1. Introduction

1.4. Structure of this Paper

The structure and organization of the paper are shown in Figure 1. Section 1 covers the questions and challenges introduction. Additionally, the research contributions related to a cross-layer architecture that is both secure and energy-efficient are analyzed. Additionally, summary of existing survey, structure of this paper, cross-layer framework and IoT Network Design are provided. IoT security measures and energy effectiveness are covered in Section 2. The autonomous, secure, and energy-efficient cross-layer structure was also covered.Moreover, IoT layered architecture, trustworthy quality standards, industrial IIoT, MAC routing and energy-efficient cross-layer design were taken into consideration.

Section 3, "Key Strategies and Trends" identifies the following subjects as key strategies: Authentication and Encryption, Machine Learning for Threat Detection, Cross-Layer Security Framework, and Energy-Efficient Routing-Optimization. Furthermore, the Integration of Artificial Intelligence, Cognitive Radio for Spectrum Efficiency, Renewable Energy Integration, Holistic Security Approaches, and Cross-Layer Optimization are Emerging Trends. Open Research Areas and Challenges are identified in Section 4, along with a description of the challenges in EE and the secure cross-layer framework. The paper’s main conclusions and future contributions are outlined in Section 5’s Conclusion (Figure 1)

Table 1. Summary of Existing Relevant Survey.

Table 1. Summary of Existing Relevant Survey.

Key Ideas	Survey Scope	Cross-Layer Inspired?	Challenges	References
LPWANs like LoRaWAN in IoT: Challenges and Cross-Layer Optimization, Cognitive Radio, EE Multi-Channel Cross-Layer MAC Framework	6G, LoRaWAN in IoT, CSMA Protocols, 6G Communication, EE in IoT Networks, CSMA Protocols	Yes	Protocol optimization, data rate, duty cycle, Massive connectivity Requirement, Energy Constraint	[4]
Cyber-physical systems: Testing platforms and vulnerability modelling, Combining Exposure Indicators and Predictive Analytics, Internal Assessment and Evaluation, H2020 ECHO Project Implementation,	Cyber-physical systems, Exposure Indicators and Predictive Analytics, Privacy-Preserving Evaluation, Cybersecurity Information Sharing	No	Data security, vulnerability modelling, Gaps Between Exposure Indicators and Predictive Analytics, Sensitive Information Protection, Trust and Transparency Among Stakeholders	[5]
Social Internet of Things (SIoT) Security, CPS in Industry, Hybrid Risk Identification Methodology, Four-Step Risk Identification Process,	SIoT security, Risk Identification in Industry, CPS Interactions, Risk Management Standards and Frameworks	Yes	Security, energy efficiency, graph-powered learning, Comprehensive Risk Identification, Complexity of CPS Interconnections, Redundancy of Risks	[6]
IoT Security Challenges and Solutions, Flying Ad Hoc Networks Challenges, Energy-Aware Routing Scheme, Path Selection Metrics, Performance Evaluation	IoT security challenges, Routing Algorithms in FANETs, Virtual Relay Tunnel (VRT) Concept, Comparison of Routing Schemes	No	Security vulnerabilities, cryptographic protocols, Dynamic Topology and High Mobility, Energy Restrictions, Efficient Path Selection	[7]
Smart City Concept, Smart City Security and Privacy: Suggested Solutions Using Blockchain and Encryption, Blockchain for Security	Adaptive cybersecurity, IoT and Cloud-based Security Issues, Data Privacy and Security Solutions, Smart City Data Management	No	Real-world network packet collection, machine learning, Resource Optimization vs. Security, Decentralized and Distributed Structure, Implementing Blockchain	[8]
Comprehensive Overview of IoT Security, Rapid Growth of IoT, IoT Security Concerns, Case Study on Camera-based IoT, Importance of Privacy and Stakeholder Roles	IoT security overview, IoT Overview and Security, Threat Analysis for Smart Camera Systems (SCS), IoT Security and Privacy	No	IoT development, security solutions, Complexity of IoT Security, Vulnerabilities in IoT Applications, Stakeholder Responsibility	[9]
NOMA-based-MIoT Communication System, 5G Technology in MIoT, NOMA-based Heterogeneous Communication System, Energy Efficiency (EE) Optimization, Iterative Approach for Optimization	MIoT Networks, MIoT communication, Energy Efficiency in MIoT, Optimization Techniques, Handling uncertain channel state Information	Yes	Energy efficiency, spectrum consumption, Complexity of Optimization, Inadequate Channel State Information, Balancing Constraints, Quality of Service	[10]
Energy-efficient Routing for Smart Dust Head Networks, Challenges with Movable Smart Dust Basestation, Flooding Approach, EE Routing Mechanism, Fuzzy Clustering and Optimization	Smart Dust energy-efficient routing, Movable BS Positioning, Routing Architectures, Optimization Techniques	No	Energy-efficient routing, network performance, High Power Usage, Network Stability, Efficient Routing	[11]
Cognitive Radio Technology for Energy-efficient IoT, IoT and Spectrum Demand, Cognitive Radio (CR) Technology, Efficient Communication Protocols, Cross-Layer Design Proposal	CR Technology for IoT, Cross-Layer Optimization, Simulation and Performance Evaluation	Yes	Spectrum optimization, Spectrum Utilization, Energy Efficiency, Network Adaptation	[12]

1.5. Cross Layer Framework

The application layer, network layer, and sensor layer are the three basic layers that make up the Internet of Things Network Architecture. Energy-efficient and security-related components are shown at each layer. Security features like authentication and encryption are part of the Application Layer, along with energy-efficient application protocols like Message Queueing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP). The Network Layer incorporates machine learning-based security solutions like Intrusion Detection Systems and middleware security frameworks. This Layer also encompasses cross-layer security frameworks and energy-efficient routing algorithms, such as those designed for Low Power and Lossy Networks (See Figure 2). The Sensor Layer addresses IoT device security with device-level encryption and authentication, along with energy-efficient hardware design considerations. This hierarchical diagram provides a structured overview of how various components contribute to ensuring both security and energy efficiency within IoT networks, as indicated by the literature.

Figure 2. An overview of IoT Cross-Layer Framework.

Figure 2. An overview of IoT Cross-Layer Framework.

2. Energy Effectiveness and Security Measures of IoT Networks

2.2. Cross Layer Security Measures

Numerous security issues, such as flaws in sensor hardware, cloud computing platforms, and network communication protocols, are highlighted in the literature analysis as being present in IoT ecosystems. Several cybersecurity techniques [15], including intrusion detection systems, authentication procedures, and encryption, are suggested as solutions to these problems. To effectively counteract complex cyber threats, cross-layer security strategies that integrate network, application, and physical layer defences are recommended. The resilience of IoT systems against new cyber-attacks is also improved by developments in AI and machine learning, which make it possible to detect and respond to threats with greater sophistication (See Figure 4). For the application layer, it is advised to use Constrained Application Protocol (COAP), Message Queuing Telemetry Transport (MQTT), and Hypertext Transfer Protocol Secure (HTTPS). Recommendations for network layer security include Media Access Control Security (MACsec), IEEE 802.1X, Virtual Private Network (VPN), and Internet Protocol Security (IPsec). Environmental protection, training, physical security policies, secure enclosures, and physical temperature resistance are advised for the sensor layer.

Table 2. Summary of Literature Review on Energy Efficient Cross-Layer Contributions.

Table 2. Summary of Literature Review on Energy Efficient Cross-Layer Contributions.

Key Contributions	Performance Evaluation Methods	Limitations	References
Enhanced LoRaWAN for IoT applications,Cross-Layer Optimization Overview, Classification of Techniques, Identification of Issues and Challenges, Performance Overview	State-of-the-Art Summary, Cross-Layer Optimisation of LoRaWAN, Overview of Challenges of LoRaWAN	Lack of empirical validation, Lack of Summary, Protocol Stack Restrictions, Optimization Gaps	[16]
Designed energy-efficient MAC solution for NB-IoT, Energy-Efficient MAC Layer Solution, Optimization Framework, Cross-Layer Approach, Probabilistic Sleep Scheduling	MINLP optimization; Lyapunov optimization, Distributed sleep scheduling, Simulation Results, High Traffic Load Testing	Reliance on simulation, Resource Constraints, Traffic Model Assumptions, Scalability	[17]
Integrated energy-efficient OF into RPL routing, Introduction of ELITE, New Routing Metric, Cross-Layer Integration, Path Selection Improvement	Energy-efficient cross-layer OF integration, RPL protocol, Comparison with Existing OFs, Simulation Results	Limited evaluation in diverse IoT environments, potential complexity in implementation, MAC Layer Dependency, Metric Specificity, Generalizability	[18]
Enhance HCN energy efficiency with NOMA, Focus on Energy Efficiency, Optimization Problem Formulation, Introduction of Quantum-inspired political optimizer(QPO) Algorithm	Hybrid resource allocation optimization, Simulation Results(Evaluation of the QPO algorithm’s performance)	Reliance on simulated comparisons, potential challenges in real-world deployment, Non-Convex Problem Complexity, Algorithm Specific	[19]
Optimized routing for energy efficiency in FANETs, Virtual relay tunnel based on a suggested energy-conscious routing strategy (ECRS), Incorporation of Multiple Metrics, Path Correlation Metric (enhance route selection)	Energy-aware routing with virtual relay tunnel, comparison against existing methods, Comparative Analysis, Simulation Studies	Limited real-world validation; potential trade-offs between efficiency and longevity, Specificity to FANETs, Complexity in Path Selection, Comparative Scope	[20]
Investigated energy management in edge computing, Energy-Efficient Secure Data Transmission, Multi-Scale Grasshopper Optimization, Robust Multi-Cascaded CNN (RMC-CNN), Dynamic Honey Pot Encryption Algorithm	Cross-layer energy optimization, Comparison with Existing Techniques, Encryption and Decryption Time Analysis,	Lack of empirical validation; potential complexity in cross-layer management, Specific Dataset Focus, Complexity of Encryption and Detection Mechanisms, Scalability and Real-Time Constraints	[21]
Developed energy-efficient MAC for CR-enabled 6G-IoT, Joint Adaptation of Physical and MAC Layer Parameters, Per-Bit Energy Efficiency Maximization	Multi-channel MAC design, Numerical Results	Reliance on simulations; potential challenges in real-world deployment, Specific to Non-Persistent CSMA, Simulation-Based Evaluation, Design Constraints in 6G-IoT, Design Constraints in 6G-IoT	[4]
Integrated energy-efficient protocols into IoT, Cross-Layer Energy Architecture Model, Focus on Green and Renewable Energy, Mathematical Modeling	Utilization of MQTT, CoAP, Zigbee, Wi-Fi for energy efficiency; support for various IoT applications, Mathematical Analysis, Power Savings Estimation,	Lack of empirical validation, Limited Exploration of Practical Implementation, Focus on Theoretical Framework, Scalability and Applicability	[22]
Investigated energy efficiency, Thorough Review of IoT for EE, Identification of Common Design Factors, Future Research Directions	Examination of hardware, software for energy management, use of historical data for forecasting, Review and Analysis, Identification of Patterns	Lack of real-world validation, Lack of Original Empirical Data, Application-Specific Variables, Focus on Heating Systems	[23]

The main ideas and takeaways from the survey papers are included into this combined introduction of a secure cross layer IoT networks, which offers a thorough synopsis of the goals and focus of the research (See Table 3 below).

2.3. Autonomous Secure and Energy Efficient Cross-Layer Framework for IoT Networks

Given the growing sophistication of cyber-attacks, the examined literature emphasizes the urgent necessity for strong security measures in IoT systems. To overcome these obstacles, autonomous characteristics must be integrated into a cross-layer framework. The main independent characteristics that can be included in such a framework are as follows:

(a)

Security Management and AI Integration in IoT Utilizing agentless SIEM modules, such as the Wazuh module, enhances IoT network security by analysing device traffic and creating alerts for anomalies without requiring endpoint software. This approach successfully protects industrial control systems in Industry 4.0 settings, as demonstrated using the SWaT dataset [24]. The integration of IoT with AI enables continuous data collection and opens new commercial opportunities through intelligent decision-making. Businesses can leverage AI to analyse IoT data with minimal human intervention, enhancing competitiveness [25]. Implementing federated learning models combined with host and network intrusion detection systems within fog computing environments significantly enhances DDoS attack detection and mitigation. This decentralized approach enhances security and lowers the possibility of single points of failure, achieving 89.753% detection accuracy [26]. utilizing machine learning techniques to identify denial-of-service attacks, such as support vector machines, random forests, and K nearest neighbours in IoT networks demonstrates strong detection capabilities, particularly in Information-Centric Networks (ICNs) [27].

(b)

Advanced Protocols and Network Integration Utilizing PCC-RPL and SLF-RPL frameworks improves the security of the RPL protocol in IoT networks by reducing wormhole attacks. SLF-RPL shows better energy efficiency, lower packet loss, and higher attack detection rates compared to PCC-RPL [28]. Integrating Software-Defined Networking (SDN) with Recursive Internetwork Architecture (RINA) enhances IoT network security, flexibility, and scalability. This method facilitates seamless edge-to-cloud connectivity and network function data sharing while maintaining operational integrity [29]. Developing secure, lightweight authentication strategies for low-power IoT devices ensures data privacy and user authentication, which is crucial for applications like Industry 4.0, smart cities, and healthcare [30]. Utilizing learning automata and clustering, this protocol enhances network performance in UV networks by optimizing cluster node count, service class, and network topology.

(c)

IoT Integration in Smart Cities and Healthcare Exploring the impact of network softwarization in the industrial sector, this study emphasizes how AI and IoT will play a part in mobile networks in the future., identifying gaps and suggesting areas for further research [31]. The integration of IoT, smart cities, and 5G technology enhances urban living by improving sustainability, efficiency, and responsiveness to citizen demands, transforming urban landscapes [32]. Proposing a Semantic IoT Middleware (SIM) for the healthcare sector addresses data interoperability, heterogeneity, and security using blockchain and AI for optimization and security enhancement [33]. Addressing data security and privacy in E-healthcare applications, this study integrates blockchain with NuCypher encryption to enhance resource use, resilience, and traceability [34].

(d)

IoT Security in Industrial and Environmental Applications Integrating Raspberry Pi clusters with BME680 sensors in Kubernetes for environmental monitoring, coupled with OpenID Connect and HashiCorp Vault for dynamic secret management, reduces vulnerabilities and improves responsiveness in IoT installations by 40% and 30%, respectively [35]. The LEMARS model combines heuristic-driven techniques and Feistel architecture to provide a lightweight encryption solution for secure satellite photography, demonstrating higher attack resilience and quality metrics [36]. Systematizing existing research on enhancing IoT resilience, this study proposes a taxonomy and classification of resilience mechanisms to address practical concerns in building reliable systems [37]. Examining static, dynamic, symbolic, and hybrid analysis techniques for finding vulnerabilities in embedded firmware, this overview suggests taxonomies and evaluates these approaches for future research [38].

Table 3. Summary of literature review on Security measures.

Table 3. Summary of literature review on Security measures.

Key Contributions	Limitations	Security Measures?	References
Cross-layer security and privacy were designed, Integration of IoT technologies with AI for security, Adoption of blockchain for decentralized coordination, Multidisciplinary approaches to ensure IoT security	Application layer security has not been explored, Resource constraints, Privacy concerns, Security issues, Lack of training data, Centralized architecture limitations	AI-based real-time data analysis, Blockchain for secure resource and data sharing, Addressing IoT and WSNs security threats dynamically	[39]
Presented various IoT framework tiers,Development of model to mitigate DDoS attacks in local networks, Utilization of Host Intrusion Detection Systems, Integration of Network Intrusion Detection System with federated learning	Think about tiered communication alone, Privacy concerns in decentralized IoT infrastructure, Potential for increased complexity in federated training/detection, Possible challenges in real-time and precise attack detection	Use of HIDS and NIDS for comprehensive attack identification, Federated learning data analysis/anomaly detection, Distributed architecture to prevent volumetric attack traffic, Near-real-time detection in fog Computing	[40]
Systematic literature review (SLR) on AI methods for IoT cybersecurity, investigation of machine learning and deep learning methods for IoT security, Finding popular techniques for high accuracy detection, such as random forests (RF) and support vector machines (SVM)	Framework for detecting intrusions at the network layer, Lacks a cross-layer strategy, Existing security and privacy challenges despite AI advancements, Need for intelligent architectural frameworks for better intrusion detection	Artificial intelligence (AI) techniques are utilized to secure Internet of Things devices. applying AI methods to identify cybersecurity threats, intelligent intrusion detection systems (IDS) with frameworks based on AI, Examination of AI techniques based on attack categories	[1]
At the most basic level of security, perception, the physical layer, and the wireless network layer were considered, Proposal of a global perspective security framework for PIoT, Focus on security issues in the perception layer of PIoT, Development of security policies and countermeasures for PIoT, Application of research results in real-world projects	Complexity of securing a large, complex cyber-physical network like PIoT, Potential challenges in implementing the proposed security framework across all layers, Complexity of securing a large, complex cyber-physical network like PIoT, Potential challenges in implementing the proposed security framework across all layers	The deployment of the autonomous safety system. security audits, residual information protection, intrusion prevention, and data backup, systems, Security framework spanning from perception layer to application layer, Specific security policies and countermeasures for addressing PIoT security issues	[2]
Discussion of existing vulnerabilities and attacks in the IoT ecosystem, Testing secure framework for IoT applications, Framework evaluates IoT applications from the initial phase	Complexity of securing IoT applications,Potential challenges in implementing comprehensive monitoring and security testing.	Monitoring and security testing framework and evaluate IoT applications, Focus on addressing security issues from the early stages of IoT application development	[3]
Examination of communication standards (ITU-T), Discussion of 4-levels of IoT security gateways, Overview of testing methods for IoT devices	Potential complexity in securing diverse communication standards, Challenges in applying uniform security measures across different levels	Identification of 4-levels of security in IoT systems, Application of security testing methods to evaluate IoT components and systems	[41]
Review of IoT threats, security requirements, challenges, Proposal of a novel paradigm combining IoT architecture with SDN, Discussion on SDN-based IoT deployment models	Challenges in unifying all IoT stakeholders on a single platform, Potential hurdles in implementing SDN-based security solutions across diverse IoT environments	Introduction of SDN-based IoT security solutions, Comprehensive overview of software-defined security (SDSec), Emphasis on network-based security solutions for the IoT paradigm	[42]
Analysis of IoT’s impact across various domains, Discussion of Service Oriented Architecture model, Divided into application network and perception layers, Examination of IoT security attacks during COVID-19	Numerous privacy concerns in rapidly developing IoT environments, Increased security attacks on IoT devices, especially during the COVID-19	Security and privacy challenges in IoT based on SOA layers, Identification of different technologies used for communication in each IoT layer, Overview of attacks targeting specific SOA layers and IoT devices	[15]

2.8. Energy Efficiency Cross Layer Design

Coal mines can communicate data in real time by integrating smart sensing devices over the Mine Internet of Things (MIoT) [10]. Reliable communication is ensured by fifth generation (5G) technology. Energy effectiveness (EE) is increased by optimizing device access and spectrum consumption in a NOMA-based MIoT communication system. Iterative methods tackle issues such as insufficient channel state information, maximizing power allocation, and assigning subchannels while considering cross-layer limitations and quality of service demands. Traditional methods result in high power usage and network breakdowns, motivating the development of energy-efficient routing mechanisms. In the Smart Dust Head (SDH) environment, the latest BS location is disseminated to nodes, enabling optimization through algorithms like Enhanced Oppositional Grey Wolf Optimization (EOGWO) for enhanced network performance [11]. This paper provides a structured overview of the relationship between Artificial Intelligence and Variable Renewable Energy (VRE) through Deep Learning (DL) applications [87]. After that, it evaluates DL-based solutions and their suitability while emphasizing important architectures. The study identifies ten DL-based strategies that facilitate VRE integration in power systems. The paper tackles energy-efficient data collection in remote IoT surveillance by designing a Medium Access Control (MAC) layer uplink solution following the Narrowband IoT (NB-IoT) scheduled access scheme. It optimizes energy consumption per uplink frame using Mixed Integer Non-Linear Programming (MINLP), and introduces a distributed sleep scheduling scheme to enhance delay and energy conservation [17]. Simulation results demonstrate its superiority over existing solutions in terms of delay, buffer length, and energy consumption under high traffic load. The paper addresses energy consumption in IoT devices by proposing ELITE, an energy-efficient cross-layer objective function (OF) integrated into the RPL routing protocol [18]. Unlike existing OF, ELITE introduces the strobe per packet ratio (SPR) metric at the MAC layer, considering transmission operations’ impact on energy consumption. By selecting paths with fewer strobe transmissions, ELITE reduces average strobes per packet by up to 25% and improves energy consumption by up to 39% The review addresses the energy consumption challenges in wireless networks, particularly focusing on energy-efficient heterogeneous cellular networks (HCNs) [19]. The paper formulates a hybrid joint resource allocation (HJRA) optimization problem and proposes a quantum-inspired political optimizer (QPO) algorithm to address the non-convex nature of the problem, showing superiority in total system EE through synchronous allocation of backhaul bandwidth, sub-channels, and power. The review addresses water scarcity in metropolitan areas and the need for intelligent water distribution systems, emphasizing IoT-Based monitoring to manage distribution challenges [88]. In smart city IoT-integrated water distribution systems, it addresses effective delay and energy offloading mechanisms and suggests communication network topologies that are customized to water network design parameters and land cover patterns. Through a case study in Kochi, India, it models delay and energy in IoT-based systems, discusses node categorization algorithms, and identifies optimal fog node placement, achieving up to 40% energy efficiency improvement. The paper addresses challenges in Flying Ad Hoc Networks (FANETs), emphasizing the importance of routing algorithms due to dynamic topology and energy constraints [7]. It introduces energy-aware routing scheme based on a virtual relay tunnel (EARVRT), an Energy-Aware Routing scheme utilizing a Virtual Relay Tunnel (VRT) to manage relay nodes, considering metrics like route energy, hop counts, and path correlation. Evaluation against existing methods demonstrates EARVRT’s superiority in delay, network longevity, energy consumption, and packet delivery rate. The literature encompasses the surge in wireless devices, emphasizing their energy consumption concerns and prompting research into energy-efficient solutions [89]. It delves into diverse topics such as computation offloading, CoAP protocols, task scheduling algorithms, and device-to-device communication enhancements, aiming to optimize energy efficiency across various wireless applications. The literature examines IoT’s connectivity and sustainability challenges due to the surge in connected devices, advocating for Cognitive Radio (CR) technology as a solution [12]. It proposes a cross-layer design optimizing modulation order and backoff probability to minimize energy consumption while meeting IoT delay requirements, PR channel availability, and user activities, demonstrating substantial energy reduction and delay satisfaction compared to single-layer approaches. The literature review addresses concerns about energy shortages due to global warming and climate change, focusing on home energy management [42]. The literature review discusses the Security concerns in the Social Internet of Things (SIoT), emphasizing the need to balance security with energy efficiency [14]. It suggests employing cross-layer architectures and leveraging graph-powered learning, effective in social networks, to strengthen SIoT security. Additionally, it outlines a study agenda for future research aimed at enhancing SIoT security. Examining the essential elements of low-power, long-range IoT connectivity, this literature study compares LoRaWAN with NB-IoT. NB-IoT is resistant to payload length effects, which is advantageous for buffered applications, according to in-field measurements. On the other hand, LoRaWAN is well-suited for longer IoT device lifetimes because to its remarkable energy efficiency, which requires 10 times less energy for similar payload delivery. Enhancing service quality, consistency, and cost management are the main goals of recent developments in IoT-enabled Wireless Sensor Networks (IWSN) [90]. A hybrid Artificial Neural Network (ANN) Simulated Annealing classifier and optimization based on MapDiminution are used to create an energy-efficient clustering and quick intrusion detection system that addresses security and energy concerns. This method lowers energy usage while achieving a high 97.57% detection accuracy. This work investigates the effective hardware implementation of feedforward artificial neural networks (ANNs) with time-multiplexed MAC blocks by recycling computational resources using approximation adders and multipliers, hence requiring less space and energy [91]. The optimal degree of approximation for hardware correctness is suggested using an algorithm. Comparing experiments with accurate hardware, MNIST and SVHN is a real-world datasets for developing machine learning algorithm databases demonstrate reductions of up to 50% energy and 10% area with negligible loss in accuracy. Energy-efficient wireless sensor networks (WSNs) are crucial for data collecting, as evidenced by the Internet of Things’ exponential expansion [92]. In this study, an energy-efficient cluster-based Lightweight On Demand Ad hoc Distance Vector Routing Protocol–Next Generation (LOADng) is developed in response to the energy limitations of smart devices. By using LOADng for routing, a seagull optimization method for optimal cluster head selection, and k-means clustering for cluster formation, the technique minimizes power consumption and maximizes network life in comparison to other options. In order to address issues with resource allocation that is energy-efficient, this literature emphasizes the significance of IoT in the technological, social, and economic spheres [93](See Figure 10 below). A new technique for forest optimization is developed in order to minimize energy consumption and delays in resource distribution, while traditional methods did not account for this factor. MATLAB simulation results show that this method performs better than generic algorithm (GA), particle swarm optimization (PSO), and distance-based algorithms, with important practical applications and increased efficiency in IoT resource management.

In order to facilitate coordinated integration in IoT environments, this research suggests a hierarchical ensemble TinyML scheme that allows individual IoT parts to make decisions that affect the entire system [94]. The two-layered TinyML-based edge computing solution was applied in a smart agriculture use case, showing advantages including decreased energy usage, response times, and wireless transmissions while improving data security and privacy. The assessment demonstrates how well the plan works to produce intelligent, context-aware applications. The swift expansion of IoT in the healthcare sector permits the acquisition of patient data in real-time, while it presents difficulties in managing copious redundant data [95]. An Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) that reduces energy consumption by reducing typical sensor readings to a single digit is presented in the study as a solution to this problem. Based on NS-2.35 simulations, EE-FDAS outperforms other approaches in terms of aggregation efficiency and energy consumption. This study proposes a clustering technique and Q-learning for work offloading to UAVs, integrating UAVs with IoT to address memory and data processing problems [96]. The suggested approach performs better in terms of data transfer and energy efficiency than the current approaches, especially in emergency situations. The usage of IoT in a smart city for energy-efficient building control is covered in this study, which also suggests a flexible, hierarchical architecture that integrates online services, people, and devices [97]. The suggested model seeks to improve overall Smart Building operations and maximize intelligent energy regulation. In the ever-expanding landscape of edge computing, the surge in energy demand poses a critical challenge that demands meticulous management for the advancement of this technology. As edge computing evolves, there is a pressing need to delve into cross-layer architecture, exploring ways to scale energy output while optimizing overall performance [21]. Envisioned as a game-changer in communication, 6G systems are set to revolutionize wireless connectivity for IoT networks [4]. CR technology emerges as a solution for spectrum support, but adapting existing protocols to the energy constraints of 6G IoT devices poses challenges. Energy-efficient protocols like MQTT, CoAP, Zigbee for sensors, and Wi-Fi for networks are integrated into next-generation IoT architecture [22]. The literature on IoT traces the development of low-power sensors and actuators, promoting RPL-based protocols and lightweight routing strategies [13]. the Internet of Things is reviewed in The literature, with a focus on energy efficiency and data dependability. To enhance energy usage, CLEERDTS integrates node location data, optimizes transmit power at the MAC layer, and chooses transmission modes at the physical layer [98]. The security issues of cyber-physical systems, especially smart grids, where Internet of Things devices are susceptible to intrusions, are discussed in this study [99]. The power and computational limitations of IoT are addressed in this study by introducing EasyChain, a lightweight blockchain with Proof-of-Authentication (PoAh) for IoE networks [100].It is implemented in Python and tested on a single-board computer. This paper introduces cluster-based scheduling and routing in geographic routing protocol (CSRGR), a dynamic cluster-based duty cycle scheduling for efficient data transmission in resource-constrained WSNs using geographic routing [101]. Lightweight blockchain assisted intruder detection system (LB-IDS) employs Lightweight blockchain multifactor authentication for node authentication and multi objective strawberry optimization (MOSO) for optimal route selection, followed by deep-Q learning (DQL) based IDS for packet classification and Blockchain for trust updates [102]. This paper introduces LB-IDS, integrating Blockchain for enhanced security in MANETs. Lightweight blockchain assisted intruder detection system (LB-IDS) employs Lightweight blockchain multifactor authentication for node authentication and multi objective strawberry optimization (MOSO) for optimal route selection, followed by deep-Q learning (DQL) based IDS for packet classification and Blockchain for trust updates [102]. In order to preserve privacy while aggregating data in low-power Internet of Things devices, this study presents LiPI [103], a lightweight PPDA method that does away with complicated cryptography and outside dependencies. The focus of this work is on the suitability of lightweight cryptography protocols for Internet of Things security while dealing with devices that have limited resources [104]. It addresses the crucial requirement for effective authentication, privacy, data integrity, and control in Internet of Things networks by comparing the performance of the PRESENT block cipher to alternative lightweight algorithms.Concerns have been raised about the energy efficiency of computing devices and technologies relevant to low-power residential and commercial buildings [23]. A lightweight symmetric cryptographic algorithm is presented in this paper [105]. The reviewed paper proposes an energy-efficient multi-channel MAC framework with a tailored CSMA protocol for CR-enabled 6G-IoT networks. Through joint adaptation of physical and MAC layer parameters, the framework aims to boost IoT network energy efficiency significantly. Numerical results demonstrate up to a 50% improvement compared to a single-channel design, offering a promising solution for 6G-IoT networks’ massive-connectivity demands. The surging (WSN-IoT) stands for Wireless Sensor Network on the Internet of Things. addresses demographic aging and job challenges but faces security threats with its resource-limited devices. Despite its critical role, research on WSN-IoT security is limited [65]. This review zooms in on security challenges, spotlighting a Contiki OS implementation of RPL’s mechanisms. It critically analyses issues, explores machine learning for management, addresses the architecture, difficulties, network attacks, and goals of the Internet of Things in Low Power Networks (IoT-LPN).

Table 4. Summary of related IoT Cyber Security Surveys.

Table 4. Summary of related IoT Cyber Security Surveys.

Survey Scope	IoT Security	Security-Measure Implemented	Limitations	References
IoT Security Research,Focuses on the deployment of IoT technology in industrial automation, Next Generation Cyber Security Architecture (NCSA) for the Industrial IoT	NCSA Implementation, Automated Cyber-Defense	Vulnerabilities, attacks, cross-layer security, Real-time Protection, Identity Token Mechanism	Limited consideration of interaction of cyber-physical devices,Specific Focus on IIoT, No Detailed Performance Evaluation, Potential Integration Challenges	[77]
In-depth analysis of the IIoT ecosystem focusing on security and digital forensics, Overview of the state-of-the-art in IIoT security and digital forensics, Highlighting key achievements, Challenges	Examination of the structural and dynamic complexity of IIoT, Exploration of vulnerabilities introduced by the continuous integration of IIoT	Analysis of cutting-edge security mechanisms deployed in IIoT ecosystems to protect processes,Survey of digital forensics literature related to IIoT, Focusing on techniques and tools to mitigate security breaches	NCSA proposed for real-time threat detection,Complexity and Integration, Evolving Threat Landscape, Need for Future Research	[69]
Cyber-physical system risk identification, Analysis of risk identification in Industry (CPS), Examination of methodologies for identifying risks across physical, Interconnection layers in CPS	Focus on the security vulnerabilities and cyber-attacks associated with interconnected devices and equipment in Industry CPS	Proposed a new hybrid methodology for risk identification in Industry, Integrating existing frameworks and standards such as ISO 31000, PMBOK, HAZOP, and NIST, Developed a four-step process that includes identifying risks from various sources	Lack of consideration for interaction of cyber-physical devices, Incompleteness of Existing Methodologies	[6]
Threat detection in industrial networks, Exploration of a framework combining using AI tools, Focus on threat detection within real industrial IoT sensor networks	Addresses the challenge of detecting threats IIoT networks while maintaining privacy and security	Big data architecture, predictive analytics	Limited to real industrial network, AI-based predictive analytics, Securely sharing results, Application of the framework as part of the H2020 ECHO project	[5]
Cross-layer authentication framework, Focuses on addressing security challenges in IIoT of 5G technology, Explores the security implications of bypassing upper authentication protocols and supporting small data transmission during initial access in IIoT systems	Highlights the vulnerabilities in IIoT due to the use of 5G, Emphasizes the need for secure cross-layer authentication frameworks to address these vulnerabilities	Device authentication vulnerabilities, 5G technology, Proposes a secure cross-layer authentication framework, Utilizes a quantum walk-based privacy-preserving, Derives the space of one-time keys for encryption	Proposal addresses security vulnerabilities, Complexity, Scalability, Performance Overhead	[58]
Adaptive Cybersecurity system, Cybersecurity for networked devices using virtual environment services	Addresses increased risks due to widespread device connectivity	Real-world network packet collection, Machine learning, Honeynet architecture, Adaptive Cybersecurity (AC) system	Performance improvements needed, dataset expansion planned, Data dependency, Scalability challenges	[61]

3. Key Strategies and Trends

3.1. Key Strategies

Figure 11. Key Strategies Of Survey Paper.

Figure 11. Key Strategies Of Survey Paper.

(a)

Authentication and Encryption: As the number of IoT devices continues to grow rapidly, ensuring robust authentication and encryption mechanisms becomes imperative to protect sensitive data and maintain privacy [9,62]. Authentication mechanisms such as cryptographic protocols and identity management systems help verify the identities of devices and users, while encryption techniques such as symmetric and asymmetric encryption ensure secure communication channels. Additionally, blockchain technology is being explored to provide tamper-proof and decentralized solutions for data integrity and transaction security in IoT environments.

(b)

Machine Learning for Threat Detection: With the increasing sophistication of cyber threats targeting IoT systems, machine learning algorithms are being leveraged for threat detection and prevention [61]. These algorithms analyze vast amounts of data generated by IoT devices to identify patterns indicative of malicious activities or anomalies. By continuously learning from new data, machine learning models can adapt and improve their accuracy in detecting and mitigating security threats, thereby enhancing the resilience of IoT networks.

(c)

Cross-Layer Security Frameworks: Cyber-physical systems (CPS) present unique security challenges due to their interconnected nature and reliance -on both physical and digital components [6]. To address these challenges, hybrid security frameworks integrating established risk management methodologies such as ISO standards with domain-specific risk models are being developed. These frameworks facilitate comprehensive risk identification and management across multiple layers of CPS architectures, from the physical layer to the application layer. By considering interactions between different layers, organizations can better assess and mitigate security vulnerabilities in their IoT deployments.

(d)

Quantum-based Authentication: With the advent of Industry IoT (IIoT) and the proliferation of 5G technology, traditional authentication mechanisms face new challenges related to device authentication vulnerabilities [58]. To address these challenges, cross-layer authentication frameworks based on quantum walk on circles are proposed. These frameworks utilize quantum principles to ensure secure device identification and authentication, thereby mitigating the risks associated with compromised authentication credentials and unauthorized access to IIoT networks.

(e)

Energy-Efficient Routing and Optimization Energy efficiency is a critical concern in IoT deployments, particularly in resource-constrained environments [7,17,18,21]. Strategies such as energy-efficient routing schemes and cross-layer optimization techniques aim to minimize energy consumption while maximizing network performance and reliability. These approaches leverage techniques such as virtual relay tunnels, mixed-integer nonlinear programming (MINLP), and optimized transmission modes to optimize energy usage at both the MAC and physical layers of IoT networks. Additionally, the integration of renewable energy sources with IoT architectures further enhances energy efficiency and sustainability, reducing reliance on traditional power sources and minimizing environmental impact.

3.2. Emerging Trends

Figure 12. Emerging Trends of Survey Paper.

Figure 12. Emerging Trends of Survey Paper.

(a)

Integration of Artificial Intelligence: The integration of artificial intelligence (AI) and machine learning algorithms is emerging as a trend to enhance IoT security and efficiency [61,87]. These technologies enable predictive analytics for threat detection and optimization of energy consumption in IoT systems. By analyzing large datasets generated by IoT devices, AI algorithms can identify patterns and anomalies indicative of security threats, enabling proactive mitigation strategies. Additionally, AI-based solutions facilitate the integration of variable renewable energy sources into power systems, improving forecasting accuracy and grid management.

(b)

Cognitive Radio for Spectrum Efficiency: With the advent of 6G IoT networks, cognitive radio (CR) technology is proposed to optimize spectrum usage and energy efficiency [4]. Multi-channel MAC frameworks tailored for CR-enabled networks aim to improve IoT network performance and connectivity by dynamically allocating spectrum resources based on network conditions and user requirements. These frameworks enhance spectrum efficiency while minimizing interference and energy consumption, thereby enabling reliable and scalable communication in dense IoT deployments.

(c)

Renewable Energy Integration The integration of renewable energy sources with IoT architectures is gaining traction for enhancing energy efficiency and sustainability [87]. Deep learning applications facilitate the integration of variable renewable energy sources into power systems, improving forecasting accuracy and grid management. By leveraging AI-based solutions, organizations can optimize energy usage and minimize reliance on traditional power sources, thereby reducing operational costs and environmental impact.

(d)

Holistic Security Approaches: As IoT ecosystems become increasingly complex, holistic security approaches are being advocated to mitigate evolving cyber threats [9] [62]. These approaches encompass robust authentication mechanisms, encryption protocols, and proactive threat detection strategies to safeguard against unauthorized access, data breaches, and other security risks. By adopting comprehensive security measures, organizations can ensure the confidentiality, integrity, and availability of their IoT deployments, thereby enhancing trust and compliance with regulatory requirements.

(e)

Cross-Layer Optimization: Cross-layer optimization techniques are being explored to improve energy efficiency and performance in IoT networks [21]. By jointly optimizing parameters across different protocol layers, such as the physical and MAC layers, organizations can minimize energy consumption while meeting the diverse requirements of IoT applications. These approaches enable dynamic adaptation to changing network conditions and user demands, enhancing reliability and scalability in IoT deployments. These findings highlight the necessity of comprehensive approaches to cyber-physical systems and IoT security, including improved cybersecurity architectures, all-encompassing security measures, and creative solutions that make use of cutting-edge technology like machine learning and quantum cryptography (See Table 5 for Key strategies and Trends).

Table 5. Key Strategies and Trends.

Table 5. Key Strategies and Trends.

Main Contributions	Trends	Application Area	Reference
Big Data Architecture, Predictive Analytics, Threat detection, Obscuring sensitive data, Evaluation framework	Enhances trust among stakeholders, Closes security gaps	Industrial networks	[5]
Cross-layer Optimization, LoRaWAN, Flexibility across protocol layers, Energy-efficient	Optimizes protocol, Enhances performance	IoT applications, LPWANs	[16]
Hybrid Methodology, Risk Identification, ISO 31000, PMBOK, HAZOP, NIST strategies	Reduces risk redundancy, Comprehensive analysis	Cyber-Physical Systems (CPS)	[6]
Social IoT (SIoT), Cross-layer Security, Data trustworthiness, Graph-powered learning strategies	Enhances network navigability, Balance energy efficiency	SIoT ecosystems	[14]
Lightweight Encryption, Key Management, Random key encryption, Information-theoretic security	Efficient and secure, Suitable for resource-limited IoT	IoT, Cyber-Physical Systems (CPS)	[52]
Cross-layer Intrusion Detection, Ensemble Learning, IoT-Sentry, Cooja IoT simulator analysis	High detection accuracy, Minimal overhead	Standardized IoT networks	[53]
IoT Authentication Strategies, Categorization by hierarchy, centralization, distribution	Comprehensive review, Encourages further research	IoT authentication	[55]
Lightweight Mutual Authentication, Smart city applications, Performance optimization	Balances security and efficiency, Outperforms existing protocols	Smart cities, Traffic and water management	[57]
Cross-layer Authentication, Quantum Walk, Device identifier encoding, Privacy-preserving protocol	High security and privacy, Low latency	IIoT, 5G networks	[58]
Honeynet Architecture, Machine Learning, Real-world attack detection, Web-based IDS-AC	Effective attack warnings, User self-update	Industrial networks, Cybersecurity	[61]
Survey of IoT Security Research, Vulnerabilities, Mitigation strategies, Future directions	Comprehensive overview, Guides future research	IoT development, Security solutions	[62]
ESPINA Protocol, IoT network technologies in delay, Improved security with keys-renewal strategy, Reduces computational cost	Energy optimization, 6G wireless connectivity, Superior to current protocols, Effective for 6G standards,6G wireless communications, Energy-efficient and secure protocols	Healthcare IoT, Embedded systems, Security-sensitive applications	[73]
CLCSR Protocol, Attack detection, Secure clustering, Lightweight cryptography	Enhances network performance, Privacy preservation	E-healthcare, Smart cities	[74]
Hierarchical Authentication, Key Agreement, Physically unclonable functions, Elliptic curve cryptography	Efficient and secure, Resistant to common attacks	Industry 4.0, IoT environments	[76]

4. Open Research Problems and Challenges

5. Conclusion

References

1. Abdullahi, M.; Baashar, Y.; Alhussian, H.; Alwadain, A.; Aziz, N.; Capretz, L.F.; Abdulkadir, S.J. Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review, 2022. [CrossRef]
2. Zhang, Y.; Zou, W.; Chen, X.; Yang, C.; Cao, J. The security for power internet of things: Framework, policies, and countermeasures. Proceedings - 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2014. Institute of Electrical and Electronics Engineers Inc., 2014, pp. 139–142. [CrossRef]
3. SǎLndescu, C.; Grigorescu, O.; Rughiniş, R.; Deaconescu, R.; Cǎlin, M. Why IoT security is failing. the Need of a Test Driven Security Approach. Proceedings - 17th RoEduNet IEEE International Conference: Networking in Education and Research, RoEduNet 2018, 2018. [CrossRef]
4. Bani Irshaid, M.; Bany Salameh, H.; Jararweh, Y. Intelligent multichannel cross-layer framework for enhanced energy-efficiency in 6G-IoT wireless networks. Sustainable Energy Technologies and Assessments 2023, 57, 103211. [CrossRef]
5. Ruggiero, B. Combining exposure indicators and predictive analytics for threats detection in real industrial IoT sensor networks 2020. pp. 423–428.
6. Santos, M.F.O.; Melo, W.S.; Machado, R. Cyber-Physical Risks identification on Industry 4.0: A Methodology Proposal. 2022 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2022 - Proceedings 2022, pp. 300–305. [CrossRef]
7. Hosseinzadeh, M.; Ali, S.; Mohammed, A.H.; Lansky, J.; Mildeova, S.; Yousefpoor, M.S.; Yousefpoor, E.; Hassan Ahmed, O.; Rahmani, A.M.; Mehmood, A. An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks. Alexandria Engineering Journal 2024. [CrossRef]
8. Karaturk, E. Security Concepts in Smart Cities 2020.
9. Elkhouly, S.M.E.; Ahmed, Y.; Yehia, E.A. The Cyber Security Vulnerabilities in the Internet of Things : A Case Study. Cf 2020, 18, 97–111.
10. Arumugam, K.; Rajesha, N.; Prasad, M.; Shanmugasundaram, N.; Rao, D.S.; Suneela, B. Spectrum Sensing Framework and Energy-Efficient Resource Allocation for Cognition Enhancement Network. 2023 International Conference on Computer Communication and Informatics, ICCCI 2023 2023, pp. 1–5. [CrossRef]
11. Rajesh, D.; Rajanna, G.S. Energy aware data harvesting strategy based on optimal node selection for extended network lifecycle in smart dust. Journal of Intelligent and Fuzzy Systems 2023, 44, 939–949. [CrossRef]
12. Salameh, H.A.; Bani Irshaid, M.; Al Ajlouni, A.; Aloqaily, M. Energy-efficient cross-layer spectrum sharing in CR green IoT networks. IEEE Transactions on Green Communications and Networking 2021, 5, 1091–1100. [CrossRef]
13. Ambika, K.; Malliga, S. Secure hyper intelligence in routing protocol with low-power (RPL) Networks in IoT. Advances in Engineering Software 2022, 173, 103247. [CrossRef]
14. Wu, Y.; Huo, Y. Cross-layer secure transmission schemes for social internet of things: Overview, opportunities and challenges. Neurocomputing 2022, 500, 703–711. [CrossRef]
15. Debnath, D.; Chettri, S.K.; Dutta, A.K. Security and Privacy Issues in Internet of Things. Lecture Notes in Networks and Systems 2022, 314, 65–74. [CrossRef]
16. Chaguile, C.C.; Alipio, M.; Bures, M. A Classification of Cross-Layer Optimization Approaches in LoRaWAN for Internet of Things. International Conference on Ubiquitous and Future Networks, ICUFN 2023, 2023-July, 259–264. [CrossRef]
17. Bhattacharjee, D.; Acharya, T.; Chakravarty, S. Energy efficient data gathering in IoT networks with heterogeneous traffic for remote area surveillance applications: A cross layer approach. IEEE Transactions on Green Communications and Networking 2021, 5, 1165–1178. [CrossRef]
18. Safaei, B.; Monazzah, A.M.H.; Ejlali, A. ELITE: An Elaborated Cross-Layer RPL Objective Function to Achieve Energy Efficiency in Internet-of-Things Devices. IEEE Internet of Things Journal 2021, 8, 1169–1182. [CrossRef]
19. Ma, J.; Gao, H.; Guo, L.; Li, H. Energy-efficient joint resource allocation for heterogeneous cellular networks with wireless backhauls. AEU - International Journal of Electronics and Communications 2024, 176, 155170. [CrossRef]
20. Hosseinzadeh, M.; Ali, S.; Hussein, A.; Lansky, J. An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks. Alexandria Engineering Journal 2024. [CrossRef]
21. Uddin, R.S.; Manifa, N.Z.; Chakma, L.; Islam, M.M. Cross-Layer Architecture for Energy Optimization of Edge Computing. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 2023, 491 LNICST, 687–701. [CrossRef]
22. Sakthidasan Sankaran, K.; Kim, B.H. Deep learning based energy efficient optimal RMC-CNN model for secured data transmission and anomaly detection in industrial IOT. Sustainable Energy Technologies and Assessments 2023, 56, 102983. [CrossRef]
23. Yaici, W.; Krishnamurthy, K.; Entchev, E.; Longo, M. Survey of Internet of Things (IoT) Infrastructures for Building Energy Systems. GIoTS 2020 - Global Internet of Things Summit, Proceedings 2020. [CrossRef]
24. Zahid, H.; Hina, S.; Hayat, M.F.; Shah, G.A. Agentless Approach for Security Information and Event Management in Industrial IoT. Electronics (Switzerland) 2023, 12. [CrossRef]
25. Bhushan, B.; Sangaiah, A.K.; Nguyen, T.N. AI Models for Blockchain-Based Intelligent Networks in IoT Systems; Vol. 6, 2023. [CrossRef]
26. de Caldas Filho, F.L.; Soares, S.C.M.; Oroski, E.; de Oliveira Albuquerque, R.; da Mata, R.Z.A.; de Mendonça, F.L.L.; de Sousa Júnior, R.T. Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning. Sensors 2023, 23, 1–35. [CrossRef]
27. Bukhowah, R.; Aljughaiman, A.; Rahman, M.M. Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions. Electronics (Switzerland) 2024, 13. [CrossRef]
28. Javed, S.; Sajid, A.; Kiren, T.; Khan, I.U.; Dewi, C.; Cauteruccio, F.; Christanto, H.J. A Subjective Logical Framework-Based Trust Model for Wormhole Attack Detection and Mitigation in Low-Power and Lossy (RPL) IoT-Networks. Information (Switzerland) 2023, 14. [CrossRef]
29. Sarabia-Jácome, D.; Giménez-Antón, S.; Liatifis, A.; Grasa, E.; Catalán, M.; Pliatsios, D. Progressive Adoption of RINA in IoT Networks: Enhancing Scalability and Network Management via SDN Integration. Applied Sciences (Switzerland) 2024, 14. [CrossRef]
30. Saurabh.; Sharma, C.; Khan, S.; Mahajan, S.; Alsagri, H.S.; Almjally, A.; Alabduallah, B.I.; Ansari, A.A. Lightweight Security for IoT. Journal of Intelligent and Fuzzy Systems 2023, 45, 5423–5439. [CrossRef]
31. Rojas, E.; Carrascal, D.; Lopez-Pajares, D.; Alvarez-Horcajo, J.; Carral, J.A.; Arco, J.M.; Martinez-Yelmo, I. A Survey on AI-Empowered Softwarized Industrial IoT Networks. Electronics (Switzerland) 2024, 13. [CrossRef]
32. Singh, S.K.; Tanwar, S.; Jadeja, R.; Singh, S.; Polkowski, Z. Secure and intelligent IoT-enabled smart cities; Vol. i, 2024; pp. 1–453. [CrossRef]
33. Elkhodr, M.; Khan, S.; Gide, E. A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness. Future Internet 2024, 16, 1–31. [CrossRef]
34. Almagrabi, A.O. Challenges and vulnerability evaluation of smart cities in IoT device based on cybersecurity mechanism. Expert Systems 2023, 40, 1–16. [CrossRef]
35. Donca, I.C.; Stan, O.P.; Misaros, M.; Stan, A.; Miclea, L. Comprehensive Security for IoT Devices with Kubernetes and Raspberry Pi Cluster. Electronics (Switzerland) 2024, 13, 1–22. [CrossRef]
36. Madhu, D.; Vasuhi, S. Lightweight Encryption Assisted Man-in-The-Middle Attack-Resilient Steganography Model for Secure Satellite Imagery Services: LEMARS. Journal of Intelligent and Fuzzy Systems 2023, 45, 2847–2869. [CrossRef]
37. Berger, C.; Eichhammer, P.; Reiser, H.P.; Domaschka, J.; Hauck, F.J.; Habiger, G. A Survey on Resilience in the IoT: Taxonomy, Classification, and Discussion of Resilience Mechanisms. ACM Computing Surveys 2022, 54. [CrossRef]
38. Magara, T.; Zhou, Y. Internet of Things (IoT) of Smart Homes: Privacy and Security. Journal of Electrical and Computer Engineering 2024, 2024. [CrossRef]
39. Wang, A.; Mohaisen, A.; Chen, S. XLF: A cross-layer framework to secure the internet of things (IoT). Proceedings - International Conference on Distributed Computing Systems 2019, 2019-July, 1830–1839. [CrossRef]
40. Tandon, A.; Srivastava, P. Location based secure energy efficient cross layer routing protocols for IOT enabling technologies. International Journal of Innovative Technology and Exploring Engineering 2019, 8, 368–374.
41. Lazarev, A. MODERN METHODS OF TESTING AND INFORMATION SECURITY PROBLEMS IN IoT. Bulletin of TUIT: Management and Communication Technologies 2021. [CrossRef]
42. Iqbal, W.; Abbas, H.; Daneshmand, M.; Rauf, B.; Bangash, Y.A. An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software-Defined Security. IEEE Internet of Things Journal 2020, 7, 10250–10276. [CrossRef]
43. Ozalp, A.N.; Albayrak, Z.; Cakmak, M.; Ozdogan, E. Layer-based examination of cyber-attacks in IoT. HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings 2022. [CrossRef]
44. Zeadally, S.; Das, A.K.; Sklavos, N. Cryptographic technologies and protocol standards for Internet of Things. Internet of Things (Netherlands) 2021, 14, 100075. [CrossRef]
45. Zeng, H.; Dhiman, G.; Sharma, A.; Sharma, A.; Tselykh, A. An IoT and Blockchain-based approach for the smart water management system in agriculture. Expert Systems 2023, 40, 1–14. [CrossRef]
46. Arulselvan, G.; Rajaram, A. Hybrid trust-based secure routing protocol for detection of routing attacks in environment monitoring over MANETs. Journal of Intelligent and Fuzzy Systems 2023, 45, 6575–6590. [CrossRef]
47. Shukla, P.; Patel, R.; Varma, S. A novel of congestion control architecture using edge computing and trustworthy blockchain system. Journal of Intelligent and Fuzzy Systems 2023, 44, 6303–6326. [CrossRef]
48. Qayyum, A.; Butt, M.A.; Ali, H.; Usman, M.; Halabi, O.; Al-Fuqaha, A.; Abbasi, Q.H.; Imran, M.A.; Qadir, J. Secure and Trustworthy Artificial Intelligence-extended Reality (AI-XR) for Metaverses. ACM Computing Surveys 2024, 56, 1–38, [2210.13289]. [CrossRef]
49. Xiao, J.; Chang, C.; Ma, Y.; Yang, C.; Yuan, L. Secure multi-path routing for Internet of Things based on trust evaluation. Mathematical Biosciences and Engineering 2024, 21, 3335–3363. [CrossRef]
50. Becherer, M.; Hussain, O.K.; Zhang, Y.; Den Hartog, F.; Chang, E. On Trust Recommendations in the Social Internet of Things - A Survey. ACM Computing Surveys 2024, 56. [CrossRef]
51. Qasem, A.; Shirani, P.; Debbabi, M.; Wang, L.; Lebel, B.; Agba, B.L. Automatic Vulnerability Detection in Embedded Devices and Firmware: Survey and Layered Taxonomies. ACM Computing Surveys 2021, 54. [CrossRef]
52. Wu, X.W.; Yang, E.H.; Wang, J. Lightweight security protocols for the Internet of Things. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017, 2017-Octob, 1–7. [CrossRef]
53. Kamaldeep.; Malik, M.; Dutta, M.; Granjal, J. IoT-Sentry: A Cross-Layer-Based Intrusion Detection System in Standardized Internet of Things. IEEE Sensors Journal 2021, 21, 28066–28076. [CrossRef]
54. Li, X.; Niu, J.; Bhuiyan, M.Z.A.; Wu, F.; Karuppiah, M.; Kumari, S. A robust ECC-Based provable secure authentication protocol with privacy preserving for industrial internet of things. IEEE Transactions on Industrial Informatics 2018, 14, 3599–3609. [CrossRef]
55. Saadeh, M.; Sleit, A.; Qatawneh, M.; Almobaideen, W. Authentication techniques for the internet of things: A survey. Proceedings - 2016 Cybersecurity and Cyberforensics Conference, CCC 2016 2016, pp. 28–34. [CrossRef]
56. Gu, T.; Mohapatra, P. BF-IoT: Securing the IoT networks via fingerprinting-based device authentication. Proceedings - 15th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2018 2018, pp. 254–262. [CrossRef]
57. Li, N.; Liu, D. for IoT and Its Applications 2017. 2, 359–370.
58. Xu, D.; Yu, K.; Ritcey, J.A. Cross-Layer Device Authentication With Quantum Encryption for 5G Enabled IIoT in Industry 4.0. IEEE Transactions on Industrial Informatics 2022, 18, 6368–6378. [CrossRef]
59. Pamarthi, S.; Narmadha, R. Literature review on network security in Wireless Mobile Ad-hoc Network for IoT applications: network attacks and detection mechanisms. International Journal of Intelligent Unmanned Systems 2022, 10, 482–506. [CrossRef]
60. Salman, O.; Abdallah, S.; Elhajj, I.H.; Chehab, A.; Kayssi, A. Identity-based authentication scheme for the Internet of Things. Proceedings - IEEE Symposium on Computers and Communications 2016, 2016-Augus, 1109–1111. [CrossRef]
61. Nguyen, K.V.; Nguyen, H.T.; Le, T.Q.; Truong, Q.N.M. Abnormal network packets identification using header information collected from Honeywall architecture. Journal of Information and Telecommunication 2023, 7, 437–461. [CrossRef]
62. Fei, W.; Ohno, H.; Sampalli, S. A Systematic Review of IoT Security: Research Potential, Challenges, and Future Directions. ACM Computing Surveys 2024, 56, 1–40. [CrossRef]
63. Baccari, S.; Hadded, M.; Touali, H.; Muhlethaler, P. A Secure Trust-aware Cross-layer Routing Protocol for Vehicular Ad hoc Networks. Journal of Cyber Security and Mobility 2021, 10, 377–402. [CrossRef]
64. Khan, A.A.; Bourouis, S.; Kamruzzaman, M.M.; Hadjouni, M.; Shaikh, Z.A.; Laghari, A.A.; Elmannai, H.; Dhahbi, S. Data Security in Healthcare Industrial Internet of Things With Blockchain. IEEE Sensors Journal 2023, 23, 25144–25151. [CrossRef]
65. Hussain, M.Z.; Hanapi, Z.M. Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review. Electronics (Switzerland) 2023, 12. [CrossRef]
66. Alhusayni, A.; Thayananthan, V.; Albeshri, A.; Alghamdi, S. Decentralized Multi-Layered Architecture to Strengthen the Security in the Internet of Things Environment Using Blockchain Technology. Electronics (Switzerland) 2023, 12. [CrossRef]
67. Alvi, A.N.; Ali, B.; Saleh, M.S.; Alkhathami, M.; Alsadie, D.; Alghamdi, B. Secure Computing for Fog-Enabled Industrial IoT. Sensors 2024, 24, 1–21. [CrossRef]
68. Bulut, C.; Wu, P.F. More than two decades of research on IoT in agriculture: a systematic literature review. Internet Research 2023. [CrossRef]
69. Kebande, V.R.; Awad, A.I. Industrial Internet of Things Ecosystems Security and Digital Forensics: Achievements, Open Challenges, and Future Directions. ACM Computing Surveys 2024, 56. [CrossRef]
70. Siqueira, F.; Davis, J.G. Service Computing for Industry 4.0: State of the Art, Challenges, and Research Opportunities. ACM Computing Surveys 2022, 54. [CrossRef]
71. Nechibvute, A.; Mafukidze, H.D. Integration of SCADA and Industrial IoT: Opportunities and Challenges. IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India) 2024, 41, 312–325. [CrossRef]
72. Federici, F.; Martintoni, D.; Senni, V. A Zero-Trust Architecture for Remote Access in Industrial IoT Infrastructures. Electronics (Switzerland) 2023, 12. [CrossRef]
73. Bomgni, A.B.; Mdemaya, G.B.; Ali, H.M.; Zanfack, D.G.; Zohim, E.G. ESPINA: efficient and secured protocol for emerging IoT network applications. Cluster Computing 2023, 26, 85–98. [CrossRef]
74. Kore, A.; Patil, S. Cross layered cryptography based secure routing for IoT-enabled smart healthcare system. Wireless Networks 2022, 28, 287–301. [CrossRef]
75. Kalyani, G.; Chaudhari, S. Cross Layer Security MAC Aware Routing Protocol for IoT Networks. Wireless Personal Communications 2022, 123, 935–957. [CrossRef]
76. Garg, S.; Kaur, K.; Kaddoum, G.; Choo, K.K.R. Toward Secure and Provable Authentication for Internet of Things: Realizing Industry 4.0. IEEE Internet of Things Journal 2020, 7, 4598–4606. [CrossRef]
77. Vijayakumaran, C.; Muthusenthil, B.; Manickavasagam, B. A reliable next generation cyber security architecture for industrial internet of things environment. International Journal of Electrical and Computer Engineering 2020, 10, 387–395. [CrossRef]
78. Qadri, Y.A.; Nauman, A.; Zikria, Y.B.; Vasilakos, A.V.; Kim, S.W. The Future of Healthcare Internet of Things: A Survey of Emerging Technologies. IEEE Communications Surveys and Tutorials 2020, 22, 1121–1167. [CrossRef]
79. Li, C.; Xu, Z.; Wang, J.; Zhao, J.; He, B.; Wang, L.; Li, J. Enhanced Clustering MAC Protocol Based on Learning Automata for UV Networks. Photonics 2024, 11. [CrossRef]
80. Jain, S.; Verma, R.K. A Taxonomy and Survey on Grid-Based Routing Protocols Designed for Wireless Sensor Networks. ACM Computing Surveys 2024, 56, 1–41. [CrossRef]
81. Al-Otaibi, S.; Khan, R.; Ali, H.; Khan, A.A.; Saeed, A.; Ali, J. A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things. Computers, Materials and Continua 2024, 78, 3805–3823. [CrossRef]
82. Akbar, M.S.; Hussain, Z.; Sheng, M.; Shankaran, R. Wireless Body Area Sensor Networks: Survey of MAC and Routing Protocols for Patient Monitoring under IEEE 802.15.4 and IEEE 802.15.6. Sensors 2022, 22. [CrossRef]
83. Soundararajan, R.; Maheswar, R.; Muthuramalingam, A.; Hossain, E.; Lloret, J. Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks. Sensors 2022, 22. [CrossRef]
84. Subramanyam, R.; Jancy, Y.A.; Nagabushanam, P. Cooperative optimization techniques in distributed MAC protocols – a survey. International Journal of Pervasive Computing and Communications 2024, 20, 285–307. [CrossRef]
85. De Vincenzi, M.; Costantino, G.; Matteucci, I.; Fenzl, F.; Plappert, C.; Rieke, R.; Zelle, D. A Systematic Review on Security Attacks and Countermeasures in Automotive Ethernet. ACM Computing Surveys 2024, 56. [CrossRef]
86. Erskine, S.K.; Chi, H.; Elleithy, A. SDAA: Secure Data Aggregation and Authentication Using Multiple Sinks in Cluster-Based Underwater Vehicular Wireless Sensor Network. Sensors 2023, 23. [CrossRef]
87. Klaiber, J.; Van Dinther, C. Deep Learning for Variable Renewable Energy: A Systematic Review. ACM Computing Surveys 2023, 56. [CrossRef]
88. Velayudhan, N.K.; S, A.; Devidas, A.R.; Ramesh, M.V. Delay and Energy Efficient Offloading Strategies for an IoT Integrated Water Distribution System in Smart Cities. Smart Cities 2024, 7, 179–207. [CrossRef]
89. Maheswar, R.; Kathirvelu, M.; Mohanasundaram, K. Energy Efficiency in Wireless Networks. Energies 2024, 17, 1–14. [CrossRef]
90. Nathiya, N.; Rajan, C.; Geetha, K. An energy-efficient cluster routing for internet of things-enabled wireless sensor network using mapdiminution-based training-discovering optimization algorithm. Sadhana - Academy Proceedings in Engineering Sciences 2024, 49. [CrossRef]
91. Esmali Nojehdeh, M.; Altun, M. Energy-Efficient Hardware Implementation of Fully Connected Artificial Neural Networks Using Approximate Arithmetic Blocks. Circuits, Systems, and Signal Processing 2023, 42, 5428–5452. [CrossRef]
92. Sharma, D.; Jain, S.; Maik, V. Energy Efficient Clustering and Optimized LOADng Protocol for IoT. Intelligent Automation and Soft Computing 2022, 34, 357–370. [CrossRef]
93. Wu, M.; Zhang, F.; Rui, X. An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm. Circuit World 2023, 49, 269–280. [CrossRef]
94. Sanchez-Iborra, R.; Zoubir, A.; Hamdouchi, A.; Idri, A.; Skarmeta, A. Intelligent and Efficient IoT Through the Cooperation of TinyML and Edge Computing. Informatica (Netherlands) 2023, 34, 147–168. [CrossRef]
95. Khan, M.N.U.; Cao, W.; Tang, Z.; Ullah, A.; Pan, W. Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT. Future Internet 2024, 16, 1–21. [CrossRef]
96. Komala, C.R.; Velmurugan, V.; Maheswari, K.; Deena, S.; Kavitha, M.; Rajaram, A. Multi-UAV computing enabling efficient clustering-based IoT for energy reduction and data transmission. Journal of Intelligent and Fuzzy Systems 2023, 45, 1717–1730. [CrossRef]
97. Ahmed, A.S.; Kurnaz, S.; Hamdi, M.M.; Khaleel, A.M.; Khaleel, A.M.; Seno, M.E. Study for Buildings with IoT System for Energy Management. ISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings 2022, pp. 53–57. [CrossRef]
98. Panchal, M.; Upadhyay, R.; Vyavahare, P.D. Cross-Layer based Energy Efficient Reliable Data Transmission System for IoT Networks. Proceedings - 2022 IEEE 11th International Conference on Communication Systems and Network Technologies, CSNT 2022 2022, pp. 527–532. [CrossRef]
99. Singhal, S.; Tripathi, S. Data-driven Secure Authentication for Smart Grid IoT Networks. Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies 2023, pp. 1–6. [CrossRef]
100. Bapatla, A.K.; Puthal, D.; Mohanty, S.P.; Yanambaka, V.P.; Kougianos, E. EasyChain: an IoT-friendly blockchain for robust and energy-efficient authentication. Frontiers in Blockchain 2023, 6, 1–19. [CrossRef]
101. Sridhar, M.; Pankajavalli, P.B. Energy - Efficient routing and scheduling using clustering in geographic routing protocol. Journal of Intelligent and Fuzzy Systems 2023, 44, 951–961. [CrossRef]
102. Sugumaran, V.R.; Rajaram, A. Lightweight blockchain-assisted intrusion detection system in energy efficient MANETs. Journal of Intelligent and Fuzzy Systems 2023, 45, 4261–4276. [CrossRef]
103. Goyal, H.; Kodali, K.; Saha, S. LiPI: Lightweight Privacy-Preserving Data Aggregation in IoT 2022. [2207.12197]. [CrossRef]
104. Murtaza, G.; Iqbal, F.; Altaf, A.; Rasheed, A. Techniques for Resource-Efficient, Lightweight Cryptography in IoT Devices for Smart Environment. Proceedings - 2023 6th International Conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2023 2023, pp. 223–228. [CrossRef]
105. Kousalya, R.; Sathish Kumar, G.A. A Survey of Light-Weight Cryptographic Algorithm for Information Security and Hardware Efficiency in Resource Constrained Devices. Proceedings - International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019 2019. [CrossRef]