A Distributed Localization Method for Wireless Sensor Networks Based on Anchor Node Optimal Selection and Particle Filter

Abstract

In wireless sensor networks, due to the significance of the location information of mobile nodes for many applications, location services are the basis of many application scenarios. However, node state and communication uncertainty affect the distance estimation and position calculation of the range-based localization method, which makes it difficult to guarantee the localization accuracy and the system robustness of the distributed localization system. In this paper, we propose a distributed localization method based on anchor nodes selection and particle filter optimization. In this method, we first analyze the uncertainty of error propagation to the least-squares localization method. According to the proportional relation between localization error and uncertainty propagation, anchor nodes are selected optimally in real-time during the movement of mobile nodes. Then we use the ranging and position of the optimally selected anchor nodes to obtain the location information of the mobile nodes. Finally, the particle filter (PF) algorithm is utilized to gain the optimal estimation of the localization results. The experimental evaluation results verified that the proposed method effectively improves the localization accuracy and the robustness of the distributed system.

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

“…QKD is a quantum cryptographic technique that enables secure communication by distributing cryptographic keys between communicating parties, utilizing the principles of quantum mechanics to ensure the keys' confidentiality [22]. Specified the present confinement plus checking arrangements, sensors node limitation, and target following innovation for WSN must be inspected according to their viewpoints the precision, expanding normal existence regarding coordination hypothesis, molecule channel, sans range hypothesis, and diverse figuring draws near [23]. An anchor node optimization approach with minimum standard deviation and minimal error propagation has been developed by examining the range-based positioning algorithm's error propagation.…”

Key Agreement and Authentication Protocols in the Internet of Things: A Survey

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Node Localization Algorithm for Irregular Regions Based on Particle Swarm Optimization Algorithm and Reliable Anchor Node Pairs

“…Gopikrishnan [6] et al proposed a unique localization framework for problems such as obstacles in irregular wireless sensor network environments, i.e., convex optimization method for localization with faster computation and also involves regular nodes in the cooperative localization process to achieve localization, which reduces the localization error to a larger extent. Iram Javed et al [7] proposed an algorithm that allows a mobile anchor node to fly in a 3D network with a C-shaped path, where the coordinates of the to-be-localized node are calculated by building a distance matrix from the RSSI (Received Signal Strength Indication) values between nodes in the network; Luo et al [8] proposed an algorithm to localize nodes in the region by moving the anchor nodes, by selecting the appropriate anchor nodes and letting them move irregularly in the area, and finally by particle filtering for distributed localization optimal estimation; Tu et al [9] proposed an algorithm to classify anchor nodes into two types, optimal and suboptimal, for distance estimation to specific unknown nodes (LRAQS), which reduces the influence of anisotropic factors in irregular regions on the localization results. For optimal anchor nodes, a probability density function is designed to compute the distance between them and the target node; for suboptimal anchor nodes, the distance is computed using the expected number of hops, and then the positional coordinates of the target node are obtained using the Bottle Sea Sheath Optimization Algorithm with quantum behavior.…”