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 inIEEE journal on selected areas in communications Vol. 34; no. 12; pp. 3577 - 3589
Main Author Sunny, Albert
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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ISSN0733-8716
1558-0008
DOI10.1109/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>.
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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