2016 IEEE International Conference on Communications (ICC) 2016
DOI: 10.1109/icc.2016.7510602
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Lifetime maximization for sensor networks with wireless energy transfer

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Cited by 17 publications
(18 citation statements)
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References 26 publications
(43 reference statements)
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“…off-line optimization as investigated for a wide-range of scenarios, including rate-based metrics [6][7][8][9] and source coding/estimation [14][15][16], [20], [21]. This type of offline optimization approaches are suitable for energy harvesting scenarios with dedicated power transfer, for instance as in [26], [27] where wireless power transfer is scheduled a priori. They also provide benchmarks for performance limits of energy harvesting systems and structural guidelines for efficient solutions in the general case.…”
Section: E Problem Statementmentioning
confidence: 99%
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“…off-line optimization as investigated for a wide-range of scenarios, including rate-based metrics [6][7][8][9] and source coding/estimation [14][15][16], [20], [21]. This type of offline optimization approaches are suitable for energy harvesting scenarios with dedicated power transfer, for instance as in [26], [27] where wireless power transfer is scheduled a priori. They also provide benchmarks for performance limits of energy harvesting systems and structural guidelines for efficient solutions in the general case.…”
Section: E Problem Statementmentioning
confidence: 99%
“…Off-line optimization approaches have been investigated for various scenarios, such as point-to-point channels [5], [6], broadcast channels [7], [8] and multiple-access channels [9] under rate based performance criterion as well as for source coding [14], [20], [21] and remote estimation scenarios [16]. From an energy harvesting perspective, these type of approaches are well-suited for scenarios where the energy arrivals can be accurately predicted, such as RF energy harvesting scenarios with dedicated power transfer scheduling as in [26], [27]. Off-line optimization approaches also provide benchmarks to evaluate the fundamental performance limitations for energy harvesting systems and structural guidelines which facilitate possibly sub-optimal but efficient solutions for the general case.…”
Section: Introductionmentioning
confidence: 99%
“…However, the data of the sensor nodes are transmitted directly to the sink due to the fact that data routing is infeasible in the underground model considered in this work. In [16], the authors assumed that the base station forms a sharp energy beam to charge a sensor node in a given timeslot. Then, they studied the problem of scheduling the energy beams to maximize the WSN lifetime, and provided a greedy algorithm to achieve the optimal solution.…”
Section: Related Workmentioning
confidence: 99%
“…They formulated a convex optimization and provided a closed form solution. In [12], the authors assumed that the base station forms a sharp energy beam to charge a sensor node in a timeslot, and studied the scheduling of the energy beams such that the WSN lifetime is maximized. Besides, Xie et.…”
Section: Related Workmentioning
confidence: 99%