Localised convex hulls to identify boundary nodes in sensor networks

Intuitively, identification of nodes close to the network edge is key to the successful setup, and continued operation, of many sensor network protocols and applications. Many virtual coordinate constructions rely on the furthest set of nodes as beacons, and sensing applications may find useful the knowledge of the network edge. In this paper we propose local convex view (lcv) as a means to identify nodes close to the network edge. It is motivated by the hypothesis that some structural information relevant to the network is buried within view of many nodes. The lcv differs from most previous methods in that it is a localized algorithm. Nodes using lcv may establish neighbourhood coordinates if no location information is available a priori. In those cases where needed information is missing, we adopt a simple probabilistic model to decide the boundary status of a node. We identify two metrics for evaluation and compare via simulation the performance of lcv against two methods with similar properties. Further simulations reveal the surprising observation that lcv seems unaffected by position estimation error. We enumerate and analyse a complete set of of node configurations seen by lcv. We conclude that the geometric properties underlying lcv are responsible for its resilience to error.