Multi-Strategy Fusion forEnhancingLocalization in Wireless Sensor Networks (WSNs)

Abstract

Localization in wireless sensor networks (WSNs) plays a crucial role in various applications thatrely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, whichintegrates optimization techniques (ABO, DSA, EHO, and KNN) and neurocomputing techniques (BP, MTLSTM,BILSTM, and Autoencoder) to enhance localization accuracy in WSNs. The work is divided into three phases: datacollection, model building, and implementation. The first and the last are carried out in the field, while the secondis made in the laboratory. The three phases involve a few general steps. The (1) Data Collection Phase includesfour steps: (a) Deploy three anchors at known locations, forming an equilateral triangle. (b) Each anchor startsbroadcasting its location. (c) Using an ordinary sensor, the RSSI of each anchor is measured at every possiblelocation where the signal of the three anchors can reach it. (d) Data is logged to a CSV file containing themeasuring location and the RSSI of the three anchors and their locations. The Model Building Phase includes (a)preprocessing of the collected data, and (b) building a model based on optimization and optimization techniques.The Implementation phase includes five steps: (a) Convoy sensors to the target field. (b) Manually deploy anchorsaccording to the distribution plan. (c) Randomly deploy ordinary sensors. (d) Each ordinary sensor starts ininitialization mode. When receiving a signal from three anchors, a sensor computes its location and stores it forfuture use. When a sensor has its location, it turns into operational mode. (e) A sensor in the operational modeattaches the location with sensed data each time it sends it to the sink or neighbors according to routing protocols(routing is not considered in this study). ABO and DSA optimization techniques show similar performance, withlower Mean Squared Error (MSE) values compared to EHO and KNN. ABO and DSA also have similar MeanAbsolute Error (MAE) values, indicating lesser average absolute errors. BP emerges as the top performer amongthe neurocomputing techniques, demonstrating better accuracy with lower MSE and MAE values compared toMTLSTM, BILSTM, and Autoencoder. Finally; The Multi-Strategy Fusion for Localization model offers aneffective approach to enhance localization accuracy in wireless sensor networks. The paper focuses on addressingthe correlation between wireless device positions and signal intensities to improve the localization process. Theobtained results and provided justification emphasize the significance and value of the model in the field oflocalization in WSNs. The model represents a valuable contribution to the development of localization techniquesand improving their accuracy to meet the needs of various applications. The model opens up opportunities for itsutilization in diverse domains such as environmental monitoring, healthcare, smart cities, and disastermanagement, enhancing its practical applications and practical significance.