Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Proceedings of the 9th annual international conference on Mobile computing and networking
Query Processing in Sensor Networks
IEEE Pervasive Computing
Capacity regions for wireless ad hoc networks
IEEE Transactions on Wireless Communications
Robust location detection with sensor networks
IEEE Journal on Selected Areas in Communications
Optimal power control, scheduling, and routing in UWB networks
IEEE Journal on Selected Areas in Communications
Transmission power control in body area sensor networks for healthcare monitoring
IEEE Journal on Selected Areas in Communications - Special issue on body area networking: Technology and applications
Distance based transmission power control scheme for indoor wireless sensor network
Transactions on computational science XI
Cluster size optimization in sensor networks with decentralized cluster-based protocols
Computer Communications
Fast algorithms and performance bounds for sum rate maximization in wireless networks
IEEE/ACM Transactions on Networking (TON)
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We consider wireless sensor networks with multiple gateways and multiple classes of traffic carrying data generated by different sensory inputs. The objective is to devise joint routing, power control and transmission scheduling policies in order to gather data in the most efficient manner while respecting the needs of different sensing tasks (fairness). We formulate the problem as maximizing the utility of transmissions subject to explicit fairness constraints and propose an efficient decomposition algorithm drawing upon large-scale decomposition ideas in mathematical programming. We show that our algorithm terminates in a finite number of iterations and produces a policy that is asymptotically optimal at low transmission power levels. Furthermore, we establish that the utility maximization problem we consider can, in principle, be solved in polynomial time. Numerical results show that our policy is near-optimal, even at high power levels, and far superior to the best known heuristics at low power levels. We also demonstrate how to adapt our algorithm to accommodate energy constraints and node failures. The approach we introduce can efficiently determine near-optimal transmission policies for dramatically larger problem instances than an alternative enumeration approach.