Abstract
Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm.
Highlights
A wireless sensor network (WSN) is a self-organization network composed of a large number of small-size, low-cost sensor nodes which can monitor physical or environmental condition [1].Recent advances of micro-electro-mechanical systems (MEMS) technology and wireless communication have propelled WSN applied to a variety of fields such as health monitoring [2], transportation management [3], business and home automation [4], global-scale wildlife [5], forest fire and environmental monitoring [6,7]
Algorithm; Section 3 gives an introduction about the modified Cuckoo Search (CS) algorithm; WSN node localization process based on the modified CS approach is explained in Section 4; simulation results and localization performance analysis are given in Section 5; Section 6 gives the conclusion of the paper
We propose a modified CS algorithm for optimizing node localization in WSN
Summary
A wireless sensor network (WSN) is a self-organization network composed of a large number of small-size, low-cost sensor nodes which can monitor physical or environmental condition [1]. Cone method solve thereduces localization problem to byrange relaxing the on proximity constraints between reported an efficient second-order cone method the localization problem by relaxing the This method requires anchors deployed aroundto thesolve perimeter of the network, otherwise the position proximity constraints between nodes. This algorithm keeps better performance than basic MDS-MAP algorithm under the uniform layouts or irregularly-shaped networks, when the number of anchors is small It needs a high consumption of battery power for each sensor to construct relative maps. To address the flip ambiguity problem, Kannan et al [25] introduced a two phase simulated annealing (SA) based localization algorithm, which first obtains an accurate estimate of location, if some nodes have flip ambiguity problem, optimization is performed based on neighborhood information of nodes and moved to the correct position. The rest of the paper is organized as follows: Section 2 provides a description of standard CS algorithm; Section 3 gives an introduction about the modified CS algorithm; WSN node localization process based on the modified CS approach is explained in Section 4; simulation results and localization performance analysis are given in Section 5; Section 6 gives the conclusion of the paper
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