Wireless Mesh Sensor nodes are deployed in harsh environments, like Industrial Wireless Mesh Sensor Networks (IWMSN). There the equipment is exposed to temperature and electrical noise, so providingareliable, interference free and efficient communication in this environment isachallenge. We proposea Multi Route Rank based Routing (MR3) protocol, which enhances the link dynamics for IWMSNand also provides interference free reliable packet delivery in harsh environments. The rank ofanode is estimated based on density, hop count, energy and Signal to Interference plus Noise Ratio (SINR). The route discovery phase finds the rank value to forward the data packet inareliable path. Once the forwarding path is established, subsequently the data packets can be propagated towards the destination without using any location information. Our simulation results show that this method improves the packet delivery ratio and the throughput tremendously, and at the same time minimizes the packet delay, in heavy traffic condition.
In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the optimal routing by satisfying the Quality of Service (QoS) constraints is an ambitious task. Multiple paths are available from the source node to the gateway for reliability, and sometimes it is necessary to deal with failures of the link in WMN. A major challenge in a MCMR-WMN is finding the routing with QoS satisfied and an interference free path from the redundant paths, in order to transmit the packets through this path. The Particle Swarm Optimization (PSO) is an optimization technique to find the candidate solution in the search space optimally, and it applies artificial intelligence to solve the routing problem. On the other hand, the Genetic Algorithm (GA) is a population based meta-heuristic optimization algorithm inspired by the natural evolution, such as selection, mutation and crossover. PSO can easily fall into a local optimal solution, at the same time GA is not suitable for dynamic data due to the underlying dynamic network. In this paper we propose an optimal intelligent routing, using a Hybrid PSO-GA, which also meets the QoS constraints. Moreover, it integrates the strength of PSO and GA. The QoS constraints, such as bandwidth, delay, jitter and interference are transformed into penalty functions. The simulation results show that the hybrid approach outperforms PSO and GA individually, and it takes less convergence time comparatively, keeping away from converging prematurely.