Cog Gap: Cognitive and Opportunistic Gateway Access for Wireless Mesh Networks

The performance of conventional gateway access optimization techniques deteriorate dramatically when traffic load is dynamic. In this article, we propose a novel gateway access algorithm called ‘Cog Gap’, which is a cognitive method and is designed for Wireless Mesh Networks to maximize the network utilization. The proposed Cog Gap utilizes a destination-hub access model, where multiple gateway nodes are connected by wired links, and packets from a source node can be sent from any connected gateway nodes to increase transmission opportunities. In Cog Gap, we use the Hidden Markov Model (HMM) and the expectation maximization method to handle uncertain traffic pattern and the loss of probing results. A traffic allocation algorithm is then proposed to optimize dynamic multi-gateway access. By modeling the route state determination and transition, we transform the opportunistic gateway access problem into a Markov decision process (MDP) problem. A heuristic and adaptive algorithm named hindsight optimal is used in solving MDP. Simulation results have proven that the proposed Cog Gap algorithm can make full use of the transmission opportunities and does not incur noticeable protocol overhead.