Priority-Based Greedy Scheduling for Confident Information Coverage in Energy Harvesting Wireless Sensor Networks

An important issue in Wireless Sensor Networks (WSNs) is to maximize the network lifetime while guaranteeing the desired coverage requirement. Recent studies have demonstrated that using rechargeable sensor nodes with energy harvesting capability has a great potential to extend the network lifetime....

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Vydané v:Proceeding of 11th International Conference on Mobile Ad-hoc and Sensor Networks 16-18 December 2015, Shenzhen, China s. 18 - 22
Hlavní autori: Zehui Xiong, Bang Wang, Zhongsi Wang
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.12.2015
Predmet:
Collaboration
Confident information coverage
Correlation
Energy harvesting
Monitoring
Peer-to-peer computing
rechargeable sensor networks
Schedules
scheduling problem
Wireless sensor networks
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Shrnutí:An important issue in Wireless Sensor Networks (WSNs) is to maximize the network lifetime while guaranteeing the desired coverage requirement. Recent studies have demonstrated that using rechargeable sensor nodes with energy harvesting capability has a great potential to extend the network lifetime. However, due to the high cost of rechargeable nodes, equipping every node with an energy harvesting unit is not practical in a large scale WSN. In this paper, we study the problem of maximizing the network lifetime of a hybrid WSN consisting of both common nodes and rechargeable nodes. Furthermore, we consider a new confident information coverage model which is more efficient for environment monitoring applications. We propose a novel priority-based greedy scheduling (PGS) algorithm to schedule the sensor nodes into a series of set covers that are activated sequentially, each satisfying the required coverage. Also, it can effectively exploit the advantages of using collaboration among sensors for the coverage requirement. Our simulations validate that the PGS algorithm can provide substantial performance improvement as compared to other peer algorithms.
DOI:10.1109/MSN.2015.20