Future Topology Changes Research Articles

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.

Maximum stability data gathering trees for mobile sensor networks

We describe a benchmarking algorithm Max.Stability-DG algorithm to determine the sequence of data gathering trees for maximum stability in mobile sensor networks such that the number of tree discoveries is the theoretical global minimum. The Max.Stability-DG algorithm assumes the availability of the complete knowledge of future topology changes and works based on a simple greedy principle: whenever a new data gathering tree is required at time instant t, we determine a spanning tree that exists for the longest time since t, transform the spanning tree to a rooted data gathering tree and use it until it exists. The whole procedure is repeated until the end of the session to obtain a sequence of longest-living stable data gathering trees. We prove the correctness of the Max.Stability-DG algorithm and also evaluate the performance of the Max.Stability-DG trees compared to minimum-distance spanning tree-based data gathering trees through extensive simulations under diverse operating conditions.

On the stability of paths, Steiner trees and connected dominating sets in mobile ad hoc networks

We propose algorithms that use the complete knowledge of future topology changes to set up benchmarks for the minimum number of times a communication structure (like paths, trees, connected dominating sets, etc.) should change in the presence of a dynamically changing topology. We first present an efficient algorithm called OptPathTrans that operates on a simple greedy principle: whenever a new source–destination ( s– d) path is required at time instant t, choose the longest-living s– d path from time t. The above strategy when repeated over the duration of the s– d session yields a sequence of long-lived stable paths such that number of path transitions is the global minimum. We then propose algorithms to determine the sequence of stable Steiner trees and the sequence of stable connected dominating sets to illustrate that the principle behind OptPathTrans is very general and can be used to find the stable sequence of any communication structure as long as there is a heuristic or algorithm to determine that particular communication structure in a given network graph. We study the performance of the three algorithms in the presence of complete knowledge of future topology changes as well as using models that predict the future locations of nodes. Performance results indicate that the stability of the communication structures could be considerably improved by making use of the knowledge about locations of nodes in the near future.