Sensor network localisation based on sorted RSSI quantisation

International Journal of Ad Hoc and Ubiquitous Computing (Impact Factor: 0.55). 01/2006; 1(4):222-229. DOI: 10.1504/IJAHUC.2006.010503
Source: DBLP


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.

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    • "centroid scheme [19] and APIT [4]. Range quantization methods like DV-Hop [5] and DHL [6] associate each 1-hop connection with an estimated distance, while others apply RSSI quantization [20]. These schemes also use multilateration techniques but rely on measures like hop count to estimate distances to anchor nodes. "

    Full-text · Chapter · Dec 2010
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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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    ABSTRACT: This paper deals with localizing nodes in a sensor network. More precisely, we consider the situation where sensor nodes localize themselves off a mobile beacon node traveling over the deployment area. We describe a genetic approach to derive semioptimal paths for a mobile beacon, where the optimal path is defined as the beacon trajectory resulting in the highest overall localization precision for sensor nodes, with certain constraints on path length. For this, we assume that the beacon periodically broadcasts its location. The sensors can extract this location information as well as the signal strength from received packets to estimate their location. In such localization scenarios, the trajectory of the beacon heavily affects the accuracy of location estimate. To evaluate paths, we employ Cramer Rao bounds (CRB) which provide an unbiased evaluation regardless of the location estimation algorithm. A genetic approach is employed to evolve paths toward an optimal trajectory, with a C++ simulator calculating sensor node CRB estimates as the objective function. We provide a description of the approach and provide insights on what the influence of our genetic algorithm is on the accumulative overall localization CRB.
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