CentroidM: a Centroid-based Localization Algorithm for Mobile Sensor Networks

In this paper, we present an adaptation of the well-known, range-free Centroid localization algorithm to deal with node mobility. This algorithm, which we call CentroidM, has the Centroid method as a stand. Positive features of the Centroid algorithm were kept while their limitations due to the dynamic characteristics of the network movement were mitigated. We consider a topology where a fraction of the nodes, called anchors, are static and are aware of their positions, while the remaining nodes are mobile. The proposed method splits the original sampling period of the Centroid algorithm into temporal windows in order to maintain a record of past information during movement. The selection of the anchor nodes is based on the received data within these temporal windows, allowing for the weighing of the anchors' coordinates. The method proved to increase the accuracy of the Centroid algorithm in static and mobile networks. The simulations were conducted under noisy environments and random mobility. Comparisons with the original algorithm show that our proposal achieves error reductions in the localization estimations up to 42% in the presence of movement and more than 30% for a static topology, leading to a significantly more accurate range-free localization process. Besides the concern regarding the accuracy of the method, the power consumption of the algorithm was addressed too. These benefits have increased 2.76 times the time spent by the CentroidM to run a localization process. However, simulation results showed it is possible to remove such overhead and still keep the achieved estimation gains near 10%.