Search Results (243)

This paper presents a novel approach for the early detection of cattle diseases. We present a uniquely integrated image classification-based project for real-time cattle disease diagnosis that combines image classification models to identify diseases accurately; a seamless, user-friendly dashboard for real-time monitoring with data visualization and instant predictions; and a mobile application that acts as a data source. The mobile application enables real-time collection of farmer and cattle-related data, including age, number of cattle, vaccination cycles, cattle images, and location metadata. Our AI-based cattle health monitoring project enables the early, efficient, scalable, and timely detection of Lumpy Skin Disease (LSD) and Foot and Mouth Disease (FMD) in cattle with high accuracy. A dataset of approximately 1600 LSD/non-LSD images and 840 FMD images was used to train multiple classification networks such as EfficientNetB0, ResNet50, VGG16, EfficientNetV2B0, and EfficientNetV2S, along with a soft-voting ensemble at inference. The proposed framework achieved a maximum testing accuracy of 98.36% for LSD classification and 99.84% for FMD classification under internal validation. These results indicate strong disease recognition capability, with ensemble-based prediction improving robustness, particularly for FMD classification. The proposed system enables practical, early, efficient, and scalable applications of AI research to improve livestock health monitoring and support the early prevention of widespread disease outbreaks. Full article

Data centers are critical digital infrastructure supporting cloud computing, artificial intelligence, and global information services. Despite their high-reliability design, they remain vulnerable to fire incidents due to continuous operation, high electrical loads, dense power systems, and the increasing use of lithium-ion batteries. Although such events are rare, their consequences can be severe, including service disruption, equipment damage, financial loss, and risks to data integrity. This study presents a systematic literature review of fire safety risk management frameworks in data centers, following PRISMA guidelines. Peer-reviewed studies published between 2020 and 2025 were retrieved from Scopus and Web of Science, screened, and appraised using structured quality criteria. Twelve empirical studies were synthesized and benchmarked against NFPA 75 and NFPA 76 standards. The findings are organized into three domains: Strategic Management, Fire Risk, and Fire Preparedness. The results show a strong focus on technical prevention and electrical hazards, while organizational readiness, emergency response, and recovery remain underexplored. Benchmarking indicates that industry standards adopt a more comprehensive lifecycle approach than the academic literature. This study reframes data center fire safety as a socio-technical reliability system and highlights critical gaps, providing a foundation for future research and improved fire safety governance and resilience. Full article

The rapid evolution of artificial intelligence technologies toward the agentic AI paradigm enables the emergence of the Agentic Web in the future Internet. Agent communication plays a critical role in constructing the Agentic Web but faces unique challenges posed by the edge–network–cloud continuum in the future Internet. This paper provides a comprehensive overview of state-of-the-art agent communication protocols and technologies, evaluating their readiness to support the construction of the Agentic Web. We first survey representative communication protocols and analyze the key technologies they employ, assessing their effectiveness in addressing the challenges for agent communications in the future Internet. We then identify critical gaps between existing approaches and the requirements of the Agentic Web, and propose a unified architectural framework grounded in virtualization and service-oriented principles to address these gaps. Such a framework may greatly facilitate the development of a pluralistic ecosystem in which various agent communication technologies and protocols can be freely developed and fully utilized. We also discuss open topics and possible directions for future research toward a fully realized Agentic Web. Full article

Infrastructure as Code (IaC) underpins automated cloud provisioning in modern DevOps environments; however, controlled comparative evaluations of leading IaC tools under identical conditions remain limited. This study presents a controlled comparative evaluation of Terraform, Pulumi, and AWS CloudFormation within a standardized Amazon Web Services environment. An identical multi-tier architecture was implemented using each tool, and repeated deployment cycles were conducted to observe differences in provisioning duration, removal time, structural maintainability, and operational characteristics. Descriptive statistical analysis across 30 controlled repetitions indicates that Terraform and Pulumi achieve comparable deployment performance, whereas CloudFormation requires more than twice the average provisioning time under the conditions evaluated. Removal durations were similar across tools but remained longest for CloudFormation. Structural analysis reveals trade-offs between declarative modular design, programmatic flexibility, and native cloud integration. The study provides a controlled, comparative framework to support evidence-based selection of IaC tools in production-oriented cloud environments. Full article

Fog computing extends cloud services to the network edge to support low-latency IoT applications. However, since fog environments are distributed and resource-constrained, intrusion detection systems must be adapted to defend against cyberattacks while keeping computation and communication overhead minimal. This systematic review presents research on intrusion detection systems (IDSs) for fog computing and synthesizes advances and research gaps. The study was guided by the “Preferred-Reporting-Items for-Systematic-Reviews-and-Meta-Analyses” (PRISMA) framework. Scopus and Web of Science were searched in the title field using TITLE/TI = (“intrusion detection” AND “fog computing”) for 2021–2025. The inclusion criteria were (i) 2021–2025 publications, (ii) journal or conference papers, (iii) English language, and (iv) open access availability; duplicates were removed programmatically using a DOI-first key with a title, year, and author alternative. The search identified 8560 records, of which 4905 were unique and included for qualitative grouping and bibliometric synthesis. Metadata (year, venue, authors, affiliations, keywords, and citations) were extracted and analyzed in Python to compute trends and collaboration. Intrusion detection systems in fog networks were categorized into traditional/signature-based, machine learning, deep learning, and hybrid/ensemble. Hybrid and DL approaches reported accuracy ranging from 95 to 99% on benchmark datasets (such as NSL-KDD, UNSW-NB15, CIC-IDS2017, KDD99, BoT-IoT). Notable bottlenecks included computational load relative to real-time latency on resource-constrained nodes, elevated false-positive rates for anomaly detection under concept drift, limited generalization to unseen attacks, privacy risks from centralizing data, and limited real-world validation. Bibliometric analyses highlighted the field’s concentration in fast-turnaround, open-access journals such as IEEE Access and Sensors, as well as a small number of highly collaborative author clusters, alongside dominant terms such as “learning,” “federated,” “ensemble,” “lightweight,” and “explainability.” Emerging directions include federated and distributed training to preserve privacy, as well as online/continual learning adaptation. Future work should consist of real-world evaluation of fog networks, ultra-lightweight yet adaptive hybrid IDS, self-learning, and secure cooperative frameworks. These insights help researchers select appropriate IDS models for fog networks. Full article

Transformer-based language models have been increasingly adopted to enhance semantic awareness in web service selection systems. However, the computational cost of large transformer encoders poses significant challenges for real-time and resource-constrained deployment scenarios. This study presents a deployment-oriented hybrid semantic–QoS framework that integrates transformer-based domain-level semantic signals with traditional Quality of Service (QoS) metrics to support scalable service selection pipelines. Rather than aiming to establish end-to-end ranking optimality, this work focuses on a comparative analysis of transformer encoders within a unified pipeline, emphasizing accuracy–latency trade-offs, resource utilization, and deployment feasibility. Four representative BERT family models—BERT, DistilBERT, RoBERTa, and ALBERT—are evaluated under identical experimental conditions. The semantic component operates at the level of domain relevance estimation, and its output is combined with QoS indicators using a controllable weighting mechanism to examine sensitivity to deployment priorities. The results reveal clear trade-offs between semantic expressiveness and computational efficiency, with lightweight models such as DistilBERT demonstrating favorable scalability and response-time characteristics despite reduced semantic capacity. The findings provide practical insights for selecting transformer encoders in QoS-aware service selection pipelines deployed in cloud, edge, or real-time environments. By framing evaluation around deployment feasibility rather than ranking optimality, this study offers guidance for balancing semantic enrichment with operational constraints in real-world service selection systems. Full article

At present, pedestrian navigation systems using smartphones have become common in daily activities. For their ubiquitous, accurate, and reliable services, map information collection is essential for constructing comprehensive spatial databases. Previously, we have developed a map information collection tool to extract building information using Google Maps, optical character recognition (OCR), geolocation, and web scraping with smartphones. However, indoor navigation often suffers from inaccurate localization due to degraded GPS signals inside buildings and Simultaneous Localization and Mapping (SLAM) estimation errors, causing position errors and confusing augmented reality (AR) guidance. In this paper, we present an improved map information collection tool to address this problem. It captures 360° panoramic images to build 3D models, apply photogrammetry-based mesh reconstruction to correct geometry, and georeference point clouds to refine latitude–longitude coordinates. For evaluations, experiments in various indoor scenarios were conducted. The results demonstrate that the proposed method effectively mitigates positional errors with an average drift correction of 3.15 m, calculated via the Haversine formula. Geometric validation using point cloud analysis showed high registration accuracy, which translated to a 100% task completion rate and an average navigation time of 124.5 s among participants. Furthermore, usability testing using the System Usability Scale (SUS) yielded an average score of 96.5, categorizing the user interface as ’Best Imaginable’. These quantitative findings substantiate that the integration of 360° imaging and photogrammetric correction significantly enhances navigation reliability and user satisfaction compared with previous sensor fusion approaches. Full article

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from $\mu =3.9$ (Challenge phase) to $\mu =4.6$ (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education. Full article

Data exfiltration poses a major cybersecurity challenge because it involves the unauthorized transfer of sensitive information. Intrusion Detection Systems (IDSs) are vital security controls in identifying such attacks; however, their effectiveness in cloud computing environments remains limited, particularly against covert channels such as Internet Control Message Protocol (ICMP) and Domain Name System (DNS) tunneling. This study compares two widely used IDSs, Snort and Suricata, in a controlled cloud computing environment. The assessment focuses on their ability to detect data exfiltration techniques implemented via ICMP and DNS tunneling, using DNSCat2 and Iodine. We evaluate detection performance using standard classification metrics, including Recall, Precision, Accuracy, and F1-Score. Our experiments were conducted on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances, where IDS instances monitored simulated exfiltration traffic generated by DNSCat2, Iodine, and Metasploit. Network traffic was mirrored via AWS Virtual Private Cloud (VPC) Traffic Mirroring, with the ELK Stack integrated for centralized logging and visual analysis. The findings indicate that Suricata outperformed Snort in detecting DNS-based exfiltration, underscoring the advantages of multi-threaded architectures for managing high-volume cloud traffic. For DNS tunneling, Suricata achieved 100% detection (recall) for both DNSCat2 and Iodine, whereas Snort achieved 85.7% and 66.7%, respectively. Neither IDS detected ICMP tunneling using Metasploit, with both recording 0% recall. It is worth noting that both IDSs failed to detect ICMP tunneling under default configurations, highlighting the limitations of signature-based detection in isolation. These results emphasize the need to combine signature-based and behavior-based analytics, supported by centralized logging frameworks, to strengthen cloud-based intrusion detection and enhance forensic visibility. Full article

The exponential proliferation of the Internet of Things (IoT) has catalyzed a paradigm shift in industrial automation and smart city infrastructure. However, this rapid expansion has engendered significant heterogeneity in communication protocols, creating critical barriers to seamless data integration and interoperability. Conventional gateway solutions frequently exhibit limited flexibility in supporting diverse protocol stacks simultaneously and often lack granular user controllability. To mitigate these deficiencies, this paper proposes a novel, modular IoT gateway architecture, designated as MIGS (Modular IoT Gateway System). The proposed architecture comprises four distinct components: a Management Component, a Southbound Component, a Northbound Component, and a Cache Component. Specifically, the Southbound Component employs instance-based isolation and independent task threading to manage heterogeneous field devices utilizing protocols such as Modbus, MQTT, and OPC UA. The Northbound Component facilitates reliable bidirectional data transmission with cloud platforms. A dedicated Cache Component is integrated to decouple data acquisition from transmission, ensuring data integrity during network latency. Furthermore, a web-based Control Service Module affords comprehensive runtime management. We explicate the data transmission methodology and formulate a theoretical latency model to quantify the impact of the Python Global Interpreter Lock (GIL) and serialization overhead. Functional validation and theoretical analysis confirm the system’s efficacy in concurrent multi-protocol communication, robust data forwarding, and operational flexibility. The MIGS framework significantly enhances interoperability within heterogeneous IoT environments, offering a scalable solution for next-generation industrial applications. Full article

This paper presents the development and cloud deployment of a system for the segmentation of electromyographic (EMG) signals oriented toward muscle fatigue monitoring in the biceps and triceps. A dataset of 30 subjects was used, resulting in 120 EMG and gyroscope files containing between four and six strength exercise series each. After a quality assessment, approximately 80% of the signals (95 files) were classified as level 1 or 2 and considered suitable for segmentation and subsequent analysis. A near real-time segmentation algorithm was designed based on signal envelopes, sliding windows, and quantile thresholds, complemented with discrete wavelet transform (DWT) filtering. Using EMG alone, segmentation accuracy reached 83% for biceps and 54% for triceps; after incorporating DWT preprocessing, accuracy increased to 87.5% and 71%, respectively. By exploiting the gyroscope’s X-axis signal as a low-noise reference, the optimal configuration achieved an overall accuracy of 80%, with 83.3% for biceps and 76.2% for triceps. The prototype was deployed on Amazon Web Services (AWS) using EC2 instances and SQS queues, and its computational cost was evaluated across four server types. On a t2.micro instance, the maximum memory usage was approximately 219 MB with a dedicated CPU and a maximum processing time of 0.98 s per signal, demonstrating the feasibility of near real-time operation under conditions with limited resources. Full article

With the development of service-based and cloud-based architectures, the number of web services is rapidly increasing, and when users use these services, they expect reliability, security, and performance. However, existing trust evaluation approaches are predominantly based on QoS characteristics (e.g., availability, throughput, and compliance) and do not fully reflect user satisfaction and expectations. This research addresses this gap by posing the following research question: How can a fuzzy-based multi-criteria decision-making (MCDM) approach incorporate user experience to enhance web service trust score evaluation under uncertainty? To address this, we propose a trust score–weighted model and an evaluation approach that extends traditional methods by integrating a fuzzy-based MCDM approach, such as fuzzy TOPSIS, fuzzy VIKOR, and fuzzy WASPAS, thereby allowing both objective service metrics and subjective user expectations to be jointly considered when evaluating service trust. The results of the proposed approach demonstrate the evaluation of trust scores through a fuzzy-based MCDM approach, allowing for the ranking of WSs. Our case study validates the model’s ability to incorporate predictive quality-of-service performance and its relevance to real-world, user-centric service selection scenarios. Full article

The rise of connected and automated vehicles has transformed in-vehicle infotainment (IVI) systems into critical gateways linking user interfaces, vehicular networks, and cloud-based fleet services. A concerning architectural reality is that hardcoded credentials like access point names (APNs) in IVI firmware create a cross-layer attack surface where local exposure can escalate into entire vehicle fleets being remotely compromised. To address this risk, we propose a cross-layer security framework that integrates firmware extraction, symbolic execution, and targeted fuzzing to reconstruct authentic IVI-to-backend interactions and uncover high-impact web vulnerabilities such as server-side request forgery (SSRF) and broken access control. Applied across seven diverse automotive systems, including major original equipment manufacturers (OEMs) (Mercedes-Benz, Tesla, SAIC, FAW-VW, Denza), Tier-1 supplier Bosch, and advanced driver assistance systems (ADAS) vendor Minieye, our approach exposes systemic anti-patterns and demonstrates a fully realized exploit that enables remote control of approximately six million Mercedes-Benz vehicles. All 23 discovered vulnerabilities, including seven CVEs, were patched within one month. In closed automotive ecosystems, we argue that the true measure of efficacy lies not in maximizing code coverage but in discovering actionable, fleet-wide attack paths, which is precisely what our approach delivers. Full article

Cloud-centric IoT frameworks remain dominant; however, they introduce major challenges related to data privacy, latency, and system resilience. Existing open-source solutions often lack standardized principles for scalable, local-first deployment and do not adequately integrate fault tolerance with hybrid automation logic. This study presents a practical and extensible local-first IoT architecture designed for full operational autonomy using open-source components. The proposed system features a modular, layered design that includes device, communication, data, management, service, security, and presentation layers. It integrates MQTT, Zigbee, REST, and WebSocket protocols to enable reliable publish–subscribe and request–response communication among heterogeneous devices. A hybrid automation model combines rule-based logic with lightweight data-driven routines for context-aware decision-making. The implementation uses Proxmox-based virtualization with Home Assistant as the core automation engine and operates entirely offline, ensuring privacy and continuity without cloud dependency. The architecture was deployed in a real-world office environment and evaluated under workload and fault-injection scenarios. Results demonstrate stable operation with MQTT throughput exceeding 360,000 messages without packet loss, automatic recovery from simulated failures within three minutes, and energy savings of approximately 28% compared to baseline manual control. Compared to established frameworks such as FIWARE and IoT-A, the proposed approach achieves enhanced modularity, local autonomy, and hybrid control capabilities, offering a reproducible model for privacy-sensitive smart environments. Full article

In hospitals, IoT has facilitated connectivity between patients and medical services using historical health data. However, its adoption in hospital catering services has been slower. This work describes the implementation of an IoT system with a three-layer architecture: the first layer collects data on patient diets and environmental conditions from the food warehouse, the second layer processes this information, establishing rules and converting raw data into valuable information, and the third layer stores the data in the cloud, presenting it in a web application. A functional system was obtained that meets the needs of catering service personnel and the hospital in which it was implemented. Full article