Abstract
In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.
Highlights
Two steps are needed for the wireless sensor networks (WSN) localization algorithm to estimate the location of an unknown node based on the Received Signal Strength Indicator (RSSI) [1]
In case of any changes in the communication environment, the parameters in the radio signal propagation model would change along location information which plays an important role in location-based service application system, leading to error increment to the distance estimated upon RSSI value or the original model becoming not applicable any more
Hu and Evans proposed a Monte Carlo localization (MCL) method for mobile sensor node, and its computational time complexity is O(Mkm), where M is the number of Monte Carlo sample points, m is the number of localization points, and k is the number of anchor nodes [11]
Summary
Two steps are needed for the wireless sensor networks (WSN) localization algorithm to estimate the location of an unknown node based on the Received Signal Strength Indicator (RSSI) [1]. Step 2 is to estimate the distance between the unknown node and each anchor node with reference to the communication RSSI value between them and obtain the estimated position value of the unknown node with reference to the coordinates of those anchor nodes This localization method requires a preliminary test for the environment [2] so as to determine the propagation parameters of the model. The localization system that can keep high accuracy in a dynamic environment will be more promising To satisfy this requirement, a maximum likelihood-based self-adaptive localization algorithm is proposed, which does not require a preliminary test for the environment in a dynamic environment but needs considerable computation amount. This paper proposes the least square-based self-adaptive WSN localization method
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