Three-Dimensional Localization Algorithm Based on Improved A and DV-Hop Algorithms in Wireless Sensor Network

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 2; p. 448
• Main Authors: Huang, Xiaohu, Han, Dezhi, Cui, Mingming, Lin, Guanghan, Yin, Xinming
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10-01-2021; MDPI
• Subjects: 3DDV-Hop; A algorithm; Accuracy; Crossovers; Error correction; Error correction & detection; Error reduction; Genetic algorithms; hop-count value; Internet of Things; Internet of Things (IoT); Localization; Multiple objective analysis; Nodes; NSGA-II; Optimization algorithms; Pareto optimization; Sensors; Sorting algorithms; Wireless networks; Wireless sensor networks; wireless sensor networks (WSNs); A algorithm; 3DDV-Hop; NSGA-II; Internet of Things (IoT); wireless sensor networks (WSNs); hop-count value; average distance per hop
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21020448

In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.