An In-Depth Examination of Localization Strategies Employed in Wireless Sensor Networks (WSNs): Methods, Challenges and Prospective Developments

Abstract

Wireless Sensor Networks (WSNs) rely heavily on localization to provide location aware services for applications including military surveillance, smart agriculture, environmental monitoring and healthcare. Morden methods that combine range-based and range-free techniques including Time of Arrival (ToA), Received Signal Strength Indicator (RSSI) and hybrid approaches have greatly increased the localization accuracy. Furthermore, machine learning based models with improved adaptability in dynamic situations incorporate: Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANN). Despite such developments, several challenges and obstacles still exist. In case of complicated terrains, environmental obstacles conjointly with multipath fading and signal interference curtail the localization accuracy. Further, the advanced techniques like Ultra-Wide Band (UWB) and directional antennas in networks with limited resources get hampered by high energy consumption and escalated hardware costs. Additionally, the lack of standard models for real time localization makes the system design even more difficult since sensor node mobility and dynamic topologies compromise the accuracy of the conventional methods. In this context, localization strategy is also seriously threatened by security issues such as spoofing and data manipulation. The current paper provides a thorough analysis of various current localization strategies employed in WSNs, thereafter classified them as machine learning based, range based, range free and hybrid approaches. The objective is to highlight the serious issues associated with the existing systems and to provide possible design suggestions for developing precise, safe and energy efficient localization frameworks. The findings of the current work are meant to expedite the future investigations more towards scalable, reliable, and contextually aware localization technologies appropriate for novel applications in the Internet of Things (IoT) and smart environments while considering both cost and security constraints.