Recently, wireless body area networks (WBANs) have attracted significant consideration in ubiquitous healthcare. A number of medium access control (MAC) protocols, primarily derived from the superframe structure of the IEEE 802.15.4, have been proposed in literature. These MAC protocols aim to provide quality of service (QoS) by prioritizing different traffic types in WBANs. A contention access period (CAP)with high contention in priority-based MAC protocols can result in higher number of collisions and retransmissions. During CAP, traffic classes with higher priority are dominant over low-priority traffic; this has led to starvation of low-priority traffic, thus adversely affecting WBAN throughput, delay, and energy consumption. Hence, this paper proposes a traffic-adaptive priority-based superframe structure that is able to reduce contention in the CAP period, and provides a fair chance for low-priority traffic. Simulation results in ns-3 demonstrate that the proposed MAC protocol, called traffic- adaptive priority-based MAC (TAP-MAC), achieves low energy consumption, high throughput, and low latency compared to the IEEE 802.15.4 standard, and the most recent priority-based MAC protocol, called priority-based MAC protocol (PA-MAC).

Carrier sense multiple access mechanism with collision avoidance (CSMA/CA) in IEEE 802.15.4‐based wireless body area networks (WBANs) may impair the transmission reliability of emergency traffic under high traffic loads, which may result in loss of high valued medical information. Majority of the recent proposals recommend an early retransmission of failed frame while ignoring the history of past failed transmissions. More importantly, these proposals do not consider the number of failed transmissions experienced by each sensor node, thereby affecting the reliability of retransmissions. In this paper, we propose a dynamic retransmission adaptive intelligent MAC (RAI‐MAC) scheme. In our proposed scheme retransmission class of each sensor node is decided by the coordinator according to the number of failed transmissions of each node as observed by the coordinator during the last superframe. Based on the retransmission class received from the coordinator, each node adjusts its next backoff value. The proposed scheme increases the probability of successful frame retransmissions without incurring extra overhead. The simulation results prove that the proposed scheme based on its adaptive retransmission mechanism achieves higher average throughput and average end‐to‐end delay, while not compromising on energy efficiency as compared to the IEEE 802.15.4 and Block Acknowledgment (Block Ack). Moreover, our scheme appears more stable in terms of average throughput, end‐to‐end delay, and energy efficiency under different values of beacon order (BO) and superframe order (SO).

Energy efficiency and fault tolerance are two of the major concerns in wireless sensor networks (WSNs) for the target coverage. Design of target coverage algorithms for a large scale WSNs should incorporate both the energy efficiency and fault tolerance. In this paper, we study the coverage problem where the main objective is to construct two disjoint cover sets in randomly deployed WSNs based on relay energy (Erelay). Further, we present an approximation algorithm called Energy Efficient Maximum Disjoint Coverage (EMDC) with provable approximation ratios. We analyze the performance of EMDC theoretically and also perform extensive simulations to demonstrate the effectiveness of EMDC in terms of fault tolerance and energy efficiency.

Ubiquitous computing has potentials to harness the flexibility of distributed computing systems including cloud, edge, and internet of things devices. Mobile edge computing (MEC) benefits time-critical applications by providing low latency connections. However, most of the resource-constrained edge devices are not computationally feasible to host deep learning (DL) solutions. Further, these edge devices if deployed under denser deployments may result in topological dependencies which if not taken into consideration may adversely affect the MEC performance. To bring more intelligence to the edge under topological dependencies, compared to optimization heuristics, this work proposes a novel collaborative distributed DL approach. The proposed approach exploits topological dependencies of the edge using a resource-optimized graph neural network (GNN) version with an accelerated inference. By exploiting edge collaborative learning using stochastic gradient (SGD), the proposed approach called CGNN-edge ensures fast convergence and high accuracy. Collaborative learning of the deployed CGNN-edge incurs extra communication overhead and latency. To cope, this work proposes compressed collaborative learning based on momentum correction called cCGNN-edge with better scalability while preserving accuracy. Performance evaluation under IEEE 802.11ax-high-density wireless local area networks deployment demonstrates that both the schemes outperform cloud-based GNN inference in response time, satisfaction of latency requirements, and communication overhead.

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate’s matrices. Moreover, it deploys the Banachiewicz–Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.