Self-Estimation of Neighborhood Distribution for Mobile Wireless Nodes

In this paper, we propose a method to estimate the node distribution for pedestrians with information terminals. The method enables us to provide situation-aware services such as intellectual navigation that tells the user the best route to go around congested regions. In the proposed method, each node is supposed to know its location roughly (i.e., within some error range) and to maintain a density map covering its surroundings. This map is updated when a node receives a density map from a neighboring node. Each node also updates the density map in a timely fashion by estimating the change of the density due to node mobility. Node distribution is obtained from the density map by choosing cells with the highest density in a greedy fashion. The simulation experiments have been conducted and the results have shown that the proposed method could keep average position errors less than 10 m.