This paper presents a comparison between some of the most used ranging localization methods based on the Received Signal Strength Indicator (RSSI) in low-power IEEE 802.15.4 wireless sensor networks. In particular, the Trilateration, the Min-Max and the Maximum-Likelihood algorithms have been compared using only a limited number of reference nodes. In order to perform an exhaustive comparison we carried out tests in an indoor environment: dozens of RSSI values for every estimation have been gathered and cleaned from outliers values. Our results show that it is possible to some extent to obtain positioning information from nodes equipped with IEEE 802.15.4 radio modules, given the position and the number of reference nodes.

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Localization in Wireless Sensor Networks (WSNs) is an important research topic: readings come from sensors scattered in the environment, and most of applications assume that the exact position of the sensors is known. Due to power restrictions, WSN nodes are not usually equipped with a global positioning system-hence, many techniques have been developed in order to estimate the position of nodes according to some measurements over the radio channel. In this paper, we propose a new technique to track a moving target by combining distance measurements obtained from both narrowband IEEE 802.15.4 and Ultrawideband (UWB) radios, and then exploiting a novel speed-based algorithm for bounding the error. This process is applied to a real dataset collected during a measurement campaign, and its performance is compared against a Kalman filter. Results show that our algorithm is able to track target path with good accuracy and low computational impact.

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