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STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Improved methods for approximating node weighted Steiner trees and connected dominating sets
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Theoretical Computer Science
Polynomial-time approximation schemes for geometric graphs
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Wireless Communications: Principles and Practice
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Energy-efficient broadcast and multicast trees in wireless networks
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On the Complexity of Computing Minimum Energy Consumption Broadcast Subgraphs
STACS '01 Proceedings of the 18th Annual Symposium on Theoretical Aspects of Computer Science
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Bounded-hop energy-efficient broadcast in low-dimensional metrics via coresets
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
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ESA'05 Proceedings of the 13th annual European conference on Algorithms
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In this paper, we present approximation algorithms for a variety of problems occurring in the design of energy-efficient wireless communication networks. We first study the k-station network problem, where for a set S of stations and some constant k, one wants to assign transmission powers to at most k senders such that every station in S can receive a signal from at least one sender. We give a (1+@e)-approximation algorithm for this problem. The second problem deals with energy-efficient networks, allowing bounded hop multicast operations, that is given a subset C of the stations S and a designated source node s@?S, we want to assign powers to the sending stations, such that every node in C can be reached by a transmission from s within k hops. For this problem, we provide an algorithm which runs in time linear in |S|. The last problem deals with a variant of the non-metric TSP problem where the edge costs correspond to the Euclidean distances to the power of some @a=1; this problem is motivated by data aggregation schemes in wireless sensor networks. We provide a simple constant approximation algorithm, which improves upon previous results when 2=