Home   >   CSC-OpenAccess Library   >    Manuscript Information
Routing in Wireless Sensor Networks: Improved Energy Efficiency and Coverage using Unmanned Vehicles
Long Kim Le, Ahmed M. Mahdy
Pages - 27 - 41     |    Revised - 31-07-2016     |    Published - 31-08-2016
Volume - 8   Issue - 2    |    Publication Date - August 2016  Table of Contents
MORE INFORMATION
KEYWORDS
Unmanned Systems, Wireless Sensor Networks, Mobile Sinks, Scheduling, Routing, Coverage.
ABSTRACT
This paper proposes a new method for collecting distributed data in Wireless Sensor Networks (WSNs) that can improve the energy efficiency and network coverage; especially in remote areas. In multi-hop communication, sink nodes are responsible for collecting and forwarding data to base stations. The nodes that are located near a sink node usually deplete their battery faster than other nodes because they are responsible for aggregating the data from other sensor nodes. Several studies have proved the advantages of using mobile sink nodes to reduce energy consumption. Nonetheless, the need for compatible and efficient routing algorithms cannot be understated. Accordingly, a hybrid routing algorithm based on the Dijkstra’s and Rendezvous algorithms is proposed. To improve the energy efficiency and coverage, Energy Efficient Hybrid Unmanned Vehicle Based Routing Algorithm (E2HUV) is proposed to create a routing path for Unmanned Aerial Vehicles (UAVs) that can be used as mobile sinks in WSNs. Performance results show that the E2HUV algorithm offers better efficiency as compared to currently existing algorithms.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
Alomari, Abdullah, et al. A scheme for using closest rendezvous points and Mobile Elements for data gathering in wireless sensor networks. Wireless Days (WD) IFIP. IEEE 2014.
Cobano, Jose A.; Martinez-De Dios, J. R.; Conde, R.; Sánchez-Matamoros, J.M.; Aníbal, Ollero. Data retrieving from heterogeneous wireless sensor network nodes using UAVs. Journal of Intelligent And Robotic Systems 2010, 133-151.
Gou, Haosong, and Younghwan Yoo. An energy balancing LEACH algorithm for wireless sensor networks. Information Technology: New Generations (ITNG) 2010; Seventh International Conference on. IEEE 2010.
Ho, Dac-Tu, Esten Ingar Grotli, and Tor Arne Johansen. Heuristic algorithm and cooperative relay for energy efficient data collection with a UAV and WSN. Computing, Management and Telecommunications (ComManTel) 2013; International Conference on. IEEE 2013.
http://store.storeimages.cdn-apple.com/4839/as-images.apple.com/is/image
http://www.tkt.cs.tut.fi/kurssit/2456/wsnmotivation2.png
Konstantopoulos, Charalampos, et al. A rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks. Parallel and Distributed Systems; IEEE Transactions on 23.5 2012, 809-817.
Lawrence, Elaine, et al. Data collection, correlation and dissemination of medical sensor information in a WSN. Networking and Services 2009; ICNS 2009; Fifth International Conference on IEEE 2009.
Leng, Jinfu. Using a uav to effectively prolong wireless sensor network lifetime with wireless power transfer. Diss. University of Nebraska 2014.
Li, Jian, and Prasant Mohapatra. An analytical model for the energy hole problem in many-to-one sensor networks. IEEE Vehicular Technology Conference. Vol. 62. No. 4. IEEE 2005.
Luo, Jun, and Jean-Pierre Hubaux. Joint mobility and routing for lifetime elongation in wireless sensor networks. INFOCOM 2005; 24th annual joint conference of the IEEE computer and communications societies; Proceedings IEEE. Vol. 3. IEEE 2005.
Matin, Mohammad. Wireless sensor networks-technology and applications. Charalambos Sergiou, Vasos Vassiliou, Publisher: InTech 2012. ISBN-13 1304639176
Salarian, Hamidreza, Kwan-Wu Chin, and Fazel Naghdy. An energy-efficient mobile-sink path selection strategy for wireless sensor networks. Vehicular Technology; IEEE Transactions 63.5 2014, 2407-2419.
Sanchez, Erwing R., et al. An adaptive power-aware multi-hop routing algorithm for wireless sensor networks. Information Technology: New Generations (ITNG) 2011; Eighth International Conference on. IEEE 2011.
Sathyaraj, B. Moses, et al. Multiple UAVs path planning algorithms: a comparative study. Fuzzy Optimization and Decision Making 7.3 2008, 257-267.
Sotheara, Say, et al. Effective data gathering protocol in WSN-UAV employing priority-based contention window adjustment scheme. Globecom Workshops (GC Wkshps) 2014; IEEE 2014
Wang, Z. Maria, et al. Exploiting sink mobility for maximizing sensor networks lifetime. System Sciences 2005; HICSS 2005; 38th Annual Hawaii International Conference on. IEEE 2005.
Wei, Peng, Quanquan Gu, and Dengfeng Sun. Wireless sensor network data collection by connected cooperative UAVs. American Control Conference (ACC) 2013; IEEE 2013.
Xing, Guoliang, et al. Rendezvous design algorithms for wireless sensor networks with a mobile base station. Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing. ACM 2008.
Zuo, Yun, Zhihao Ling, and Yifeng Yuan. A hybrid multi-path routing algorithm for industrial wireless mesh networks. EURASIP Journal on Wireless Communications and Networking 2013, 1-12.
Mr. Long Kim Le
Texas A&M University-Corpus Christi - United States of America
lle3@islander.tamucc.edu
Dr. Ahmed M. Mahdy
Texas A&M University-Corpus Christi - United States of America