Energy Efficient and Collision Reduction Routing Method for Wireless Sensor Networks using Cognitive Radio
Article No.: 5, Pages 1 - 3
Abstract
Working in ISM band becomes overcrowded, shared unlicensed spectrum band, leads to a reduction in the quality of communication. This makes increase in packet loss caused by collisions and results in the necessity of packets retransmissions. In wireless sensor networks a large amount of energy of sensor nodes will be wasted during retransmissions. Cognitive radio is the technology makes it possible for sensor nodes to make use of licensed bands. In this paper a routing technique for cognitive radio wireless sensor networks is presented, that is based on a cross-layer design that jointly considers route and spectrum selection. This method has two main phases: next hop selection and channel selection. The routing is done hop-by-hop with local information and decisions, which are more compatible with sensor networks. Primary user action and prevention from interfering with them is considered in all spectrum decisions. It uniformly distributes frequency channels between neighboring nodes, which lead to a local reduction in collision probability. This clearly affects energy consumption in all sensor nodes. The route selection is energy-aware and a learning based technique is used to reduce the packet delay with respect to hop-count. The imitation reveals that by applying cognitive radio technology to WSNs and selecting a proper channel, we can consciously decrease collision probability. This saves energy of sensor nodes and improves the network lifetime.
References
[1]
RojinTizvar, Maghsoud Abbaspour, Mahdi Dehghani, "A collision- and energy-aware routing method for cognitive radio wireless sensor networks" Wireless Networks (2014) 20:2037--2052
[2]
Behzad Razavi, Fellow, IEEE Cognitive Radio Design Challenges and Techniques IEEE Journal of solid state circuits, vol. 45, no. 8, August 2010
[3]
F.C. Commission. (November 2002). Spectrum policy task force, Technical report.
[4]
Mitola, I. J., & Maguire, J. G. Q. (1999). Cognitive radio: Makingsoftware radios more personal. IEEE Personal Communications Magazine, 6(4), 13--18.
[5]
C. K. Siew and D. J. Goodman, "Packet data transmission over mobileradio channels," IEEE Trans. Veh. Technol., vol. 38, no. 2, 1989.
[6]
A. He et al., "Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications," ACM Mobile Computing and Communication. Rev., vol. 13, no. 2, pp. 37--48, 2009
[7]
T. R. Newman et al., "Cognitive engine implementation for wireless multicarrier transceivers," Wiley J. Wireless Communication. and Mobile Computing, vol. 7, no. 9, pp. 1129--1142, 2007
[8]
C. Clancy, J. Hecker, E. Stuntebeck, and T. O'Shea, "Applications of machine learning to cognitive radio networks," IEEE Wireless Communication, vol. 14, no. 4, pp. 47--52, Aug. 2007
[9]
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393--422.
[10]
Yau, K. L., Komisarczuk, P., & Teal, P. (2009). Cognitive radio based wireless sensor networks: Conceptual design and open issues. In IEEE 34th conference on local computer networks, (LCN 2009) (pp. 955--962).
[11]
Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine, 46(4), 40--48.
[12]
Akyildiz, I. F., et al. (2006). NeXt generation / dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks: The International Journal of Computer and Telecommunications Networking, 50(13), 2127--2159.
[13]
Fortuna, C., & Mohorcic, M. (2009). Trends in the development of communication networks: Cognitive networks. Computer Networks: The International Journal of Computer and Telecommunications Networking, 53(9), 1354--1376.
[14]
Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network, 23(4), 34--40.
- Energy Efficient and Collision Reduction Routing Method for Wireless Sensor Networks using Cognitive Radio
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Published In
July 2016
262 pages
ISBN:9781450341790
DOI:10.1145/2967878
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- University of North Texas: University of North Texas
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Published: 06 July 2016
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ICCCNT '16
ICCCNT '16: 7th International Conference on Computing Communication and Networking Technologies
July 6 - 8, 2016
TX, Dallas, USA
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ICCCNT '16 Paper Acceptance Rate 48 of 101 submissions, 48%;
Overall Acceptance Rate 48 of 101 submissions, 48%
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