An Algorithm to Determine Energy-aware Maximal Leaf Nodes Data Gathering Tree for Wireless Sensor Networks

We propose an Energy-aware Maximal Leaf Nodes Data Gathering (EMLN-DG) algorithm for periodic data collection and transmission in wireless sensor networks. For each round of data gathering, an EMLN-DG tree spanning the entire sensor network is formed based on the residual energy level available at the nodes and the number of uncovered neighbors of a node during tree formation. Only nodes that have a relatively larger number of neighbors as well as a higher energy level are included as intermediate nodes in the EMLN-DG tree. By maximizing the number of leaf nodes in a DG tree and considering the energy level available at the nodes while forming the tree, we reduce energy consumption per round as well as balance the energy level across all the nodes in the network. This contributes to a significantly larger network lifetime, measured as the number of rounds before the first node failure due to exhaustion of battery charge. Performance comparison studies with the well-known data gathering algorithms such as LEACH and PEGASIS illustrate that EMLN-DG can help to sustain the network for a significantly larger number of rounds and at the same time incur a lower, or if not comparable, energy loss, delay and energy loss*delay per round of data gathering.Comment: 14 pages, 5 figures, Journal of Theoretical and Applied Information Technology, Vol. 15, No. 1, May 201

Node Failure Time and Coverage Loss Time Analysis for Maximum Stability Vs Minimum Distance Spanning Tree based Data Gathering in Mobile Sensor Networks

A mobile sensor network is a wireless network of sensor nodes that move arbitrarily. In this paper, we explore the use of a maximum stability spanning tree-based data gathering (Max.Stability-DG) algorithm and a minimum-distance spanning tree-based data gathering (MST-DG) algorithm for mobile sensor networks. We analyze the impact of these two algorithms on the node failure times and the resulting coverage loss due to node failures. Both the Max.Stability-DG and MST-DG algorithms are based on a greedy strategy of determining a data gathering tree when one is needed and using that tree as long as it exists. The Max.Stability-DG algorithm assumes the availability of the complete knowledge of future topology changes and determines a data gathering tree whose corresponding spanning tree would exist for the longest time since the current time instant; whereas, the MST-DG algorithm determines a data gathering tree whose corresponding spanning tree is the minimum distance tree at the current time instant. We observe the Max.Stability-DG trees to incur a longer network lifetime (time of disconnection of the network of live sensor nodes due to node failures), a larger coverage loss time for a particular fraction of loss of coverage as well as a lower fraction of coverage loss at any time. The tradeoff is that the Max.Stability-DG trees incur a lower node lifetime (the time of first node failure) due to repeated use of a data gathering tree for a longer time.Comment: 16 pages, 11 figure