Abstract
In wireless sensor networks, time division multiple access (TDMA) -based MAC can eliminate collisions, hence save energy and guarantee a bounded delay. However, the slot scheduling problem in TDMA is an NP problem. To minimized the total slots needed by a set of data collection tasks and saving the energy consumed on switching between the active and sleep states, a novel particle swarm optimization (PSO)-based scheduling algorithm called PSOSA is proposed in TDMA sensor networks. This algorithm can take full advantage of the searching ability of PSO, which is powerful for solving NP problems. Simulation results show that PSOSA requires less slots and energy to finish a set of data collection tasks. Moreover, compare with coloring algorithms, PSOSA have more flexibility to deal with a multi-objective optimization problem.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pei, G., Chien, C.: Low Power TDMA in Large Wireless Sensor Networks. In: Proc. MILCOM 2001, vol. 1, pp. 347–351 (2001)
Li, J., Lazaroul, G.Y.: A Bit-Map-Assisted Energy-Efficient MAC Scheme for Wireless Sensor Networks. In: IPSN 2004, Berkeley, California, USA, April 26-27 (2004)
Jolly, G., Younis, M.: An Energy-Efficient, Scalable and Collision-Free MAC layer Protocol for Wireless Sensor Networks. Wireless Communications and Mobile Computing 5(3), 285–304 (2005)
Cui, S., et al.: Energy-Delay Tradeoffs for Data Collection in TDMA-based Sensor Networks. In: The 40th annual IEEE International Conference on Communications, Seoul, Korea, May 16-20 (2005)
Gandham, S., Dawande, M., Prakash, R.: Link scheduling in sensor networks: distributed edge coloring revisited. In: INFOCOM 2005 (2005)
Perumal, K., Patro, R.K., Mohan, B.: Neighbor Based TDMA slot assignment algorithm for WSN. In: INFOCOM 2005 (2005)
Florens, C., McEliece, R.: Packet distribution algorithms for sensor networks. In: IEEE INFOCOM 2003 (2003)
Wang, J., Choi, H., Hughes, E.A.: Scheduling on Sensor Hybrid Network. In: IEEE ICCCN (2005)
Gandham, S., et al.: Distributed Minimal Time Convergecast Scheduling in Wireless Sensor Networks. In: The 26th International Conference on Distributed Computing Systems (ICDCS 2006), Lisboa, Portuga, July 4-7 (2006)
Ergen, S.C., Varaiya, P.: Pedamacs: Power efficient and delay aware medium access protocol for sensor networks. Master Thesis, Electrical Engineering and Computer Science, Graduate Division, University of California, Berkeley
Ergen, S.C., Varaiya, P.: TDMA Scheduling Algorithms for Sensor Networks, Technical Report, Department of Electrical Engineering and Computer Sciences University of California, Berkeley (July 2005)
Shih, E., et al.: Energy-Efficient Link Layer for Wireless Microsensor Networks. In: Proceedings of the Workshop on VLSI 2001 WVLSI 2001, Orlando, Florida (April 2001)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp. 39–43
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1998), Piscataway, NJ, pp. 69–73 (1998)
Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proc. of the 2001 Congress on Evolutionary Computation, pp. 81–86 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mao, J., Wu, X., Wu, Z., Wang, S. (2006). A Novel Energy-Aware TDMA Scheduling Algorithm for Wireless Sensor Networks. In: Cheng, X., Li, W., Znati, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2006. Lecture Notes in Computer Science, vol 4138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11814856_31
Download citation
DOI: https://doi.org/10.1007/11814856_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37189-2
Online ISBN: 978-3-540-37190-8
eBook Packages: Computer ScienceComputer Science (R0)