Energy-Efficient Node Scheduling for Dense Wireless Sensor Networks Using Voronoi Diagram

Abstract:

A wireless sensor network consists of distributed devices which can monitor physical and environmental conditions and have a lot of applications, such as environmental monitoring, habitat monitoring, object tracking, nuclear reactor controlling, fire detection and traffic monitoring. Although it has a lot of potential applications, a wireless sensor network is highly energy constrained. Limited energy resource to the sensor becomes an obstacle for its applications and efficient usage of energy helps in improving the network lifetime. Suppose a network has a high-density of sensor nodes, there will be lots of problems such as the intersection of sensing, redundant data, communication interference and energy waste. So it is necessary to set up a management application to maintain these resources. On the other hand, a high-density network can be fault tolerant and more accurate than low-density sensor networks. In this paper, we proposed a new scheduling for sensor networks which can decide the nodes to be on or off and maintain the coverage area for the original one. This management schema may take a node out of service temporally in order to save energy. Our design uses a Voronoi Diagram, which decomposes the sensing area into regions around each node.

• Periodical: Applied Mechanics and Materials (Volumes 236-237)
• Pages: 1054-1066
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.236-237.1054
• Online since: November 2012
• Authors: Hai Feng Lin, Anna Jiang, Di Bai, Yun Fei Liu
• Keywords: Energy Efficient, Node Scheduling, Voronoi Diagram, Wireless Sensor Network (WSN)

References

[1] IEEE standard for wireless lan medium access control(mac) and physical layer(phy) specification (1997).

DOI: 10.1109/ieeestd.1997.85951

[2] Jan Vitek Bogdan Carbunar, Ananth Grama. Distributed and dynamic voronoi overlays for coverage detection and distributed hash tables in ad hoc networks. Proceeding of the Tenth International Conference on Parallel and Distribued Systems (2004).

DOI: 10.1109/icpads.2004.1316137

[3] Nicolas D. Georganas Di Tian. Acoverage-preserving node scheduling scheme for large wireless sensor networks. First ACM international Workshop on Wireless Sensor Networks and Applications pp.32-41, (2002).

DOI: 10.1145/570738.570744

[4] J.Y. Halpern L. Li. Minimum energy mobile wireless networks revisited. IEEE International Conference on Communication (2001).

[5] Linnyer B. Ruiz Antonio A.F. Loureiro Antonio O. Fernandes Jose Marcos S. Nogueira Marcos Augusto M. Vieira, Luiz Filipe M. Vieira. Scheduling nodes in wireless sensor networks: A voronoi approach. Proceedings of the 28th Annual IEEE International Conference on Local Network (2003).

DOI: 10.1109/lcn.2003.1243168

[6] Fronco P. Praparata and Michael Ian Shamos. Computational geometry (1985).

[7] A. Chandrakasan W. Heinzelman and H. Balakrishnan. energy efficient communication protocols for wireless microsensor networks. 33rd international conference on system sciences (2000).

DOI: 10.1109/hicss.2000.926982

[8] Dan Wang Yuanzhu P. Chen and Jian Zhang. Variable-base tacit communication: A new energy efficient communication scheme for sensor networks. First International Conference on Integrated Internet Ad hoc and Sensor Networks(2006).

DOI: 10.1145/1142680.1142715