Due to the increasing pervasiveness of various wireless machines in Industrial Internet of Things (IIoT) networks, the need for timely updates on information freshness has become more critical. Social IIoT (SIIoT) framework facilitates the development of interconnected networks in smart factories that can establish social relationships between humans and machines. The widespread implementation of device-to-device (D2D) communication in SIIoT networks introduces cross-talk interference, leading to an untimely update on information freshness has become a critical challenge. The Age of Information (AoI) performance metric is used to measure the Information freshness. Dynamic channel behavior plays a crucial role in enhancing the performance of D2D-enabled SIIoT Networks. This article addresses the impact of AoI and user channel behavior on network performances. We propose a mean field game theoretic optimization algorithm (PMOA) that utilizes a Hypergraph model and game theoretic concept. The problem is solved in two stages: first, a hypergraph-based channel selection model is developed to minimize interference under outdated channel state information (CSI). Second, AoI is optimized by adopting the Fokker-Planck equation (FPK). This approach meets constraints on maximum transmission power, data rate variability, and timeliness of information freshness of the devices. The PMOA algorithm is validated through simulation and achieves a significant improvement with a 13.41% increase in network throughput and a reduction in AoI by 39.38% compared to existing benchmark schemes. The implementation of the proposed algorithm may be suitable for updating the information freshness of industrial devices driven by SIIoT-based recommendation systems.

Summary In the rapidly evolving landscape of fifth generation Internet of Things (5G‐IoT) networks, Device‐to‐Device (D2D) communication has emerged as a promising paradigm to enhance secrecy transmission rate (STR) and connectivity. However, the security of D2D communications in the presence of eavesdroppers remains a critical challenge. This article investigates the problem of optimizing D2D transmit power to achieve secure D2D communication while considering the presence of random eavesdroppers in 5G‐IoT networks. We propose a novel secrecy‐based power control approach (SRMWPCA) approach to model the random distribution of eavesdroppers in the network, taking into account their varying distances from D2D pairs and deliberately increasing interference at the eavesdropper's link. By leveraging tools from stochastic geometry, we derive an analytical expression for the secrecy transmission probability (STP), which quantifies the probability of eavesdroppers successfully decoding the D2D transmission. In this analysis, we have incorporated practical considerations such as channel fading, path loss, and interference from other devices. To enhance the security of D2D communication, we formulate an optimization problem to determine the optimal transmit power levels for D2D pairs, subject to constraints on the secrecy transmission probability and interference to the cellular network. We propose an efficient algorithm to find the power allocation that maximizes the secrecy outage performance while meeting these constraints. Simulation results demonstrate the effectiveness of the proposed approach in achieving secure D2D communication in 5G‐IoT networks with random eavesdroppers. The performance of the proposed SRMWPCA approach improved by 23.25% and 20.9% compared with standard approaches in terms of the secrecy rate and throughput of the users from malicious attacks.

Summary The evolution of IoT, 5G and 6G aims to provide almost zero latency. Computation tasks size is different for different users. A framework for task computation achieving almost zero latency for stochastic demand is a challenge. A relay‐based D2D mobile edge computing (MEC) system is proposed. The idle device present in the networks is used as relay resources (RS). Mobile devices (MDs) communicate task to relay resources (RS) using D2D communication link. The RS perform computation and offloaded to edge server (ES). It aims to minimize total cost, energy expenditure and overall latency. Problem is formulated as mixed‐integer nonlinear‐constrained problem (MINCP). A three‐step algorithm to optimize relay selection, power allocation and computation resource allocation is proposed. In the initial step optimal relay selection is obtained by the Kuhn‐Munkres (KM) algorithm. In the next step, power allocation is obtained using Q‐learning. In the last step, the main problem is converted into a cost optimization problem deciphered by the proposed algorithm. The substantial simulation results indicate the relay‐based MEC system to achieve an astounding outcome in terms of latency, energy consumption and cost. Compared with other baseline methods, the proposed algorithm can achieve reduced energy consumption and cost for almost zero latency.

Abstract Quantum dot Cellular Automata (QCA) is a promising paradigm after CMOS technology due to low power consumption and small area requirements. As the counter is the most important and fundamental component of the QCA sequential logic family, therefore current effort has been made to design and analysis a dual‐mode counter. The reported dual‐mode nano counter can be operated as a Ring counter (R‐counter) as well as it can be operated as a Twisted‐ring counter (T‐counter). To achieve this, two inverters, four majority voters, and four D flip‐flops have been fully utilized. The main advantage of this design is that a single nano‐counter circuit can perform both tasks of R‐counter and T‐counter, which therefore eliminates the design constraint of double QCA layouts. In addition to that, the proposed 4‐bit QCA layout is easily expandable to n‐bit as per needs. With compared to the best‐reported design, the proposed dual‐mode counter has 75% and 29% less delay and cell count respectively. The area‐delay cost, QCA‐specific cost, and energy‐delay cost of the proposed circuit are approximately 13 times, 228 times, and 61 times superior to the previously reported best design. The designs were verified using the popular QCA layout design software QCADesigner 2.0.3. Simultaneously, two energy estimate tools, QCAPro and QCADesigner‐E (QDE), were utilized to investigate the circuits' energy dissipation.

Abstract Detection and accurate position estimation have become an essential task for any static underwater acoustic sensor networks. Many localization schemes have been introduced in the field of terrestrial WSN over the last few decades. These schemes are still not perfectly realizable in a harsh underwater environment due to their erroneous communication channel and infrastructure‐less environment. Limited localization coverage, propagation error, and accuracy are the major drawbacks in acoustic communication. This article proposed an adaptive virtual anchor node based on the improved shortest path algorithm with PSO technique. The main advantages of this proposed scheme are divided into two parts. First, introducing an enhanced shortest path algorithm can reduce the propagation error generated through multi‐hop zigzag movement and improve the coverage. Second, the particle swarm optimization algorithm with virtual anchor nodes can significantly enhance the accuracy by reducing the errors to localize the unknown nodes. Investigational results manifest that the proposed methodology performed better than the Improved DV‐Hop +PSO, IRL‐WOA, and basic DV‐Hop methods as far as their restriction. The proposed method improved 23%, 30%, and 35% better localization error, 10%, 15%, and 22% better relative localization error, and 10%, 18%, and 25% better accuracy respectively.

Abstract Deployment of a small unmanned aerial vehicle (UAV) mounted 5G base station is a promising solution for providing seamless network connectivity to users in a modern, data‐centric thrust areas. The key challenge is to find the location, the height and the optimum number of mounts. A machine programming based approach is proposed here for optimal placement of UAV‐mounted base station. The location of the deployment is determined using three clustering algorithms such asK‐means,K‐medoids, and fuzzy cluster means. Different sets of UAV‐mounted base stations have been deployed with variable user density at different heights. The impact on the network performance has been quantified through measurements of received power, signal to interference plus noise ratio (SINR), and path loss per active user equipment (UEs). To gain further insights, a scenario where UEs are connected only to the terrestrial base station, that is, the network is devoid of any sort of UAV mounted base station is evaluated. Numerical computations reaffirm that the proposed technique reduces the average path loss of the active UEs. Moreover, the use of height‐mounted base station also alleviates the issues arising due to low SINR values. The said technique shows immense potential in terms of seamless connectivity to end users in events of emergency and remote deployment scenarios, where ground‐based base station is not possible. The big transition of on‐ demand connectivity for 5G networks shall be benefitted from purpose‐built UAV infrastructure with specific locations or areas in mind.

Abstract Device‐to‐device (D2D) communication plays a vital role in communication technologies with resource management and power control, which are the major issues for researchers in the era of 2020. In 5G networks, machine learning algorithms play a crucial role to manage these issues. In the proposed work, the problem is formulated for the uplink underlaid model of D2D communication. The Lyapunov optimization technique is utilized with a combination of machine learning techniques for resource allocation in D2D communication. First, the maximization of uplink and overall system capacity is formulated with resource management, which guarantees the signal to interference noise ratio for the D2D users. The optimization is a mixed integer non‐linear problem which uses the Lyapunov optimization method to optimize the bit error rate value and iterative algorithm to optimize the power value with different constraints. After attaining the optimized value, the support vector machine technique is utilized to ensure the spectral efficiency of an overall system in autonomous mode. Simulation results show that the proposed method provides higher reliability and power efficiency with higher system capacity in comparison to prevailing technologies.