Multi-Strategy Fusion for Enhancing Localization in Wireless Sensor Networks (WSNs)

Abstract

Localization in wireless sensor networks (WSNs) plays a crucial role in various applications that rely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, which integrates 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: data collection, model building, and implementation. The first and the last are carried out in the field, while the second is made in the laboratory. The three phases involve a few general steps. The (1) Data Collection Phase includes four steps: (a) Deploy three anchors at known locations, forming an equilateral triangle. (b) Each anchor starts broadcasting its location. (c) Using an ordinary sensor, the RSSI of each anchor is measured at every possible location where the signal of the three anchors can reach it. (d) Data is logged to a CSV file containing the measuring 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 anchors according to the distribution plan. (c) Randomly deploy ordinary sensors. (d) Each ordinary sensor starts in initialization mode. When receiving a signal from three anchors, a sensor computes its location and stores it for future use. When a sensor has its location, it turns into operational mode. (e) A sensor in the operational mode attaches 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, with lower Mean Squared Error (MSE) values compared to EHO and KNN. ABO and DSA also have similar Mean Absolute Error (MAE) values, indicating lesser average absolute errors. BP emerges as the top performer among the neurocomputing techniques, demonstrating better accuracy with lower MSE and MAE values compared to MTLSTM, BILSTM, and Autoencoder. Finally; The Multi-Strategy Fusion for Localization model offers an effective approach to enhance localization accuracy in wireless sensor networks. The paper focuses on addressing the correlation between wireless device positions and signal intensities to improve the localization process. The obtained results and provided justification emphasize the significance and value of the model in the field of localization in WSNs. The model represents a valuable contribution to the development of localization techniques and improving their accuracy to meet the needs of various applications. The model opens up opportunities for its utilization in diverse domains such as environmental monitoring, healthcare, smart cities, and disaster management, enhancing its practical applications and practical significance.

Keywords

• K-Nearest Neighbors. Localization. Neural Network. RSSI. Wireless Sensor Networks.