Comparative study between metaheuristic algorithms for internet of things wireless nodes localization

Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption

Parameter selection and performance comparison of particle swarm optimization in sensor networks localization

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors\u27 memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm

Wireless Sensor Localization: Error Modeling and Analysis for Evaluation and Precision

Wireless sensor networks (WSNs) have shown promise in a broad range of applications. One of the primary challenges in leveraging WSNs lies in gathering accurate position information for the deployed sensors while minimizing power cost. In this research, detailed background research is discussed regarding existing methods and assumptions of modeling methods and processes for estimating sensor positions. Several novel localization methods are developed by applying rigorous mathematical and statistical principles, which exploit constraining properties of the physical problem in order to produce improved location estimates. These methods are suitable for one-, two-, and three-dimensional position estimation in ascending order of difficulty and complexity. Unlike many previously existing methods, the techniques presented in this dissertation utilize practical, realistic assumptions and are progressively designed to mitigate incrementally discovered limitations. The design and results of a developed multiple-layered simulation environment are also presented that model and characterize the developed methods. The approach, developed methodologies, and software infrastructure presented in this dissertation provide a framework for future endeavors within the field of wireless sensor networks