A Clustering Routing Algorithm For WSN Based On Swarm Intelligence Optimization Technique

As a new kind of information acquisition and processing mode, wireless sensor network(WSN) has become a research focus at home and abroad. Because of the limitation of energy of the sensor node itself, the performance of routing algorithm has an important influence for WSN. This main studied focus of the paper is clustering routing algorithm for WSN, and the clustering and routing mechanism is two important research contents of clustering routing algorithm for WSN. The former is responsible for the optimization of network topological structure. It is focus on the optimization of the number of cluster heads and reasonable configuration elements of the cluster heads. The later is focus on the best route to transmit the collected data to base station. Good clustering and routing mechanism can effectively balance the network energy consumption, increase the life cycle of the network.(1) The paper proposes the improved particle swarm algorithm(IBPSO) on the basis of in the basic particle swarm optimization algorithm(BPSO) which improved the inertia weight and accelerating factor. The algorithm mainly made two improved aspects compared with the BPSO algorithm: The speed of the particle can be adjusted according to the situation of convergence by introduting individual optimal and global optimal factor in inertia weight; In the view of losses of the diversity of population in the late iterations on BPSO algorithm, and it is easy convergence to the local optimal solution, the acceleration factor is improved and makes the acceleration factor is no longer a fixed value, but according to the experience of the particle itself memory and group’s role in the movement to make dynamic adjustment, which affects the size of the particle velocity. Function test results show that the improved IBPSO global search ability and convergence speed of the algorithm has certain improvement.(2) In view of the randomness of selecting cluster heads of the LEACH algorithm, the paper use particle swarm algorithm to improve the clustering mechanism. The paper proposes a heterogeneous clustering algorithm for WSN based on particle swarm optimization algorithm(PSO-CRA). The algorithm improve the fitness function of article swarm algorithm and consider the residual energy of nodes, the distance of node and node, the distance between nodes and sink node. The paper use improved particle swarm optimization algorithm to the clusters stage and get the optimal cluster head selection. The simulation experiments show that this algorithm avoids the cluster head node energy consumption caused by poor selecting imbalance and promote efficient use of network node energy compared with LEACH algorithm.(3) In view of the cluster head nodes using a single jump way to communicate directly with the sink node on LEACH algorithm, thus increasing the network communication traffic. The paper use ant colony algorithm to improve the routing mechanism. This paper proposes a more jump clustering routing algorithm for WSN based on ant colony optimization(ACO-CRA).Using dynamic adaptability and optimization ability of ant colony, and consider the transmission distance and residual energy of node on the ant colony algorithm, then to find the optimal path of energy efficient for communication between cluster heads and base station.The simulation results show that the proposed algorithm can improve the network energy utilization.(4) This paper proposes a clustering routing algorithm for WSN based on particle swarm optimization and ant colony optimization(PSOACO-CRA). It is based on researching clustering and routing mechanism and using particle warm optimization algorithm and ant colony algorithm. The algorithm optimize the heads selection by using particle swarm optimization algorithm in the stage of selecting clusters, which balanced energy consumption in the network node. In data transmission phase transmission path was optimized by using ant colony optimization algorithm, and to find the optimal path of nodes and sink node, which reduce the energy consumption on the path. The simulation results show that the performance of the proposed algorithm has improved compared with PSO-CRA and the ACO-CRA algorithm.