Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Dynamic Power Management in Wireless Sensor Networks
IEEE Design & Test
An environmental energy harvesting framework for sensor networks
Proceedings of the 2003 international symposium on Low power electronics and design
Timing-sync protocol for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Characterizing the capacity region in multi-radio multi-channel wireless mesh networks
Proceedings of the 11th annual international conference on Mobile computing and networking
Resource allocation and cross-layer control in wireless networks
Foundations and Trends® in Networking
IEEE Transactions on Mobile Computing
Energy-aware routing in sensor networks: A large system approach
Ad Hoc Networks
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
IEEE Wireless Communications
Cross-layer optimization of wireless networks using nonlinear column generation
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
Emerging techniques for long lived wireless sensor networks
IEEE Communications Magazine
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
Optimal energy management policies for energy harvesting sensor nodes
IEEE Transactions on Wireless Communications
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We study wireless multihop energy harvesting sensor net works employed for random field estimation. The sensors sense the random field and generate data that is to be sent to a fusion node for estimation. Each sensor has an energy harvesting source and can operate in two modes: Wake and Sleep. We consider the problem of obtaining jointly optimal power control, routing and scheduling policies that ensure a fair utilization of network resources. This problem has a high computational complexity. Therefore, we develop a computationally efficient suboptimal approach to obtain good solutions to this problem. We study the optimal solution and performance of the suboptimal approach through some numerical examples.