Dynamic cluster algorithm for improving percolation of targets in a sensor network (DC-AIPT)

Abstract

Abstract A target tracking in wireless sensor networks consists of two main functions: The detection and the tracking of the target along its trajectory by means of sensors deployed in an area of interest. Generally, these sensors are not maintainable after deployments. Dynamic clustering algorithms seem to be an effective mechanism for increasing the network’s lifetime. Indeed, this type of algorithms only activates the nodes that are on the trajectory of the target when the latter is at their reach. All other sensors must be in sleep mode. The effectiveness of a monitoring solution must take into account the quality of monitoring, connectivity, and the power consumption that are directly affected by the distribution and density of the nodes. We propose to construct optimal dynamic clusters on the target trajectory based on a probabilistic model integrating two fundamental parameters: energy consumption and accuracy. This last metric is evaluated, for the first time in the target tracking algorithms, by the notion percolation.