Fast approximation algorithms for fractional packing and covering problems
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Online computation and competitive analysis
Online computation and competitive analysis
Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems.
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Stochastic Optimization is (Almost) as easy as Deterministic Optimization
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Maximum-lifetime routing algorithms for networks with omnidirectional and directional antennas
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Maximum Lifetime Broadcasting in Wireless Networks
IEEE Transactions on Computers
Approximating Fractional Packings and Coverings in O(1/epsilon) Iterations
SIAM Journal on Computing
Stochastic analyses for online combinatorial optimization problems
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Fast algorithm for multicast and data gathering in wireless networks
Information Processing Letters
Throughput-competitive on-line routing
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
Online Primal-Dual Algorithms for Covering and Packing
Mathematics of Operations Research
A Preemptive Algorithm for Maximizing Disjoint Paths on Trees
Algorithmica - Special Issue: Scandinavian Workshop on Algorithm Theory; Guest Editor: Joachim Gudmundsson
Novel algorithms for the network lifetime problem in wireless settings
Wireless Networks
Introduction to Stochastic Programming
Introduction to Stochastic Programming
Optimal flow distribution among multiple channels with unknown capacities
Theoretical Computer Science
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We introduce a general model for online packing problems with applications to lifetime optimization of wireless sensor networks. Classical approaches for lifetime maximization make the crucial assumption that battery capacities of sensor nodes are known a priori. For real-world batteries, however, the capacities are only vaguely known. To capture this aspect, we introduce an adversarial online model where estimates become more and more accurate over time, that is, when using the corresponding resources. Our model is based on general linear packing programs and we assume the remaining capacities to be always specified by lower and upper bounds that may deviate from each other by a fixed factor α. We analyze the algorithmic consequences of our model and provide a general -competitive framework. Furthermore, we show a complementary upper bound of.