VARSA: An Efficient VAriable Radius Sensor Activation Scheme for Target Tracking using Wireless Sensor Networks
Pages 69 - 75
Abstract
Energy efficiency advancement is vital in target tracking Wireless Sensor Networks (WSNs) due to resource constraints of tiny detectors. On the other side, Tracking quality must also be assured while minimizing energy consumption. The main aim of target tracking algorithms is to depict the trajectory of the target at a sink node using the aggregated data from all sensor nodes. Monitoring an Area of Interest (AoI) while other sensors are in sleep mode has been shown to effectively improve energy efficiency in WSNs. In this paper, we propose a VAriable Radius Sensor Activation (VARSA) algorithm to decrease the sensing energy consumption of tracking applications using WSNs. VARSA uses a dynamic sensing radius adjustment and sends the appropriate sensing radius to the next predicted sensor to wake up with. In addition to sending sensors into sleep mode when they are not in the AoI, we propose to decrease the sensing radius of the sensors in the AoI in real time to further decrease the consumed energy of sensing. Simulation results demonstrate that the proposed algorithm significantly decreases the sensing energy consumption, while providing better tracking quality over time.
References
[1]
N. Aschenbruck, R. Ernst, E. Gerhards-Padilla, and M. Schwamborn. Bonnmotion: a mobility scenario generation and analysis tool. In Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, page 51. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2010.
[2]
N. Bartolini, T. Calamoneri, T. La Porta, C. Petrioli, and S. Silvestri. Sensor activation and radius adaptation (sara) in heterogeneous sensor networks. ACM Transactions on Sensor Networks (TOSN), 8(3):24, 2012.
[3]
N. Chapman. Deer, the animal answer guide. Zoological Journal of the Linnean Society, 166(2):464--464, 2012.
[4]
H.-C. Chu and R.-H. Jan. A gps-less self-positioning method for sensor networks. In Parallel and Distributed Systems, 2005. Proceedings. 11th International Conference on, volume 2, pages 629--633. IEEE, 2005.
[5]
F. Deldar and M. H. Yaghmaee. Designing a prediction-based clustering algorithm for target tracking in wireless sensor networks. In Computer Networks and Distributed Systems (CNDS), 2011 International Symposium on, pages 199--203. IEEE, 2011.
[6]
L. Gu and J. A. Stankovic. Radio-triggered wake-up capability for sensor networks. In IEEE Real-Time and Embedded Technology and Applications Symposium, pages 27--37, 2004.
[7]
H. Jamali Rad, M. Azarafrooz, H. Shahriar Shahhoseini, and B. Abolhassani. A new adaptive power optimization scheme for target tracking wireless sensor networks. In Industrial Electronics & Applications, 2009. ISIEA 2009., volume 1, pages 307--312.
[8]
C. Kompis and S. Aliwell. Energy harvesting technologies to enable remote and wireless sensing. Sensors and Instrumentation-Knowledge Transfer Network, 2008.
[9]
S.-M. Lee, H. Cha, and R. Ha. Energy-aware location error handling for object tracking applications in wireless sensor networks. Computer Communications, 30(7):1443--1450, 2007.
[10]
N. Shrivastava, R. M. U. Madhow, and S. Suri. Target tracking with binary proximity sensors: fundamental limits, minimal descriptions, and algorithms. In Proceedings of the 4th international conference on Embedded networked sensor systems, pages 251--264. ACM, 2006.
[11]
G. Wang, M. Bhuiyan, Z. Alam, and L. Zhang. Two-level cooperative and energy-efficient tracking algorithm in wireless sensor networks. Concurrency and Computation: Practice and Experience, 22(4):518--537, 2010.
[12]
Z. Wang, E. Bulut, and B. K. Szymanski. Distributed energy-efficient target tracking with binary sensor networks. ACM Transactions on Sensor Networks (TOSN), 6(4):32, 2010.
[13]
G. Xing, C. Lu, R. Pless, and Q. Huang. On greedy geographic routing algorithms in sensing-covered networks. In Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing, pages 31--42. ACM, 2004.
[14]
S. Zhang, G. Li, L. Xiao, L. Wang, and X.-n. Zhou. Distributed targets tracking with dynamic power optimization for wireless sensor networks. In Informatics and Management Science IV, pages 221--229. Springer, 2013.
[15]
J. Zheng, M. Z. A. Bhuiyan, S. Liang, X. Xing, and G. Wang. Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks. Future Generation Computer Systems, 2013.
[16]
Z. Zhou, S. R. Das, and H. Gupta. Variable radii connected sensor cover in sensor networks. ACM Transactions on Sensor Networks (TOSN), 5(1):8, 2009.
Index Terms
- VARSA: An Efficient VAriable Radius Sensor Activation Scheme for Target Tracking using Wireless Sensor Networks
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Published In
November 2015
114 pages
ISBN:9781450337588
DOI:10.1145/2810362
- General Chair:
- Mirela Sechi M.A. Notare,
- Program Chairs:
- Ángel Cuevas Rumín,
- Miguel Lopez-Guerrero
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Published: 02 November 2015
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November 2 - 6, 2015
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Overall Acceptance Rate 83 of 272 submissions, 31%
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