The Improved LEACH-C Protocol with the Cuckoo Search Algorithm

LEACH-C routing protocol chooses the cluster head by simulated annealing algorithm, although the simulated annealing algorithm can optimize network communication distance, however, the convergence of annealing algorithm is very slow. Improved LEACH-C uses the cuckoo search algorithm to optimize network communication distance. To meet discrete distribution of node, the continuous Lévy flight length is discretized. To increase the convergent speed, the annealing probability calculation introduced to the Cuckoo Search Algorithm. The simulation result shows that the improved LEACH-C Algorithm has good convergence speed, reaches low stable objective function values. From the improved LEACH-C algorithm, the cluster heads distribution is relatively uniform, the total WSNs energy consumption is low, and the node death rate gets low. Introduction The cuckoo search algorithm (Cuckoo Search CS) is a new intelligent optimization algorithm proposed by University of Cambridge scholars Yang X. S. and Suash D. in 2009[1]. With simple and less parameters, the CS algorithm is concerned by scholars such as Zheng Hongqing[2], who proposed an adaptive step cuckoo search algorithm[3], Song Yujian, who applied the cuckoo algorithm to the multi resources equilibrium optimization[4]. The wireless sensor network(WSNs) consists of a large number of sensors. These sensors usually have limited battery energy. So, to design an energy efficient WSNs routing protocol is a key technology of WSNs[5]. Clustering protocol has been proved to be a valid WSNs routing protocol[6]. LEACH-C protocol[7] is a classical centralized WSNs routing protocol. This protocol uses simulated annealing algorithm to calculate the cluster heads, however, the convergence speed of the simulated annealing algorithm is slow and can not guarantee the results to achieve the global optimal. Compared to simulated annealing algorithm, cuckoo search algorithm uses Lévy flight to search the solution space, and can effectively expand the search scope so as to improve the convergence speed of algorithm. Also the cuckoo algorithm is a swarm optimization algorithm and can reach to the global optimal solution within a shorter period of time. WSNs nodes are distributed in the discrete multidimensional space, and Lévy flight track is continuous. If the Cuckoo algorithm uses the Lévy flight to find the new cluster head to replace the old one, there exists the problem that when the Lévy flight coordinates for the new point cannot be correspond to the real WSNs node position. In this paper we will adapt the cuckoo algorithm to fit the discrete WSNs nodes 530 Advances in Computer Science Research (ACRS), volume 54 International Conference on Computer Networks and Communication Technology (CNCT2016)