This paper focuses on designing a medium access control and channel usage algorithm in multi-rate wireless local area networks for improving the efficiency and fairly sharing channel resources among the contending nodes. Aiming for the problems that the high collision is often caused by binary exponential backoff mechanism in the legacy IEEE 802.11 and the shared channel can be overused by low bitrate nodes, we propose a differentiated reservation (DR) algorithm to reduce the collision among the contending nodes by setting their backoff counter as a deterministic value once accessing successfully to the channel. Furthermore, to eliminate the performance anomaly, some nodes are permitted to send multiple packets in one transmission opportunity according to their feature. Moreover, we present the implementation of the DR algorithm that is readily applied to both the existing 802.11 DCF and 802.11e EDCA networks with minimum modification. In addition, we also investigate the limitation of the DR algorithm and propose a group-based differentiated reservation (GDR) algorithm applied to high dense scenarios. The results of the theoretical analysis and simulation validate that our proposed algorithms (DR and GDR) can obtain high throughput, good airtime fairness, and low collision rate.

This article investigates the high-reliable data transmission for multihop and multichannel wireless sensor networks (WSNs), which jointly optimizes the channel allocation and channel access mechanisms. We propose a novel wireless paradigm empowered by mobile-edge computing (MEC) and deep reinforcement learning (DRL) to improve the data process ability of WSNs and formulate the joint resource allocation problem for reliability maximization as a partially observable Markov decision process (POMDP). Meanwhile, we introduce the distributed decision-making (DDM) framework to decouple channel optimization into two subproblems: 1) channel allocation and 2) channel access. Correspondingly, we present an asynchronous channel allocation algorithm for multiagent scenario and enable the neighbor cooperation to tackle the nonstationary problem, which can significantly improve the network convergence speed. Besides, we present a collision-free channel access algorithm including three submodules that can simultaneously eliminate vanishing nodes, hidden terminal, and exposed terminal problems in large-scale WSNs. Simulation results demonstrate that the proposed algorithm significantly improves network performance in terms of convergence, throughput, collision, and packet delivery ratio (PDR).