A cross-layer learning automata based gateway selection method in multi-radio multi-channel wireless mesh networks

Wireless networks' applications are increasing gradually necessitating their performance to enhance. Evolution of these networks over time indicates the need for algorithms which can operate based on their dynamic nature. Wireless mesh networks provide Intranet and Internet access for different applications in various environments. It is expected that the traffic load will be high on these networks. As gateway nodes are responsible for the traffic load transmission, gateway selection is known as one of the important research issues in that it can lead to optimized use of the network capacity and reduce congestion effects. In addition, utilizing multi-radio multi-channel architecture is one of the promising methods for increasing performance and decreasing interference. Channel assignment determines the most appropriate channel-radio associations for transmitting and receiving data through different channels simultaneously. Taking into account this architecture, this paper was written to propose a distributed gateway selection algorithm along with a cross-layer concept which predicts environment dynamics by learning automata. Experimental results demonstrate that the proposed method in various configurations on average improves packet delivery ratio 17.66%, throughput 5.36%, network overhead ratio 6.34%, and average end-to-end delay 15.94% higher than reinforcement learning-based best path routing algorithm (RLBPR), the best studied algorithm; therefore, it leads to more efficient utilization of network capacity compared to nearest gateway, minimum load index, expected transmission count, best path to best gateway, and RLBPR algorithms.