Network Performance Evaluation of M2M With Self Organizing Cluster Head to Sink Mapping

In this paper, a machine-to-machine (M2M) networks are arranged hierarchically to support an energy-efficient routing protocol for data transmission from terminal nodes to a sink node via cluster heads in a wireless sensor network (WSN) at perio network congestion caused by heavy M2M traffic is tackled using the load balancing solutions to maintain high levels of network performance. First, a multilevel clustering multiple sinks with IPv6 protocol over low wireless personal area networks is promoted to prolong network lifetime. Second, the enhanced network performance is achieved through non-linear integer-based optimization. A self-organizing cluster head to sink algorithm (SOCHSA) is proposed, hosting discrete particle swarm optimization (DPSO) and genetic algorithm (GA) as evolutionary algorithms to solve the network performance optimization problem. Network Performance is measured based on key performance indicators for load fairness and average residual network energy. The SOCHSA algorithm is tested by two benchmark problems with two and three sinks. DPSO and GA are compared with the exhaustive search algorithm to analyze their performances for each benchmark problem. Both algorithms achieve optimum network performance evaluation values of 108.059 and 108.1686 in the benchmark problems P1 and P2, respectively. Using three sinks under the same simulation settings, the average residual energy is improved by 2% when compared with two sinks. Computational results prove that DPSO outperforms GA regarding complexity and convergence, thus being best suited for a proactive Internet of Things network. The proposed mechanism satisfies different network performance requirements of M2M traffic by instant identification and dynamic rerouting.