Sensor network localisation based on sorted RSSI quantisation
Range estimation is essential in many sensor network localisation algorithms. Although wireless sensor systems usually have available received signal strength indication (RSSI) readings, this information has not been effectively used in the existing localisation algorithms. In this paper, we present a novel approach to localisation of sensors in an ad hoc sensor network based on a sorted RSSI quantisation algorithm. This algorithm can improve the range estimation accuracy when distance information is not available or too erroneous. The range level used in the quantisation process can be determined by each node, using an adaptive quantisation scheme. The new algorithm can be implemented in a distributed way and achieves significant improvement over existing range-free algorithms. The performance advantage for various sensor networks is shown, with experimental results from our extensive simulation with a realistic radio model.
Available from: Winston K. G. Seah
- "centroid scheme  and APIT . Range quantization methods like DV-Hop  and DHL  associate each 1-hop connection with an estimated distance, while others apply RSSI quantization . These schemes also use multilateration techniques but rely on measures like hop count to estimate distances to anchor nodes. "
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ABSTRACT: Localising sensor nodes is an essential process for self-organising Wireless Sensor Networks (WSNs). A few recently-proposed localisation algorithms use Received Signal Strength Indication (RSSI) readings usually available in WSNs for node localisation. However, the behaviours of RSSI-based localisation systems have not been thoroughly investigated. In this paper, we formulate the problem of localisation using quantised RSSI as a parameter estimation problem and derive the Cramer-Rao Lower Bound (CRLB) for the localisation problem. We then study the effect of quantisation level and network configuration parameters on the lower bound of localisation error variance.
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