Adaptation of sampling in target tracking sensor networks

Sampling is one of the most common and repeated tasks in a target tracking sensor network. However, tuning the sampling rate parameter can be a challenging issue considering all the sensor network restrictions. In this paper, we propose two adaptive sampling algorithms in a target tracking sensor network while considering a multi-objective fitness function. The restrictions used as objective functions are energy consumption and prediction error which provide a direct feedback to the sampling rate adaptation algorithms. We support our proposed methods with well structured experimental evaluations.