An Improved DV-Hop Localization Algorithm Based on Human Conception Optimization with Time Varying Acceleration Coefficients for Wireless Sensor Network

Abstract

Wireless Sensor Network (WSN) is widely used in a variety of practical applications. WSN may be used to sense objects, gather information, analyze it, and then transmit it again. The significance of optimization techniques is crucial for the accurate and reliable estimation of the sensor nodes' location. The positioning accuracy of traditional DV-Hop localization algorithm is not entirely satisfactory instead of it is quite simple, stabilized, feasible, and requires less hardware. Thus to enhance the positioning accuracy without increasing the hardware cost of a sensor node, this article provides an improved DV-Hop localization algorithm using Human Conception Optimization (HCO). The proposed method adds a parameter to alter the anchor nodes' hop size. Furthermore, it is analyzed with traditional DV-Hop, IDV-Hop algorithm, DV-Hop based PSO, and DV-Hop based CTO. The simulation results support the conclusion that, the proposed algorithm performs better than the competing algorithms by minimizing the localization error, localization error variance, and the localization accuracy with varying the number of anchor nodes, total number of nodes, and the communication range.