Recognizing Boundaries in Wireless Sensor Networks Based on Local Connectivity Information

This paper develops an efficient and distributed boundary detection algorithm to precisely recognize wireless sensor network (WSN) boundaries using only local connectivity information. Specifically, given any node in a WSN, the proposed algorithm constructs its 2-hop isocontour and locally makes a rough decision on whether this node is suspected to be on boundaries of the WSN by examining the associated 2-hop isocontour. Then, a heuristic operation is performed to refine this decision, with the result that the suspected boundary node set is significantly shrunk. Lastly, tight boundary cycles corresponding to both inner and outer WSN boundaries are derived by searching the suspected boundary node set. Furthermore, regarding WSNs with relatively low node densities, the proposed algorithm is adapted to improve the quality of boundary detection. Even though the proposed algorithm is initially presented under the assumption of the idealized unit disk graph (UDG) model, we further consider the more realistic quasi-UDG (QUDG) model. In addition, a message complexity analysis confirms the energy efficiency of the proposed algorithm. Finally, we carry out a thorough evaluation showing that our algorithm is applicable to both dense and sparse deployments of WSNs and is able to produce accurate results.