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).

Conventional DCF and RTS/CTS mechanisms perform the channel contention by a distributed and independent manner, which can lead to severe cochannel interference and low channel utilization in multiple APs dense deployment scenario. In this paper, we propose a channel scheduling cooperation algorithm called CCT-SDN (centralized concurrent transmission based on SDN) that enables multiple APs (Access Points) to perform cooperatively a centralized downlink transmission control, thus achieving higher system throughput and channel utilization by avoiding cochannel interference and implementing concurrent transmission. This design inherits the merit of the conventional distributed random channel access and adopts standardized OpenFlow protocol and Software Defined Network (SDN) architecture to make a centralized concurrent downlink traffic transmission decision among APs. Meanwhile, we also present a novel neighborhood relation storage scheme called SPRIM to enhance the retrieving efficiency of SDN controller, which enables CCT-SDN to perform a real-time control. Moreover, we also develop a theoretical model to prove the improvement of CCT-SDN. Furthermore, our solution does not require any modifications to existing ubiquitous 802.11 terminal devices and thus is likely to be widely deployed. Finally, extensive simulation results on Mininet-WiFi verify that CCT-SDN can achieve significant performance in terms of aggregate throughput, channel utilization, and packet loss rate in different deployment scenarios.

In Wireless Local Area Networks (WLANs), cochannel deployment can bound channel access delay and improve network capacity due to mitigating the collision and interference among different Access Points (APs). In this paper, we present a network model and an interference model for multiple APs cochannel deployment and propose a channel assignment mechanism which formulates the channel assignment problem into a time slot allocation problem. Meanwhile, we assign the channel based on the vertex coloring algorithm and make extra polls by utilizing the time slot reservation strategy to improve the channel assignment. Furthermore, we optimize the polling list of APs through classifying the clients to improve the channel utilization. The simulation results show that our proposed algorithm can improve the performance in terms of network throughput, transmission delay, and packet loss rate compared with the DCF (Distributed Coordination Function) and TMCA algorithms.