Title
Maximizing Energy Efficiency of Period-Area Coverage with UAVs for Wireless Rechargeable Sensor Networks
Abstract
Wireless Rechargeable Sensor Networks (WRSNs) with perpetual network lifetime have been used in many Internet of Things (IoT) applications, like smart city and precision agriculture. Rechargeable sensors together with Unmanned Aerial Vehicles (UAVs) are collaboratively employed for fulfilling periodic coverage tasks. However, traditional coverage solutions are normally based on static deployment of sensors and not suitable for such coverage requirements. In this paper, we propose a new concept of coverage problem named Period-Area Coverage (PAC) which requires data of the overall area must be collected periodically. We focus on maximizing the energy efficiency of UAVs and propose two heuristic scheduling schemes to balance energy cost. Moreover, we adopt adjustable sensing range to further promote efficiency and develop a charging re-allocation mechanism for UAVs. Test-bed experiments and extensive simulations demonstrate that the proposed schemes can enhance energy efficiency by 18.2% compared to prior arts.
Year
DOI
Venue
2019
10.1109/SAHCN.2019.8824918
2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
Keywords
Field
DocType
UAVs,perpetual network lifetime,smart city,precision agriculture,periodic coverage tasks,coverage requirements,coverage problem,energy efficiency,wireless rechargeable sensor networks,Internet of Things applications,rechargeable sensors,unmanned aerial vehicles,coverage solutions,period-area coverage,heuristic scheduling schemes,energy cost
Wireless,Software deployment,Efficient energy use,Computer science,Internet of Things,Precision agriculture,Smart city,Wireless sensor network,Distributed computing,Area coverage
Conference
ISSN
ISBN
Citations 
2155-5486
978-1-7281-1208-4
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chi Lin185.86
Chunyang Guo200.34
Wan Du321817.80
Jing Deng43887221.06
Lei Wang543364.21
Guowei Wu67514.81