Received Signal Strength based Localization in Wireless Sensor Networks

Abstract

Context: The localization of the sensor nodes has become a critical issue in the context of wireless sensor networks (WSNs). A convenient way to estimate the location of the static objects is to exploit received signal Strength (RSS) attenuation with the distance, as it does not require any extra hardware. This has become feasible as received signal strength indicator (RSSI) is a common and standard feature in almost all the sensor nodes radios. RSSI can be recorded automatically by the received messages. On the contrary received signal strength is variable, noisy, unstable and challenging to use in practice. This doctoral dissertation investigates the feasibility of sensor nodes for distance estimation as well as for localization, with the available resources for wireless sensor network. This work makes use of the RSS efficiently. Further it provides experimental support to the hypothesis that appropriate modeling of received signal strength behavior and propagation modeling parameters are prerequisite to the applicability of wireless sensor network in real-time applications. This dissertation is associated with: (1) the selection criteria for the sensor nodes to be chosen for distance estimation as well as for localization, (ii) identifying the several factors that influence the variability of the received signal strength, (iii) modeling of the RF propagation in wireless sensor networks, (iv) defining the basic sensor network deployment constraints which can ensure efficient distance estimation as well as localization. IRIS motes from MEMSIC were deployed in real-time indoor environments to assess the practical values of the received signal strength, RF models. In this work, RSS variability, various environmental conditions were investigated. Several factors that influences signal propagation which causes RSS variation and the existing constraints from wireless sensor network were reckoned to design suitable model for indoor localization. The distance estimation and localization contemplation, by means of factors influencing RSS, distance estimation, localization were combined to validate the implication in real-time indoor environments, which concerns performance of the distance estimation as well as localization.