Uneven Clustering Routing Algorithm Based On Optimal Clustering For Wireless Sensor Networks

Wireless sensor networks(WSN) typically consist of a large number of energy-constrained sensor nodes with limited onboard battery resources which form a dynamic multi-hop network. In a lot of applications supported by wireless sensor networks, node energy is difficult to renew. Once a node runs out of energy. It will influence the function of the entire network, and then accelerate network death. The reconstruction of the network, requires a lot of manpower and financial resources, therefore, energy optimization is a critical issue in the design of wireless sensor networks.Hierarchical routing algorithm as an energy optimization strategy has been widely regarded as one of the effective ways to save energy for wireless sensor networks. We discuss the current hierarchical routing algorithm such as random clustering routing algorithm, uniform clustering routing algorithm, uneven clustering routing algorithm. In this paper, we propose clustering routing algorithms based on optimal clustering. The algorithm consider the calculation of optimal cluster number, cluster head selection, cluster radius calculation, and isolated node management. Therefore, in the part of calculating the optimal number of cluster heads, we reconstructed the energy consumption model by adding data fusion rate and other parameters which make it more reasonable and deduced the optimal number of cluster heads formula. Secondly, in part of selecting cluster heads, we propose a cluster heads selection algorithm based on the selection probability and a cluster heads selection algorithm based on nodes’ residual energy. In cluster heads selection algorithm based on the selection probability cluster heads selected by considering residual energy, initial energy, average energy consumption, and node degree(the number of neighbor nodes within the cluster range). In cluster heads selection algorithm based on nodes’ residual energy cluster heads selected directly by considering nodes’ residual energy and nodes density, reducing the number of iteration and make it more direct and more efficient than the cluster heads selection algorithm based on the selection probability. Thirdly, in order to solve "hot spot" problem, a self-adaptive uneven clustering algorithm is proposed, which takes the node degree into consideration. Besides, we present a solution to solve "isolated nodes problem". Isolated nodes join the nearest clusters according to the cost for joining near cluster and sending data to the base station through the nearest cluster head.At last, in order to optimize the global parameters, Quantum Genetic Uneven Clustering Routing Algorithm for Wireless Sensor Networks QGCRA and Quantum Genetic Energy Efficient Iteration Clustering Routing Algorithm for Wireless Sensor Networks QGEEIC are proposed, the clustering parameters are optimized by quantum genetic algorithm based on double-chain encoding method. In order to improve the adaptability to cluster structure of wireless sensor networks, the rotation angle and the fitness function of a quantum gate have been improved. The network structure is more reasonable and make the network more energy efficient.After extensive simulation in NS2, the simulation results show its superiority in terms of network lifetime, the number of alive nodes, and the total energy consumption. The time that the network no longer provides acceptable quality results in QGEEIC is prolonged by about 77.60% than that in LEACH, is prolonged by about 67.25% than that in HEED, is prolonged by about 54.20% than that in EEUC, is prolonged by about 35.89% than that in UCRA, is prolonged by about 23.04% than that in QGCRA, is prolonged by about 12.74% than that in EEIC. The algorithm to some extent to achieve optimized energy consumption and prolong the network lifetime.