A Distributed Algorithm for Minimum-Weight Spanning Trees
ACM Transactions on Programming Languages and Systems (TOPLAS)
Distributed Algorithms
Self-stabilizing multicast protocols for ad hoc networks
Journal of Parallel and Distributed Computing - Special issue on wireless and mobile ad hoc networking and computing
Algorithmic aspects of topology control problems for ad hoc networks
Mobile Networks and Applications
Maximizing network lifetime of broadcasting over wireless stationary ad hoc networks
Mobile Networks and Applications
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
EURASIP Journal on Wireless Communications and Networking
Energy concerns in wireless networks
IEEE Wireless Communications
Minimum energy mobile wireless networks
IEEE Journal on Selected Areas in Communications
Lifetime maximization for multicasting in energy-constrained wireless networks
IEEE Journal on Selected Areas in Communications
Optimizing in-network aggregate queries in wireless sensor networks for energy saving
Data & Knowledge Engineering
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We present a simple and efficient distributed method for determining the transmission power assignment that maximises the lifetime of a data-gathering wireless sensor network with stationary nodes and static power assignments. Our algorithm determines the transmission power level inducing the maximum-lifetime spanning subgraph of a network by means of a distributed breadth-first search for minmax-power communication paths, i.e. paths that connect a given reference node to each of the other nodes so that the maximum transmission power required on any link of the path is minimised. The performance of the resulting Maximum Lifetime Spanner (MLS) protocol is validated in a number of simulated networking scenarios. In particular, we study the performance of the protocol in terms of the number of required control messages, and compare it to the performance of a recently proposed Distributed Min-Max Tree (DMMT) algorithm. For all network scenarios we consider, MLS outperforms DMMT significantly. We also discuss bringing down the message complexity of our algorithm by initialising it with the Relative Neighbourhood Graph (RNG) of a transmission graph rather than the full graph, and present an efficient distributed method for reducing a given transmission graph to its RNG.