Author : Mayuri Kherde and Roshani Awatade

A novel Connectivity- based Boundary Extraction scheme for large-scale sensor networks. Connectivity-based Boundary Extraction for the topology has shown great impact on the performance of such services as location, routing, and path planning in wireless sensor networks. Connectivity-based Skeleton Extraction (CASE) algorithm, to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. Boundary detection algorithms, which are used to extract accurate Boundary nodes. Segmentation algorithm does not require sensor locations and only uses network connectivity information. Each node is given a ‘flow direction’ that directs away from the network boundary. A node with no flow direction becomes a sink, and attracts other nodes in the same segment. We evaluate the performance improvements by integrating shape segmentation with applications such as distributed indices and random sampling. To find the boundary nodes by using only connectivity information propose a simple, distributed algorithm that correctly detects nodes on the boundaries and connects them into meaningful boundary cycles.

Keywords: Connectivity-based, Boundary Extraction, Randomly distributed, Boundary detection

Article published in International Journal of Current Engineering and Technology, Vol.6, No.1 (Feb-2016)