A fast and simple randomized parallel algorithm for maximal matching
Information Processing Letters
Online power-aware routing in wireless Ad-hoc networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Convex Optimization
The impact of imperfect scheduling on cross-layer congestion control in wireless networks
IEEE/ACM Transactions on Networking (TON)
Energy-aware routing in sensor networks: A large system approach
Ad Hoc Networks
Scheduling Efficiency of Distributed Greedy Scheduling Algorithms in Wireless Networks
IEEE Transactions on Mobile Computing
Wireless mesh networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Energy optimal control for time-varying wireless networks
IEEE Transactions on Information Theory
Throughput and Fairness Guarantees Through Maximal Scheduling in Wireless Networks
IEEE Transactions on Information Theory
Maximum battery life routing to support ubiquitous mobile computing in wireless ad hoc networks
IEEE Communications Magazine
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
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In this work, we study the problem of minimizing the total power consumption in a multihop wireless network subject to a given offered load. It is well-known that the total power consumption of multihop wireless networks can be substantially reduced by jointly optimizing power control, link scheduling, and routing. However, the known optimal cross-layer solution to this problem is centralized and with high computational complexity. In this paper, we develop a low-complexity and distributed algorithm that is provably power-efficient. In particular, under the node-exclusive interference model and with suitable assumptions on the power-rate function, we can showthat the total power consumption of our algorithm is at most (2 + Ɛ) times as large as the power consumption of the optimal (but centralized and complex) algorithm, where is an arbitrarily small positive constant. Our algorithm is not only the first such distributed solution with provable performance bound, but its power-efficiency ratio is also tighter than that of another suboptimal centralized algorithm in the literature.