Joint Scheduling and Sensing Allocation in Energy Harvesting Sensor Networks With Fusion Centers
Energy harvesting wireless networks have become a reality in recent years. Designing policies that store and utilize the harvested energy, for achieving the desired network performance, remains one of the key challenges in such networks. To this end, based on the quality of monitoring , we formulate...
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Published in | IEEE journal on selected areas in communications Vol. 34; no. 12; pp. 3577 - 3589 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 0733-8716 1558-0008 |
DOI | 10.1109/JSAC.2016.2611962 |
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Abstract | Energy harvesting wireless networks have become a reality in recent years. Designing policies that store and utilize the harvested energy, for achieving the desired network performance, remains one of the key challenges in such networks. To this end, based on the quality of monitoring , we formulate a long-term time-averaged joint scheduling and sensing allocation problem in wireless sensor networks with finite energy and data buffers, subject to certain data and battery quality-of-service constraints. Relaxing the finite energy buffer assumption, using virtual queues and techniques from Lyapunov optimization, we obtain the JSSA algorithm. We show that by appropriately choosing the control parameter of the JSSA algorithm, we can achieve a performance gap that decays inversely proportional to the battery capacity. The implementation overhead of the JSSA algorithm scales as <inline-formula> <tex-math notation="LaTeX">O(n) </tex-math></inline-formula>. Therefore, we also present a low-complexity distributed version of this algorithm whose implementation overhead scales as <inline-formula> <tex-math notation="LaTeX">O(\log n) </tex-math></inline-formula>. |
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AbstractList | Energy harvesting wireless networks have become a reality in recent years. Designing policies that store and utilize the harvested energy, for achieving the desired network performance, remains one of the key challenges in such networks. To this end, based on the quality of monitoring , we formulate a long-term time-averaged joint scheduling and sensing allocation problem in wireless sensor networks with finite energy and data buffers, subject to certain data and battery quality-of-service constraints. Relaxing the finite energy buffer assumption, using virtual queues and techniques from Lyapunov optimization, we obtain the JSSA algorithm. We show that by appropriately choosing the control parameter of the JSSA algorithm, we can achieve a performance gap that decays inversely proportional to the battery capacity. The implementation overhead of the JSSA algorithm scales as <inline-formula> <tex-math notation="LaTeX">O(n) </tex-math></inline-formula>. Therefore, we also present a low-complexity distributed version of this algorithm whose implementation overhead scales as <inline-formula> <tex-math notation="LaTeX">O(\log n) </tex-math></inline-formula>. Energy harvesting wireless networks have become a reality in recent years. Designing policies that store and utilize the harvested energy, for achieving the desired network performance, remains one of the key challenges in such networks. To this end, based on the quality of monitoring , we formulate a long-term time-averaged joint scheduling and sensing allocation problem in wireless sensor networks with finite energy and data buffers, subject to certain data and battery quality-of-service constraints. Relaxing the finite energy buffer assumption, using virtual queues and techniques from Lyapunov optimization, we obtain the JSSA algorithm. We show that by appropriately choosing the control parameter of the JSSA algorithm, we can achieve a performance gap that decays inversely proportional to the battery capacity. The implementation overhead of the JSSA algorithm scales as [Formula Omitted]. Therefore, we also present a low-complexity distributed version of this algorithm whose implementation overhead scales as [Formula Omitted]. |
Author | Sunny, Albert |
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SubjectTerms | Algorithms Batteries Buffers distributed scheduling Energy efficiency Energy harvesting Lyapunov optimization Optimization Quality of service Resource management Scheduling Sensors Sustainable development Wireless networks Wireless sensor network Wireless sensor networks |
Title | Joint Scheduling and Sensing Allocation in Energy Harvesting Sensor Networks With Fusion Centers |
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