Abstract
Localization is a key stage in building a routing matrix between nodes in wireless sensor networks. It is essential for guaranteeing the proper exchange of sensor node measurements and the conclusions made at the network level. This paper reviews various range-based localization techniques used in wireless sensor networks. It presents a mathematical model for determining sensor locations based on Received Signal Strength Indicator measurements and studies the energy consumption of the proposed localization technique. The localization technique under consideration uses Trilateration in order to find initial estimates of the coordinates of the sensor nodes. It then applies Recursive Least Square Estimation in order to refine the estimates and account for the noise accompanying the RSSI measurements. Both decentralized and centralized implementations are compared in terms of mathematical complexity, processing energy requirements, communication energy consumption, and scalability. Simulations based on the Mica2Dot platform show that besides being scalable, the decentralized localization approach becomes more energy efficient than the centralized one in terms of communication and processing power consumption as the sensors’ distribution density increases.
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