Weight convergence analysis of DV-hop localization algorithm with GA

Xingjuan Cai; Penghong Wang; Zhihua Cui; Wensheng Zhang; Jinjun Chen

doi:10.1007/s00500-020-05088-z

Weight convergence analysis of DV-hop localization algorithm with GA

Soft comput. 2020;24(23):18249-18258. doi: 10.1007/s00500-020-05088-z. Epub 2020 Jun 18.

Authors

Xingjuan Cai¹, Penghong Wang¹, Zhihua Cui¹, Wensheng Zhang², Jinjun Chen^{1

3}

Affiliations

¹ School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024 China.
² State Key Laboratory of Intelligent Control and Management of Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
³ Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, 3000 Australia.

Abstract

The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4R.

Keywords: Convergence analyses; DV-hop; Genetic algorithm (GA); Mathematical weight model; Positional precision.