Mobile Edge Computing (MEC) enables computational task offloading from resource-constrained End Devices (EDs) to nearby edge servers, thereby reducing latency and energy consumption. This problem has been extensively studied in the literature, however, existing approaches often neglect realistic device constraints, particularly battery dynamics, and rely on limited and non-reproducible datasets. To address these gaps, this paper proposes a Battery-Dependent Partial Offloading Scheme (BDPOS) that integrates the current battery levels of EDs into the cost model, resulting in energy-aware and optimal offloading decisions. The proposed framework jointly optimizes three objectives: determining the optimal number of components, identifying task partitioning, and selecting offloading policies to identify the overall minimum-cost policy. To ensure reproducibility, BDPOS is further used to generate multiple synthetic datasets of varying sizes. A comprehensive comparative analysis of multiple AI models within a sequential Multi-Task Learning (MTL) framework is conducted on these datasets for intelligent offloading in MEC environments. The evaluated models include Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), Minimal Gated Unit (MGU), and Temporal Convolutional Network (TCN). The datasets are pre-processed and optimized using Bayesian-based hyperparameter tuning before model training. The trained models are then evaluated using multiple performance metrics, the Wilcoxon signed-rank test, and computational cost analysis. Moreover, the energy consumption of the proposed algorithm is compared with existing schemes in the literature. Simulation results demonstrate that the proposed technique BDPOS, significantly reduces overall energy consumption compared to existing strategies, while the MTL-based GRU model achieves superior performance, attaining 87.96% accuracy in component optimization, a mean absolute error of 0.0780 for task partitioning, and 63.37% accuracy for offloading policy prediction. These findings highlight the importance of battery-aware modelling and provide actionable insights into the design of efficient, data-driven offloading strategies for next-generation MEC systems.

The Internet of Things (IoT) interconnects vast numbers of sensors and devices that operate under different cryptographic infrastructures, making secure cross-domain communication essential. Most existing signcryption schemes are designed for a single infrastructure or, at best, two fixed ones, which limits applicability in heterogeneous and evolving IoT deployments. To address this need, we introduce LH3SC, a seamless elliptic-curve heterogeneous signcryption scheme that supports three infrastructures: certificateless cryptography, public key infrastructure, and identity-based cryptography, with a CLC sender. LH3SC enables devices across these domains to communicate securely without major architectural changes. The security analysis establishes IND-CCA2 confidentiality and EUF-CMA unforgeability, and the performance evaluation demonstrates lower computation and communication costs than representative schemes. These properties make LH3SC suitable for resource-constrained IoT settings, including healthcare automation, smart grids, and other distributed systems that require seamless cross-domain security.

The growing demand for real-time computing applications on mobile devices is burdening their processing power and battery life. Mobile edge computing helps by allowing these tasks to be offloaded to nearby servers having more processing power. However, when it comes to multiple servers and tasks, choosing the optimal components for offloading becomes challenging. This is because we need to balance between reducing the amount of data transferred and keeping communication latency low. To address this problem, an energy-efficient parallel computation offloading mechanism through deep learning (EPCOD), is proposed. An algorithm using deep learning (DL) is developed and trained as a decision-making system. This system selects the best combination of application components taking into account various factors, such as energy consumption, network conditions, computational load , data transfer volume, and communication latency. A cost function that includes all these factors is developed to calculate the cost for each possible offloading policy combination. By analyzing a large dataset, we find the best policies. Additionally, we use a DL network to efficiently handle this computational task. Simulation results demonstrate that EPCOD effectively minimizes both latency and energy consumption, achieving a high accuracy of deep neural network of up to 73.5%.

In unmanned aerial vehicle (UAV)-assisted wake-up radio (WuR)-enabled internet of things (IoT) networks, UAVs can instantly activate the main radios (MRs) of the sensor nodes (SNs) with a wake-up call (WuC) for efficient data collection in mission-driven data collection scenarios. However, the spontaneous response of numerous SNs to the UAV’s WuC can lead to significant packet loss and collisions, as WuR does not exhibit its superiority for high-traffic loads. To address this challenge, we propose an innovative receiver-initiated WuR UAV-assisted clustering (RI-WuR-UAC) medium access control (MAC) protocol to achieve low latency and high reliability in ultra-low power consumption applications. We model the proposed protocol using the M / G / 1 / 2 queuing framework and derive expressions for key performance metrics, i.e., channel busyness probability, probability of successful clustering, average SN energy consumption, and average transmission delay. The RI-WuR-UAC protocol employs three distinct data flow models, tailored to different network traffic scenarios, which perform three different MAC mechanisms: channel assessment (CCA) clustering for light traffic loads, backoff plus CCA clustering for dense and heavy traffic, and adaptive clustering for variable traffic loads. Simulation results demonstrate that the RI-WuR-UAC protocol significantly outperforms the benchmark sub-carrier modulation clustering protocol. By varying the network load, we capture the trade-offs among the performance metrics, showcasing the superior efficiency and reliability of the RI-WuR-UAC protocol.

Intelligent Transportation Systems (ITS) supported by smart vehicles have revolutionized modern transportation, offering a wide range of applications and services, such as electronic toll collection, collision avoidance alarms, real-time parking management, and traffic planning. However, the open communication channels among various entities, including smart vehicles, roadside infrastructure, and fleet management systems, introduce security and privacy vulnerabilities. To address these concerns, we propose a novel security framework, named blockchain-assisted lightweight authenticated key agreement security framework for smart vehicles-enabled ITS (BASF-ITS), which ensures data protection both during transit and while stored on cloud servers. BASF-ITS employs a combination of efficient cryptographic primitives, including hash functions, XOR operator, ASCON, elliptic curve cryptography, and physical unclonable functions (PUF), to design authenticated key agreement schemes. The inclusion of PUF significantly enhances the system’s resistance to physical attacks, preventing tampering attempts. To ensure data integrity when stored on the cloud, our framework incorporates blockchain technology. By leveraging the immutability and decentralization of the blockchain, BASF-ITS effectively safeguards data at rest, providing an additional layer of security. We rigorously analyze the security of BASF-ITS and demonstrate its strong resistance against potential security ass aults, making it a robust and reliable solution for smart vehicle-enabled ITS. In a comparative analysis with contemporary competing schemes, BASF-ITS emerges as a promising approach, offering superior functionality traits, enhanced security features, and reduced computation, communication, and storage costs. Furthermore, we present a practical implementation of BASF-ITS using blockchain technology, showcasing the computational time versus the “transactions per block” and the “number of mined blocks”, confirming its efficiency and viability in real-world scenarios.Note to Practitioners—This article is motivated by designing an efficient, lightweight, and anonymous blockchain-enabled authenticated security framework that can fix the security and privacy concerns in insecure environments for ITS applications, such as automated road speed enforcement, collision avoidance alarm systems, and traffic planning and management, etc. Authenticated key agreement schemes are extensively used to secure communications in the ITS environment. However, the existing state-of-the-art schemes are not efficient in terms of performance, are not resilient against potential security attacks, and do not support anonymity, untraceability, and unlinkability. Therefore, we propose the authenticated security framework to secure communication among the participating entities in the ITS environment. It utilizes efficient cryptographic primitives, such as hash function, XOR-operator, ASCON, elliptic curve cryptography, and PUF. It is shown that the proposed framework can be deployed as a robust tool to address the ITS security problems efficiently. Moreover, the proposed framework is lightweight and efficient and can be easily deployed in various ITS applications and other resource-constrained environments. However, the participating entities, such as vehicles and roadside units, must be PUF-enabled to deploy the proposed framework.

Delayed delivery of packets hinders the performance of time-sensitive Internet of Things (IoT) applications and incurs increased power consumption. Stochastic routing schemes solve the problem of saving all participating nodes from getting their power drained out quickly. However, stochastic routing incurs the problem of delivery delays and reliable end-to-end delivery. This paper proposes a novel routing scheme, called $Q_{i j}$ routing, to solve these problems. The proposed $Q_{i j}$ routing scheme is a combination of a classic routing scheme, called Distance Vector Algorithm, with a novel re-definition of the cost of a link to find the best path from source to destination. $Q_{i j}$ takes into account the wireless link reliability of any connection between two nodes, and the transmission delay of IoT devices working together in a distributed network. With the presented mathematical model, a routing table is maintained that let an individual node in a network find the distinctly prominent reliable path among many routes from source to destination. The superior efficiency of $Q_{i j}$ routing scheme over eminent stochastic routing schemes is proven through simulation results in terms of reduced end-to-end expected delivery delay and increased expected delivery ratio.

It has been observed that the use of radio-frequency fingerprinting (RF-FP) for location estimation (LE) of vehicles can significantly improve secrecy capacity (SC) for urban scenarios in $6^{th}$ generation cellular vehicle-to-everything (6G C-V2X) communication. However, in most of the literature, interference is considered constant for simplicity. Thus, there is a need to investigate the impact of varying levels of interference and incomplete channel state information (CSI) on the performance of SC. This study examines the impact of shadowing and interference in the presence of heavy traffic and obstructions. We have proposed a technique that analyzes the effects of varying levels of interference for LE via RF-FP to improve SC with incomplete CSI. Simulation results demonstrate that interference has a significant impact on SC which depends on the distance and location of the eavesdropping vehicle. However, a decrease in SC is not solely caused by the increase in interference, since other factors, such as the speed and relative position of the illegitimate vehicles as well as incomplete CSI, can also significantly affect the SC performance, as demonstrated through simulations.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for the next generation of wireless networks. However, the random nature of wireless channels can significantly reduce performance. Recently, reconfigurable intelligent surfaces (RISs) have emerged as a passive smart solution to combat the effects of wireless radio links. Integrating NOMA with RIS is a smart solution to control the stochastic behaviour of wireless channels with improved coverage and enhanced throughput. It can substantially improve the outage probability (OP) of wireless networks. This paper investigates the outage performance of a multi-antenna downlink NOMA system with RIS assistance. We determine the OP utilising the statistical characteristics of the signal-to-noise ratio of the reflection channel from the base station to the users through RIS for both near and far users. The proposed RIS-aided system outperforms conventional NOMA and orthogonal multiple access (OMA) systems without RIS. We validate our analytical results using Monte Carlo simulations. The simulation results show that the RIS-aided NOMA system outperforms conventional systems and the scenario without RIS assistance.

Abstract It is anticipated that sixth‐generation (6G) systems would present new security challenges while offering improved features and new directions for security in vehicular communication, which may result in the emergence of a new breed of adaptive and context‐aware security protocol. Physical layer security solutions can compete for low‐complexity, low‐delay, low‐footprint, adaptable, extensible, and context‐aware security schemes by leveraging the physical layer and introducing security controls. A novel physical layer security scheme that employs the concept of radio frequency fingerprinting (RF‐FP) for location estimation is proposed, wherein the RF‐FP values are collected at different points with in the cell. Then, based on the estimated location, the nearest possible road‐side unit for sending the information signal is located. After this, the effects on secrecy capacity (SC) and secrecy outage probability (SOP) in the presence of multiple eavesdropper per unit time are analysed. It has been shown via simulations that the proposed RF‐FP scheme increases SC by up to 25% for the same signal‐to‐noise ratio (SNR) values as those of the benchmarks, while the SOP tends to decrease by up to 30% as compared to the benchmark scheme for the same SNR value. Thus, the proposed RF‐FP‐based location estimation provides much better results as compared to the existing physical layer security schemes.

The next-generation Internet of vehicles (IoVs) seamlessly connects humans, vehicles, roadside units (RSUs), and service platforms, to improve road safety, enhance transit efficiency, and deliver comfort while conserving the environment. Currently, numerous entities communicate in the IoVs environment via insecure public channels that are susceptible to a variety of security assaults and threats. To address these security challenges, we design an anonymous authenticated key exchange mechanism for the IoVs in smart transportation supported by blockchain, referred to as AAKE-BIVT. AAKE-BIVT securely transmits traffic information to a cluster head, before heading to a nearby RSU utilizing the established secret session keys via mutual authentication and key agreement. A cloud server (CS) then securely aggregates data from related RSUs and generates transactions. The CS combines the transactions into blocks in a peer-to-peer network of CSs, and the blocks are confirmed and added to the blockchain via a voting-based consensus method. By means of rigorous informal security studies and formal security analysis through the random oracle model, we reveal that the proposed AAKE-BIVT is resistant to a broad range of potential security assaults in the IoVs environment. Furthermore, a comparative study reveals that AAKE-BIVT outperforms existing state-of-the-art techniques, in terms of security and functionality while being more efficient in terms of communication and computation. Additionally, the blockchain simulation validates the implementation viability of our proposed AAKE-BIVT.

The proliferation of Internet of Things (IoT) devices in the 5G era has resulted in increased security vulnerabilities and zero-day attacks, underscoring the importance of network intrusion detection systems (NIDS). However, existing NIDS have limitations in terms of accuracy, recall rates, false alarm rates, and generalization capabilities, and they cannot meet the IoT's requirements for low latency and limited computing resources. To overcome these challenges, we propose a NIDS based on a pseudo-siamese stacked autoencoder (PSSAE), deployed in the fog computing layer. Our system uses unsupervised training of stacked autoencoders (SAEs) to extract deep semantic features of normal and abnormal traffic, followed by supervised learning with labels to improve characterization and classification capabilities. The results show that our proposed method's accuracy and detection rate (DR) is 2% to 15% and 1%–14% higher than the existing techniques using the KDDTest+ dataset, respectively. Our proposed method outperformed the existing methods by 1% to 4% using the KDDTest+ dataset. The F1-Score is higher by 3%–11.55% using the KDDTest+ dataset. On the other hand, using the KDDTest-21 dataset, the accuracy of our proposed method also outperformed the existing technique by 6.09%–13.81%. The DR and F1-Score are higher by 7.02% and 5.57%, respectively, using the KDDTest+ dataset. This is due to the fact that each layer of the network trained by SAEs is more capable of extracting the semantic features of the data than the DNN-trained network directly.

Internet of Things (IoT) is a promising technology that attains significant consideration in diverse industrial areas, i.e., agriculture, engineering, logistics, trading, ecological examining, security surveillance, energy, and healthcare. IoT gains much more attention with the rapid advancement of wireless communication and sensor networks as millions of intelligent devices get involved in IoT. These intelligent devices' raw data must be captured and processed to support decision-making. However, IoT applications trust the central server for information storage, processing, and mediators for wireless transmission. Consequently, it can leak the information and lead to high costs and delays. Hence, data security is the leading interest for the IoT. Blockchain technology can be deployed to overcome the security and effectiveness of the gigantic data in IoT. Blockchain is studied as a key to permitting storing, processing and sharing of data in an efficient, secure manner. In addition, reinforcement learning can convene the high data rate requirements. It will help us to optimize the performance of the blockchain-enabled IoT framework.

Mobile edge computing (MEC) is used for compu-tationally complex applications by offloading it to the nearby edge server either partially or entirely. The problem arises of selecting whether the component is to be offloaded to the mobile edge server (MES) for execution, or it needs to be executed locally. Therefore, we propose a time-efficient decision offloading scheme (TEDOS) to derive a data set and train an artificial neural network (ANN) on the derived data set. TEDOS provide the smart decision on the optimal permutation of the divided components based on delay. We developed a mathematical model for delays in communication, execution and component queuing. We obtained a final delay for all possible permutations of component offloading policies. Our model obtained 91 % accurate results as compared to the existing schemes. The simulation result shows that our proposed model outperforms the state-of-the-art.

The cognitive radio-enabled$massive$Internet of Things (CR-$m$IoT) is envisioned to shape the future of densely connected IoT devices in the sixth-generation networks to support the hyperconnected society. In conventional CR networks, secondary users (SUs) sense the whole block of spectrum to find idle channels, which is an energy-consuming, delay-inducing, and processing-intensive task. With the large scale of resource-constrained heterogeneous devices in CR-$m$IoT, the sensing process becomes a major hurdle for CR-$m$IoT devices to achieve efficient utilization of the limited device and network resources. Thus, a novel multiparameter-based flexible scheme is proposed for idle channel prediction and channel ranking, which considers priorities as well as heterogeneity of users. The scheme uses a probabilistic approach and employs multiple parameters simultaneously to evaluate the suitability of a channel before selecting it for transmission. In addition, valid channel obsolescence, a major problem inherent with channel prediction and ranking, is countered by the proposed scheme. The scheme is evaluated under the impact of variable primary and SUs’ arrivals and under multiple channel failures rates and variable sensing and frame time duration. The proposed scheme is also compared with its own modified version that disregards channel failures, and with the random channel selection approach followed by IEEE 802.22. The overall evaluation is conducted under realistic spectrum sensing. Simulation results show that for different parameter values, the proposed scheme improves the collision probability by 11%–55%, reduces sensing time and energy by 60% and 65%, respectively, and enhances throughput by 4%–70%, and spectrum utilization efficiency by 11%–40%.

Ensuring security and privacy in the Internet of Things (IoT) while taking into account the resource-constrained nature of IoT devices is challenging. In smart home (SH) IoT applications, remote users (RUs) need to communicate securely with resource-constrained network entities through the public Internet to procure real-time information. While the 6LoWPAN adaptation-layer standard provides resource-efficient IPv6 compatibility to low-power wireless networks, the basic 6LoWPAN design does not include security and privacy features. A resource-efficient authenticated key exchange (AKE) scheme becomes imperative for 6LoWPAN-based resource-constrained networks to render indecipherable communication functionality. This article presents a lightweight user AKE scheme for 6LoWPAN-based SH networks (LAKE-6SH) to achieve authenticity of RUs and establish private session keys between the users and network entities by employing the SHA-256 hash function, exclusive-OR operation, and a simple authenticated encryption primitive. Informal security validation illustrates that LAKE-6SH is protected against different pernicious security attacks. The security is further validated formally through the random oracle model. Moreover, through Scyther validation, it is demonstrated that LAKE-6SH is secure. In addition, it is demonstrated that LAKE-6SH renders better security features aside from its low communication and computational overheads.

Cognitive radio networks (CRNs) can facilitate ultra-reliable communication among IoT devices in the 6G environment by enhancing channel availability (CA) for primary and secondary users. However, CA does not necessarily lead to successful connection establishment unless receiver’s accessibility (RA) is guaranteed. This motivates us to propose the notion of connection availability (CoA) that incorporates RA into CA. We also introduce the idea of service maintainability (SM) that includes the effect of RA in service retainability. Additionally, spectrum utilization efficiency (SUE) is expressed and analyzed with and without considering the impact of RA. For performance evaluation, a channel reservation algorithm with customizable configurations is proposed. Furthermore, an analytical model is used to investigate the network performance for all key performance indicators (KPIs) under multiple channel failures and PU arrival rates and determine valuable tradeoffs among KPIs.

Cognitive radio networks (CRNs) promise to accommodate billions of Internet of Things (IoT) devices within scarce spectrum by allowing secondary users (SUs) to use licensed spectrum. However, the devices need uninterruptible communication, which the conventional CRNs cannot fulfill. This necessitates successful service completion probability (SSCP) enhancement in CRNs. Further, maintaining fairness among SUs, in terms of availing network services, is a matter of consideration for ensuring the network services to be fairly available to all SUs. In this paper, we propose a hybrid CRN (HCRN) scheme to analyze two problems. Firstly, we investigate SSCP enhancement by utilizing hybrid underlay-interweave mode of CRNs and propose a dynamic channel reservation algorithm to support interrupted users. Secondly, we propose a multi-attributes based fairness-driven channel determination (MFD) algorithm for channel interruption, which ensures fairness among SUs in availing network services. Furthermore, continuous-time Markov chain is used for modelling, and mathematical formulations are derived for SSCP. The proposed scheme is evaluated under various network traffic loads and channel failure rates. Numerical results show significant improvement in SSCP and reduction in forced termination rate as compared to the benchmark. Similarly, the MFD algorithm brings a prominent improvement in fairness.

Maintaining link stability among randomly deployed network nodes is one of the key challenges for effective communication in mobile ad hoc networks (MANETs). Under uniform speed and random trajectory of mobile nodes, there must be a unified model to determine an adequate strategy that addresses the issue of link stability in MANETs. We present a novel dynamic link connectivity (DLC) strategy that maintains link stability through efficient link connectivity among the neighboring nodes using Hello messaging. We also perform stochastic analysis of the proposed strategy, which predicts the future link status among the neighboring nodes at different time steps of a Markov process. We find that the link stability is affected by the received signal strength, signal‐to‐noise ratio, transition rates between the connection and disconnection states, and probabilities of link connectivity and disconnectivity at steady state. Analytical and simulation results indicate efficacy of the proposed strategy in terms of reduced communication overhead, lower propagation delay, and better energy efficiency of the network. The results also demonstrate that the proposed strategy minimizes the average response time, increases the throughput, and reduces the packet loss ratio, thereby, maintaining efficient link stability among the neighboring nodes.

This study concerns the problem of controlling multiclass (elastic, inelastic and unresponsive) Internet traffic without sacrificing quality of service (QoS) by adopting a unified ‘resource allocation and traffic management’ approach. The aim is to minimise the need for relying on dedicated QoS traffic control mechanisms in order to avoid spiralling complicatedness that, in practice, leads to ‘robust yet fragile’ Internet. In order to address this challenge, the authors first introduce an end-to-end non-convex network utility maximisation-based resource allocation algorithm to guarantee enhanced QoS to elastic and inelastic flows. Then, a pricing-based fair and scalable traffic management scheme, called Purge, is introduced to protect transmission control protocol-friendly traffic from unfairness attacks by unresponsive flows. Finally, the main contribution of this work, the unified algorithm, is developed by adapting Purge to complement link-control of the proposed resource allocation algorithm to enable it to enforce fairness while maintaining a scalable network core. The unified approach thus delivers QoS guarantees for multiclass traffic.

Network utility maximization (NUM) provides an important perspective to conduct rate allocation where optimal performance, in terms of maximal aggregate bandwidth utility, is generally achieved such that each source adaptively adjusts its transmission rate. Behind most of the recent literature on NUM, common assumptions are that traffic flows are elastic and that their utility functions are strictly concave. This provides design simplicity but, in practice, limits the applicability of resulting protocols, in that severe QoS problems may be encountered when bandwidth is shared by inelastic flows. This paper investigates the problem of distributively allocating data transmission rates to multiclass services, both elastic and inelastic, and overcomes the restrictive and often unrealistic assumptions. The proposed method is based on the Lagrangian Relaxation for a dual formulation that decomposes the higher dimension NUM into a number of subproblems. We use a novel Surrogate Subgradient based stochastic method to solve the dual problem. Unlike the ordinary subgradient methods, surrogate subgradient can compute optimal prices without the need to solve all the subproblems. For the lower dimension, nonlinear and nonconvex subproblems we use a hybrid particle swarm optimization (PSO) and sequential quadratic programming (SQP) method, where the objective is to achieve fast convergence as well as accuracy. We demonstrate the efficiency of the proposed rate allocation algorithm, in terms maintaining QoS for multiclass services, and validate its scalability and accuracy for large scale flows.