Research On Clustering Topology Control Technology In WSN

Clustering topology control can effectively reduce energy consumption, balance load and prolong lifetime of the network. The key factors to optimize the topology control algorithm and improve performance of the network are discussed. On the basis of advantages and disadvantages analysis of existed clustering algorithms, two new clustering algorithms are proposed. The first algorithm is named MCEO (Multi-hop Clustering algorithm based on Energy Optimization) and the second one is named MCWM (Multi-hop Clustering algorithm based on Weighting Model in WSN).In MCEO, cluster heads are chosen adaptively according to nodes’ residual energy, accumulated number of selected cluster head and current rounds. A weighting which takes both nodes’ residual energy and distance into consideration is calculated while clustering. The optimal multi-hop communication path is constructed by adding nodes with the maximum weighting, calculating the path weighting which considers cluster heads’ energy, distance to next hop and minimum hops.In MCWM, within communication radius, cluster heads are chosen as the maximum value among nodes’ residual energy, summation of distances to neighbor nodes and node degree. A weighting which takes both nodes’ residual energy and distance into consideration is calculated while clustering. The optimal multi-hop communication path is constructed by adding nodes with the maximum weighting, calculating the path weighting which considers cluster heads’ energy, distance to next hop and minimum hops. The communication radius, network restructuring period and factors of cluster head weighting are optimized as well.The simulation results indicate that MCEO and MCWM performs better than LEACH, HEED and EOUCP while reducing energy consumption and prolonging lifetime of the network. When the distance from nodes to base station and the regional boundary is increasing, the network performance degradation of MCEO and MCWM is smaller.The clusters’ number,size and distribution of MCEO are optimized. Duo to more effective clustering mechanism, MCWM performs better than MCEO in reducing energy consumption. The optimal cluster head factor, network restructuring period and communication radius are respectively a=0.5,6=0.25, c=0.25, T=150, R=30.Three-layer structure MCWM algorithm performs better than two-layer structure MCWM in reducing energy consumption.The simulation results show that the MCEO and MCWM algorithm can effectively reduce energy consumption, balance nodes load and prolong lifetime of the network. These two algorithms have better suitability with base station long distance communication, and they are suitable for the wireless sensor network which includes a large number of nodes and requires long-distance data transmission.