Introduction to algorithms
Distributed computing: a locality-sensitive approach
Distributed computing: a locality-sensitive approach
A Distributed Algorithm for Minimum-Weight Spanning Trees
ACM Transactions on Programming Languages and Systems (TOPLAS)
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
A faster distributed protocol for constructing a minimum spanning tree
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
The bin-covering technique for thresholding random geometric graph properties
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
IEEE Transactions on Parallel and Distributed Systems
Fast low degree connectivity of ad-hoc networks via percolation
ESA'07 Proceedings of the 15th annual European conference on Algorithms
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
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Traditionally, the performance of distributed algorithms has been measured in terms of time and message complexity. Message complexity concerns the number of messages transmitted over all the edges during the course of the algorithm. However, in energy-constraint radio or wireless networks (e.g., sensor networks), energy is a critical factor in measuring the efficiency of a distributed algorithm. Transmitting a message between two nodes has an associated cost (energy) and moreover this cost can depend on the two nodes (e.g., the distance between them among other things). Thus in addition to the time and message complexity, it is important to consider energy complexity that accounts for the total energy associated with the messages exchanged among the nodes in a distributed algorithm, and design energy-efficient distributed algorithms for energy-constraint networks. This paper addresses the minimum spanning tree (MST) problem, an important problem in distributed computing. We study energy-efficient distributed algorithms for the Euclidean MST problem assuming random distribution of nodes. We show a non-trivial lower bound of Ω(log n) on the energy complexity of any distributed MST algorithm. We then give a distributed algorithm that constructs an optimal MST with energy complexity O(logn) on average and O(log n log log n) with high probability. This is an improvement over the previous best known bound on the average energy complexity of Ω(log2 n). All the above results assume that nodes do not know their geometric coordinates. If the nodes know their own coordinates, then we give an algorithm with O(1) energy complexity (which is the best possible) that gives an O(1) approximation to the MST.