Improved Distance vector based Kalman Filter localization algorithm for wireless sensor network

Localization is a crucial concern in many wireless sensor networks (WSN) applications. Moreover, getting accurate information about the geographic positions of nodes in the sensing field is necessary in order to make the collected data useful and meaningful. In this context, the heuristic distance vector called DV-Distance is widely used for range-free localization in multi-hop WSN thanks to its advantages such as its simplicity and good accuracy. However, its localization accuracy may be relatively low due to environmental conditions. In this paper, we propose an improvement of the DV-Hop algorithm based on distance computation via a received signal strength indicator (RSSI) and a simple polynomial approximation technique. The proposed technique consists of RSSI measurement for distance estimation between sensor nodes thanks to low cost and simplicity compared to other techniques based on time and angle of arrival, i.e. TOA, AOA and TDOA which require more specialized equipment. However, as RSSI values may be affected by conditions environment such as noise, we propose to use the Kalman Filter in order to deal with this issue and improve localization accuracy. Test results proved that the proposed technique has a higher localization accuracy than DV-Distance by up to 50% and by up to 18% when compared with the state-of-the-art algorithms cited above and other DV-Hop-based localization methods.