.png){kind=icon}
Return
An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network
.png){kind=icon}
OA
AI
DOI：10.3390/s16091390.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
wireless sensor network
localization
cuckoo search algorithm
average localization error
convergence rate
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
7.2W
Citations:
20.9W
Organization
Cited Papers
ShareSaveShareSaveShareSaveA Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality.png)
SENSORSOAAI
SENSORSIF3.5
OAAIShareSaveShareSave
View more