WSN backbone formation using non-probabilistic spanning tree algorithm

Abstract

Wireless sensor network (WSN) consists of battery operated sensor nodes that are used to communicate information among nodes in the network. A large WSN network is often difficult to analyze due to its complexity. Analysis of such networks requires the use of graph sampling techniques. Graph sampling helps in obtaining a sample graph that has properties similar to that of the original graph. Graph sampling algorithms show biasness towards high degree nodes. For taking into account the low degree nodes, we propose to use a spanning tree protocol that helps in obtaining an efficient connected graph. Use of spanning tree protocol provides a tree-based structure to the graph which can then be used to obtain a sampled graph offering better connectivity. To analyze such networks, we implement the concept of non-probabilistic spanning tree along with default connecting dominating set (CDS) strategy. The existing solution takes into account only the construction of the backbone nodes. In this paper, we use the concept of non-probabilistic spanning tree approach that provides better connectivity, which is then used to construct the backbone resulting in energy conservation.