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 (Icy) 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 Icy differs from most previous methods in that it is a localised algorithm. Nodes using Icy 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 Icy seems unaffected by position estimation error. We enumerate and analyse a complete set of node configurations seen by icy. We conclude that the geometric properties underlying Icy are responsible for its resilience to error.
localisation convex hulls sensor networks boundary detection edge detection