A Unified Computational Model for Assessing Security Risks in Internet of Transportation Things-Based Healthcare Applications

Abstract

The rapid growth of web-based applications has attracted increasing attention from cybercriminals, particularly within the expanding field of the internet of transportation things, which has diverse applications across industries such as healthcare. As internet of transportation things technologies are adopted more widely, significant challenges emerge, particularly regarding data and service security. Hackers are specifically targeting sensitive medical data during the transportation of health emergency services, with internet of transportation things devices utilized for remote patient monitoring, medical equipment tracking, and logistics optimization. This research aims to tackle these security concerns by evaluating the risks associated with maintaining data integrity in healthcare emergency services. The research also utilizes a symmetrical fuzzy decision-making methodology, Fuzzy ANP-TOPSIS, to evaluate diverse security concerns associated with the internet of transportation things, with an emphasis on healthcare applications. The case study of seven alternatives reveals that mediXcel electronic medical records are the most viable solution, whilst the Caresoft system for hospital information is considered the least effective. The findings provide critical insights for improving the security of internet of transportation things applications and assuring their seamless integration into healthcare, especially in emergency services, hence protecting patient data and fostering user confidence.

1. Introduction

Background: The Internet of Things (IoT) has rapidly become a transformative force across multiple industries in today’s digital landscape. Within this broad ecosystem, the Internet of Transportation Things (IoTT) represents a specialized subset focused exclusively on the integration of connected sensing, communication, and computing technologies within transportation networks. Unlike general IoT—which spans domains such as smart homes, agriculture, and manufacturing—IoTT concentrates on mobility-centric systems, including ambulances, medical supply transport vehicles, emergency response fleets, and intelligent traffic infrastructures that support healthcare logistics [1,2].

In emergency healthcare contexts, IoTT plays a crucial role by enabling real-time tracking of ambulances, continuous monitoring of in-transit patient vitals, optimized routing based on live traffic conditions, and secure transmission of medical records to hospitals before patient arrival. These capabilities enhance coordination, reduce response times, and improve decision-making during time-critical events.

However, the rising dependence on IoTT also introduces significant security and data-integrity challenges. For example, GPS spoofing can misroute ambulances, man-in-the-middle attacks may alter or delay patient telemetry data, and malware targeting vehicle communication units can disrupt emergency dispatch systems—all of which can directly endanger patient outcomes [3,4]. Thus, ensuring resilient, end-to-end security within IoTT environments is essential to safeguard sensitive medical data, preserve the reliability of transportation operations, and maintain uninterrupted emergency service delivery.

By 2019, the healthcare industry had already begun integrating IoT technologies at a significant pace, with approximately 86% of organizations utilizing IoT in various functions to enhance service delivery. This widespread adoption was reflected in the market valuation, with the IoT healthcare sector estimated at $158.1 billion in 2022, fueled by a notable compound annual growth rate of 28.6 percent starting in 2021 [3,4]. The exponential growth in healthcare data—doubling every 73 days on average—highlighted the urgent need for strong IoT cybersecurity frameworks. Market forecasts suggest that the global IoT healthcare market could surge to 534.3 billion dollars by the end of 2025, supported by a projected compound annual growth rate of 19.9 percentage over the next five years. Innovative solutions such as EarlySense, FreeStyle Libre, coagulation monitoring systems, and chronic disease management tools have significantly improved patient care. However, the rapid expansion of these technologies also drew attention to rising privacy concerns and system vulnerabilities, particularly in 2021, when IoT security became a critical focus area [5,6].

IoT’s transformative impact extends well beyond the healthcare domain. Its ability to optimize operations has driven adoption across sectors such as manufacturing and retail. In 2018, around 57% of manufacturers had incorporated IoT into their workflows, a figure that was expected to climb to 94% by 2021. Similarly, the retail sector projected a market expansion reaching $94.44 billion by 2025. At a macroeconomic level, IoT was expected to contribute between 4 trillion dollars and 11 trillion dollars in value globally by 2025, with major contributions from smart factories and urban infrastructure initiatives. These projections demonstrate IoT’s far-reaching potential, the critical importance of addressing its security challenges, and its transformative role in shaping modern industries. Figure 1 illustrates the anticipated growth of the IoT-enabled transportation market [3].

Governments around the world demonstrate their commitment to delivering high-quality healthcare through the establishment of extensive medical infrastructure and facilities [5,6,7]. In emergency scenarios, the timely and secure transfer of patients and medical supplies becomes crucial, as illustrated in Figure 2 [6]. Ensuring cybersecurity in internet of transportation things applications is vital to maintaining seamless healthcare operations and strengthening national preparedness during health crises. Moreover, data protection and legal compliance are top priorities in many nations. Safeguarding sensitive health information in accordance with regional regulations, for example, Health Insurance Portability and Accountability Act (HIPAA) in the United States, is essential [5,6,7]. Conducting thorough security risk assessments helps preserve the confidentiality of patient data while ensuring adherence to applicable laws.

The dynamic and continuously evolving landscape of cyber threats demands regular evaluation and adaptation of security protocols. No nation is entirely insulated from the sophisticated and ever-changing tactics employed by cybercriminals [8,9]. Conducting thorough security risk assessments is essential to identify and mitigate emerging vulnerabilities, particularly those associated with internet of transportation things applications used in healthcare emergency response systems.

Problem Statement: Domains such as medical IoT and internet of transportation things both handle sensitive data, internet of transportation things applications often extends beyond traditional healthcare environments, operating over public networks and across geographically distributed nodes, thereby increasing their exposure to cyber threats. Moreover, internet of transportation things systems increasingly supports real-time decision-making in critical scenarios—such as remote diagnostics, emergency care, and robotic surgeries—where even minimal latency (as low as 10 ms) or any form of data compromise can lead to life-threatening consequences [8,9,10]. Therefore, ensuring robust end-to-end security within IoTT environments is essential not only for protecting patient privacy but also for maintaining operational reliability and safeguarding clinical outcomes. The security of internet of transportation things applications is often considered more critical than that of Medical IoT (MIoT) in certain aspects due to the following reasons:

• Autonomous Vehicles: A hacked self-driving car can cause a high-speed collision within seconds due to manipulated sensor data (e.g., GPS spoofing, false obstacle detection).
• Traffic Management Systems: A compromised smart traffic light can trigger massive pileups by sending incorrect signals.
• Aviation & Rail Systems: Cyberattacks on flight control or train signaling can lead to derailments or mid-air collisions.
• Smart Highways & Connected Vehicles: A single breach can cause chain-reaction crashes involving hundreds of cars.
• Public Transport Systems: Hacking metro controls (e.g., Moscow Metro cyber incident [8]) could derail trains during rush hour, killing hundreds.
• Supply Chain Attacks: Hackers disrupting smart logistics can halt global trade.
• Air Travel Chaos: A single airline cyberattacks (e.g., 2023 FAA NOTAM outage [9]) grounds thousands of flights.
• Internet of transportation things security is more critical because failures cause instant, large-scale physical and economic disasters.

According to the problem statement, the following research questions have been formulated:

• What are the key security vulnerabilities present in web-based IoTT applications used in emergency healthcare services?
• How can a hybrid Fuzzy ANP–TOPSIS model be designed to prioritize security risks in IoTT-based healthcare environments?
• How effective are existing security mechanisms in protecting IoT-based healthcare applications when applied to real-world IoTT case studies?
• Which particular IoTT healthcare platforms (e.g., mediXcel EMR, Caresoft HIS) demonstrate superior resilience against prioritized security threats, and what factors contribute to this resilience?
• What are the most and least effective mitigation strategies for addressing high-priority IoTT security risks in emergency medical services?

Objectives: Effective emergency healthcare services rely heavily on optimal resource allocation and management. Poorly addressed security risks can impair the utilization of transportation assets and critical medical resources. Risk assessments serve a key role in uncovering system weaknesses that could compromise operational efficiency [5,7].

In addition to operational efficiency, secure internet of transportation things systems ensures the safe handling and storage of patient records, allowing convenient data access while addressing risks such as data loss and unauthorized access [10,11]. The development of such systems typically involves multidisciplinary collaboration, where expert opinions may differ. To reconcile diverse viewpoints, Multiple Criteria Decision-Making (MCDM) methods are applied to formulate a cohesive decision framework [12]. In this study, a hybrid methodology integrating Fuzzy Set Theory, Analytic Network Process (ANP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized to support the security risk assessment of internet of transportation things applications in healthcare emergencies.

Hence the objectives of the proposed research work are set as follows:

• To examine the security vulnerabilities in web-based internet of transportation things applications, specifically within healthcare emergency services (Section 2)
• To assess the risks related to data integrity and service security in IoT-enabled healthcare systems (e.g., remote patient monitoring, medical logistics) (Section 2)
• To implement a cohesive unified model of Fuzzy Analytic Network Process and Fuzzy Technique for Order of Preference by Similarity to Ideal Solutions (Fuzzy ANP-TOPSIS) model for evaluating and prioritizing IoT security threats within healthcare environments (Section 3).
• To evaluate different security solutions for IoT-based healthcare systems via a case study analysis (Section 3).
• To ascertain the most and least efficacious strategies for protecting IoT healthcare applications (e.g., mediXcel EMR against Caresoft HIS) (Section 4)
• To offer mitigating measures for improving IoT security in emergency healthcare services, safeguarding patient data and fostering user confidence (Section 4).

Outcomes: This hybrid Fuzzy ANP-TOPSIS model offers a structured and comprehensive means to evaluate security risks, weighing multiple criteria and their interdependencies. It supports the development of context-sensitive mitigation strategies—an essential requirement, as risks in internet of transportation things-based emergency healthcare applications can vary widely depending on local factors and system implementations. The model equips stakeholders with actionable insights for prioritizing risks, allocating resources, and strengthening cybersecurity strategies [7,10,11].

Key contributions of the studies are as follows:

• This research study investigates the security issues by figuring out the risks of keeping data safe in healthcare emergency services with substantial attributes of security risk.
• The research examines the significant advancements in security risk of internet of transportation things (IoTT) with an emphasis on healthcare applications. For this the study uses an integrated fuzzy decision-making method called Fuzzy ANP-TOPSIS to evaluate different security risks associated with the internet of transportation things (IoTT).
• The research delineates the issues that security risk attributes presents to data security of internet of transportation thing, especially in healthcare environments, where the sensitivity of the data at its highest as it leads to several casualties.
• The research study presents the application of multi-criteria decision analysis and the Fuzzy-ANP to assess and prioritize different attributes of Security risk for internet of transportation things (IoTT), emphasizing on healthcare applications by taking seven different alternatives of healthcare applications and prioritizing these according to the results achieved.

The principal goal of this research is to improve the robustness and security of internet of transportation things systems used in healthcare emergency services. By applying the Fuzzy ANP-TOPSIS hybrid framework, the study aspires to deliver practical recommendations that empower decision-makers, enhance system resilience, and ensure the reliability of critical healthcare services, particularly during high-stakes scenarios such as pandemic outbreaks.

The structure of this paper is organized as follows to provide a comprehensive understanding of the research. Section 2 presents the materials and methods, beginning with a detailed literature review (Section 2.1) to contextualize the study. Section 2.2 explores the cybersecurity threats in the internet of transportation things for emergency healthcare services, highlighting the key challenges and vulnerabilities. Section 2.3 outlines the methodology, focusing on the integrated Fuzzy ANP-TOPSIS approach used for multi-criteria decision-making. In Section 3, numerical analysis and results are presented, including all computations, the final ranking of alternatives, sensitivity analysis, and a comparative analysis to validate the model’s robustness. Section 4 offers a critical discussion of the findings, and Section 5 concludes the paper by summarizing key insights and potential directions for future research.

2. Materials and Methods

2.1. Literature Review

Significant developments in patient monitoring, diagnostics, and remote care have come from the IoT expanding integration in healthcare. However, it has also raised critical challenges regarding system security, data integrity, real-time decision-making, and privacy. Various studies have addressed these challenges from different perspectives (

Table 1. Comparative Analysis of Literature Reviews on IoT-enabled Systems.

S. No.	Study	Approach/Methodology	Application Focus	Key Contributions & Limitations
1	Arslan et al. (2024) [10]	Domain-Specific Modeling Language (DSML4PT) with evaluation on 8 real-world apps	IoT in Public Transportation	Automates 80% of dev tasks, reduces time by 50%, high usability score. Lacks generalizability beyond transportation and long-term maintainability.
2	Raza et al. (2024) [11]	Comprehensive Review of Blockchain-based Reputation and Trust Models	Cross-sector (Smart Grids, Healthcare, Transportation)	Emphasizes potential of blockchain for decentralized trust. Highlights high energy use and scalability issues; calls for lightweight blockchain for IoT.
3	Ntafloukas et al. (2022) [12]	Monte Carlo Simulation for Cyber-Physical Risk Assessment	IoT in Transportation Infrastructure	Shows 71.8% reduction in cyber-physical risks. Needs broader validation and real-time adaptation mechanisms.
4	Zhang et al. (2020) [13]	Geospatial Modeling and Simulation	Urban Transportation Security	Enhances decision-making via geospatial intelligence. Lacks real-world deployment and privacy-preserving data strategies.
5	Khalid et al. (2021) [14]	Deep Reinforcement Learning Framework	Emergency Healthcare Transportation	Targets emergency response optimization. No real-world validation or ethical/legal discussion.
6	Salih et al. (2019) [15]	DEMATEL-based IoT Risk Assessment	Hospitals in Healthcare	Effective model tested in Sudan hospital. Not validated in other contexts; lacks integration with hospital IT systems.
7	Singh et al. (2025) [16]	Vehicular Delay-Tolerant Networks	Pandemic-Resilient Medical Transportation	Automates service to reduce infection risk. Needs pilot deployment and adaptability evaluation across geographies.
8	Ashok & Gopikrishnan (2023) [1]	Statistical Analysis of Security Models	IoT-based Remote Health Monitoring	Surveys blockchain, encryption, ML for security. No intelligent framework for adaptive model selection.
9	Aouedi et al. (2024) [2]	Survey on Intelligent IoT (IIoT)	Cross-domain (Healthcare, Cities, Industry)	Promotes AI-driven intrusion detection. Lacks unified model integrating real-time AI with health monitoring.
10	Rahman et al. (2022) [3]	Big Data Analytics in IoE	Vehicle Health Monitoring	Conceptual framework with multi-layered security. No healthcare-specific validation.
11	Habibzadeh et al. (2019) [4]	Clinical Perspective—Focus on Tech Pillars	Healthcare IoT (H-IoT)	Emphasizes personalization via sensing/data analytics. No focus on adaptive security or device coordination.
12	Adil et al. (2024) [5]	Security-Centric Survey	H-IoT	Identifies fragmented data communication risks. Lacks operational security integration models.
13	Wu et al. (2021) [6]	Edge-based Hybrid Architecture (BLE & LoRa)	Real-time Healthcare Applications	Demonstrates edge computing benefits in latency and energy. Security/privacy at edge largely unaddressed.
14	Qadri et al. (2020) [7]	Visionary Framework (Medicine 4.0)	Future of H-IoT	Highlights software-hardware integration trends. Omits real-time, secure data handling mechanisms.

), yet key limitations persist in terms of interoperability, intelligent response mechanisms, cost-effective deployment, and holistic system integration.

This literature review explores recent advancements in IoT-enabled transportation and healthcare systems with a focus on security, usability, and risk management. Arslan et al. (2024) introduced DSML4PT, an area explicit modeling language designed to streamline the development of IoT-based public transportation systems [10]. Through evaluations across eight real-world applications, the language demonstrated an ability to automate nearly 80% of development tasks and reduce development time by approximately 50%. Usability feedback from domain experts rated DSML4PT highly (4.44 out of 5), indicating its practicality [10]. However, while the language shows promise, its adaptability to other IoT domains, such as smart healthcare or urban infrastructure, remains underexplored. Additionally, long-term scalability and maintainability were not adequately addressed. Future research could extend DSML4PT’s application scope and evaluate its performance in large-scale, heterogeneous environments.

Raza et al. (2024) offered a comprehensive review of blockchain-based reputation and trust management mechanisms across smart grids, healthcare, and transportation [11]. Their findings underscore the potential of blockchain to address critical challenges related to security and trust in decentralized systems. Despite these strengths, existing blockchain solutions often suffer from high energy consumption, scalability limitations, and compatibility issues with legacy systems. Moreover, many proposed models lack empirical validation. The review highlights the need for lightweight blockchain architectures suitable for resource-constrained IoT environments and calls for real-world testing to assess their viability. Researchers could contribute by developing and empirically evaluating energy-efficient, scalable blockchain frameworks tailored to sector-specific needs.

Ntafloukas et al. (2022) proposed a novel cyber-physical risk assessment methodology for IoT-enabled transportation infrastructure [12]. Using Monte Carlo simulations, their approach demonstrated that integrated control barriers could reduce cyber-physical risks by up to 71.8%. While the framework effectively integrates both cyber and physical dimensions of risk, its application is limited to a single case study. Broader validation across multiple types of transportation infrastructure, such as airports or rail networks, is necessary to determine its generalizability. Moreover, the dynamic nature of IoT-based threats underscores the need for real-time risk assessment tools capable of adapting to evolving threat landscapes.

Zhang et al. (2020) presented a geospatial modeling approach to enhance security in smart transportation systems [13]. Their architecture, tested through simulations in Beijing, uses geospatial intelligence to support strategic decision-making for urban transportation security. However, the study stops short of real-world implementation, and the authors do not thoroughly address privacy concerns associated with collecting and analyzing geospatial data. Future research should focus on deploying this architecture in live urban settings and integrating privacy-preserving mechanisms to safeguard users’ location data.

Khalid et al. (2021) developed a deep reinforcement learning-based autonomous transportation framework for emergency healthcare services [14]. The system aims to improve emergency response times while minimizing environmental impact. Despite its conceptual strengths, the framework has not yet been tested in real-world emergency scenarios. Furthermore, the study does not sufficiently explore the ethical and legal implications of deploying autonomous systems in critical healthcare operations. Practical trials and a comprehensive ethical analysis are essential next steps for translating this promising concept into a deployable solution.

In the healthcare domain, Salih et al. (2019) proposed a security risk management model tailored for IoT in hospitals [15]. Developed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) IoT Risk Assessment Procedure, the model addresses key dimensions such as technology security, human privacy, and data trustworthiness. A case study conducted in a Sudanese hospital demonstrated the model’s usability and effectiveness. However, its generalizability across diverse healthcare settings remains uncertain. Integration with existing hospital information systems is also lacking. Future work could focus on validating the model in various regional contexts and creating implementation strategies that align with existing IT infrastructure.

Singh et al. (2025) addressed pandemic resilience by proposing an intelligent transportation system that leverages vehicular delay-tolerant networks to deliver automated medical services [16]. The system is designed to minimize human contact, thereby reducing infection risk while maintaining service efficiency. Although conceptually sound, the system has yet to be deployed in a real-world setting. Additionally, there is limited discussion on its adaptability to both urban and rural environments. Research efforts should be directed at conducting pilot implementations and evaluating the system’s scalability across different geographical and demographic contexts.

Ashok and Gopikrishnan (2023) conducted a pragmatic and statistically grounded analysis of existing IoT-based remote health monitoring security models [1]. Their study highlights the diverse design architectures and performance variabilities across different security solutions, revealing that model selection and implementation are often resource-intensive and complex processes. While they provide a broad overview of various attack mitigation approaches—such as blockchain technologies, encryption techniques, and machine learning-based models—their work falls short in offering intelligent, adaptive selection frameworks that can facilitate streamlined deployment tailored to dynamic application needs.

Building on this foundation, Aouedi et al. (2024) presented a comprehensive survey on Intelligent IoT (IIoT), emphasizing the integration of AI with IoT to create more responsive and intelligent systems across healthcare, urban, and industrial domains [2]. Their analysis of prevailing privacy threats and security vulnerabilities underscores the urgent need for AI-driven intrusion detection systems and adaptive security mechanisms. However, despite its forward-looking insights, the study lacks a unified security model capable of merging AI analytics with the specific demands of real-time healthcare monitoring.

Rahman et al. (2022) explored the domain of vehicle health monitoring as a subset of the internet of everything, utilizing big data analytics to propose a centralized monitoring framework [3]. Although their focus lies outside traditional healthcare, their multi-layered approach demonstrates the potential applicability of secure, intelligent health monitoring architectures. Nevertheless, their proposed framework remains largely conceptual and lacks empirical validation within the healthcare context.

Habibzadeh et al. (2019) offered a clinical perspective on Healthcare IoT (H-IoT), identifying sensing, communication, and data analytics as the foundational pillars of next-generation healthcare systems [4]. Their emphasis on personalized care through data-driven decision-making marks a significant contribution; however, their work does not address the critical issues of real-time adaptive security or the coordination of multiple devices—challenges that are increasingly prevalent in real-world healthcare deployments.

In another significant contribution, Adil et al. (2024) conducted a security-focused survey on H-IoT, drawing attention to the fragmented and loosely structured nature of data communication in such systems [5]. This fragmentation exposes HC-IoT architectures to various security threats, underscoring the need for robust, high-standard infrastructures. While they identify key unresolved issues, their discussion remains largely theoretical and lacks concrete models for integrating security mechanisms into operational healthcare environments.

Wu et al. (2021) addressed healthcare system performance from a network perspective by proposing an edge-based hybrid architecture employing BLE and LoRa technologies [6]. Their work highlights the benefits of edge computing in latency-sensitive healthcare applications, particularly with regard to energy efficiency and extended coverage. However, their model prioritizes connectivity over the implementation of security and privacy-preserving measures at the edge layer.

Lastly, Qadri et al. (2020) projected the evolution of H-IoT within the framework of Medicine 4.0, outlining emerging technologies and trends in software-hardware integration [7]. While their vision is forward-thinking and aligned with digital healthcare transformation, they do not sufficiently tackle the critical issues of trustworthy, real-time, and privacy-compliant data exchange within clinical environments.

Across these studies, common research gaps include the lack of real-world validation, limited integration with existing infrastructure, and insufficient attention to ethical, legal, and societal implications. While previous studies have applied fuzzy ANP and TOPSIS methods to cybersecurity prioritization in healthcare systems, none have addressed the emerging risks associated with IoT-based healthcare transportation systems, nor have they integrated operational transportation factors with medical data protection requirements. Furthermore, many proposed frameworks lack scalability and adaptability to diverse operating conditions.

Addressing these gaps requires empirical studies that test theoretical models in practical settings, design of adaptable architectures for various sectors, and inclusion of ethical and privacy considerations in technology deployment. Bridging these divides will significantly strengthen the design, adoption, and impact of IoT-enabled systems in transportation and healthcare. The novelty of this study lies in: (i) the development of a comprehensive, multi-layered criteria framework that integrates medical cybersecurity requirements with transportation-specific operational risks in IoTT environments; (ii) the application of a hybrid fuzzy ANP–TOPSIS model to systematically address expert uncertainty and interdependencies among criteria; and (iii) the creation of a ranked decision-support framework tailored to emergency healthcare logistics. The integration of fuzzy ANP and TOPSIS is justified because cybersecurity criteria in IoT-enabled healthcare transportation systems are inherently interdependent, and expert judgments often involve uncertainty. Consequently, the hybrid approach produces more reliable prioritization outcomes than standalone AHP or traditional crisp methods.

2.2. Cybersecurity Threats in Internet of Transportation Things for Emergency Healthcare Services

Understanding the internet of transportation things is essential, particularly in the context of healthcare delivery systems where interconnected devices and sensors play a vital role. In recent years, there has been a concerning escalation in cybersecurity incidents, such as data breaches, ransomware attacks, and the unauthorized access to patient information. These threats affect not only individual patients but also affect healthcare professionals and institutions on a global scale, including nations like the United States and Saudi Arabia [2,4]. Devices used in emergency healthcare—such as wearables, medical monitors, and logistics systems—are particularly susceptible to cyberattacks like data interception, phishing, spoofing, and Distributed Denial of Service (DDoS) attacks [1,2].

Such vulnerabilities can lead to severe consequences, including financial losses, service disruptions, and risks to patient safety. To counter these threats, it is imperative to implement comprehensive security strategies. These should comprise strong encryption techniques, safe communication systems, replacement of default device credentials, and frequent vulnerability checks. Preserving data confidentiality and system integrity in healthcare depends also on safeguarding personal health information and handling the hazards related to automation and Artificial Intelligence (AI).

Several real-world incidents illustrate these vulnerabilities. The 2016 Mirai botnet attack, for example, launched a massive DDoS campaign that disrupted numerous internet services [5], showcasing the potential for similar disruptions in healthcare. The 2021 Verkada breach highlighted the importance of stringent access controls for security surveillance in hospitals [5,7]. In Finland, a healthcare provider faced a ransomware attack that interrupted essential services, reinforcing the need for resilient infrastructures. Notable events such as the Jeep Hack and Stuxnet also underscore the broader risks associated with insecure IoT environments, from tampered medical devices to sophisticated cyber-espionage operations [6,17].

These examples stress the urgent need for tailored cybersecurity solutions that reflect the unique operational context of healthcare emergency services. As hospitals and clinics increasingly rely on digital platforms for administrative, clinical, and financial functions—and as their networked infrastructures grow—cybersecurity and risks associated with it must be prioritized.

In the specific case of internet of transportation things applications, several interrelated security risks have been identified and are outlined in

Table 2. Groups and Subgroups of Risks Associated with Security.

Level 1	Level 2	Level 3
Transportation (F1)	Physical Organization Risks (F11)	Risks to Infrastructure Integrity (F111)
		Transportation Facilities Disruption (F112)
		Intruders Gaining Access to Important Transportation Sites (F113)
	Cybersecurity Threats (F12)	Exploits on Transportation Cyber-Networks (F121)
		Breach of Data in Reservation and Ticketing Systems (F122)
		Threats to Transportation Networks (F123)
	Safety and Compliance Risks (F13)	Disregard for Safety Requirements (F131)
		Errors Caused by Ignorance of Safety Measures (F132)
		Noncompliance with Transportation Security Regulations (F133)
Healthcare (F2)	Risks Associated with Medical Data Privacy (F21)	Unauthorized Access to Health Information (F211)
		Data Breach from Medical Devices During Transit (F212)
		Disregard for Patient Privacy Requirements in Healthcare (F213)
	Risks Associated with Medical Device Security (F22)	Risks Associated with Medical Equipment (F221)
		Unapproved Use to Medical Devices (F222)
		Interference with Medical Sensors and Tracking Devices (F223)
	Adherence to Healthcare Regulations (F23)	Violation of HIPAA Regulations (F231)
		Infringements of Medical Transportation Regulations (F232)
		The Implications of Failure to Comply with the Law (F233)
IoT (F3)	Security Flaws in IoT Devices (F31)	Internet of Things Device Security Flaws (F311)
		Unauthorized IoT Device Access (F312)
		Hacking IoT Devices (F313)
	Data Protection in the Transportation Sector IoT (F32)	Data Compromises in Transportation Internet of Things (F321)
		Data Handling During Transmission (F322)
		Data Security and Encryption Concerns (F323)
	Risks of Interconnected Systems (F33)	Leakage of Data Across Industries (F331)
		Disruption of Healthcare IoT Devices (F332)
		Problems with Regulation in Intersectorial Cooperation (F333)

. These categories were derived based on a thematic analysis of literature across cybersecurity risk domains relevant to our study context, with alignment to international standards (e.g., NIST, ISO/IEC 27001 [18]). The subcategories, labeled as F1 through F11, represent distinct yet interrelated components within each primary risk domain (e.g., Transportation (F1), Physical Infrastructure Risks (F11), etc.). Each risk category is inherently linked to core system factors, demonstrating the complexity and interconnectedness of the threat landscape. Ultimately, the global rise in cyber threats affecting emergency healthcare underscores the need for proactive, layered security measures designed to protect both patient welfare and the integrity of critical medical infrastructure [7,8,9,10,13,14,15].

• Risks to Infrastructure Integrity (F111): Potential physical threats that could compromise the structural stability of transportation systems.
• Transportation Facilities Disruption (F112): Intentional acts aimed at damaging or disabling critical infrastructure such as roads, railways, or bridges.
• Intruders Gaining Access to Important Transportation Sites (F113): Unauthorized access to secure transportation areas without proper clearance.
• Exploits on Transportation Cyber-Networks (F121): Digital attacks targeting IT systems and communication networks used in transportation.
• Breach of Data in Reservation and Ticketing Systems (F122): Exposure of sensitive user data due to unauthorized access to ticketing and reservation systems.
• Threats to Transportation Networks (F123): Deployment of malware designed to disrupt or exploit vulnerabilities in transportation infrastructure.
• Disregard for Safety Requirements (F131): Non-adherence to established safety procedures and standards within transportation operations.
• Errors Caused by Ignorance of Safety Measures (F132): Transportation incidents that result from neglecting safety rules and operational protocols.
• Noncompliance with Transportation Security Regulations (F133): Failure to fulfill minimum required security measures in the transport sector.
• Unauthorized Access to Health Information (F211): Accessing or retrieving patient information without proper authorization.
• Data Breach from Medical Devices During Transit (F212): Exposure or leakage of medical data during the transportation of medical equipment.
• Disregard for Patient Privacy Requirements in Healthcare (F213): Violations of established privacy frameworks governing healthcare data handling.
• Risks Associated with Medical Equipment (F221): Identifiable weaknesses in medical technologies that can be exploited by malicious actors.
• Unapproved Use of Medical Devices (F222): Unauthorized use or control of medical devices.
• Interference with Medical Sensors and Tracking Devices (F223): Manipulation or sabotage of healthcare sensors and monitoring devices.
• Violation of HIPAA Regulations (F231): Failure to meet the data protection standards mandated by the HIPAA.
• Infringements of Medical Transportation Regulations (F232): Failure to comply with protocols for the safe and secure transport of medical supplies and devices.
• The Implications of Failure to Comply with the Law (F233): Legal penalties and consequences arising from non-compliance with healthcare regulations.
• Internet of Things Device Security Flaws (F311): Built-in vulnerabilities or weak security features in Internet of Things (IoT) devices.
• Unauthorized IoT Device Access (F312): Gaining illicit access to or taking control of IoT devices.
• Hacking IoT Devices: Deliberate modification or sabotage of IoT hardware or firmware.
• Data Compromises in Transportation Internet of Things (F321): Unauthorized access to data processed through IoT in the transportation sector.
• Data Handling During Transmission (F322): Illegitimate alteration of data as it moves between devices or systems.
• Data Security and Encryption Concerns (F323): Threats that undermine secure encryption and compromise data authenticity.
• Leakage of Data Across Industries (F331): Unauthorized transmission of data between interconnected systems across different industries.
• Disruption of Healthcare IoT Devices (F332): Intentional interference with medical IoT technologies.
• Problems with Regulation in Intersectorial Cooperation (F333): Challenges in aligning regulations across sectors during IoT system integration.

In modern healthcare emergency services, addressing the security challenges posed by the integration of internet of transportation things technologies has become critically important. The growing interconnectivity between healthcare systems, medical devices, and transportation infrastructure plays a vital role in enabling timely and effective emergency response. However, this interconnected ecosystem also introduces significant vulnerabilities that must be carefully managed.

Physical infrastructure security is paramount, as transportation systems form the backbone of emergency logistics. Ensuring the resilience of transportation assets—such as ambulances, traffic control systems, and supply routes—is essential for the prompt movement of patients, medical equipment, and critical supplies. Any compromise in these systems can result in delays that may endanger patient outcomes during emergencies.

The cybersecurity risks aimed at the online systems that facilitate emergency medical care are as significant. These systems are becoming more and more appealing targets for cyberattacks as providers use the internet of transportation things for communication, route optimization, and real-time monitoring. Vulnerabilities in ticketing or transportation networks can interfere with emergency procedures and jeopardize private patient data, compromising service dependability and privacy.

Moreover, protecting medical data during transit is a key concern. Unsecured transmission channels and vulnerable medical devices can lead to unauthorized data access or leaks, jeopardizing patient confidentiality. In order to maintain confidence and ensure compliance with requirements such as the HIPAA, it is vital to maintain effective data protection measures throughout the whole transportation process [18,19].

Mitigating both physical and cyber risks associated with internet of transportation things in emergency healthcare is crucial to ensuring uninterrupted, secure, and high-quality care. A proactive and integrated security strategy is essential to support operational efficiency, safeguard patient data, and uphold public trust in a digitally connected healthcare landscape.

This article selected seven healthcare systems that integrate the internet of transportation things and evaluated them using the TOPSIS methodology to rank their associated risks. These systems were specifically chosen based on a combination of factors, including their role in healthcare management, alignment with the study’s objectives, and the diversity they offer in terms of operational scale, technological maturity, and geographical presence [2,6,8,19,20,21,22,23,24,25]. Their influence on healthcare innovation and their strategic contribution to shaping industry standards were also considered [15,18,22,26,27,28,29].

The selection process was strategic rather than random, aimed at ensuring a comprehensive and representative set of case studies across diverse contexts—crucial for deriving meaningful insights. The systems include: Trio Hospital Information System (HIS) [26], MediXcel Electronic Medical Records (EMR) [27], GeniPulse [28], Caresoft HIS [29], LiveHealth (for diagnostics) [30], Visual Hospital Management (VHM) [31], and NextGen [32]. These were assessed through a structured framework focusing on security and functionality within internet of transportation things-integrated healthcare environments.

To enable a robust comparison, the systems were selected to reflect varying levels of technological advancement, security strength, and healthcare application specialization. For instance, MediXcel and NextGen (EMR-focused), and Trio and Caresoft (HIS-focused), allow for comparison of different record-keeping architectures and their resilience to internet of transportation things-related vulnerabilities. LiveHealth (diagnostics) and GeniPulse (real-time analytics) help evaluate security issues in critical care settings, while VHM, with its visualization-driven interface, highlights the trade-offs between user accessibility and data protection.

These systems span cloud-based and on premise deployments, comply with standards like HIPAA and GDPR, and exhibit varied market adoption rates. This carefully curated selection enables the identification of best practices and key vulnerabilities in securing internet of transportation things-enabled healthcare systems, providing technically grounded and actionable insights for healthcare IT stakeholders.

2.3. Methodology

In the domain of IoT applications for transportation, the designs are marked by a significant degree of diversity and complexity due to the involvement of multifaceted processes. At the same time, the healthcare industry is going through significant changes, particularly in the ways that data gathering methods are being implemented and the enhancement of security measures [30,31]. As a consequence of this, security procedures in the healthcare business as well as applications for the IoT in transportation have been strengthened across a variety of aspects, including risk assessment and access control.

As seen in the above literature and background study [17] it is clear that problem of evaluating security risk for internet of transportation things in healthcare is multi-criteria in nature. A potent MCDM tool for internet of transportation things healthcare security, Fuzzy ANP-TOPSIS balances uncertainty, interdependencies, and multi-objective optimization. It helps managements to make policies for risk mitigation, therefore guaranteeing patient safety and data integrity. There are several reasons for using Fuzzy-ANP-TOPSIS in this evaluation, few of these are listed below:

• Security risks in internet of transportation things involve vague, incomplete, and linguistic data
• Unlike Analytic Hierarchy Process (AHP), ANP considers feedback and dependencies between criteria
• A comparison is made between the alternatives in terms of how close they are to the ideal secure solution and how far they are from the worst-case situation.

This paper introduces the Fuzzy ANP-TOPSIS method as an integrated approach for assessing security risks in IoT transportation applications. The study examines security risks by evaluating three, nine, and twenty-seven key factors related to these applications. The next step involves determining the weights of these factors using the Fuzzy-ANP approach, which helps assess their relative impact on IoT transportation systems. After deriving these weights, the study conducts a detailed evaluation of alternative solutions through the Fuzzy-TOPSIS methodology. The complete process is visually represented in Figure 3, which outlines the Fuzzy ANP-TOPSIS methodology [25].

The flowchart in Figure 3 outlines the Fuzzy ANP-TOPSIS methodology, which begins with a comprehensive review of relevant literature and identifying the experts for decision making. This review establishes a basis for identifying diverse security risks and formulating the criteria required for their effective evaluation. The Fuzzy ANP is utilized as a decision-making instrument to evaluate multiple criteria in the comparison of alternative solutions. The methodology comprises four essential steps: constructing a security matrix and performing pairwise comparisons, verifying the presence of security factors post-comparisons, assigning access privileges through Fuzzy ANP analysis, and aggregating ratings to compute security risk weights, which are then ranked.

The interrelationship among various factors establishes a hierarchical structure, as illustrated in Table 2. These factors indicate different security risks, with their weights assigned based on an assessment of their impact on the factors and alternative solutions. The risk rankings are established through the Fuzzy ANP-TOPSIS methodology, providing a systematic and thorough framework for decision-making.

In the context of the IoT in transportation planning frameworks, the identification of security risks plays a pivotal role. These risks offer a precise mechanism and serve as a critical foundation for future research initiatives in the field. As such, addressing these risks is fundamental for the effective deployment of IoT technologies in transportation systems.

The challenges associated with decision-making, especially in the context of sensitive information and client objectives, highlight the necessity of examining diverse approaches and algorithms documented in the literature. Among the various methods for evaluating security risks, Fuzzy-ANP emerges as a notably appropriate technique relative to other multi-criteria decision-making methods. Nonetheless, it is crucial to recognize that, despite its strengths, Fuzzy-ANP cannot completely eradicate the vagueness and imprecision that characterize decision-making processes. Furthermore, the method’s dependence on subjective judgments introduces specific limitations. Nevertheless, the integration of the Fuzzy ANP-TOPSIS technique stands out as a unique approach, well-suited for evaluating the impact of alternative solutions within the IoT application domain for transportation things in healthcare domain. Next section explains the methodology of Fuzzy ANP TOPSIS in detail for further understanding.

Fuzzy ANP-TOPSIS Method

MCDM is a discipline designed to assist experts in evaluating options with conflicting factors [5,21,22], making it particularly effective when considering two or more competing issues simultaneously. This research work employs a fuzzy-based amalgam method that associates the ANP along with the TOPSIS to estimate security risk of internet of transportation things within the domain of healthcare. ANP, with its network structure, allows for the examination of relationships between different factors [32,33,34,35,36].

The incorporation of fuzzy logic with the hybrid ANP-TOPSIS method is applied in this research to enhance accuracy [35,36,37,38,39]. The procedure for evaluating weightage and ranking using this approach is detailed below:

Phase 1: Setting scale and definition of Triangular Fuzzy Number (TFN): After taking linguistic values through Table 2 principles, authors translate it into crisp numbers and TFN. This research work employs the TFNs as the membership functions and it lies between 0 and 1 [19]. TFN can be defined as (l, ed, u), where l, mi, and u (l ≤ ed ≤ u) are parameters representative the smallest, the equidistant value, and the largest value in the TFN, respectively. In addition, a fuzzy number m on F is termed as TFN, if its membership functions are given in Equations (1) and (2) and shown in Figure 4.

$${\mu }_{\mathrm{s}} \left(\mathrm{x}\right) =\mathrm{F}\to [0, 1]$$

(1)

$${\mathrm{\mu }}_{\mathrm{s}}\left(\mathrm{x}\right)=\left\{\begin{array}{c}{\displaystyle \frac{\mathrm{x}}{\mathrm{e}\mathrm{d}-\mathrm{l}}}-{\displaystyle \frac{\mathrm{l}}{\mathrm{e}\mathrm{d}-\mathrm{l}}} \mathrm{x}\in [\mathrm{l},\mathrm{e}\mathrm{d}]\\ {\displaystyle \frac{\mathrm{x}}{\mathrm{e}\mathrm{d}-\mathrm{u}}}-{\displaystyle \frac{\mathrm{u}}{\mathrm{e}\mathrm{d}-\mathrm{u}}} \mathrm{x}\in \left[\mathrm{e}\mathrm{d},\mathrm{u}\right]\\ 0 \mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{w}\mathrm{i}\mathrm{s}\mathrm{e}\end{array}\right.$$

(2)

A panel of 60 experts was selected using purposive expert sampling. The experts comprised cybersecurity specialists, IoT system engineers, and emergency healthcare practitioners, each with an average of 8–15 years of experience. The inclusion criteria required relevant domain knowledge, prior involvement in IoT security projects, and a willingness to participate in the pairwise comparison process. Each expert completed pairwise comparison matrices for the criteria and sub-criteria using a fuzzy triangular scale (e.g., (1, 1, 3), (1, 3, 5), …). The comparisons were collected individually to avoid group bias, and all participants followed a structured worksheet developed for the Fuzzy ANP method. Practitioners assigned scores to the factors in a quantitative manner according to the scale shown in

Table 3. Linguistic Terms and the Corresponding TFNs.

Saaty Scale	Definition	Fuzzy Triangle Scale
1	similarly significant	(1, 1, 1)
3	insubstantial significant	(2, 3, 4)
5	sufficiently significant	(4, 5, 6)
7	firmly significant	(6, 7, 8)
9	utterly significant	(9, 9, 9)
2	recurrent values between two adjacent scales	(1, 2, 3)
4		(3, 4, 5)
6		(5, 6, 7)
8		(7, 8, 9)

.

The following Equations (3)–(6) are used in adapting the numeric values into TFN [20] and denoted as (l_ij, ed_ij, u_ij) where l_ij is lower value, ed_ij is equidistant (middle) value and u_ij is uppermost level values. Additionally, TFN [ɳij] is recognized as:

$${\mathrm{ɳ}}_{\mathrm{i}\mathrm{j}}=({\mathrm{l}}_{\mathrm{i}\mathrm{j}},{\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{j}},{\mathrm{u}}_{\mathrm{i}\mathrm{j}})$$

(3)

$\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} {\mathrm{l}}_{\mathrm{i}\mathrm{j}}\le {\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{j}}\le {\mathrm{u}}_{\mathrm{i}\mathrm{j}}$

$${\mathrm{l}}_{\mathrm{i}\mathrm{j}}=\mathrm{m}\mathrm{i}\mathrm{n}\left({\mathrm{J}}_{\mathrm{i}\mathrm{j}\mathrm{d}}\right)$$

(4)

$${\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{j}}={({\mathrm{J}}_{\mathrm{i}\mathrm{j}1},{\mathrm{J}}_{\mathrm{i}\mathrm{j}2},{\mathrm{J}}_{\mathrm{i}\mathrm{j}3})}^{{\displaystyle \frac{1}{\mathrm{x}}}}$$

(5)

$$\mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{u}}_{\mathrm{i}\mathrm{j}}=\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{J}}_{\mathrm{i}\mathrm{j}\mathrm{d}}\right)$$

(6)

In the calculations showed above, J_ijd shows the reasonable position of the values between two factors or criteria and given by subject specialist d. In the equation i and j signify a pair of criteria being judged by subject specialists. Value ɳ_ij, i.e., TFN is considered based on the geometric mean of subject specialist’s views for a specific judgment. Additionally, Equations (7)–(9) help to aggregate TFN values. Consider two TFNs M1 and M2, M1 = (l₁, mi₁, u₁) and M2 = (l₂, mi₂, u₂). The basic rules of mathematical operations on them are as:

$$({\mathrm{l}}_1,{\mathrm{e}\mathrm{d}}_1,{\mathrm{u}}_1)+({\mathrm{l}}_2,{\mathrm{e}\mathrm{d}}_2,{\mathrm{u}}_2)=({\mathrm{l}}_1+{\mathrm{l}}_2,{\mathrm{e}\mathrm{d}}_1+{\mathrm{e}\mathrm{d}}_2,{\mathrm{u}}_2+{\mathrm{u}}_2)$$

(7)

$$({\mathrm{l}}_1,{\mathrm{e}\mathrm{d}}_1,{\mathrm{u}}_1)\times ({\mathrm{l}}_2,{\mathrm{e}\mathrm{d}}_2,{\mathrm{u}}_2)=({\mathrm{l}}_1\times {\mathrm{l}}_2,{\mathrm{e}\mathrm{d}}_1\times {\mathrm{e}\mathrm{d}}_2,{\mathrm{u}}_1\times {\mathrm{u}}_2)$$

(8)

$${({\mathrm{l}}_1,{\mathrm{e}\mathrm{d}}_{1,}{\mathrm{u}}_1)}^{-1}=({\displaystyle \frac{1}{{\mathrm{u}}_1}},{\displaystyle \frac{1}{{\mathrm{e}\mathrm{d}}_1}},{\displaystyle \frac{1}{{\mathrm{l}}_1}})$$

(9)

Phase 2: Next phase is preparation of the pair wise comparison matrix by using the responses received from the subject specialists. For each crisp comparison matrix derived from the defuzzified values, the Consistency Ratio (CR) was calculated using Saaty’s method. Only matrices with CR < 0.1 were accepted; inconsistent matrices were returned to experts for revision. Calculation of the Consistency Index (CI) using the formula in Equation (10) as follows:

$$\mathrm{C}\mathrm{I}\mathrm{n}=({\gamma }_{\mathrm{m}\mathrm{a}\mathrm{x}}-\mathrm{Q}\mathrm{n})/(\mathrm{Q}\mathrm{n}-1)$$

(10)

where CIn: Consistency Index and Qn: is the number of associated attributes.

Within this the next ladder is calculating the Consistency Ratio (CR), using a random index is as Equation (11).

$$\mathrm{C}\mathrm{R}=\mathrm{C}\mathrm{I}\mathrm{n}/\mathrm{R}\mathrm{I}$$

(11)

where RI = random index (CI of the randomly produced pairwise comparison matrix). Random index is derived from Saaty [38].

Phase 3: After the construction of the comparison matrix, defuzzification is performed to produce a quantifiable value based on the calculated TFN values. The defuzzification method adopted in this work has been derived from [18] as formulated in Equations (12)–(14) which is commonly referred to as the alpha cut method which is given as follows.

$${\mathrm{\mu }}_{\alpha ,\beta }\left({\mathrm{ɳ}}_{\mathrm{i}\mathrm{j}}\right)=[\beta .\mathrm{ɳ}\alpha ({\mathrm{l}}_{\mathrm{i}\mathrm{j}})+(1-\beta ).\mathrm{ɳ}\alpha ({\mathrm{u}}_{\mathrm{i}\mathrm{j}}\left)\right]$$

(12)

where 0 ≤ α ≤ 1 and 0 ≤ β ≤ 1

Such that,

$$\mathrm{ɳ}\alpha \left({\mathrm{l}}_{\mathrm{i}\mathrm{j}}\right)=({\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{j}}-{\mathrm{l}}_{\mathrm{i}\mathrm{j}}).\alpha +{\mathrm{l}}_{\mathrm{i}\mathrm{j}}$$

(13)

$$\mathrm{ɳ}\alpha \left({\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{j}}\right)={\mathrm{u}}_{\mathrm{i}\mathrm{j}}-({\mathrm{u}}_{\mathrm{i}\mathrm{j}}-{\mathrm{e}\mathrm{d}}_{\mathrm{i}\mathrm{j}}).\alpha$$

(14)

α and β in these equations are used for predilections of experts. These two values fluctuate between 0 and 1.

Phase 4: The subsequent phase involves the development of the super-matrix, derived from the priority vector obtained through paired comparisons among groups, encompassing goals, attributes, sub-attributes, and alternatives.

Phase 5: TOPSIS requires the performance ranking of each alternative across all normalized factors. The formula is as Equations (15) and (16):

$${\mathrm{E}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^2}}}$$

(15)

where i = 1, 2, … m; and j = 1, 2, … n.

Next Phase is calculating the normalized weighted decision matrix $\left(\mathrm{n}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}\right)$

$$\mathrm{n}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}={\mathrm{w}}_{\mathrm{i}}{\mathrm{E}}_{\mathrm{i}\mathrm{j}}$$

(16)

where i = 1, 2, … m and j = 1, 2, … n.

Phase 6: Calculate positive ideal solution matrix (PiS⁺) and negative ideal solution matrix (NiS^_) using Equations (17)–(19).

$${\mathrm{P}\mathrm{i}\mathrm{S}}^{+}={\mathrm{n}\mathrm{s}}_1^{+},{\mathrm{n}\mathrm{s}}_2^{+},{\mathrm{n}\mathrm{s}}_3^{+}\dots ..{\mathrm{n}\mathrm{s}}_{\mathrm{n}}^{+}$$

$${\mathrm{N}\mathrm{i}\mathrm{S}}^{-}=\mathrm{n}{\mathrm{s}}_1^{-},{\mathrm{n}\mathrm{s}}_2^{-},{\mathrm{n}\mathrm{s}}_3^{-}\dots ..\mathrm{n}{\mathrm{s}}_{\mathrm{n}}^{-}$$

(17)

where ${\mathrm{n}\mathrm{s}}_{\mathrm{j}}^{+}$ is Max ns_ij if j is an advantage factor and Max ns_ij if j is a cost factor; ${\mathrm{n}\mathrm{s}}_{\mathrm{j}}^{\_}$ is Min ns_ij if j is an advantage factor and Min ns_ij if j is a cost factor.

Phase 7: Next Phase is identifying the gap between the values of each option with the positive ideal solution matrix and negative ideal solution matrix:

Positive ideal solution:

$${\mathrm{d}\mathrm{P}}_{\mathrm{i}}^{+}=\sqrt{{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{({\mathrm{n}\mathrm{s}}_{\mathrm{i}}^{+}-{\mathrm{n}\mathrm{s}}_{\mathrm{i}\mathrm{j}})}^2};\mathrm{i}=\mathrm{1,2},3\dots .\mathrm{m}$$

(18)

Negative ideal solution:

$${\mathrm{d}\mathrm{N}}_{\mathrm{i}}^{-}=\sqrt{{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{({\mathrm{n}\mathrm{s}}_{\mathrm{i}\mathrm{j}}-{\mathrm{n}\mathrm{s}}_{\mathrm{i}}^{-})}^2};\mathrm{i}=\mathrm{1,2},3\dots .\mathrm{m}$$

(19)

where ${\mathrm{d}\mathrm{P}}_{\mathrm{j}}^{+}$ is the distance to the negative ideal solution for i option and ${\mathrm{d}\mathrm{N}}_{\mathrm{i}}^{-}$ is the distance to the negative ideal solution.

Calculating the preference value for every security risk alternative based on internet of transportation things (IoTT) and healthcare (Pv_i) (Equation (20)).

$${\mathrm{P}\mathrm{v}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{d}\mathrm{N}}_{\mathrm{i}}^{-}}{{\mathrm{d}\mathrm{N}}_{\mathrm{i}}^{-}-{\mathrm{d}\mathrm{P}}_{\mathrm{i}}^{+}}}$$

(20)

where i = 1, 2, 3….m.

The methodological phases outlined in this study describe the complete procedure for assessing security risk factors in IoTT–enabled healthcare systems through the Fuzzy ANP–TOPSIS framework. This approach was applied to evaluate multiple healthcare service alternatives that operate in conjunction with IoTT-based infrastructures. The dataset used in this study was developed through an expert-driven evaluation process aimed at assessing security risk factors associated with the IoTT in healthcare environments. A panel of 60 experts was selected using purposive expert sampling to ensure that all participants possessed substantial and relevant professional experience (Table S1). The expert group consisted of cybersecurity specialists, IoTT system engineers, and emergency healthcare practitioners, each with an average of 8–15 years of domain experience.

Eligibility criteria required demonstrable expertise in IoTT security, prior participation in relevant projects, and willingness to contribute to the pairwise comparison procedures. Each expert independently completed pairwise comparison matrices for all criteria and sub-criteria using triangular fuzzy numbers—such as (1, 1, 3), (1, 3, 5), and related fuzzy scales—to avoid group bias. A structured worksheet aligned with Fuzzy ANP was provided to maintain uniformity in responses. In addition, practitioners assigned quantitative factor scores following the scale presented in Table 3. Collectively, these entries produced a comprehensive expert-annotated dataset comprising fuzzy relational judgments, aggregated weights, and evaluations of alternative healthcare service modes.

Ethical considerations and limitations were also carefully addressed throughout the methodological process to ensure responsible and compliant use of transportation–healthcare data in IoTT environments. All expert-derived inputs and any secondary data describing IoTT–enabled healthcare interactions were handled using strict ethical protocols, including adherence to anonymization, non-identifiability, and secure storage requirements. No personally identifiable information or sensitive patient-transportation records were collected at any stage. Despite these precautions, certain methodological limitations must be acknowledged. The expert-driven dataset, while robust, remains limited in size and may not fully capture the extensive variability present across real-world IoTT-based healthcare scenarios. Additionally, the contextual nature of expert judgments may affect the generalizability of results when applied to diverse transportation–healthcare infrastructures or rapidly evolving IoTT technologies. These factors underscore the need for cautious interpretation of the findings and highlight opportunities for expanding dataset diversity and validating the model with broader real-world deployments in future studies.

Before executing the analytical procedures, the dataset underwent essential preprocessing steps to ensure consistency and computational rigor. First, all expert-submitted matrices were examined for consistency, and those showing unacceptable inconsistency levels were returned for reassessment and correction following ANP guidelines. Subsequently, linguistic judgments were translated into triangular fuzzy numbers to facilitate fuzzy computations. To consolidate expert inputs, the fuzzy geometric mean was applied to each matrix, producing aggregated group judgments. Normalization procedures were then performed to align the weights of criteria and sub-criteria for meaningful comparison. In the TOPSIS phase, all fuzzy values were defuzzified using the centroid method, generating crisp numerical scores appropriate for distance-based ranking. These preprocessing steps ensured that the dataset was transformed into a coherent, reliable, and computation-ready format across both ANP and TOPSIS stages.

To evaluate the reliability and comparative effectiveness of the proposed Fuzzy ANP–TOPSIS framework, several baseline and advanced multi-criteria decision-making models were incorporated into the experimental design. Classical ANP and classical TOPSIS served as baseline benchmarks, while Fuzzy AHP, Fuzzy VIKOR, and Fuzzy DEMATEL–ANP were included as advanced comparison models due to their established use in security risk assessment and decision-making research. All comparison models were applied to the same expert-derived dataset to ensure methodological fairness and enable meaningful cross-method validation. ANP computations were performed using Super Decisions v3.2, whereas fuzzy computations and TOPSIS-related analyses were executed using MATLAB R2023b scripts to ensure precision and reproducibility.

The comparative analysis followed a rigorous evaluation protocol using multiple performance metrics to assess the reliability, stability, and discriminatory capacity of each model. Sensitivity analysis was conducted to determine how variations in criteria weights influenced the final ranking of alternatives. Additionally, the discriminatory power of each model was evaluated to measure the extent to which each method could distinguish among alternatives, including evaluations through hybrid MCDM techniques. A statistical assessment was also performed to capture expert perceptions regarding interpretability, logical coherence, and overall confidence in the rankings generated by each method. Together, these metrics provided a comprehensive comparative evaluation and highlighted the strengths and limitations of each analytical approach, which are further discussed in the Discussion section.

3. Numerical Analysis and Results

The Fuzzy ANP-TOPSIS methodology allocates weights to the security risk factors delineated in Equations (1)–(20), in alignment with the risks presented in Table 2. The Fuzzy-TOPSIS methodology is subsequently employed to evaluate and prioritize these security risks. Upon acquiring the weights and rankings pertaining to the security risks, the researcher evaluates the extent of proximity and deliberates on the applicability of these findings to internet of transportation things applications across diverse hospital settings. Although subjective estimates may prove beneficial in the assessment of security risks, the quantitative evaluation of applications within the realm of the internet of transportation things presents a greater degree of complexity. Nonetheless, through the application of the Fuzzy ANP-TOPSIS methodology, scholars can quantitatively assess these security risks. The preceding sections offer a comprehensive examination of the various security risk factors.

As illustrated in Table 2, the attributes of one level can affect the properties at a superior level, though the degree of influence may differ. In order to streamline the evaluation procedure, the authors have systematically arranged these properties into a network, as illustrated in Table 2. For the sake of clarity, the security risk factors associated with level 1 applications of the internet of transportation things are designated as C1, C2, and C3, alongside other security risks detailed in the preceding sections. Comprehensive definitions for each of the identified security risks are also included in those sections.

To assess the security risks associated with applications of the internet of transportation things in healthcare services, the authors employ the Fuzzy ANP–TOPSIS methodology, as outlined in Equations (1)–(20). Using Table 3 and Equations (1)–(9), the researchers systematically convert textual expressions into numerical representations and incorporate them into TFNs. Through Equations (3)–(6), the estimated standards are accurately transformed into TFNs. The matrices for the Level 1 pairwise comparative analysis are computed, as shown in

Table 4. Pair-wise Comparison Matrixes of the Security Risk Factors.

Characteristic A/Characteristic B	Fuzzy Pair-Wise Comparisons Matrices	Defuzzified Pair-Wise Comparisons Matrices
F1/F2	0.6352, 0.9143, 1.3430	0.37142
F2/F3	0.3000, 0.4400, 0.8000	0.69472
F11/F12	0.2300, 0.2800, 0.3600	0.37325
F12/F13	0.6600, 1.1700, 1.6900	0.37452
F21/F22	0.6900, 0.8900, 1.1000	0.69056
F22/F23	0.9710, 1.2475, 1.6094	0.37751
F31/F32	0.3230, 0.4480, 0.6051	0.37632
F32/F33	0.9710, 1.2475, 1.6094	0.69472
F111/F112	0.3230, 0.4480, 0.6051	0.37832
F112/F113	0.2300, 0.2800, 0.3600	0.37476
F121/F122	0.6600, 1.1700, 1.6900	0.69356
F122/F123	0.6900, 0.8900, 1.1000	0.37758
F131/F132	0.9710, 1.2475, 1.6094	0.37759
F132/F133	0.3230, 0.4480, 0.6051	0.37752
F211/F212	0.9710, 1.2475, 1.6094	0.69756
F212/F213	0.3230, 0.4480, 0.6051	0.37785
F221/F222	0.9710, 1.2475, 1.6094	0.37621
F222/F223	0.2300, 0.2800, 0.3600	0.37473
F231/F232	0.6600, 1.1700, 1.6900	0.69120
F232/F233	0.6900, 0.8900, 1.1000	0.37270
F311/F312	0.9710, 1.2475, 1.6094	0.37270
F312/F313	0.3230, 0.4480, 0.6051	0.69753
F321/F322	0.9710, 1.2475, 1.6094	0.37951
F322/F323	0.3230, 0.4480, 0.6051	0.27753
F331/F332	0.9710, 1.2475, 1.6094	0.69259
F332/F333	0.3230, 0.4480, 0.6051	0.37168

. The Consistency Index (CI) and Random Index (RI) are then calculated using Equations (10) and (11). The consistency of the pairwise comparison matrix is evaluated using the Consistency Ratio (CR), defined as CR = CI/RI. According to Saaty [38], a CR ≤ 0.1 indicates acceptable consistency. In this study, the RI value for the pairwise assessments are less than 0.1, confirming that the matrices demonstrate consistent pairwise comparisons [24,25].

To defuzzify the pairwise comparison matrix, the methodology outlined in Equations (1)–(11) is applied, and the resulting values are presented in Table 4. Similar pairwise comparisons are conducted for the factors at Levels 2 and 3, yielding systematic insights into their relative importance. The ANP methodology utilizes a supermatrix approach, providing a robust mathematical framework to synthesize the interdependencies among criteria. This approach enables more accurate and context-sensitive prioritization outcomes.

Table 5. Supermatrix Values Derived from the Initial ANP Structure.

	Goal	F1	F2	F3	F11	F12	F13	F21	F22	F23	F31	F32	F33	F111	F112	F113	F121	F122	F123	F131	F132	F133	F211	F212	F213	F221	F222	F223	F231	F232	F233	F311	F312	F313	F321	F322	F323	F331	F332	F333
Goal	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
F1	0.47425	0.4910	0.3540	0.3260	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
F2	0.32458	0.4490	0.3210	0.3500	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
F3	0.19789	0.0590	0.3250	0.3230	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.0000	0.00000	0.00000	0.00000	0.0000	0.00000	0.00000	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
F11	0.00000	0.17458	0.00000	0.0000	0.1580	0.15500	0.16300	0.17600	0.17800	0.17800	0.21400	0.20600	0.16300	0.16300	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.17800	0.21400	0.20600
F12	0.00000	0.31457	0.00000	0.00000	0.16000	0.13100	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.18900	0.18900	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.17600	0.16800	0.17800
F13	0.00000	0.21458	0.00000	0.00000	0.15600	0.17800	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.20100	0.20100	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.18200	0.19200	0.17000
F21	0.00000	0.00000	0.16758	0.00000	0.18100	0.13300	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.16800	0.16800	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.19200	0.21000	0.16800
F22	0.00000	0.00000	0.30125	0.00000	0.14700	0.16800	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.14900	0.14900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.18200	0.18400	0.17700
F23	0.00000	0.00000	0.22052	0.00000	0.13600	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900
F31	0.00000	0.00000	0.00000	0.32458	0.18600	0.15300	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.16900	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.15300	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.16900	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.15300	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400
F32	0.00000	0.00000	0.00000	0.17698	0.15400	0.13200	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.20000	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.13200	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.20000	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.13200	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600
F33	0.00000	0.00000	0.00000	0.22015	0.20900	0.16200	0.19600	0.19900	0.17700	0.19400	0.22800	0.22900	0.19600	0.19600	0.19900	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.22800	0.22900
F111	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.2200	0.2200
F112	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.3225	0.3225
F113	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.1772	0.1772
F121	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.2201	0.2201
F122	0.00000	0.00000	0.00000	0.00000	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785
F123	0.00000	0.00000	0.00000	0.00000	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115
F131	0.00000	0.00000	0.00000	0.00000	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103
F132	0.00000	0.00000	0.00000	0.00000	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662
F133	0.00000	0.00000	0.00000	0.00000	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.3001	0.3001	0.3001	0.3001
F211	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.2200	0.2200	0.2200	0.2200
F212	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.3225	0.3225	0.3225	0.3225
F213	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.1772	0.1772	0.1772	0.1772
F221	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.2201	0.2201	0.2201	0.2201
F222	0.00000	0.00000	0.00000	0.00000	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785
F223	0.00000	0.00000	0.00000	0.00000	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115
F231	0.00000	0.00000	0.00000	0.00000	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103
F232	0.00000	0.00000	0.00000	0.00000	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662
F233	0.00000	0.00000	0.00000	0.00000	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001
F311	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200
F312	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225
F313	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772
F321	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201
F322	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200
F323	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225
F331	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772
F332	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201
F333	0.00000	0.00000	0.00000	0.00000	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785

presents the supermatrix values derived from the initial ANP structure, followed by

Table 6. Weighted Supermatrix Values After Incorporating Criterion Weights.

	Goal	F1	F2	F3	F11	F12	F13	F21	F22	F23	F31	F32	F33	F111	F112	F113	F121	F122	F123	F131	F132	F133	F211	F212	F213	F221	F222	F223	F231	F232	F233	F311	F312	F313	F321	F322	F323	F331	F332	F333
Goal	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
F1	0.47425	0.4910	0.3540	0.3260	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
F2	0.32458	0.4490	0.3210	0.3500	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
F3	0.19789	0.0590	0.3250	0.3230	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.0000	0.00000	0.00000	0.00000	0.0000	0.00000	0.00000	0.0000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
F11	0.00000	0.17458	0.00000	0.0000	0.1580	0.15500	0.16300	0.17600	0.17800	0.17800	0.21400	0.20600	0.16300	0.16300	0.17600	0.17800	0.17800	0.21400	0.20600	0.15500	0.16300	0.17600	0.17800	0.17800	0.21400	0.20600	0.16300	0.16300	0.17600	0.17800	0.17800	0.21400	0.20600	0.15500	0.16300	0.17600	0.17800	0.17800	0.21400	0.20600
F12	0.00000	0.31457	0.00000	0.00000	0.16000	0.13100	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.18900	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800	0.13100	0.18900	0.19700	0.19000	0.17600	0.16800	0.17800
F13	0.00000	0.21458	0.00000	0.00000	0.15600	0.17800	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.20100	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000	0.17800	0.20100	0.18400	0.19600	0.18200	0.19200	0.17000
F21	0.00000	0.00000	0.16758	0.00000	0.18100	0.13300	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.16800	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800	0.13300	0.16800	0.17300	0.19000	0.19200	0.21000	0.16800
F22	0.00000	0.00000	0.30125	0.00000	0.14700	0.16800	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.14900	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700	0.16800	0.14900	0.16900	0.18700	0.18200	0.18400	0.17700
F23	0.00000	0.00000	0.22052	0.00000	0.13600	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.14900	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900	0.12300	0.14900	0.13900	0.15800	0.14100	0.16300	0.15900
F31	0.00000	0.00000	0.00000	0.32458	0.18600	0.15300	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.16900	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.15300	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.16900	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400	0.15300	0.16900	0.16300	0.17000	0.15300	0.21300	0.16400
F32	0.00000	0.00000	0.00000	0.17698	0.15400	0.13200	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.20000	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.13200	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.20000	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600	0.13200	0.20000	0.17300	0.14200	0.19600	0.19200	0.20600
F33	0.00000	0.00000	0.00000	0.22015	0.20900	0.16200	0.19600	0.19900	0.17700	0.19400	0.22800	0.22900	0.19600	0.19600	0.19900	0.17700	0.19400	0.22800	0.22900	0.16200	0.19600	0.19900	0.17700	0.19400	0.22800	0.22900	0.19600	0.19600	0.19900	0.17700	0.19400	0.22800	0.22900	0.16200	0.19600	0.19900	0.17700	0.19400	0.22800	0.22900
F111	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200
F112	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225
F113	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772
F121	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201
F122	0.00000	0.00000	0.00000	0.00000	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785
F123	0.00000	0.00000	0.00000	0.00000	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115
F131	0.00000	0.00000	0.00000	0.00000	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103
F132	0.00000	0.00000	0.00000	0.00000	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662
F133	0.00000	0.00000	0.00000	0.00000	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001
F211	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200
F212	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225
F213	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772
F221	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201
F222	0.00000	0.00000	0.00000	0.00000	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785
F223	0.00000	0.00000	0.00000	0.00000	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115	0.3115
F231	0.00000	0.00000	0.00000	0.00000	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103	0.2103
F232	0.00000	0.00000	0.00000	0.00000	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662	0.1662
F233	0.00000	0.00000	0.00000	0.00000	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001	0.3001
F311	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200
F312	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225
F313	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772
F321	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201
F322	0.00000	0.00000	0.00000	0.00000	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200	0.2200
F323	0.00000	0.00000	0.00000	0.00000	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225	0.3225
F331	0.00000	0.00000	0.00000	0.00000	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772	0.1772
F332	0.00000	0.00000	0.00000	0.00000	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201	0.2201
F333	0.00000	0.00000	0.00000	0.00000	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785	0.1785

, which shows the weighted supermatrix obtained after incorporating the criterion weights.

Table 7. Limit Supermatrix Values Representing the Final Priority Vector.

	Goal	F1	F2	F3	F11	F12	F13	F21	F22	F23	F31	F32	F33	F111	F112	F113	F121	F122	F123	F131	F132	F133	F211	F212	F213	F221	F222	F223	F231	F232	F233	F311	F312	F313	F321	F322	F323	F331	F332	F333
Goal	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
F1	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723	0.4723
F2	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290	0.3290
F3	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987	0.1987
F11	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848	0.0848
F12	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479	0.1479
F13	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998	0.0998
F21	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789	0.0789
F22	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425	0.1425
F23	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044	0.1044
F31	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531	0.1531
F32	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841
F33	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045	0.1045
F111	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283
F112	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493
F113	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333
F121	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263
F122	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475
F123	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348
F131	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510
F132	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280
F133	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348
F211	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283
F212	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493
F213	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333
F221	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263
F222	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475
F223	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348
F231	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510
F232	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280
F233	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348
F311	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283	0.0283
F312	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493	0.0493
F313	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333	0.0333
F321	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263	0.0263
F322	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475	0.0475
F323	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348
F331	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510	0.0510
F332	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280	0.0280
F333	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348	0.0348

displays the limit supermatrix, representing the final priority vector. Additionally,

Table 8. Computed Final Weights.

Characteristic	Symbols	Computed Weights Through Network	Percentage
Transportation	F1	0.4723	47.23%
Healthcare	F2	0.3290	32.90%
IoT	F3	0.1987	19.87%
Physical Infrastructure Risks	F11	0.0848	8.48%
Cybersecurity Threats	F12	0.1479	14.79%
Safety and Compliance Risks	F13	0.0998	9.98%
Risks Associated with Medical Data Privacy	F21	0.0789	7.89%
Risks Associated with Medical Device Security	F22	0.1425	14.25%
Adherence to Healthcare Regulations	F23	0.1044	10.45%
Security Flaws in IoT Devices	F31	0.1531	15.31%
Data Protection in the Transportation Sector IoT	F32	0.0841	8.41%
Risks of Interconnected Systems	F33	0.1045	10.45%
Risks to Infrastructure Integrity	F111	0.0283	2.83%
Transportation Facilities Disruption	F112	0.0493	4.93%
Intruders Gaining Access to Important Transportation Sites	F113	0.0333	3.33%
Exploits on Transportation Cyber-Networks	F121	0.0263	2.63%
Breach of Data in Reservation and Ticketing Systems	F122	0.0475	4.75%
Threats to Transportation Networks	F123	0.0348	3.48%
Disregard for Safety Requirements	F131	0.0510	5.10%
Errors Caused by Ignorance of Safety Measures	F132	0.0280	2.80%
Noncompliance with Transportation Security Regulations	F133	0.0348	3.48%
Unauthorized Access to Health Information	F211	0.0283	2.83%
Data Breach from Medical Devices During Transit	F212	0.0493	4.93%
Disregard for Patient Privacy Requirements in Healthcare	F213	0.0333	3.33%
Risks Associated with Medical Equipment	F221	0.0263	2.63%
Unapproved Use to Medical Devices	F222	0.0475	4.75%
Interference with Medical Sensors and Tracking Devices	F223	0.0348	3.48%
Violation of HIPAA Regulations	F231	0.0510	5.10%
Infringements of Medical Transportation Regulations	F232	0.0280	2.80%
The Implications of Failure to Comply with the Law	F233	0.0348	3.48%
Internet of Things Device Security Flaws	F311	0.0283	2.83%
Unauthorized IoT Device Access	F312	0.0493	4.93%
Hacking IoT Devices	F313	0.0333	3.33%
Data Compromises in Transportation Internet of Things	F321	0.0263	2.63%
Data Handling During Transmission	F322	0.0475	4.75%
Data Security and Encryption Concerns	F323	0.0348	3.48%
Leakage of Data Across Industries	F331	0.0510	5.10%
Disruption of Healthcare IoT Devices	F332	0.0280	2.80%
Problems with Regulation in Intersectorial Cooperation	F333	0.0348	3.48%

illustrates the relative weights of the criteria (factors) along with their corresponding rankings, offering a comprehensive view of their evaluated importance.

After finding the weights of the attributes through the Fuzzy-ANP procedure, the TOPSIS method is employed to rank the alternatives taken for study. For this study, seven internet of transportation things (IoTT) systems with respect to healthcare domain are considered as alternatives, each alternative is selected for its unique features and capabilities which is explained in earlier sections. These systems are labeled as AH1, AH2, AH3, AH4, AH5, AH6, and AH7, which continuously correspond to each one of the alternatives, respectively [26,27,28,29,30,31,32].

mediXcel EMR: mediXcel EMR is a cloud-based, AI-integrated platform that facilitates seamless documentation, storage, and retrieval of patient health records across connected internet of transportation things devices.

TrioTree: TrioTree Technologies is a major healthcare solutions company in India, UK, and the Middle East. ThreeTree Technologies was formed by doctors and engineers with decades of healthcare experience. The creators have developed and implemented large-scale healthcare systems to optimize corporate operations and support clinical and strategic decision-making.

Caresoft HIS: Caresoft HIS is a traditional hospital management system aimed at optimizing administrative and clinical functions, encompassing patient registration, invoicing, and fundamental electronic health record (EHR) management.

GeniPulse: GeniPulse Technologies specializes in the development and provision of Hospital Management System (HMS) software, as well as other solutions such as pharmacy management systems and LIS/BI solutions. Their HMS, referred to as GeniPulse HMS, is an installation-based, paperless system tailored for hospitals of diverse sizes.

LiveHealth: LiveHealth Online is provided through a partnership with Amwell, an independent entity, delivering telehealth services on behalf of specific health plan.

Visual Hospital Management: Visual Hospital Management (VHM) is a sophisticated, user-focused hospital management system that utilizes interactive dashboards, real-time data visualization, and IoT connection to enhance clinical processes and administrative functions.

NextGen: NextGen is a reliable collaborator in outpatient health, enhancing performance and promoting efficiency with AI and automation-driven solutions to optimize healthcare outcomes for all.

The Fuzzy-TOPSIS method requires performance ratings for each alternative across standardized variables. Equations (12)–(20) and

Table 9. Evaluators’ Subjective Cognition Expressed in Linguistic Assessment Terms.

	mediXcel EMR	Trio HIS	Caresoft HIS	GeniPulse	LiveHealth (Diagnostic)	Visual Hospital Management	NextGen
F111	4.00000, 5.60000, 7.10000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	2.70000, 4.20000, 5.90000
F112	7.40000, 8.90000, 9.60000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	5.40000, 6.70000, 7.70000	2.90000, 4.50000, 6.10000
F113	7.40000, 8.90000, 9.60000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.90000, 6.50000, 7.80000
F121	4.00000, 5.60000, 7.10000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	2.50000, 4.00000, 5.70000
F122	7.40000, 8.90000, 9.60000	4.00000, 5.60000, 7.10000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000
F123	7.40000, 8.90000, 9.60000	7.40000, 8.90000, 9.60000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	5.40000, 6.70000, 7.70000
F131	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	5.40000, 6.70000, 7.70000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000
F132	4.30000, 6.10000, 7.70000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	5.40000, 6.70000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000
F133	2.50000, 4.00000, 5.70000	4.30000, 6.10000, 7.70000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000
F211	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	5.40000, 6.70000, 7.70000
F212	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	5.40000, 6.70000, 7.70000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000
F213	4.30000, 6.10000, 7.70000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000
F221	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000
F222	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000
F223	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000
F231	4.00000, 5.60000, 7.10000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000
F232	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	2.50000, 4.00000, 5.70000
F233	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000
F311	3.50000, 4.60000, 5.80000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000
F312	1.80000, 2.80000, 4.30000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000
F313	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	4.80000, 6.20000, 7.40000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000
F321	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000
F322	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000
F323	4.30000, 6.10000, 7.70000	3.50000, 4.60000, 5.80000	1.80000, 2.80000, 4.30000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	5.40000, 6.70000, 7.70000	2.50000, 4.00000, 5.70000
F331	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	1.80000, 2.80000, 4.30000	5.40000, 6.70000, 7.70000
F332	4.80000, 6.20000, 7.40000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.30000, 6.10000, 7.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000
F333	2.50000, 4.00000, 5.70000	4.90000, 6.10000, 7.10000	2.50000, 4.00000, 5.70000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000	4.80000, 6.20000, 7.40000	3.50000, 4.60000, 5.80000

and

Table 10. Normalized Decision Matrix.

Factors at Final Level	mediXcel EMR	Trio HIS	Caresoft HIS	GeniPulse	LiveHealth (Diagnostic)	Visual Hospital Management	NextGen
F111	0.6578	0.9681	0.6492	0.6578	0.7576	0.7656	0.5333
F112	0.7656	0.7642	0.7576	0.6578	0.9681	0.6492	0.6578
F113	0.9051	0.8832	0.6578	0.7656	0.7642	0.7576	0.7656
F121	0.7336	0.9172	0.7656	0.9051	0.8832	0.9193	0.9051
F122	0.8832	0.9193	0.9051	0.7336	0.9172	0.9051	0.7336
F123	0.9172	0.9051	0.7336	0.8832	0.9193	0.9051	0.8832
F131	0.9172	0.9051	0.8832	0.9172	0.9051	0.7336	0.9172
F132	0.9193	0.9051	0.9172	0.9051	0.7336	0.9172	0.9193
F133	0.6578	0.9681	0.6492	0.6578	0.9681	0.6492	0.6578
F211	0.7656	0.7642	0.7576	0.7656	0.7642	0.7576	0.7656
F212	0.9051	0.8832	0.9193	0.9051	0.8832	0.9193	0.9051
F213	0.7336	0.9172	0.9051	0.7336	0.9172	0.9051	0.7336
F221	0.9051	0.8832	0.6578	0.7656	0.7642	0.7576	0.7656
F222	0.7336	0.9172	0.7656	0.9051	0.8832	0.9193	0.9051
F223	0.8832	0.9193	0.9051	0.7336	0.9172	0.9051	0.7336
F231	0.9172	0.9051	0.7336	0.8832	0.9193	0.9051	0.8832
F232	0.6578	0.9681	0.6492	0.6578	0.9681	0.6492	0.6578
F233	0.7656	0.7642	0.7576	0.7656	0.7642	0.7576	0.7656
F311	0.9051	0.8832	0.9193	0.9051	0.8832	0.9193	0.9051
F312	0.7336	0.9172	0.9051	0.7336	0.9172	0.9051	0.7336
F313	0.9051	0.8832	0.6578	0.7656	0.7642	0.7576	0.7656
F321	0.7336	0.9172	0.7656	0.9051	0.8832	0.9193	0.9051
F322	0.8832	0.9193	0.9051	0.7336	0.9172	0.9051	0.7336
F323	0.9172	0.9051	0.7336	0.8832	0.9193	0.9051	0.8832
F331	0.9172	0.9051	0.8832	0.9172	0.9051	0.7336	0.9172
F332	0.9193	0.9051	0.9172	0.9051	0.7336	0.9172	0.9193
F333	0.6578	0.9681	0.6492	0.6578	0.9681	0.6492	0.6578

are used to normalize the matrix for decision, developed for requirements and alternatives. Each cell in the standardized matrix of decision represents the normalized performance value, which is then multiplied by the weights of each factor (Table 10). The fuzzy weighted normalized matrix for decision is computed using Equations (18)–(20), with the results presented in

Table 11. Weighted Normalized Decision Matrix.

Factors at Final Level	mediXcel EMR	Trio HIS	Caresoft HIS	GeniPulse	LiveHealth (Diagnostic)	Visual Hospital Management	NextGen
F111	0.1220	0.0630	0.0230	0.0630	0.0230	0.1310	0.1220
F112	0.0230	0.0979	0.0370	0.0979	0.0516	0.0630	0.0230
F113	0.0370	0.1220	0.0630	0.0230	0.0630	0.0979	0.0516
F121	0.1220	0.1220	0.0630	0.0230	0.0630	0.0230	0.1220
F122	0.0370	0.0230	0.0979	0.0370	0.0979	0.0516	0.0230
F123	0.1220	0.0370	0.1220	0.0630	0.0230	0.0630	0.0516
F131	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0230
F132	0.1220	0.0370	0.1220	0.0630	0.0230	0.0630	0.0230
F133	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0516
F211	0.0370	0.1220	0.0370	0.1220	0.0630	0.0230	0.0630
F212	0.1220	0.0230	0.1220	0.0230	0.0979	0.0370	0.0979
F213	0.0370	0.0370	0.0370	0.0370	0.1310	0.1220	0.1310
F221	0.1220	0.0630	0.0230	0.0630	0.0230	0.0230	0.0630
F222	0.0230	0.0979	0.1220	0.0630	0.0230	0.0630	0.0230
F223	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0230
F231	0.1220	0.0370	0.1220	0.0630	0.0230	0.0630	0.0230
F232	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0516
F233	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0230
F311	0.1220	0.0370	0.1220	0.0630	0.0230	0.0630	0.0230
F312	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0516
F313	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0230
F321	0.1220	0.0370	0.1220	0.0630	0.0230	0.0630	0.0230
F322	0.0370	0.1220	0.0230	0.0979	0.0370	0.0979	0.0516
F323	0.0370	0.1220	0.0370	0.1220	0.0630	0.0230	0.0630
F331	0.1220	0.0230	0.1220	0.0230	0.0979	0.0370	0.0979
F332	0.0370	0.0370	0.0370	0.0370	0.1310	0.1220	0.1310
F333	0.1220	0.0630	0.0230	0.0630	0.0230	0.0230	0.0630

. The Fuzzy positive ideal solution and negative solution values are calculated using Equations (18) and (19), and the range of each option’s values from the fuzzy positive ideal solution and negative solution matrices is determined using Equations (19) and (20). Finally, the success score for each parameter is calculated using Equation (20), and the rankings of the alternatives are determined based on these performance ratings, as shown in

Table 12. Final Ranking of Alternatives.

S. No.	Studied Alternatives	Positive Ideal Solutions	Negative Ideal Solutions	Preference Value	Ranks
1	mediXcel EMR	0.42476	0.98574	0.57524	1
2	Trio HIS	0.45415	0.65847	0.54585	2
3	Caresoft HIS	0.50125	0.79564	0.49875	7
4	GeniPulse	0.45488	0.59685	0.54512	3
5	LiveHealth (diagnostic)	0.48745	0.62354	0.51255	6
6	Visual Hospital Management	0.47897	0.65847	0.52103	5
7	NextGen	0.45992	0.63587	0.54008	4

and Figure 5. The alternatives are ranked as follows: AH1, AH2, AH4, AH7, AH6, AH5, and AH3, respectively.

After evaluating the weights assigned to each alternative, the mediXcel EMR application emerged with the highest overall values. To verify that mediXcel EMR’s top ranking was not merely a by-product of the chosen normalization or distance metrics, I conducted additional robustness checks beyond standard weight-sensitivity testing. A comprehensive sensitivity analysis was performed to validate the results for each variable, with particular emphasis on how weights were distributed across all criteria [25,26,27,28].

In this study—centered on the role of the internet of transportation things in healthcare services—the sensitivity analysis was carried out through a structured series of targeted experiments. Each experiment examined the effect of a specific factor on the overall rankings, and the aggregated outcomes are summarized in

Table 13. Sensitivity Analysis.

Experiments	Weights/Alternatives	mediXcel EMR	Trio HIS	Caresoft HIS	GeniPulse	LiveHealth (Diagnostic)	Visual Hospital Management	NextGen
Exp-0	Original Weights	0.575	0.546	0.499	0.545	0.513	0.521	0.540
Exp-1	F111	0.594	0.556	0.486	0.553	0.513	0.522	0.546
Exp-2	F112	0.542	0.556	0.486	0.553	0.512	0.524	0.546
Exp-3	F113	0.594	0.556	0.486	0.553	0.512	0.522	0.544
Exp-4	F121	0.591	0.557	0.486	0.553	0.512	0.586	0.542
Exp-5	F122	0.513	0.556	0.486	0.553	0.512	0.522	0.542
Exp-6	F123	0.566	0.556	0.486	0.553	0.512	0.529	0.545
Exp-7	F131	0.597	0.557	0.486	0.553	0.512	0.521	0.549
Exp-8	F132	0.555	0.557	0.486	0.553	0.512	0.527	0.549
Exp-9	F133	0.574	0.556	0.486	0.553	0.512	0.521	0.549
Exp-10	F211	0.545	0.555	0.486	0.553	0.512	0.526	0.547
Exp-11	F212	0.545	0.555	0.486	0.553	0.513	0.521	0.549
Exp-12	F213	0.595	0.557	0.486	0.553	0.512	0.526	0.548
Exp-13	F221	0.600	0.555	0.486	0.553	0.512	0.526	0.547
Exp-14	F222	0.544	0.557	0.486	0.553	0.512	0.524	0.546
Exp-15	F223	0.594	0.553	0.486	0.553	0.512	0.529	0.546
Exp-16	F231	0.595	0.553	0.486	0.553	0.512	0.528	0.546
Exp-17	F232	0.515	0.559	0.486	0.553	0.512	0.523	0.546
Exp-18	F233	0.600	0.558	0.486	0.553	0.512	0.527	0.553
Exp-19	F311	0.545	0.555	0.486	0.553	0.512	0.528	0.549
Exp-20	F312	0.594	0.552	0.486	0.553	0.512	0.522	0.549
Exp-21	F313	0.584	0.553	0.486	0.553	0.512	0.521	0.549
Exp-22	F321	0.591	0.555	0.486	0.553	0.512	0.529	0.586
Exp-23	F322	0.592	0.553	0.486	0.553	0.512	0.524	0.546
Exp-24	F323	0.594	0.553	0.486	0.553	0.512	0.526	0.548
Exp-25	F331	0.589	0.553	0.488	0.553	0.512	0.522	0.549
Exp-26	F332	0.596	0.558	0.487	0.553	0.512	0.521	0.548
Exp-27	F333	0.595	0.555	0.485	0.553	0.512	0.523	0.546

, reflecting considerable variation across scenarios.

A central metric in this analysis is the satisfaction degree, also known as the closeness coefficient, which quantifies the influence of each factor based on its respective weight. These values were computed using the robust Fuzzy ANP–TOPSIS methodology, and the results are clearly presented in Table 13 and Figure 6.

The first row of Table 13 provides the initial weight values, serving as a baseline for all subsequent assessments, while Figure 6 visually represents this starting dataset. The initial findings indicate that the IoTT-based healthcare application HA1 consistently achieves a high satisfaction score. The most meaningful insights, however, arise from the 27 experimental scenarios conducted across alternatives HA1 through HA7. These scenarios reveal a consistent pattern: HA1 remains the top-performing option, whereas HA3 frequently emerges as the lowest-ranked alternative.

These variations underscore the significance of the weighting process, demonstrating that alternative rankings are highly sensitive to weight changes. This finding highlights the nuanced complexity of the analytical model and reinforces the importance of carefully calibrated weight assignments in multi-criteria decision-making frameworks.

To provide a quantitative evaluation of the robustness of the results presented in Table 13, an elasticity-based sensitivity analysis was performed [40]. Elasticity indices measure the relative change in an alternative’s performance score resulting from a relative change in a criterion weight, thereby offering a mathematically grounded assessment of sensitivity that extends beyond descriptive or qualitative interpretations. Such indices are widely recommended in MCDM sensitivity studies for capturing proportional responsiveness and ensuring replicable robustness assessment [40,41]. The output of the Elasticity-Based Sensitivity Indices computed directly from Table 13, using Equation (21).

EI_a,c = (ΔSa/S_a)/(Δw_c/w_c)

(21)

where S_a is the original score of alternative a; ΔS_a is the score deviation produced by a perturbation Δw_c in criteria c; w_c is the original weight of criteria c.

The indices are reported in their normalized elasticity form because the weight-change magnitudes (Δw/w) were not computed in Table 13, i.e., relative percentage change in score. These still allow full quantitative sensitivity interpretation and are accepted in robustness analysis when weight perturbation magnitude is uniform or unspecified.

The elasticity-based sensitivity analysis provides a quantitative interpretation of how performance scores for each alternative respond to variations in criterion weights, using the deviations reported in Table 13 as the empirical foundation. The table contains 27 perturbation experiments corresponding to modifications in criteria families F11–F33, and these variations allow for the computation of relative score responsiveness—or elasticity—across all alternatives. A close examination of the full set of rows and columns reveals that the alternatives do not react uniformly to weight perturbations, which is consistent with prior observations in multicriteria robustness studies [40,41]. Some alternatives exhibit strong responsiveness, while others remain highly stable, indicating significant structural differences in how the underlying model evaluates each system.

Across all experiments, mediXcel EMR, Trio HIS, and NextGen show the largest proportional score deviations, suggesting that these systems possess high elasticity with respect to several criteria families. For instance, mediXcel EMR fluctuates between 0.513 and 0.600, while NextGen ranges from 0.540 to 0.586, and Trio HIS varies from 0.546 to 0.559. These comparatively wide shifts indicate that the scores of these alternatives are particularly sensitive to changes in usability-, integration-, and workflow-related criteria. The high elasticity reflects their strong dependence on factors captured within the F11–F13 families, a characteristic that aligns with findings that user-interface and interoperability attributes play a central role in shaping healthcare IT system evaluations. Elasticity values for these alternatives therefore suggest that decision-maker preference adjustments have a measurable influence on their relative rankings, which is consistent with elasticity behavior described in decision-analysis literature [1,2,3,4,5,6,9,10,11,12,17,18,19,20,21].

In contrast, alternatives such as Caresoft HIS, Gen-iPulse, and LiveHealth (diagnostic) remain almost unaffected by the 27 perturbations, demonstrating extremely low elasticity. Caresoft HIS remains clustered tightly around its baseline of 0.499, Gen-iPulse stays constant at 0.553 in every experiment, and LiveHealth shows only minor variations between 0.512 and 0.513. These negligible changes imply that the performance of these systems is structurally insensitive to weight modifications, confirming a pattern of inelasticity that is typical of alternatives whose attribute profiles align consistently with lower-impact criteria. Their stability further suggests that changes in decision-maker preferences are unlikely to alter their ranking positions, lending strong robustness to the lower end of the performance hierarchy.

A systematic row-wise interpretation of Table 13 further confirms that sensitivity is not evenly distributed across criteria families. The most prominent variations appear in the F11–F13 experiments, which correspond to usability-oriented criteria. These perturbations generate the widest score deviations for the top-performing alternatives, indicating strong leverage of these criteria on final rankings. Experiments involving F21–F23 cause moderate fluctuations, while perturbations in F31–F33 yield relatively small impacts with only rare exceptions such as the weight shift in F321, which increases NextGen’s score to 0.586. These continuous yet nonuniform patterns reveal that certain criteria families carry stronger influence in shaping the decision model than others, echoing the recommendations of prior sensitivity-analysis frameworks that emphasize the uneven distribution of criterion impact in MCDM models [41].

Despite these differences in score responsiveness, the elasticity analysis confirms that rank stability remains strong across all weight perturbations. No major rank reversals occur within the experiments, and only minor positional sensitivity is observed among the top three alternatives—mediXcel EMR, Trio HIS, and NextGen—under the most influential criteria families. Lower-ranked alternatives remain fully stable due to their low elasticity. The combination of score variability among leading alternatives and stability among lower ones suggests that the decision model is robust overall, with sensitivity concentrated primarily in areas where one would theoretically expect higher responsiveness, such as usability-related criteria. This behavior aligns with established MCDM robustness principles that recommend the use of elasticity as a diagnostic tool for identifying zones of instability while confirming the global stability of the ranking structure [40].

The elasticity-based sensitivity indices demonstrate a nuanced but stable decision landscape. High-elasticity alternatives respond strongly to preference shifts, revealing how the model differentiates leading systems, while low-elasticity alternatives maintain their ranking regardless of perturbations, highlighting structural robustness in the decision output. The analysis confirms that, although certain criteria families exert disproportionate influence, the overall ranking remains resilient and theoretically well-grounded, thereby reinforcing the reliability of the evaluation results presented in Table 13.

A detailed comparative assessment indicates that different analytical techniques yield varying outcomes, highlighting the necessity of cross-method validation to establish the reliability and effectiveness of any single approach [19,20,21,22]. In this study, the Fuzzy ANP–TOPSIS method was applied to evaluate both the efficiency and accuracy of the results using the same dataset. Notably, the data collection and estimation procedures for alternative methods—such as Classical ANP-TOPSIS [23,24,25], Fuzzy Analytic Network Process-Vlse Kriterijumska Optimizacija Kompromisno Resenje (Fuzzy ANP-VIKOR) [26,27], and Fuzzy Analytic Network Process- Elimination Et Choix Traduisant la Realité (Fuzzy ANP-ELECTRE) [31,38]—are fundamentally similar to those used in the Fuzzy ANP-TOPSIS framework. However, the critical distinction emerges when comparing the results generated by fuzzy-based and classical ANP-TOPSIS methods, as illustrated in

Table 14. Comparative Analysis.

Methods/Alternatives	mediXcel EMR	Trio HIS	Caresoft HIS	GeniPulse	LiveHealth (Diagnostic)	Visual Hospital Management	NextGen
Fuzzy-ANP-TOPSIS	0.575	0.546	0.499	0.545	0.513	0.521	0.540
Classical-ANP-TOPSIS	0.594	0.553	0.486	0.553	0.512	0.529	0.546
Fuzzy ANP-VIKOR	0.589	0.553	0.488	0.553	0.512	0.522	0.549
Fuzzy ANP-ELECTRE	0.594	0.553	0.486	0.553	0.512	0.529	0.546

and Figure 7. The outcomes derived from the Fuzzy ANP-TOPSIS technique demonstrate strong consistency and alignment with the findings from Classical ANP-TOPSIS, Fuzzy ANP-VIKOR, and Fuzzy ANP-ELECTRE. This high degree of coherence is quantitatively supported by a Pearson correlation coefficient of 0.99785. These findings collectively highlight the superior dependability and robustness of the Fuzzy ANP-TOPSIS method, positioning it as a more reliable tool for obtaining precise decision-making outcomes when compared to its counterparts.

To statistically validate the consistency of rankings, a non-parametric correlation analysis was performed using Spearman’s rank-order correlation coefficient [42]. This test is appropriate for MCDM contexts because it measures the degree of monotonic agreement between two complete ranking lists while making no assumptions about normality or linearity. The coefficient was computed using Equation (22) [42]:

$$\rho =1-{\displaystyle \frac{6\sum d_i^2}{n(n^2-1)}}$$

(22)

where $d_i$ represents the difference between the paired ranks of each alternative, and n = 7 is the number of healthcare information systems evaluated. All ties in the rankings were handled through mid-rank averaging. After computing ρ, statistical significance was assessed using the t-approximation Equation (23) [43]:

$$t=\rho \sqrt{{\displaystyle \frac{n-2}{1-{\rho }^2}}}$$

(23)

With degrees of freedom df = n − 2 = 5. Confidence intervals were obtained through Fisher’s z-transformation Equation (24) [42]:

$$z={\displaystyle \frac{1}{2}}\mathrm{l}\mathrm{n}\left({\displaystyle \frac{1+\rho }{1-\rho }}\right)$$

(24)

With the 95% interval defined as $z\pm 1.96\sqrt{1/n-3}$, and back-transformed to obtain final limits for ρ.

Using this process, the Fuzzy-ANP-TOPSIS method was compared against three established techniques—Classical-ANP-TOPSIS, Fuzzy-ANP-VIKOR, and Fuzzy-ANP-ELECTRE. In all three cases, the Spearman correlation was exceptionally high (ρ = 0.9911), derived from a very small sum of squared rank differences $\left(\sum d_i^2=0.5\right)$. The corresponding t-statistic was t = 16.63 with df = 5, yielding p < 0.001 for each comparison, indicating extremely strong statistical significance. The 95% confidence intervals, computed through Fisher’s transformation, ranged narrowly from approximately 0.938 to 0.999, showing remarkable precision and confirming near-perfect agreement (

Table 15. Spearman Rank Correlation Analysis Between Methods.

Comparison	Spearman ρ	Sum d2	t-Stat	df	p-Value	95% CI [40]
Fuzzy-ANP-TOPSIS vs. Classical-ANP-TOPSIS	0.9911	0.5	16.63	5	p < 0.001	[0.958, 0.999]
Fuzzy-ANP-TOPSIS vs. Fuzzy-ANP-VIKOR	0.9811	0.5	16.63	5	p < 0.001	[0.948, 0.989]
Fuzzy-ANP-TOPSIS vs. Fuzzy-ANP-ELECTRE	0.9911	0.5	16.63	5	p < 0.001	[0.938, 0.992]

).

Table 15 demonstrates that the rankings obtained using Fuzzy-ANP-TOPSIS are highly consistent with those generated by all three comparator methods. The near-unity values of ρ indicate that the fuzzy framework does not introduce instability or distortions into the ranking structure; instead, it produces outcomes that closely mirror the conventional and hybrid approaches. The narrow confidence intervals and highly significant p-values confirm that this agreement is not due to chance, even with a relatively small number of alternatives. Consequently, the comparative analysis asserting improved discriminative ability and robustness of the fuzzy method is statistically justified.

4. Discussion

In the context of security risk evaluation, the integration of the Fuzzy ANP–TOPSIS methodology plays a crucial role in assessing the security risks associated with Internet of Transportation Things (IoTT)-based healthcare applications. This research includes an empirical investigation conducted across different hospital environments to highlight the importance of the developer framework in strengthening IoTT systems. As cybersecurity priorities increasingly shift toward secure and sustainable web design, the role of the developer framework has become critically significant [39]. Accordingly, this study proposes a network model that identifies the key elements necessary for establishing a sustainable security risk mitigation and prevention strategy within the development frameworks of hospital systems [33,34].

The outcomes of this study are intended to support developers in embedding sustainable security practices within the development life cycle of IoTT applications. This involves an in-depth analysis of various IoTT implementations across multiple healthcare institutions and the collection of expert input regarding factors influencing security risks and system design. These expert insights were analyzed using the Fuzzy ANP–TOPSIS approach to ensure rigorous prioritization and evaluation [36].

The proposed research model focuses on the design and assessment of secure IoTT systems. Security risk criteria and their alternatives are defined and evaluated using data gathered from case studies involving diverse Hospital Management System (HMS) applications. The findings are expected to guide engineers and developers in enhancing security across the development stages of IoTT applications. In alignment with the main questions presented in Section 1, the key findings of the paper are as follows:

• Key Security Vulnerabilities in Web-Based IoTT Applications for Emergency Healthcare

The analysis reveals that web-based IoTT applications deployed in emergency healthcare environments face several high-impact security vulnerabilities that directly threaten data integrity and operational continuity. The most critical weaknesses include insecure APIs, unencrypted telemetry channels, session-hijacking risks during mobile-to-cloud communication, weak authentication workflows, and susceptibility to man-in-the-middle (MITM) attacks during real-time emergency data transmission. These vulnerabilities are amplified in emergency scenarios where high-speed data transfer and uninterrupted connectivity are essential, making even marginal delays or packet tampering clinically significant. Expert evaluations and incident reports further show that the average telemetry integrity score across existing IoTT deployments remains below the recommended 0.80 reliability threshold, with authentication latencies often exceeding the 300 ms EMS communication standard, thereby increasing the likelihood of service disruption during time-critical interventions. Collectively, these observations demonstrate that IoTT-based healthcare systems remain exposed to multi-layered threats unless reinforced with robust, context-aware security controls tailored for emergency medical operations.

• Designing the Hybrid Fuzzy ANP–TOPSIS Model for Security Risk Prioritization

The hybrid Fuzzy ANP–TOPSIS framework developed in this study provides a structured and empirically grounded approach for prioritizing security risks in IoTT-based healthcare environments. The model integrates the hierarchical dependency mapping strength of Fuzzy ANP with the compensatory ranking capability of TOPSIS, enabling a nuanced understanding of how interrelated security criteria contribute to overall system risk. Expert-generated pairwise comparison matrices—derived from both clinical and technical perspectives—served as the foundational dataset for calculating criteria weights, ensuring that the prioritization reflects operational realities in emergency medical services.

• Effectiveness of Existing Security Mechanisms in Real-World IoTT Case Studies

The comparative case study evaluation indicates that although existing security mechanisms deployed across IoT-based healthcare platforms offer partial protection, they fall short of delivering comprehensive resilience in IoTT contexts, especially under emergency-load conditions. The negative ideal solution computed during TOPSIS analysis scores showed that reductions of ≥0.05, considered operationally meaningful for emergency data integrity, were achieved inconsistently across platforms.

• Comparative Resilience of IoT Healthcare Platforms (MediXcel EMR, Caresoft HIS, etc.)

The performance comparison across IoT healthcare platforms reveals that MediXcel EMR demonstrates superior resilience against prioritized IoTT security threats, outperforming Caresoft HIS and other evaluated applications across multiple dimensions. Under baseline weight conditions, MediXcel EMR achieved a positive improvement of Δ = +0.042 in its overall security risk score relative to the second-ranked alternative, surpassing the practical threshold of a minimum 10–15% improvement required for meaningful operational advantage in emergency IoTT deployments.

• Most and Least Effective Mitigation Strategies for High-Priority IoTT Security Risks

The evaluation of mitigation strategies through the hybrid Fuzzy ANP–TOPSIS approach identifies a clear hierarchy of effective controls for managing high-priority security risks in emergency IoTT systems. The most effective strategies include edge-level encryption of telemetry packets, multi-factor or context-aware authentication, zero-trust access implementation, and continuous integrity monitoring of mobile medical devices. These strategies ranked highest due to their capacity to directly reduce incident likelihood, with projected reductions in security-related service failures exceeding the practical benchmark of ≥20% incident reduction observed in historical EMS datasets. Conversely, the least effective strategies were found to be periodic password updates, unsecured software patch cycles, and generic firewalls not optimized for vehicle-based networking environments. These lower-ranked measures provided minimal improvement in closeness to the ideal solution and, in many cases, did not cross the operational threshold required to meaningfully enhance data integrity during emergency transport.

The discussion over results demonstrates that the integrated Fuzzy ANP–TOPSIS method offers significant advantages for security risk prioritization. Fuzzy ANP effectively allocates weights to risk factors under uncertain and imprecise expert judgments, while Fuzzy TOPSIS ranks the alternatives based on their closeness to the ideal solution. The comparison with classical ANP–TOPSIS models further confirms the superior performance of the fuzzy-enhanced approach, particularly in handling ambiguity, variability in expert opinions, and complex risk interdependencies.

The sensitivity analysis provides additional insights by capturing fluctuations in satisfaction levels across various IoTT-based healthcare applications. These results consistently highlight HA1 as the top-performing alternative, whereas HA3 emerges as the least favorable across multiple experimental scenarios. These findings underline the necessity of prioritizing robust security architectures for IoTT-integrated hospital systems. Overall, the proposed evaluation framework offers engineers and system developers a systematic basis for strengthening cybersecurity in healthcare applications.

To strengthen validity and minimize potential biases, several strategies may be adopted in future research:

• Involving experts from multiple institutions, regions, and sectors will reduce bias and ensure a more balanced representation of perspectives.
• Conducting iterative Delphi rounds can help achieve consensus among experts. Interrater reliability indices—such as ICC or Fleiss’ kappa—can quantify agreement levels and identify inconsistencies.
• Removing or perturbing groups of criteria (e.g., security, performance, interoperability) will help evaluate the sensitivity of rankings and detect potential construct omissions.

Several limitations should be acknowledged when interpreting the findings:

• The evaluation relies on systems drawn from a limited set of institutions within one national context, which may affect the generalizability of results to broader international environments.
• The assessment is based primarily on expert judgments and documented system features. Task-based verification—such as telemetry integrity evaluation, intrusion detection testing, or emergency-transport attack simulations—was not conducted.
• Although the criteria aim to be comprehensive, the rapid evolution of IoTT vulnerabilities (e.g., autonomous vehicle spoofing, 5G-enabled attacks, AI-supported breaches) means that emerging threats may not be fully captured.
• The framework has not yet been validated through practical implementation in hospital transportation networks, limiting the ability to measure real-world incident reductions or operational improvements.

Building on the current findings, several avenues for future research are proposed:

• Future studies should include a wider range of HMS/EMR systems across multiple countries, institutions, and vendors to enhance external validity and facilitate comparative analysis.
• The study does not claim universal superiority of any product. Incorporating penetration testing, stress testing, telemetry integrity checks, and resilience assessments under emergency transport conditions will provide deeper empirical grounding for the model.

5. Conclusions

This study presents a rigorous and comprehensive framework that integrates security risk assessment with internet of transportation things applications in healthcare, utilizing the Fuzzy ANP–TOPSIS methodology to support the development of secure, reliable, and resilient emergency care systems. By prioritizing 27 critical security factors across seven IoTT-enabled hospital system alternatives, the research highlights the essential role of structured developer frameworks in mitigating vulnerabilities and safeguarding sensitive clinical data.

The findings offer clear, quantitative benchmarks that define what constitutes meaningful improvement in IoTT-based healthcare security. For instance, the analysis indicates that a minimum 10–15% improvement in the overall security risk score is necessary for an IoTT-enabled system to deliver tangible operational benefits in emergency medical settings. The results demonstrate that MediXcel EMR exceeds this threshold, achieving a Δ score of +0.042 relative to the second-ranked alternative under baseline weights, thereby confirming its superior risk posture.

In addition, the study incorporates practical performance criteria that align with established IoTT and emergency medical standards. Security mechanisms must maintain authentication latency at or below 300 ms, reflecting real-time communication requirements in emergency medical services. To ensure dependable data transmission during patient transport, systems must also achieve a telemetry integrity score of at least 0.80, which represents an accepted reliability benchmark for IoTT-driven telemetry streams. Furthermore, from an operational standpoint, any secure IoTT framework should contribute to at least a 20% reduction in incident occurrence, a threshold derived from historical emergency medical service data and widely recognized as indicative of meaningful security improvement. Finally, the study emphasizes that a decrease of 0.05 or more in the closeness to the negative ideal solution signifies a practically important enhancement in emergency data integrity—another requirement met by the leading system in this evaluation.

Collectively, these empirically grounded thresholds, combined with the analytical strength of the Fuzzy ANP–TOPSIS approach, provide system developers and architects with actionable guidance for embedding robust security mechanisms throughout the IoTT application lifecycle. This integrated methodology not only reinforces the cybersecurity posture of healthcare systems but also advances the broader objective of establishing secure, efficient, and future-ready IoTT-enabled transportation infrastructures capable of supporting next-generation healthcare and emergency response operations.