The Integration of the Internet of Things (IoT) Applications into 5G Networks: A Review and Analysis

Abstract

The incorporation of Internet of Things (IoT) applications into 5G networks marks a significant step towards realizing the full potential of connected systems. 5G networks, with their ultra-low latency, high data speeds, and huge interconnection, provide a perfect foundation for IoT ecosystems to thrive. This connectivity offers a diverse set of applications, including smart cities, self-driving cars, industrial automation, healthcare monitoring, and agricultural solutions. IoT devices can improve their reliability, real-time communication, and scalability by exploiting 5G’s advanced capabilities such as network slicing, edge computing, and enhanced mobile broadband. Furthermore, the convergence of IoT with 5G fosters interoperability, allowing for smooth communication across diverse devices and networks. This study examines the fundamental technical applications, obstacles, and future perspectives for integrating IoT applications with 5G networks, emphasizing the potential benefits while also addressing essential concerns such as security, energy efficiency, and network management. The results of this review and analysis will act as a valuable resource for researchers, industry experts, and policymakers involved in the progression of 5G technologies and their incorporation with IT solutions.

1. Introduction

1.1. Generations of Mobile Networks

Throughout several generations, mobile networks have undergone substantial technological and service breakthroughs. Analogue voice communication was first introduced by First Generation (1G) networks. 1G mobile networks provided basic telephone services and were primarily analog. It marked the beginning of wireless communication for commercial use [1]. Digital voice and rudimentary data services like SMS and MMS were introduced with the Second Generation (2G). 2G introduced digital communications, improved transmission quality, and focused on voice communication. It also led to the emergence of personal communications services (PCSs) [1]. Third Generation (3G) networks greatly improved data transmission capacity by enabling video calling and mobile internet access. 3G brought about advancements in supporting voice and data services and the introduction of wireless local area networks (WLANs) [1].

High-definition video streaming and sophisticated mobile internet services were made possible by Fourth Generation (4G), or LTE, which significantly increased data speeds. 4G technology significantly improved the data transmission speed and calling capabilities, paving the way for modern applications and services [2]. With ultra-high speeds, low latency, and huge connections for IoT devices, Fifth Generation (5G) networks mark a significant advancement that makes applications like driverless cars and smart cities possible. 5G networks are characterized by high-speed connectivity, low latency, high resilience, and the ability to support critical and massive machine-type communication, in addition to mobile broadband access [3,4].

It is anticipated that subsequent generations, like Sixth Generation (6G), will expand upon these developments by emphasizing even faster speeds, lower latency, and integrating AI and advanced machine learning for more intelligent network management [5].

Table 1. Key developments and characteristics of mobile generations [6].

Generation	Introduction Year	Key Features	Technologies/Standards	Applications	Data Rate	Latency
1G	1980s	Analog voice	AMPS, TACS	Voice calls	2.4 kbps	High
2G	1990s	Digital voice, SMS	GSM, CDMA, TDMA	Voice calls, text messaging	64 kbps	High
3G	2000s	Mobile data, video calls	UMTS, CDMA2000	Video calls, mobile internet	2 Mbps	Medium
4G	2010s	High-speed internet, IP-based	LTE, WiMAX	HD video streaming, video conferencing	100 Mbps–1 Gbps	Low
5G	2020s	Ultra-fast internet, IoT, low latency	NR (New Radio), mmWave	IoT, smart cities, AR/VR	1–10 Gbps	Ultra-low
6G	2030s (expected)	AI integration, extremely high data rates	Under development	Seamless integration of billions of IoT devices, AI, and smart cities	1 Tbps	Near-zero

summarizes the key developments and characteristics of each generation from 1G to 5G and briefly touches on the prospects of 6G.

The evolution of mobile networks has been driven by the increasing demand for data traffic, the proliferation of smartphones, and the need for high-speed connectivity [7]. Each generation has built upon the previous one, addressing the limitations and challenges of the preceding technology to meet the growing demands of users. The deployment of new-generation systems has led to increased energy consumption, costs, and carbon footprints, necessitating a focus on energy efficiency in the transition to newer generations [8]. Figure 1 demonstrates the evolution of the generations of mobile networks over the years.

1.2. Features and Characteristics of 5G Networks

5G networks bring significant advancements in data rates, latency, device density, and support for diverse applications, enabled by innovative technologies like massive MIMO, network slicing, and SDN/NFV. However, they also present challenges in thermal management, interference, and security that need to be addressed for successful deployment and operation. 5G networks represent a considerable development over previous generations of mobile communication technologies, with new features and capabilities to fulfill the expanding needs for connectivity, speed, and dependability. Some of the main features of 5G networks are as follows:

• High Data Rates and Bandwidth. 5G networks function at considerably higher frequencies (30–300 GHz) than 4G, allowing for greatly increased data rates. They enable data rates of several tens of Gbps, crucial for applications needing rapid data transfer [9].
• Low Latency. A key feature of 5G is its extremely low latency, around 1 millisecond, which is essential for real-time uses like autonomous driving and remote surgical procedures [10].
• High Device Density. 5G networks can support a very high density of devices, with connection densities of millions of devices per square kilometer, facilitating massive Machine-Type Communications (mMTCs). This is particularly important for the Internet of Things (IoT) and smart city applications [10,11].
• Enhanced Mobile Broadband (eMBB). 5G networks can accommodate an exceptionally high concentration of devices, achieving connection densities of millions of devices for every square kilometer, enabling massive Machine-Type Communications (mMTCs) [9,11]. This situation centers on delivering high-speed internet connectivity, facilitating services such as high-definition video streaming and virtual reality. This situation centers on delivering high-speed internet connectivity, facilitating services such as high-definition video streaming and virtual reality.
• Ultra-Reliable Low Latency Communications (URLLCs)
• URLLC is tailored for uses that demand highly reliable and low-latency communication, like industrial automation and essential services [9,11].
• Massive Machine-Type Communications (mMTCs). mMTC accommodates a significant quantity of IoT devices, facilitating smart cities, smart homes, and various IoT applications [9,11].

5G networks are a substantial advancement in mobile communication technology, incorporating numerous essential breakthroughs that improve performance, efficiency, and connectivity. The main innovations of this technology are as follows:

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High Bandwidth and Low Latency. 5G networks offer extremely high data rates and ultra-low latency, which are crucial for real-time applications such as telemedicine, autonomous vehicles, and smart cities [12,13,14,15].

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Massive Multiple-Input Multiple-Output (MIMO) and Beamforming. These technologies improve spectral efficiency and network capacity by allowing multiple data signals to be transmitted and received simultaneously [13].

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Millimeter Wave Spectrum. The utilization of higher-frequency bands (millimeter waves) enables faster data transmission and supports a higher density of devices [16].

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Network Slicing. This enables the construction of many virtual networks within a single physical 5G network, each customized for unique applications or services, enhancing flexibility and resource management [13].

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Edge Computing. Enhances the speed of applications that demand real-time data processing by processing data closer to its source [13].

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Dynamic Spectrum Sharing. This invention makes better use of the existing spectrum by dynamically distributing frequencies based on demand [16].

5G technology offers significant advancements over its predecessors, including higher data transmission speeds, reduced latency, and increased bandwidth. These improvements enable a wide range of applications across various industries:

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Internet of Things (IoT): 5G is a foundational technology for IoT, enabling the connection of billions of devices with high reliability and low power consumption. This supports applications in smart cities, smart agriculture, and industrial automation [14,15].

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Smart Grids and Energy: In the energy sector, 5G facilitates the development of smart grids by providing high-bandwidth and low-latency communication, essential for the real-time monitoring and management of power systems [12].

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Healthcare: Telemedicine and remote surgery benefit from 5G’s low latency and high reliability, allowing for real-time data transmission and the remote control of medical devices [17].

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Autonomous Vehicles: 5G supports the development of connected and autonomous vehicles by enabling real-time communication between vehicles and infrastructure, enhancing safety and efficiency [13,14,15].

Numerous technological, legislative, and operational issues must be considered when deploying and implementing 5G networks. The following summarizes the main issues and concerns:

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Infrastructure Investment: Significant investment is required to build the infrastructure necessary for widespread 5G deployment, including new base stations and fiber optic networks [15,18].

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Security and Privacy: The increased connectivity and data transmission capabilities of 5G pose new security challenges, necessitating robust cybersecurity measures and compliance with privacy regulations [18,19].

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Interoperability: Ensuring compatibility between various IoT devices and existing technologies is crucial for the seamless integration of 5G networks [18].

In summary, 5G is a sophisticated wireless communication technology; however, its high-power consumption, cost, and infrastructure requirements sometimes bring limitations for its application in long-range IoT solutions, especially in distant areas or when low resources are available. Therefore, wireless technology should be chosen based on the range, power consumption, data transmission rate, signal behavior, and unique solution’s requirements. Among several IoT connectivity alternatives, LoRa (Long Range), for example, stands out because of its extended range and low power consumption, making it the preferable choice for long-range IoT applications. On the other hand, other technologies such as Wi-Fi offer high data transfer speeds and capacity; however, it is less commonly used for IoT solutions because devices in Wi-Fi networks consume a lot of power, and the range of the network is limited [1,2,3,4,5,6,7,8,9,10].

1.3. IoT in 5G

With improved connectivity, reduced latency, and faster data rates, the combination of 5G technology and IoT is poised to revolutionize several industries. Nevertheless, it also presents serious security issues that require careful planning, funding, and cutting-edge security solutions. With continuous research and development opening the door for creative applications and solutions, the Internet of Things appears to have a bright future in the 5G age [20,21]. The usefulness, effectiveness, and scalability of Internet of Things (IoT) applications are improved by the incorporation of 5G technology, which provides several noteworthy advantages. The following are the main benefits of 5G for IoT:

• Improved Connectivity and Capacity: 5G technology facilitates the efficient communication of vast networks of smart devices by supporting a huge number of IoT devices. For uses like healthcare, industrial automation, and smart cities, this is essential [19,20].
• Reduced Latency: 5G’s extremely low latency is one of its biggest benefits; this is crucial for real-time applications like remote surgery and driverless cars [22].
• Greater Data Rates: To manage the massive amounts of data produced by IoT devices, 5G provides noticeably faster data transmission rates than its predecessors [23].

5G networks offer several improvements over earlier generations, marking a substantial leap forward in mobile communication technology. The following are some significant technological developments:

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Network Slicing: Network slicing, or the creation of several virtual networks inside a single physical 5G network, is made possible by 5G. This makes it possible to provide tailored connectivity options for various IoT applications, guaranteeing top security and performance [21].

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Advanced Communication Technologies: 5G’s capacity to serve a variety of IoT applications is enhanced by technologies like visible light communication (VLC), millimeter wave (mmWave), and multiple-input-multiple-output (MIMO) [22].

The introduction of 5G networks has the potential to completely alter the operational and economic environments in several industries. Significant improvements in data rates, latency, dependability, and capacity are anticipated with this new generation of mobile networks, which should stimulate significant operational and economic growth. The following are the two main economic and operational implications:

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Cost-Benefit Analysis: Although the 5G infrastructure requires a large initial investment, there are long-term advantages, such as increased energy efficiency, lower maintenance costs, and the capacity to accommodate more IoT devices, which lowers overall costs [24].

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Strategic Investments: Considering the operational and financial implications, businesses are urged to make strategic investments in 5G to fully utilize its potential for IoT applications [20,24].

While promising unprecedented speed, low latency, and connectivity, 5G introduces a range of new security challenges due to its complex and heterogeneous nature. The following are the two main concerns:

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Increased Security Risks: IoT device vulnerabilities and the requirement for strong security measures to fend off assaults like distributed denial of service (DDoS) are among the additional security issues brought about by the integration of IoT with 5G [25,26,27].

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Security Solutions: To reduce these risks and guarantee safe IoT deployments in 5G networks, innovative security frameworks like block chain and attribute-based access control (ABAC) can be put into place [24,27].

The IoT and 5G networks have the potential to completely transform several industries. The following are some important avenues for this integration’s future:

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Emerging Technologies. The capabilities and uses of IoT will be further enhanced by the convergence of 5G with emerging technologies such as cloud computing, machine learning, and artificial intelligence (AI) [21].

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Research and Development: To solve the issues and enhance the functionality of 5G-enabled IoT systems, continuous research is necessary, with a particular emphasis on areas like resource allocation, energy optimization, and security [22,26].

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Security Enhancements. There are serious security issues when IoT and 5G are combined. It is essential to provide thorough security frameworks to safeguard IoT devices and data. This entails fixing weaknesses and putting strong security measures in place. In 5G networks, blockchain can offer a decentralized and safe way to manage IoT devices, guaranteeing safe connectivity and thwarting attacks [25,27].

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Edge Computing and AI Integration. While edge computing’s close proximity to IoT devices can improve real-time processing and lower latency, which is especially advantageous for applications that need to analyze and respond to data instantly, artificial intelligence (AI) can improve data processing, optimize network management, and improve decision-making processes in 5G-enabled IoT systems [28].

The main contributions of the research work are as follows:

• In the Introduction, there was a thorough examination of the changing role of the IoT in 5G networks. By initially outlining the development and comparative attributes of mobile network generations, the paper creates a foundational understanding of technological progress. It then explores the essential features and advancements of 5G networks, paving the way for an in-depth discussion on the transformative possibilities of IoT integration. Lastly, the paper underscores the potential effects and emerging trends of IoT-enabled 5G systems, providing valuable insights into how this convergence could transform communication, automation, and smart infrastructure.
• The literature review synthesizes recent research on the integration of 5G, IoT, and AI across key sectors—smart cities, industrial automation, healthcare, agriculture, and autonomous vehicles—highlighting their transformative potential, challenges, and real-world implementations. It provides a structured comparison of studies, identifying gaps (e.g., 5G reliability in smart cities, AI-ethics trade-offs) and future directions (e.g., 5G optimization, scalable IoT solutions). The review serves as a comprehensive reference for researchers and practitioners, bridging theoretical advancements with practical applications.
• Regarding the use of IoT in 5G, a comprehensive classification of AI and IoT applications in 5G across various sectors, including education, security, healthcare, and entertainment, is conducted. Moreover, an analysis of sector-specific impacts, emerging trends, and market growth emphasizes how these technologies are driving innovation and enhancing efficiency in industries such as healthcare, agriculture, and smart cities.
• Concerning the security measures needed, we present a comprehensive, multi-layered analysis of security challenges and corresponding countermeasures for integrating IoT applications within 5G networks. Moreover, we highlighted the emerging role of AI-driven security mechanisms in strengthening the resilience of 5G-enabled IoT systems.
• Finally, the challenges facing IoT integration with 5G networks are addressed in this research work since considering that addressing these challenges necessitates collaborative efforts among researchers, industry leaders, and regulatory bodies to develop standardized frameworks, robust security measures, and energy-efficient communication protocols.

This review differs from existing works as follows:

• The review offers a comprehensive, sector-specific analysis of IoT applications within 5G networks, supported by structured comparison tables and statistical insights.
• It provides a unique focus on emerging technologies and security challenges, making it a practical and forward-looking reference for researchers and practitioners.
• It delivers a comprehensive and multifaceted analysis of the integration of IoT applications into 5G networks, addressing both technical and economic aspects, real-world applications, network slicing, security concerns, and the integration of emerging technologies.
• This holistic approach sets apart from existing literature and offers valuable insights for stakeholders looking to leverage the full potential of 5G–IoT integration.

This is how the remainder of the paper is structured. A discussion of a relevant piece of literature is provided in Section 2. Section 3 discusses the use of IoT in 5G networks across several industries. In Section 4, security measures and applications are examined to ensure successful integration. In Section 5, the difficulties encountered are reviewed, and in Section 6, conclusions and future directions are presented, followed by a list of relevant references. The framework structure of the research is given in Figure 2.

2. Literature Review

The convergence of 5G networks, IoT, and AI is driving transformative advancements across critical sectors, enabling smarter, more efficient systems while addressing global challenges like urbanization, sustainability, and resource management. This literature review synthesizes recent research on the application of these technologies in smart cities, industrial automation, healthcare, agriculture, and autonomous vehicles, highlighting their potential, limitations, and real-world implementations. From 5G’s role in enabling real-time IoT monitoring for urban infrastructure to AI-driven predictive maintenance in Industry 4.0, the reviewed studies demonstrate significant strides in scalability and efficiency.

2.1. Study Selection Criteria

The studies included in this review were selected based on the following criteria:

• Relevance to 5G-IoT-AI Integration: Focus on peer-reviewed articles that explicitly address the intersection of 5G, IoT, and AI in the target sectors.
• Technological Scope: Emphasis on studies evaluating real-world implementations, architectural frameworks, or empirical validations (e.g., pilot projects, simulations, or case studies). Theoretical proposals were included only if they offered novel in-sights.
• Impact and Innovation: Prioritization of research demonstrating measurable improvements in scalability, efficiency, or sustainability, or identifying critical challenges (e.g., latency, privacy, interoperability).
• Geographical Diversity: Inclusion of studies from both early-adopter regions (e.g., Singapore, the EU) and emerging economies to capture global trends and deployment disparities.
• Methodological Rigor: Preference for studies with clear methodologies (e.g., systematic reviews, experimental validations, or bibliometric analyses) and reproducible results.
• Rationale for Criteria:
  • Relevance and Scope ensure alignment with the paper’s focus on interdisciplinary integration, avoiding indirect work (e.g., standalone AI algorithms unrelated to 5G/IoT).
  • Impact-driven selection highlights actionable insights for practitioners, while geographical diversity reveals contextual challenges (e.g., 5G coverage gaps in rural agriculture [29]).
  • Methodological rigor safeguards against unreliable evidence, particularly important for emerging technologies where hype may overshadow empirical limitations (e.g., 5G’s uneven performance in smart cities [30]).

These criteria collectively address the need for a balanced, evidence-based synthesis of the field’s state-of-the-art, gaps, and future directions.

2.2. Smart Cities

The real-world performance of 5G networks in supporting smart city applications, focusing on an IoT-based pilot project for road asset monitoring using waste collection trucks over six months, is evaluated in [30]. While 5G demonstrates potential for mobile video streaming, the study reveals significant performance variations that may hinder its reliability for data-intensive or near-real-time applications. Unlike prior evaluations limited to 5G core testing or speed tests, this work provides a comprehensive assessment through both application-specific and independent analyses. The findings highlight current limitations in 5G deployments, suggesting they may not yet fully meet the demands of emerging smart city services, particularly where consistent high bandwidth and low latency are crucial. The study contributes practical insights into 5G’s readiness for scalable smart city solutions amid uneven network coverage.

The role of 5G in sustainable smart buildings is explored in [31], highlighting its potential to enhance construction, operation, and management through high-class services and efficient functionalities. Focusing on Singapore—a leading smart city and early adopter of 5G—the study examines global trends, R&D efforts, and test-bedding initiatives in 5G labs. It provides a comprehensive review of 5G developments, use cases, and government-supported projects in Singapore, particularly for smart buildings and built environment improvements. The research serves as a valuable benchmark for future smart city development, offering insights for researchers and industries in the context of big data and sustainable urban growth.

The study in [32] contributes a comprehensive bibliometric analysis of smart city security and surveillance, examining 745 Scopus-indexed articles (1977–2023) to map key trends and challenges. It highlights the tension between AI-driven technological advancements and privacy concerns, emphasizing the need for ethical AI deployment in urban management. Through co-citation, co-occurrence, and bibliographic coupling analyses, the study identifies critical thematic clusters and influential works in the field. The research offers actionable insights for policymakers and urban planners to balance urban safety with privacy protection, advocating for robust governance frameworks. Ultimately, it underscores the importance of fostering public trust through ethical standards in AI-powered security systems for smart cities. The work in [33] proposes an IoT-based smart parking system that leverages mobile applications and cloud technology to efficiently manage parking spaces and reduce congestion. The system not only provides real-time information on available parking spots but also helps users locate the optimal space. By utilizing 5G-enabled IoT sensors, it ensures seamless connectivity and low-cost data transmission between devices and the cloud. A cloud-based architecture simplifies data storage, management, and sharing across multiple workstations. Overall, the design enhances parking efficiency, improves vehicle management, and supports scalable, industry-friendly solutions through 5G integration.

Similarly [34] proposes an IoT-based environmental monitoring system that enables real-time and historical data visualization through a web app, accessible from anywhere via the internet. The key contribution lies in its seamless integration of hardware, network, and software configurations to provide customizable data insights while prioritizing security, responsiveness, and efficient data management. The system supports diverse sensor technologies, allowing users to monitor air quality, weather, and pollution levels with flexibility in data display. By bridging edge-to-cloud connectivity, it enhances accessibility and usability for both individuals and researchers, overcoming time and location constraints. Overall, the study advances environmental monitoring by offering a scalable, user-friendly solution for analyzing large volumes of sensor data.

Finally, [35] explores the integration of 5G and IoT technologies, discussing their architectures, common implementations, and key challenges, particularly focusing on interference issues. It provides a detailed analysis of interference in wireless applications, specifically in 5G and IoT systems, along with potential optimization techniques to mitigate these problems. The study emphasizes the critical need to address interference and enhance network performance to ensure reliable IoT connectivity, which is vital for efficient business operations. Additionally, it highlights how businesses can leverage these technologies to boost productivity, minimize downtime, and improve customer satisfaction. The paper also underscores the transformative potential of converging networks and services in expanding internet accessibility and enabling innovative applications.

Table 2. Structured comparison for the studies in the context of smart cities.

Paper	Focus Area	Key Contributions	Methodology/Approach	Findings/Limitations	Technology Highlighted
[30]	5G for Smart City IoT	Evaluates 5G performance in real-world smart city applications (road asset monitoring)	6-month pilot project with IoT-based monitoring using waste collection trucks	5G shows potential but has performance variations; it may not meet real-time demands fully	5G, IoT
[31]	5G for Smart Buildings	Examines 5G’s role in sustainable smart buildings (Singapore case study)	Review of global trends, R&D, and Singapore’s 5G test bed initiatives	5G enhances construction/management; serves as a benchmark for future smart city projects	5G, Smart Buildings
[32]	Smart City Security	Bibliometric analysis of security/surveillance in smart cities (745 Scopus articles)	Co-citation, co-occurrence, and bibliographic coupling analyses (1977–2023)	Highlights AI-privacy tension; calls for ethical AI governance in urban surveillance	AI, Surveillance, Ethics
[33]	IoT-based Smart Parking	Proposes a cloud-based smart parking system with real-time spot optimization	Mobile apps and 5G-enabled IoT sensors for cloud connectivity	Improves parking efficiency, scalability, and low-cost data transmission	IoT, 5G, Cloud
[34]	IoT Environmental Monitoring	Real-time environmental data visualization via web app (air quality, weather, etc.)	Hardware–software integration with edge-to-cloud connectivity	Scalable, user-friendly solution for remote monitoring and data analysis	IoT, Edge/Cloud, Web App
[35]	5G-IoT Integration	Analyzes interference challenges in 5G-IoT systems and optimization techniques	Review of architecture, implementations, and interference mitigation strategies	Interference issues critical; optimization needed for reliable IoT business applications	5G, IoT, Interference Mitigation

below provides a comparison of the studies discussed.

2.3. Industrial Automation

In [36], a novel architectural design for 5G-enabled Industrial Internet of Things (IIoT) to enhance smart manufacturing processes in cyber-physical manufacturing systems (CPMSs) is proposed. The architecture leverages key 5G features such as enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), ultra-reliable low-latency communication (URLLC), and narrowband IoT (NB-IoT) to address the limitations of 3G/4G in handling high data volumes, low latency, and real-time monitoring. By integrating these 5G capabilities with IIoT, the proposed framework improves industrial automation, collaboration, and efficiency in smart manufacturing. The contribution lies in enabling advanced industrial services through a 5G-driven IIoT architecture, optimizing CPMS for future smart industries.

The contribution in [37] proposes a 5G-enabled Network Application (NetApp) for predictive maintenance in energy-related critical infrastructures, leveraging AI to enhance failure prediction and prevent equipment damage. NetApp employs containerized components to collect time-series operational data from power plants and detect anomalies in energy generator performance using an autoencoder-based approach. By enabling real-time monitoring and early fault detection, the system facilitates timely maintenance interventions. The evaluation results confirm the effectiveness of the proposed solution in improving predictive maintenance for critical infrastructure. Overall, the paper contributes an AI-driven, 5G-integrated framework to optimize operational reliability in the energy sector.

The work in [38] reviews recent research on Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs), focusing on communication and control challenges in industrial settings. It highlights the stringent latency and reliability requirements for wireless technologies used in AGV/AMR coordination and fleet management, which many existing solutions fail to meet. The study discusses integration challenges and the limitations of current state-of-the-art technologies in smart manufacturing environments. Additionally, it identifies research gaps in applying 5G networks for AGV/AMR fleet management and proposes novel integration approaches, such as leveraging the tactile Internet, 5G network slicing, and virtual reality to enable future smart factories. The paper contributes by motivating innovative solutions to enhance AGV/AMR deployment in industrial automation.

The research in [39] proposes a genetic algorithm (GA)-based resource management approach integrated with machine learning for predictive maintenance in fog computing within Industry 4.0. The study focuses on optimizing task distribution and predictive maintenance by comparing GA with traditional scheduling algorithms (MinMin, MaxMin, FCFS, Round Robin) in terms of the time, cost, and energy efficiency using FogWorkflowSim simulations. The results show that GA outperforms other methods, achieving a 0.48% faster execution time, 5.43% lower cost, and 28.10% lower energy usage compared to the second-best approach. Additionally, the predictive maintenance model, built using two-class logistic regression on real-time data, achieves high accuracy (95.1% training, 94.5% testing), demonstrating its effectiveness in failure prediction and resource optimization.

A systematic literature review (SLR) on IoT-based Supply Chain Management (SCM) from 2018 to 2022 is presented in [40], addressing the lack of comprehensive reviews in this growing research area. It highlights how IoT technologies—such as GPS, RFID, and NFC—enhance SCM by enabling real-time tracking, asset management, and operational efficiency. The review provides a detailed analysis of IoT applications, technologies, sensors, and devices used across various SCM stages. Additionally, it offers insights into the challenges, benefits, and economic impacts of the IoT in SCM. The findings serve as a valuable resource for researchers and practitioners seeking a holistic understanding of IoT-based SCM systems.

The study in [41] explores the synergy between Virtual Commissioning (VC) and Digital Twins (DTs), two key Industry 4.0 technologies, addressing a gap in the existing research. It identifies a strong relationship between VC and DTs, emphasizing their shared components like digital models, and demonstrates how their combined use enhances model reusability and overall value. The study proposes strategies for integrating VC and DTs, including mutual support and full integration, while also suggesting future research directions. By highlighting their complementary roles, the paper contributes to optimizing industrial processes through effective VC-DT collaboration.

Table 3. Structured comparison for the studies in the context of industrial automation.

Paper	Focus Area	Key Contributions	Methodology/Approach	Findings/Limitations	Technology Highlighted
[36]	5G-IIoT for Smart Manufacturing	Proposes 5G-enabled IIoT architecture for CPMS (eMBB, mMTC, URLLC, NB-IoT)	Integrates 5G features to overcome 3G/4G limitations in industrial automation	Enhances real-time monitoring, collaboration, and efficiency in smart factories	5G, IIoT, CPMS
[37]	AI/5G for Predictive Maintenance	AI-driven 5G NetApp for fault detection in energy infrastructures (autoencoder-based)	Containerized data collection and anomaly detection in power plants	Improves equipment reliability; enables real-time monitoring and maintenance	5G, AI, Autoencoders
[38]	AGV/AMR Communication	Reviews wireless challenges for AGV/AMR fleet management in smart factories	Analyzes latency/reliability gaps; proposes 5G slicing, tactile Internet, VR solutions	Current tech falls short; 5G integration needed for coordination in Industry 4.0	AGV, AMR, 5G Slicing
[39]	Fog Computing in Industry 4.0	GA and ML for predictive maintenance and resource optimization in fog computing	Compares GA with MinMin/MaxMin/FCFS/RoundRobin via FogWorkflowSim	GA outperforms: 0.48% faster, 5.43% cheaper, 28.1% less energy; 95% prediction accuracy	GA, ML, Fog Computing
[40]	IoT in Supply Chain Management	SLR on IoT-based SCM (2018–2022): tracking, asset management, efficiency	Reviews GPS/RFID/NFC applications, challenges, and economic impacts	IoT enhances real-time SCM but faces integration and scalability hurdles	IoT, RFID, GPS, NFC
[41]	VC & Digital Twins	Explores VC-DT synergy for Industry 4.0; proposes integration strategies	Identifies shared components (digital models) and collaboration frameworks	Combined use boosts model reusability and process optimization	Virtual Commissioning, Digital Twins

below provides a comparison of the studies discussed.

2.4. Healthcare

The study in [42] highlights the transformative potential of 5G technology in healthcare, emphasizing its role in enabling smart healthcare systems and improving medical resource allocation. It discusses the key features and service pillars of 5G that support healthcare innovations, such as remote patient monitoring and AI-integrated smart devices. The study identifies major applications of 5G in healthcare, including enhanced patient autonomy and real-time decision-making through interconnected devices. While acknowledging challenges like limited coverage in obstructed areas, the paper anticipates greater collaboration between network providers and medical device manufacturers to advance smart medical care. Overall, the contribution lies in outlining how 5G can revolutionize healthcare accessibility, efficiency, and technological integration.

The research in [43] emphasizes the transformative potential of 5G wireless networks in healthcare, emphasizing the need for greater awareness to accelerate adoption and avoid the delays seen in past technologies like minimally invasive surgery. The authors review 13 relevant studies, analyzing 5G’s capabilities in clinical applications, education, research, and administrative infrastructure, while addressing both opportunities and challenges. They discuss how 5G enables an integrated ecosystem of innovative technologies that can revolutionize healthcare delivery. The study also examines nontechnical barriers to implementation and advocates for increased awareness to shorten the acceptance timeline. By synthesizing the current evidence, the paper aims to promote the faster adoption of 5G-driven solutions for improved patient care.

The transformative role of IoT in healthcare is explored in [44] by analyzing specific sensor technologies and communication methods. It highlights successful applications such as remote patient monitoring, personalized treatments, and improved healthcare efficiency. The study also addresses key challenges, including data security, interoperability, and optimal data utilization. By showcasing real-world implementations, the paper demonstrates how IoT enhances patient care and resource management. Ultimately, it aims to guide practitioners and researchers in leveraging IoT to advance healthcare delivery.

The research in [45] surveys the integration of 5G technology, IoT, and AI to enhance smart hospitals by improving medical services and patient care. The study focuses on optimizing 5G waveforms (NOMA, FBMC, OFDM) through a proposed PAPR reduction algorithm to boost power efficiency and spectral access. Advanced detection and spectrum sensing algorithms are applied to enhance signal detection and reduce spectrum leakage, improving throughput and spectral efficiency. Additionally, the paper highlights the role of the IoT and AI in enabling remote healthcare services, cost reduction, and better patient outcomes. The findings demonstrate that these technologies can significantly enhance hospital efficiency and healthcare quality.

A systematic review of 5G-enabled smart healthcare applications is presented in [46], highlighting its contributions to enhancing medical services through high-speed data transmission, ultra-low latency, and improved network capacity. The study emphasizes 5G’s role in supporting IoMT devices, remote care, and reducing in-person hospital visits via eMBB and URLLC features. Using the PRISMA methodology, the review analyzes research trends, revealing that most existing works are proposals (56.81%) or theoretical studies (22.73%), with limited implementations (15.91%) due to 5G’s ongoing global deployment. The paper also addresses key technical aspects, health benefits, and security protocols necessary for developing smart healthcare systems. Despite current limitations, the study underscores the promising potential of 5G in transforming healthcare delivery.

The study in [47] highlights the transformative potential of telesurgery—a telemedicine application using robotic systems and ICT—to enable remote surgical procedures, overcoming geographical barriers and reducing patient travel. It emphasizes telesurgery’s benefits, including cost containment and enhanced access to minimally invasive robotic surgery. However, despite growing interest, the study notes its limited clinical adoption due to unresolved challenges. The article reviews these advantages, identifies current limitations, and explores potential solutions through ongoing research to facilitate broader implementation. Overall, the work contributes by assessing telesurgery’s promise while addressing critical barriers to its practical use.

Table 4. Structured comparison for the studies in the context of healthcare.

Paper	Focus Area	Key Contributions	Methodology/Approach	Findings/Limitations	Technology Highlighted
[42]	5G in Smart Healthcare	Outlines 5G’s role in remote monitoring, AI integration, and resource allocation	Reviews 5G features (eMBB, URLLC) and healthcare applications	Improves accessibility but faces coverage challenges in obstructed areas	5G, AI, IoMT
[43]	5G Adoption in Healthcare	Advocates for awareness to accelerate 5G adoption in clinical/research settings	Analyzes 13 studies on 5G’s clinical/administrative potential	Nontechnical barriers (e.g., awareness) delay implementation	5G, Telemedicine
[44]	IoT in Healthcare	Examines IoT sensors for remote monitoring and personalized care	Reviews real-world IoT applications (e.g., wearables) and challenges	Highlights data security and interoperability issues	IoT, Wearables
[45]	5G–IoT-AI in Smart Hospitals	Proposes PAPR reduction algorithm for 5G waveforms (NOMA, FBMC, OFDM)	Optimizes signal detection/spectrum sensing for throughput/efficiency gains	Enhances power efficiency, remote care, and cost reduction	5G, AI, IoT, NOMA
[46]	5G Smart Healthcare Review	PRISMA-based review of 5G’s role in IoMT and remote care	Analyzes 56.81% proposals vs. 15.91% implementations due to ongoing 5G deployment	eMBB/URLLC enables low-latency care but face security challenges	5G, IoMT, PRISMA
[47]	Telesurgery	Assesses robotic telesurgery’s potential to overcome geographical barriers	Reviews benefits (cost reduction) and adoption challenges (latency, ethics)	Limited clinical adoption despite technological readiness	Robotics, 5G, ICT

below provides a comparison of the studies discussed.

2.5. Agriculture

A comprehensive review of how emerging technologies like the IoT, AI, and ML are transforming agriculture across key areas such as smart farming, precision livestock, and regenerative agriculture is presented in [48]. By synthesizing current research, innovations, and case studies, it highlights the role of these technologies in boosting productivity, sustainability, and efficiency. The study explores practical applications in open-field farming, vertical farming, and zero-waste systems, offering insights for farmers and consumers. Additionally, it addresses future perspectives on leveraging these advancements to tackle global food security and environmental challenges while supporting sustainable development goals. Ultimately, the work serves as a valuable resource for understanding the impact of cutting-edge technologies in modern agriculture.

The work in [49] contributes to the field of Food Traceability Systems (FTS) by analyzing existing definitions of traceability and proposing a simpler, more comprehensive definition. It also introduces a new architecture for IoT-based FTS and a classification framework for the technologies used, categorizing them into Identification and Monitoring Technologies (IMTs), Communication Technologies (CTs), and Data Management Technologies (DMTs). Additionally, the paper discusses the applications of emerging technologies like 5G and Distributed Ledger Technology (DLT) to enhance FTS. By addressing gaps in standardization and technology classification, the study aims to improve food safety, regulatory compliance, and consumer confidence in food supply chains. The work also explores future trends in IoT-based traceability systems.

An IoT-based biological state monitoring system designed for automated livestock health and welfare inspection in intensive farming is proposed in [50]. The key contribution is a distributed master–slave hardware setup using microprocessors, laser sensors, and wireless modules to collect real-time livestock posture data, analyzed via a one-dimensional spatial posture description method. The system employs an RS485 bus and MQTT protocol to transmit JSON-formatted data to an Alibaba Cloud server, enabling centralized monitoring through the mobile app. Experimental results confirm the system’s effectiveness in remote data acquisition, real-time reporting, and status alarms, meeting the accuracy requirements for large-scale farming. This solution enhances livestock management by providing timely, reliable monitoring across multiple breeding sites. The work in [51] explores the role of the 5G-based Internet of Things (IoT) in advancing smart agriculture, addressing the need for increased production, sustainability, and efficiency. It reviews recent developments in 5G–IoT for agriculture, proposing a conceptual framework that includes its architecture, key technologies, and applications. The study highlights practical use cases across various smart agriculture scenarios, demonstrating the transformative potential of 5G–IoT. Additionally, it identifies key challenges and scientific problems, offering research directions to overcome them. Finally, the paper outlines future trends and the broader application value of 5G-enabled smart agriculture.

The transformative potential of 5G technology in smart farming is presented in [29], highlighting its role in enhancing connectivity, real-time monitoring, and data-driven agricultural practices. It addresses key challenges such as network infrastructure, edge computing, AI integration, and security while emphasizing alignment with Sustainable Development Goals (SDGs) like Zero Hunger and Industry Innovation. The study discusses critical applications, including UAV operations, AR/VR, predictive maintenance, and AI-driven analytics, to boost productivity and sustainability. Additionally, it identifies research gaps and calls for stakeholder collaboration to optimize 5G deployment in agriculture. Ultimately, the paper underscores 5G’s potential to advance sustainable farming, reduce the environmental impact, and support global food security and technological innovation.

Table 5. Structured comparison for the studies in the context of agriculture.

Paper	Focus Area	Key Contributions	Methodology/Approach	Findings/Limitations	Technology Highlighted
[48]	IoT/AI/ML in Agriculture	Comprehensive review of smart farming, precision livestock, and regenerative agriculture	Synthesizes research, innovations, and case studies	Boosts productivity/sustainability; addresses food security and SDGs	IoT, AI, ML
[49]	IoT-based Food Traceability	Proposes simplified FTS definition and new IoT–FTS architecture and classification	Classifies technologies into IMT, CT, DMT; explores 5G/DLT applications	Improves food safety/consumer confidence; gaps in standardization	IoT, 5G, DLT
[50]	Livestock Monitoring	IoT-based system for real-time livestock health inspection (posture analysis)	Distributed master-slave hardware with RS485/MQTT protocols and cloud/mobile integration	Effective for remote monitoring in large-scale farming; meets accuracy requirements	IoT, MQTT, Cloud Computing
[51]	5G-IoT in Smart Agriculture	Conceptual framework for 5G-IoT architecture/applications in agriculture	Reviews 5G–IoT developments and use cases; identifies challenges	Enhances production/sustainability; needs research to address implementation hurdles	5G, IoT
[29]	5G in Smart Farming	Examines 5G’s role in UAVs, AI analytics, and SDG-aligned farming	Discusses challenges (infrastructure, security) and AI/AR/VR applications	Promotes sustainable farming; calls for stakeholder collaboration	5G, AI, UAVs, AR/VR

below provides a comparison of the studies discussed.

2.6. Transportation and Autonomous Vehicles

The role of 5G technology in advancing Logistics 4.0 by enabling reliable, real-time communication for managing large-scale data exchange between interconnected systems was studied in [52]. Through a systematic literature review, it identifies key logistics areas where 5G can be implemented, highlighting its potential benefits and the complementary technologies (IoT, AI, big data) that enhance its adoption. The study also outlines the expected improvements in logistics processes, such as optimization and efficiency gains. Additionally, it discusses the current challenges hindering widespread 5G integration in logistics and suggests future research directions to address these barriers. Overall, the paper contributes by mapping 5G’s transformative potential in Smart Logistics while addressing adoption challenges.

The work in [53] studies the integration of cutting-edge technologies, such as the IoT, edge intelligence, 5G, and blockchain, into autonomous vehicle (AV) architectures to enhance safety, efficiency, and sustainability in intelligent transportation systems. It provides a comprehensive review of these technologies’ impact, implementation challenges, and potential solutions. The study also discusses the seamless integration of these advancements to meet AV requirements and highlights future research directions. By consolidating key enabling technologies in a single review, the paper serves as a valuable reference for researchers and stakeholders in AV development, addressing critical challenges and opportunities in the field.

The survey in [54] explores the integration of Fifth Generation (5G) and beyond (B5G) networks with autonomous vehicles (AVs), highlighting how 5G’s enhanced capabilities—such as ultra-reliable low latency communication and massive connectivity—can support AV requirements. It reviews current advancements in AVs, automation levels, and enabling technologies while discussing the role of 5G in facilitating AV deployment. The paper also examines emerging technologies for 5G–AV integration, security concerns, and standardization efforts. Additionally, it identifies key challenges, lessons learned, and future research directions to advance AV technologies in the 5G and B5G era.

The work in [55] investigates how 5G technology will revolutionize smart cities and intelligent transportation systems (ITSs) by enabling massive connectivity, high mobility support, and network ubiquity, overcoming the limitations of 4G. It highlights 5G’s role as a key enabler for the Internet of Things (IoT) and the Internet of Vehicles (IoV), facilitating seamless integration in densely populated or high-speed environments. The discussion comprehensively addresses the technical, economic, and legal challenges associated with 5G deployment in these domains. By analyzing its impact on autonomous vehicles and vehicular communications, the paper provides insights into the transformative potential of 5G in the coming years. Overall, it underscores 5G’s significance in advancing smart urban and transportation ecosystems.

The role of 5G networks in enhancing Vehicle-to-Everything (V2X) communication, emphasizing its potential to improve road safety, traffic efficiency, and energy savings through high-speed, low-latency connectivity, is investigated in [56]. It evaluates V2X performance on 5G compared to older networks, covering key paradigms like V2V, V2I, and V2P, while addressing challenges such as infrastructure integration, security, and privacy. The study also examines the impact of V2X on smart cities, autonomous vehicles, and Intelligent Transportation Systems (ITSs). Additionally, it highlights emerging technologies like edge computing, AI, and blockchain that could transform V2X systems. Serving as a comprehensive guide, the paper provides valuable insights for policymakers, researchers, and scholars in the field.

Table 6. Structured comparison of the studies in the context of transportation and autonomous vehicles.

Paper	Focus Area	Key Contributions	Methodology/Approach	Findings/Limitations	Technology Highlighted
[52]	5G in Logistics 4.0	Maps 5G’s role in Smart Logistics (IoT/AI/big data integration)	Systematic literature review of 5G applications in logistics	Optimizes efficiency but faces adoption challenges (e.g., infrastructure, cost)	5G, IoT, AI, Big Data
[53]	AV Tech Integration	Reviews IoT/edge/5G/blockchain for AV safety and sustainability	Comprehensive review of enabling technologies and implementation challenges	Highlights the need for seamless integration of heterogeneous technologies	AVs, 5G, Blockchain, Edge AI
[54]	5G/B5G for AVs	Analyzes 5G’s URLLC/massive connectivity for AV deployment	Surveys AV automation levels, 5G integration, and security/standardization efforts	Identifies security concerns and the need for B5G advancements	5G, B5G, Autonomous Vehicles
[55]	5G for Smart Cities/ITS	Examines 5G’s role in IoT/IoV for dense/high-speed environments	Discusses technical/economic/legal challenges of 5G deployment	Overcomes 4G limitations but faces regulatory hurdles	5G, IoV, ITS
[56]	5G–V2X Communication	Evaluates 5G’s impact on V2X (safety, traffic, energy efficiency)	Compares 5G-V2X with older networks; explores edge/AI/blockchain synergies	Enhances low-latency V2V/V2I/V2P but requires infrastructure upgrades	5G, V2X, Edge Computing, AI

below provides a comparison of the studies discussed.

3. Application of IoT in 5G Networks

3.1. Implementation of AI and IoT-Driven 5G Solutions

There are several applications of IoT in 5G networks, such as education, health, smart cities, agriculture, and energy.

Table 7. Classification of IoT and AI applications in 5G.

Sector	Application	References
Health	Smart hospital	Kumar [57], Chen [58], Siriwardhana [59]
	Telemedicine	Chen [58], Chakola [60], Ahmad [61]
	Patient monitoring	Jiménez [62], Cerqueira [63], Balasundaram [64]
	Treatment	Khatun [65], Anglano [66], Humayun [67]
Education	Distance learning	Mazloomi [68], Li [69]
	Smart classroom	Pang [70], Memos [71], Yadav [72]
	Security	Kitkowska [73]
Agriculture	Irrigation	Lu [74], Dumitrascu [75]
	Monitoring	Bostani [76], Makondo [77]
	Prediction	Bhatia [78]
	Decision making	Louta [79], Shaikh [80]
Industry	Manufacturing	Xiang [81], Humayun [82]
	Maintenance	Moloudian [83], Hoang [84]
	Safety	Kabir [85], Wanasinghe [86]
City Development	Transportation	Modina [87], Honda [88]
	Waste management	Tomas [89], Cao [90]
	Monitoring	Segura-Garcia [91], Palattella [92]
Retail	Marketing	Prasad [93], Gomez [94]
	Smart shopping	Al-Samawi [95], Dutkiewicz [96]
Energy	Smart grids	Zhang [97], Saleem [98]
	Energy management	Daas [99], Israr [100]
	Infrastructure monitoring	Morato [101], Sotres [102]
	Smart meters	Lee [103], Ahmadzadeh [104]
Finance	Risk monitoring	Wang [105], Ahmed [106]
	Smart applications	Vanmathi [107]
Entertainment	Smart Tv	Suneesh [108], Patzold [109]
	Virtual reality	Wu [110], Kusuma [111]
Defense	Surveillance	Muhammad [112], Quintana-Ramirez [113]
	Wearable devices	Gravina [114], Gravina [115]
	Smart vehicles	Gao [116], Zhang [117]

demonstrates a comprehensive classification of IoT applications in 5G networks across various sectors, including health, education, agriculture, industry, city development, retail, energy, finance, entertainment, and defense. Each sector is further divided into specific applications, reflecting the broad spectrum of technological advancements enabled by the convergence of IoT and 5G.

Emerging technologies such as AI, the IoT, and 5G have a strategic role in transforming healthcare, with a focus on smart hospitals, remote patient and elderly monitoring, and security. Several studies demonstrate the potential of these technologies in reducing latency and enhancing real-time applications in smart healthcare environments. Kumar et al. [57] proposed a hybrid detection technique for improving latency in 5G-based smart hospitals, while Jiménez et al. [62] discussed the integration of 5G in remote patient monitoring systems. Siriwardhana et al. and Chamola et al. [60] examine the role of 5G, AI, blockchain, and IoT in managing the COVID-19 pandemic, working on how Artificial Intelligence methodologies improve diagnostics, patient tracking, treatment, and healthcare logistics.

Another application of AI in 5G is the security issues in healthcare systems. Chen et al. [58] proposed a Zero-Trust model to enhance security awareness and protection in smart healthcare. Ahmad et al. [61] and Balasundaram et al. [64] discussed the potential of IoT and wearable sensors for remote health monitoring and emergency care. Recent studies with Anglano et al. [66] and Humayun et al. [67] emphasize cloud-based wearable health tracking systems and the importance of emerging technologies in improving healthcare access in remote areas. Proposed methods and models demonstrate high accuracy of the AI and IoT based 5G in the health sector.

AI and IoT-based 5G have several applications in the education sector, from smart classrooms to teaching methodologies [67]. Li et al. [69] proposed an AI-based 5G model for campuses that have sustainable energy efficiency, secure rooms, and automation in several locations. Peng et al.’s [69] model creates a virtual representation of the classroom, enabling the real-time monitoring of environmental conditions, student engagement, and resource utilization using IoT sensors. Virtual-reality-based applications are also very efficient in education, especially remote learning and adult education [71]. These systems are very helpful in managing classrooms and campuses, such as attendance, security checks, monitoring, and examinations [72]. Kitkowska et al. [73] proposed another monitoring system in elementary schools to monitor student activities, campus security, and enhancements for privacy protection.

Recently, the agricultural usage of technology has become more important. The integration of IoT, AI, and 5G technologies brings efficiency and quality to agricultural products. Irrigation, monitoring, decision-making, and prediction are the main applications of the AI-based systems. Lu et al. [74] proposed the real-time monitoring of soil moisture, temperature, and environmental conditions, optimizing water usage and improving crop yields. Sensors collect data such as the temperature, moisture, water level, and soil quality, and machine-learning-based predicted models help farmers to make decisions. It is easier to monitor large farm areas, detect plant diseases, and arrange water levels [75,76,77]. Bhatia et al. [78] discussed the impact of IoT and AI applications, including smart agriculture, smart cities, and industrial automation, highlighting key challenges and opportunities. Robotics is also a very critical field for smart farming. Agri robots are very useful in monitoring and replacing labor in agricultural fields [79,80].

In today’s industry, sensors, robots, and AI models are frequently used in all sectors. Industry 5.0 emphasizes human-centric and sustainable manufacturing by integrating AI, robots, and IoT [81,82]. Security and privacy in industrial fields challenge 5G-enabled Industrial IoT, requiring advanced encryption and authentication methods [83]. Also, IoT-based smart home frameworks ensure safety through biometric authentication and intrusion detection in industry [84]. In the energy sector, IoT and AI play a vital role in predictive maintenance and real-time monitoring to enhance efficiency in oil and gas operations [85,86].

There are various applications of the AI-based IoT and 5G in sustainable city development in transportation, monitoring, and waste management. Modina et al. and Honda et al. worked on traffic control systems in city centers [87,88]. There is also machine-learning-based smart sensor systems in waste management. AI-based methods have high accuracy in waste collection and classification [89,90]. City monitoring systems are based on video cameras and IoT sensors, which are mostly used for security and safety reasons [91,92]. All of these smart systems are integrated with AI-based algorithms that process data, classify, predict, and make decisions in a short time. Human-based monitoring systems are time-consuming and have lower accuracy than machine-based systems. Another application of AI-based 5G technologies is retail, such as marketing [93,94] and e-shopping [95,96]. Prasad et al. [93] analyzes the economic and security challenges of AI-based 5G in emphasizing market trends and risk-mitigation strategies. Gomes et al. [94] analyzed business models for managing IoT–mobile devices in digital hospitals, addressing privacy and security concerns. Other works discuss how 5G enhances IoT applications in daily life and shopping and highlight improvements in connectivity and automation [95,96]. AI and IoT technologies have recently been used in energy sectors, such as smart grid, energy management, infrastructure monitoring, and smart meters. The authors in [97,98,99,100,101,102,103,104,105] worked on the integration of 5G, artificial intelligence (AI), and the Internet of Things (IoT) in smart grid and energy management systems. Zhang et al. [97] discuss AI services enabled by 5G Multi-Access Edge Computing in smart grids, while Saleem et al. [98] discuss the role of IoT in enhancing cyber-physical systems within the 5G-powered smart grid. Daas et al. [99] introduce a graph-theory-based energy management framework in energy management systems. Experimental results show that the proposed AI and IoT-based techniques are very efficient and have high accuracy in energy management.

Risk monitoring [105,106] and smart applications [107] in finance, smart TV [108,109], and virtual reality [110,111] applications also benefit from AI and IoT technologies via 5G. Defense and security is another critical field using these technologies, such as in surveillance, wearable devices, and smart vehicles [112,113,114,115,116,117]. Machine-to-machine communication is very important in 5G. Muhammad et al. [112] propose an efficient fire detection system for uncertain surveillance environments such as dusty weather. Quintana-Ramirez et al. [113] introduced an edge cloud-based video surveillance system for public transportation security. Gravina and Fortino [114] provide a discussion of wearable body sensor networks that can be used in the security and defense sector.

3.2. Statistical Insights and Market Trends

Table 8. Statistics for AI and IoT in 5G applications.

Sector	Application	Statistics
Health [57,58,116]	-5G remote patient monitoring via IoT -Telemedicine	31% of patients in the USA had remote monitoring in 2021. AI integration will increase this number.
Education [69,70,71,118,119]	-Distance learning, especially virtual reality/augmented reality-based learning -Smart classrooms	-The 5G-enabled education market will reach $404 billion by 2026. -VR in the education market will grow 40.7% by 2027.
Smart cities [76,118,119,120]	-Real-time traffic monitoring -Surveillance and automated response systems for public safety	-By 2026, there will be over 60 million 5G smart city connections globally. -60% of devices will have smart city applications by 2026. -Market size is expected to be $820.7 by 2027.
Agriculture [74,77,120]	-IoT sensors monitor soil conditions, crop health, and weather patterns. -Monitoring with wearable devices	The smart farming market is growing, which is $21.8 billion in 2024 and is expected 113.16$ billion by 2034.
Industry [81,82,119]	-Enhancing automation and real-time communication -IoT-based maintenance and early detection	The IoT and AI-based 5G technology market will reach $263.4 billion by 2027.
Finance [106,107,118]	-Smart ATM: Real-time monitoring and maintenance in ATM machines -Fraud detection	-Finance sectors reach $2.03 billion in the 2023 market via 5G. -Finance technology in the IoT and AI market will reach $309.98 billion by 2028.
Defense [105,118,119,120]	-AI-based 5G has more reliable communication technologies in the military -IoT-based drones and other technologies improve monitoring capabilities	-The global market is expected to reach $90 billion in 2025. -AI based 5G defense technology market increased 28% from 2020 to 2025.
Energy [98,101,119]	-Smart grids increase energy consumption, increase energy efficiency, and reliability	-The global smart energy market will reach $60 billion by 2027. -5G based grid market is expected to increase by 24% from 2022 to 2030.

demonstrates some statistics for 5G applications in several sectors. In healthcare, 5G technology usage is increasing, especially in monitoring. Statistics show that 31% of US patients got remote monitoring in 2021. It is obvious that AI-based sensor technology will be efficient in the health sector in the future. The education sector is another important and popular use of technology, such as smart schools, distance learning, and virtual reality applications. This sector will reach more than $400 billion in the market. IoT-based home and city applications are very popular, and with AI applications, they are growing in the market. Energy and defense sectors are also growing and very highly expected in the market in the future, as given in Table 8.

The shift from 4G to 5G networks has had a major impact on how the Internet of Things (IoT) is used across different sectors. With 4G, we saw the rise of early IoT applications like smart thermostats, home assistants, wearable fitness trackers, and simple agricultural sensors. These systems were useful but relatively limited, as 4G could only support around 100,000 connected devices per square kilometer and had latency in the range of 30 to 50 milliseconds. This made it suitable for everyday consumer use but not ideal for more complex tasks that required real-time data or large-scale device integration. The network’s slower uplink speeds also made it harder to manage the huge volumes of data generated by large sensor networks, especially in industrial settings. 5G brings dramatically faster speeds, a much lower latency, and the capacity to support up to a million devices in the same area. This means that the IoT can now be used in much more advanced and demanding scenarios. We are seeing 5G-powered applications in self-driving cars, where real-time communication is essential, as well as in remote surgeries where precision and speed are critical. It is also enabling smarter city infrastructure like traffic systems and surveillance [72,91], along with more efficient industrial automation, predictive maintenance, and energy management through smart grids.

Table 9. Technological and regional statistics of IoT deployment in 4G and 5G.

Region	IoT in 4G	IoT in 5G
China	3B+ IoT devices (4G)	900M+ 5G IoT (30% of global share by 2025)
USA	1.5B+ 4G IoT connections	100M+ 5G IoT devices (smart cities, agriculture)
EU	~700M total IoT devices over 4G	150M+ 5G IoT devices by 2023, esp. in Germany & NL
South Korea	4G-based wearables and mobile IoT	15M+ 5G IoT devices (autonomous driving, hospitals)
UAE/KSA	Minimal 4G IoT in 2015	100M+ 5G devices planned for 2025 (smart cities, security)

demonstrates technological and regional statistics of 4G and 5G. The deployment of IoT devices has grown rapidly across mobile generations, with significant regional differences in adoption. In the 4G era, IoT growth was largely driven by consumer electronics in the USA, Europe, and East Asia, focusing on wearables, home automation, and telematics. However, the emergence of 5G has shifted focus toward industrial and mission-critical IoT in countries with strong infrastructure investments [85,103,104].

4. Security Measures of Integrating IoT Applications in 5G Networks

4.1. General Overview

It is a great opportunity to change the different sectors of the economy with the help of the integration of IoT applications into 5G networks, besides the improvement of data transmission speeds, the decrease in delivery time, and the increase in the number of connections. However, this convergence also creates several security risks that must be addressed to ensure that IoT devices and networks are secure. Recent identifications have highlighted the need for robust security frameworks to address the increasing risks of cyberattacks in 5G-based IoT environments [121]. Some of these security risks include data leakage, DoS attacks, and access control, which are a great risk to both individuals and organizations that use IoT applications in the 5G network [122]. The combination of IoT and 5G networks is introduced to transform the way people can interact with technology. IoT devices are gathering a large amount of data, which needs to be transported through secure and robust networks. These characteristics of 5G networks, high-speed data transfer, low latency, and massive machine-type communications, make it suitable for IoT applications [123]. However, this integration creates new security threats such as MitM attacks, DDoS attacks, and firmware exploits that need to be prevented to protect the data and the network’s integrity [123].

4.2. Security Challenges in Integrating IoT Applications in 5G Networks

In this paper, the deployment of IoT applications on top of the 5G network is presented to create new opportunities for almost all sectors of the economy by enhancing the speed of data transmission and reducing the time delay in the network. However, the integration of such IoT devices and networks presents certain crucial security risks that must be addressed to make the IoT devices and networks reliable and safe.

The combination of IoT applications with 5G networks enables multiple sectors to benefit from faster data transfer speeds and lower latency. The implementation of IoT applications in 5G networks creates essential security problems that must be solved to protect network reliability and device safety. The growing attack surface remains a major concern because the expected 75 billion connected IoT devices in 2025 will generate extensive new entry points for unauthorized access and data breaches that require strong security measures, including encryption and AI-based anomaly detection [124]. Data privacy stands as a major concern because IoT devices gather personal and health and industrial information, which needs protection through strict regulations like GDPR; research shows growing IoT data breaches, which demonstrate the requirement for decentralized security systems that use blockchain authentication and AI intrusion detection [121]. The extensive connectivity of 5G networks creates conditions that make the system vulnerable to congestion and Distributed Denial of Service (DDoS) attacks, which attackers can exploit using IoT-based botnets [121]. The use of default or weak passwords by many IoT devices creates authentication and authorization challenges because it increases their vulnerability to unauthorized access, but this risk can be reduced by implementing multi-factor authentication, digital certificates, and biometric systems [125]. IoT devices remain exposed to exploitation because of outdated firmware and unpatched software, which requires AI-powered vulnerability scanning and secure over-the-air (OTA) updates to maintain security in 5G-enabled environments [126]. The safe deployment of IoT in 5G networks requires solving these security challenges by implementing advanced encryption methods and secure authentication protocols, intelligent threat detection systems, and consistent firmware updates.

4.3. Security Measures for Integrating IoT Applications in 5G Networks

To address the security issues that are likely to be encountered when incorporating IoT applications into 5G networks, Figure 3 shows the comprehensive 5G-Based Protection for IoT Devices.

4.3.1. Encryption and Data Protection

Given that IoT devices generate a high amount of sensitive information, they have become a preferred target for cyber-attackers. Encryption is one of the key factors in the protection of data from unauthorized access. The use of end-to-end encryption mechanisms (E2EEs) through algorithms like AES, TLS, and DTLS guarantee the safety of data in transit [126]. Furthermore, homomorphic encryption and quantum secure cryptographic methods are being researched to improve the security of the next generation 5G IoT networks. It is crucial to establish robust encryption policies whenever dealing with the vast amounts of confidential information that IoT devices generate.

Table 10. Comparison of some encryption techniques.

Encryption Technique	Description	Advantages	Limitations	Reference
Advanced Encryption Standard (AES)	A symmetric encryption algorithm is widely used for securing data.	High efficiency and strong security for data at rest and in transit.	Require secure key management; not inherently suitable for end-to-end encryption.	[127]
Transport Layer Security (TLS)	A cryptographic protocol designed to provide secure communication over a computer network.	Ensures data integrity and privacy between applications and users.	Vulnerable to certain attacks if not properly configured; relies on PKI for certificate management.	[128]
Datagram Transport Layer Security (DTLS)	An adaptation of TLS for datagram-based applications, such as those using UDP.	Provides similar security guarantees to TLS for applications requiring low latency.	Susceptible to packet loss and reordering; complexity in implementation.	[128]
Homomorphic Encryption	Allows computations to be performed on ciphertexts, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintext.	Enables data processing without exposing sensitive information; it is beneficial for cloud computing and data analytics.	Computationally intensive; overhead performance currently limits practical applications.	[129]
Quantum-Resistant Cryptography	Cryptographic algorithms are designed to be secure against attacks by quantum computers.	Provides long-term security in the advent of quantum computing; essential for future-proofing cryptographic systems.	Many algorithms are still under research and standardization, with potential performance trade-offs.	[130]

presents some encryption techniques.

These encryption techniques must be implemented and tailored to the specific requirements of IoT applications within 5G networks to protect sensitive data from increasingly common cyber threats.

4.3.2. Secure Authentication Mechanisms

Due to the issues with scalability and security, traditional authentication mechanisms, such as username and password, are not suitable for the IoT–5G environment. The security of the system is increased by secure authentication techniques such as multi-factor authentication (MFA), biometric authentication, and blockchain-based identity management. Decentralized and tamper-proof identity verification is achieved using blockchain technology, thus reducing the risk of identity fraud. Furthermore, PKI and ZTA are also important for preventing unauthorized access and establishing trust in the IoT networks.

Table 11. Comparison of various authentication techniques.

Authentication Mechanism	Description	Advantages	Limitations	Reference
Multi-Factor Authentication (MFA)	Combines two or more authentication factors (e.g., passwords, biometrics, tokens) to verify user identity.	Enhances security by requiring multiple forms of verification; reduces reliance on passwords alone.	May introduce usability challenges; requires additional infrastructure.	[131]
Biometric Verification	Utilizes unique biological traits (e.g., fingerprints, facial recognition) for authentication.	Provides high security due to the uniqueness of biometric data; convenient for users.	Privacy concerns, potential for false positives/negatives; requires specialized hardware.	[131]
Blockchain-Based Identity Management	Employs decentralized ledger technology to manage and verify identities securely.	Tamper-resistant; eliminates single points of failure; enhances transparency.	Scalability issues, high computational requirements, and regulatory uncertainties.	[132]
Public Key Infrastructure (PKI)	Uses asymmetric cryptography to issue and manage digital certificates for authentication.	Establishes a trusted environment; widely adopted standard.	Complex certificate management; potential vulnerabilities if private keys are compromised.	[133]
Zero Trust Architecture (ZTA)	Assumes no implicit trust within the network; continuously verifies every user and device attempting to access resources.	Minimizes attack surfaces; adaptable to modern threats; enforces strict access controls.	Implementation complexity may require significant changes to existing infrastructure.	[134]

presents a comparison of different authentication techniques.

4.3.3. Network Slicing Security

5G enables network slicing, meaning that many virtual networks can be run on a single physical network. Although this feature increases the efficiency and flexibility of the network, it also creates security issues. The implementation of micro segmentation, access control lists (ACLs), and the real-time monitoring of each slice enhances the protection against cyber threats. Furthermore, Secure Access Service Edge (SASE) frameworks aid in the enforcement of security policies across the network slices.

Table 12. Comparison of various security mechanisms applicable to network slicing.

Security Mechanism	Description	Advantages	Limitations	Reference
Micro-Segmentation	Divides the network into smaller, isolated segments to limit the lateral movement of threats.	Enhances security by containing breaches within segments; provides granular security controls.	Complexity in implementation requires comprehensive network visibility.	[135]
Access Control Lists (ACLs)	Rules that permit or deny traffic flow based on predefined security policies.	Simple to implement; effective in controlling traffic and enforcing policies.	It can become unmanageable in large-scale networks; it lacks the intelligence to adapt to new threats.	[136]
Real-Time Monitoring	Continuous observation of network traffic to detect and respond to anomalies.	Enables prompt threat detection and response; improves situational awareness.	High resource consumption; potential for false positives.	[137]
Secure Access Service Edge (SASE)	Converges networking and security functions into a unified, cloud-delivered service model.	Simplifies security management; provides consistent security policies across all network slices.	Dependence on cloud providers; integration challenges with existing infrastructure.	[138]

compares several security mechanisms that can be applied to network slicing and describes their characteristics.

4.3.4. AI-Driven Threat Detection and Response

AI and ML are important for proactive threat management in 5G IoT networks. They work by having Security Information and Event Management (SIEM) systems that are driven by AI to analyze network traffic patterns to detect anomalies and prevent cyberattacks in real time [132]. Intrusion detection and prevention systems (IDPSs) that use AI can detect malware, Distributed Denial of Service (DDoS) attacks, and unauthorized access attempts with greater effectiveness by AI. AI and ML are very vital in improving threat detection and mitigation of risks in the 5G IoT networks.

Table 13. Comparison of AI-driven security systems.

AI-Driven Security System	Description	Advantages	Limitations	Reference
AI-Powered Threat Intelligence Platforms	Utilize AI to gather and analyze threat data, providing real-time insights and proactive defense strategies.	Enhances situational awareness; enables swift response to emerging threats.	Dependence on data quality; potential for high false-positive rates.	[139]
AI-Driven Intrusion Detection and Prevention Systems (IDPSs)	Employ ML algorithms to identify and mitigate unauthorized access attempts and network anomalies.	Adaptive learning capabilities improve detection accuracy over time.	Requires continuous training; may struggle with novel attack vectors.	[140]
AI-Based Anomaly Detection Systems	Analyze patterns in network traffic to identify deviations indicative of potential threats.	Effective in detecting unknown threats; reduces reliance on signature-based detection.	High computational requirements; potential for false alarms.	[141]
AI-Enhanced Security Information and Event Management (SIEM) Systems	Integrate AI to process and correlate security events from various sources, facilitating comprehensive threat analysis.	Streamlines incident response; improves threat visibility across the network.	Complexity in integration; potential data overload without proper filtering.	[142]

compares AI-based security solutions and their characteristics.

4.3.5. Secure Firmware and Software Updates

Many IoT devices are incapable of receiving security updates at regular intervals and are therefore prone to exploitation. The use of secure OTA updates is to guarantee that firmware and software patches are being delivered in a secure manner. Code signing, firmware integrity verification, and hardware security features like TPMs stop any unauthorized changes. It is imperative to have secure firmware and software update mechanisms for the security of IoT devices in 5G networks.

Table 14. Comparison of various security mechanisms applicable to firmware and software updates.

Security Mechanism	Description	Advantages	Limitations	Reference
Secure Over-the-Air (OTA) Updates	Enables wireless delivery of firmware and software updates to IoT devices, ensuring timely patching of vulnerabilities.	Facilitates efficient and widespread deployment of updates; reduces the need for physical access to devices.	Requires robust security protocols to prevent unauthorized access during transmission.	[143]
Code Signing	Utilizes digital signatures to verify the authenticity and integrity of firmware and software before installation.	Prevents installation of malicious or tampered code; ensures that only code from trusted sources is executed.	Relies on secure key management; compromised signing keys can undermine security.	[143]
Firmware Integrity Verification	Employs checksums or cryptographic hashes to validate firmware integrity during boot or update processes.	Detects unauthorized modifications; enhances trust in device operation.	May not prevent initial compromise; requires secure storage of integrity metrics.	[144]
Trusted Platform Modules (TPMs)	Hardware-based security components that store cryptographic keys and perform secure operations.	Provides hardware-rooted security; enhances protection against software-based attacks.	Increases hardware complexity and cost; integration with existing systems.	[145]

compares various security mechanisms that can be applied to firmware and software updates, and their key features are summarized.

4.3.6. Edge Computing Security

Thus, edge computing cuts the latency and increases the real-time processing of IoT-5G applications. However, it also has a security risk, such as unauthorized data access and tampering. Enhancing edge security involves implementing data encryption, secure hardware enclaves, and decentralized identity management. The concept of confidential computing that uses Trusted Execution Environments (TEEs) is a further step towards increased data security at the edge. Security measures for edge computing environments for IoT–5G applications must be strong to counter possible threats.

Table 15. Comparison of various security mechanisms applicable to edge computing.

Security Mechanism	Description	Advantages	Limitations	Recent Research Reference
Data Encryption	Encrypts data to prevent unauthorized access during transmission and storage.	Ensures confidentiality and integrity of data; protects against eavesdropping and tampering.	Requires effective key management; may introduce computational overhead.	[146]
Secure Hardware Enclaves	Utilizes hardware-based isolated execution environments to protect sensitive data and code.	Provides strong security against physical and software attacks; enhances trust in edge devices.	Increases hardware complexity and cost; limited scalability.	[147]
Decentralized Identity Management	Employs distributed ledger technologies like blockchain for identity verification without central authorities.	Enhances privacy and security; reduces single points of failure; improves scalability.	Faces challenges in standardization and interoperability; potential performance issues.	[148]
Confidential Computing with Trusted Execution Environments (TEEs)	Uses TEEs to process data securely within protected areas of processors, ensuring code and data confidentiality.	Protects data in use; mitigates risks of data exposure during processing; enhances overall security posture.	Limited availability across devices; potential overhead performance.	[149]

compares the edge computing security mechanisms with their key features.

4.3.7. Regulatory Compliance and Standardization

Cybersecurity regulations and industry standards compliance are a must to make IoT devices secure in 5G networks. Guidelines for securing IoT ecosystems are provided by regulations such as GDPR, ISO/IEC 27001 [150] and the NIST Cybersecurity Framework. Organizations also should adopt emerging 5G security standards from the 3rd Generation Partnership Project (3GPP) and the Internet Engineering Task Force (IETF) to enhance security [151].

4.3.8. Comparison of Security Measures

In conclusion of the security measures,

Table 16. Comparison between security measures.

Security Measure	Description	Advantages	Challenges	Reference
Network Segmentation	Dividing the network into smaller, isolated segments	Limits the spread of attacks and contains breaches	Requires careful planning and management	[152]
Strong Authentication	Implementing MFA and digital certificates	Ensures only authorized devices can access the network	It can be complex to implement and manage	[153]
Encryption	Encrypting data in transit and at rest	Protects sensitive information from unauthorized access	Can introduce latency, requires key management	[153]
Regular Updates	Regularly updating firmware and software	Addresses known vulnerabilities, enhances security	Requires automated mechanisms, can be resource-intensive	[153]
IDPS	Deploying intrusion detection and prevention systems	Detects and prevents malicious activities	Can generate false positives, requires continuous monitoring	[154]
SIEM	Implementing security information and event management	Provides real-time analysis of security alerts	Requires integration with multiple systems, and can be complex to configure	[154]
Zero Trust Architecture	Assuming no device or user should be trusted by default	Prevents unauthorized access, limits potential damage	Requires a cultural shift, can be complex to implement	[155]
Edge Computing	Processing data closer to the source of generation	Reduces data exposure, enhances security	Requires investment in edge infrastructure, can be complex to manage	[156]

compares the suggested security measures for integrating IoT applications into 5G networks.

4.4. Summary

4.4.1. Overview

The release of such integration in various sectors with the help of IoT applications with 5G networks can change the whole scenario. But the use of such integration raises critical security issues that are very important to solve to guarantee the reliability and safety of IoT devices and networks. With the help of the suggested security measures, organizations can overcome these challenges and realize the full potential of the IoT in 5G networks. The use of integrating IoT in 5G networks is expanding to include smart healthcare, the industrial IoT, smart transportation, and even smart agriculture. In the process of developing these applications, it is crucial to consider security aspects to avoid compromising sensitive information and the network. In conclusion, the enhanced version of IoT applications is only possible in integration with 5G networks if security is addressed in an overall approach with the help of the latest technologies, strong policies, and ongoing surveillance. As a result, organizations can fully implement the IoT in their 5G networks with the knowledge that their data is secure.

4.4.2. Discussion and Recommendations

The security mechanisms selection and deployment analysis in Table 9, Table 10, Table 11, Table 12, Table 13, Table 14 and Table 15 provides essential information for optimizing encryption and authentication technologies and best practices for IoT and 5G technology applications in critical sectors. For example, for preferred encryption methods, the evaluation shows that AES stands as an efficient encryption standard, which receives broad support for both data transmission and storage applications. Real-time healthcare applications need TLS/DTLS because this protocol supports UDP protocols and provides low overhead while ensuring data integrity and minimal delay. The current computational complexity of homomorphic encryption and quantum-resistant cryptography makes them suitable for high-security environments with ample processing resources, but they offer future-proofing capabilities. Accordingly, for recommended authentication techniques, healthcare systems benefit most from multi-factor authentication (MFA) and biometric verification because they protect sensitive personal data while requiring strict access control measures. Blockchain-based identity management works well for large-scale and decentralized environments such as smart cities or logistics because it provides tamper-resistance and auditability, but scalability needs proper management.

• General Recommendations:
  • For any sector deploying 5G–IoT applications, a layered security architecture combining encryption, secure authentication, and continuous threat monitoring is imperative.
  • Regulatory compliance with standards such as GDPR, ISO/IEC 27001, and 3GPP must be maintained across all layers.
  • AI-driven SIEM and IDPS systems are highly effective in environments with high data volume and dynamic threat landscapes, such as industrial automation and defense.

5. Challenges Facing the Integration of IoT Applications in 5G Networks

The integration of the Internet of Things (IoT) with 5G networks presents a transformative opportunity for various industries, ranging from healthcare and smart cities to autonomous vehicles and industrial automation. However, despite its promising potential, several challenges delay the seamless integration of IoT applications within 5G environments. This section explores key challenges, including network scalability, security vulnerabilities, interoperability issues, energy efficiency concerns, and latency requirements. Additionally, it discusses potential solutions, such as network slicing, artificial intelligence (AI)-driven management, and energy-efficient communication protocols, which can enhance IoT deployment within 5G ecosystems. Understanding these challenges is crucial for researchers, policymakers, and industry leaders to ensure the successful deployment of IoT applications within 5G networks.

The convergence of IoT and 5G technology is expected to revolutionize digital connectivity, enabling high-speed, low-latency communication between billions of connected devices. While 5G networks provide enhanced bandwidth, ultra-reliable low latency communication (URLLC) [157], and massive machine-type communication (mMTC) [158], the deployment of IoT applications in such an environment faces numerous hurdles. The rapid expansion of connected devices and the diverse requirements of IoT services pose significant challenges in terms of scalability, security, interoperability, and energy consumption [159].

Moreover, as IoT devices are integrated into smart cities, healthcare systems, industrial automation, and connected vehicles, ensuring seamless and secure communication becomes more complex. The coexistence of different communication standards, diverse hardware architectures, and heterogeneous data transmission protocols further complicates integration efforts [160]. Therefore, addressing these challenges is essential to fully unlock the potential of IoT in 5G networks. The below section highlights the primary obstacles that must be addressed for a smooth and efficient integration of IoT applications in 5G networks and proposes possible solutions for overcoming these challenges.

5.1. Network Scalability and Congestion

One of the fundamental challenges of integrating IoT with 5G is managing network scalability. The 5G ecosystem is designed to support an immense number of connected devices per square kilometer, but handling the explosive growth of IoT devices remains a complex issue. The sheer volume of data generated by IoT devices can lead to network congestion, impacting overall performance and quality of service (QoS) [161].

Additionally, efficient spectrum management is critical, as the increased demand for connectivity may lead to resource allocation challenges. With billions of devices transmitting data simultaneously, congestion can degrade network reliability and introduce delays in mission-critical applications. To mitigate these issues, researchers and network engineers must explore innovative solutions such as the following:

• Network slicing, which allows multiple virtual networks to be created on a shared physical infrastructure, optimizing resources for specific IoT use cases [162].
• Edge computing which reduces the load on central cloud servers by processing data closer to the source [163,164].

AI-driven traffic management dynamically allocates bandwidth and optimizes data transmission pathways [162].

Implementing these technologies will help 5G networks accommodate the exponential growth of IoT devices while maintaining optimal service quality [163].

5.2. Security and Privacy Concerns

IoT applications within 5G networks are highly susceptible to security threats, including cyberattacks, data breaches, and unauthorized access. Since IoT devices often have limited computational capabilities, implementing robust security protocols remains a challenge. The distributed nature of IoT networks increases the attack surface [165], making them vulnerable to threats such as the following:

• Denial-of-service (DoS) attacks, which can overwhelm networks and render services unavailable [166].
• Man-in-the-middle (MITM) attacks, where attackers intercept and manipulate communication between devices [167,168].
• Unauthorized data interception, leading to potential breaches of sensitive information [169].

Privacy concerns also arise due to the vast amount of personal and sensitive data collected by IoT devices, particularly in healthcare and smart city applications. To enhance the security and privacy of IoT applications within 5G networks, several measures can be implemented, such as the following:

• End-to-end encryption ensures that data remains protected throughout transmission.
• Blockchain technology provides decentralized and tamper-resistant security for IoT transactions.
• AI-based threat detection mechanisms that can identify and mitigate cyber threats in real-time.

Ensuring robust security measures will be crucial in fostering trust and promoting the widespread adoption of the IoT in 5G networks [170].

5.3. Interoperability Challenges

The heterogeneity of IoT devices and communication protocols creates interoperability challenges in 5G networks. IoT applications span multiple industries, each utilizing different hardware, software, and data formats. The lack of standardized protocols and communication frameworks makes it difficult for devices from different manufacturers to communicate seamlessly [171,172].

Achieving interoperability requires the following:

• Developing universal communication standards to facilitate cross-platform compatibility.
• Implementing middleware solutions that translate protocols and enable seamless data exchange.
• Encouraging industry collaborations and regulatory efforts to establish standardized frameworks for IoT communication.

Addressing interoperability challenges will be key to ensuring that IoT devices can seamlessly operate within the 5G ecosystem, fostering innovation and scalability [172].

5.4. Energy Efficiency Constraints

IoT devices often operate in resource-constrained environments, with limited battery life and low power consumption requirements. The integration of IoT applications into 5G networks demands energy-efficient communication protocols and device management strategies [173].

Continuous data transmission and frequent network handovers consume significant power, making it challenging for battery-operated IoT devices to sustain long-term connectivity. To address this issue, advancements in energy-efficient wireless communication technologies are needed, such as the following:

• Wake-up radios, which minimize power consumption by activating devices only when needed [174].
• Low-power wide-area networks (LPWANs) optimize long-range communication with minimal energy usage [175].
• Optimized sleep–wake cycles, reducing unnecessary data transmissions to conserve battery life [176].

These strategies will enhance the sustainability of IoT deployments within 5G networks, extending the lifespan of connected devices and reducing operational costs [173].

5.5. Meeting Latency Requirements

Many IoT applications, such as autonomous vehicles, industrial automation, and remote healthcare, require ultra-low latency communication to function effectively. While 5G networks promise latencies as low as one millisecond, achieving consistent low-latency performance in real-world scenarios is challenging [177].

Network congestion, signal interference, and inefficient routing can increase response times, affecting the reliability of latency-sensitive IoT applications. Potential solutions include the following:

• Edge computing reduces latency by processing data closer to the source rather than relying on centralized cloud servers [178].
• Network slicing ensures that latency-sensitive applications receive prioritized network resources [179].
• Advanced routing algorithms dynamically optimize data transmission paths for minimal delays [180].

These measures will be essential for maintaining real-time performance and ensuring the success of latency-sensitive IoT applications within 5G networks [177].

The integration of IoT applications within 5G networks holds tremendous potential but comes with significant challenges. Scalability, security vulnerabilities, interoperability, energy efficiency, and latency constraints are key areas requiring further research and innovation. Addressing these challenges necessitates collaborative efforts among researchers, industry leaders, and regulatory bodies to develop standardized frameworks, robust security measures, and energy-efficient communication protocols. By overcoming these hurdles, the synergy between IoT and 5G can drive the next wave of technological advancements, transform industries, and enhance digital connectivity worldwide [181].

6. Conclusions and Future Directions

The incorporation of IoT applications into 5G networks offers a significant opportunity for the advancement of smart cities. By utilizing 5G’s ultra-low latency, substantial bandwidth, and extensive device connectivity, urban infrastructures can be improved to provide more efficient, sustainable, and citizen-focused services. Systems such as smart traffic management, real-time public safety surveillance, intelligent energy grids, and automated waste disposal are already showcasing the benefits of this integration. However, several hurdles must still be overcome to unlock the complete potential of 5G-enabled smart cities. These challenges include safeguarding large amounts of sensitive urban data, ensuring the energy-efficient functionality of numerous distributed IoT sensors, and managing varying, dynamic network environments. Furthermore, achieving full compatibility among diverse smart city subsystems necessitates standardized frameworks and protocols. This study explores the core technical applications, key challenges, and prospects of integrating IoT applications with 5G networks, highlighting the transformative benefits while addressing critical issues such as security, energy efficiency, and network management.

The results of this review concerning the use of IoT within 5G networks are expected to provide significant insights for the research community. By consolidating current advancements, obstacles, and emerging trends, this study aims to foster ongoing innovation and direct future research in the area. It will act as a valuable resource for researchers, industry experts, and policymakers involved in the progression of 5G technologies and their incorporation with IT solutions.

Future research and development should focus on the following areas to further promote smart city innovation:

• Urban Data Security and Privacy: Developing scalable, real-time encryption and threat detection systems to protect citizen and infrastructure data.
• Sustainable Infrastructure: Creating energy-aware communication and computing systems to support eco-friendly and resilient smart city operations.
• Edge Intelligence: Increasing the utilization of edge and fog computing to minimize latency and enable localized decision-making for essential services such as emergency responses and traffic management.
• Interoperability Standards: Formulating unified frameworks that guarantee seamless integration across devices, vendors, and city departments.
• Citizen Engagement and Accessibility: Building inclusive platforms enables residents to interact with and derive benefits from smart city services through user-friendly interfaces and personalized data features. As cities progress and embrace digital technologies, the synergistic potential of 5G and the IoT will be vital in crafting urban settings that are not only smarter but also more habitable, secure, and attuned to the needs of their residents.