Energy-efficient Low-delay TDMA Scheduling Algorithm for Industrial Wireless Mesh Networks

  • Zuo, Yun (School of Information Science and Engineering, East China University of Science and Technology) ;
  • Ling, Zhihao (School of Information Science and Engineering, East China University of Science and Technology) ;
  • Liu, Luming (School of Information Science and Engineering, East China University of Science and Technology)
  • Received : 2012.02.18
  • Accepted : 2012.09.28
  • Published : 2012.10.31

Abstract

Time division multiple access (TDMA) is a widely used media access control (MAC) technique that can provide collision-free and reliable communications, save energy and bound the delay of packets. In TDMA, energy saving is usually achieved by switching the nodes' radio off when such nodes are not engaged. However, the frequent switching of the radio's state not only wastes energy, but also increases end-to-end delay. To achieve high energy efficiency and low delay, as well as to further minimize the number of time slots, a multi-objective TDMA scheduling problem for industrial wireless mesh networks is presented. A hybrid algorithm that combines genetic algorithm (GA) and simulated annealing (SA) algorithm is then proposed to solve the TDMA scheduling problem effectively. A number of critical techniques are also adopted to reduce energy consumption and to shorten end-to-end delay further. Simulation results with different kinds of networks demonstrate that the proposed algorithm outperforms traditional scheduling algorithms in terms of addressing the problems of energy consumption and end-to-end delay, thus satisfying the demands of industrial wireless mesh networks.

Keywords

References

  1. HART Communication Foundation, "TDMA Data Link Layer," http://WirelessHART.hartcomm.org/, Apr 2007.
  2. K. Veeramachaneni and L. A. Osadciw, "Optimal Scheduling in Sensor Networks Using Swarm Intelligence," in Proc. of 38th Annual Conference on Information Sciences and Systems, pp. 17-19, March 15-19, 2004.
  3. S. Gandham, M. Dawande and R. Prakash, "Link Scheduling in Wireless Sensor Networks: Distributed Edge-Coloring Revisited," Journal of Parallel and Distributed Computing, vol. 68, no. 8, pp. 1122-1134, Aug 2008. https://doi.org/10.1016/j.jpdc.2007.12.006
  4. S. C. Ergen and P. Varaiya, "TDMA Scheduling Algorithms for Wireless Sensor Networks," Wireless Networks, vol. 16, no. 4, pp. 985-997, May 2010. https://doi.org/10.1007/s11276-009-0183-0
  5. G. Y. Pei and C. Chien, "Low Power TDMA in Large Wireless Sensor Networks," in Proc. of IEEE Military Communication Conference, vol. 1, pp. 347-351, Oct 2001.
  6. G. Jolly and M. Younis, "An Energy-Efficient, Scalable and Collision-Free MAC Layer Protocol for Wireless Sensor Networks," Wireless Communications and Mobile Computing, vol. 5, no. 3, pp. 285-304, May 2005. https://doi.org/10.1002/wcm.222
  7. L. Q. Shi and A. O. Fapojuwo, "TDMA Scheduling with Optimized Energy Efficiency and Minimum Delay in Clustered Wireless Sensor Networks," IEEE Transactions on Mobile Computing, vol. 9, no. 7, pp. 927-940, Jul 2010. https://doi.org/10.1109/TMC.2010.42
  8. S. S. Kulkarni and M. Arumugam, "SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for Sensor Networks," In Sensor Network Operations, S. Phoha, T. F. La Porta and C. Griffin (Eds.), Wiley- IEEE Press, pp. 186-215, Apr 2006.
  9. J. C. Ma, W. Lou, Y. W. Wu, X. Y. Li and G. H. Chen, "Energy Efficient TDMA Sleep Scheduling in Wireless Sensor Networks," in Proc. of 28th IEEE International Conference on Computer Communications, pp. 630-638, Apr 19-25, 2009.
  10. J. L. Mao, Z. M. Wu and X. Wu, "A TDMA Scheduling Scheme for Many-to-One Communications in Wireless Sensor Networks," Computer Communications, vol. 30, no. 4, pp. 863-872, Feb 2007. https://doi.org/10.1016/j.comcom.2006.10.006
  11. S. M. Islam, S. Ghosh, S. Das, A. Abraham and S. Roy, "A Modified Discrete Differential Evolution based TDMA Scheduling Scheme for Many to One Communications in Wireless Sensor Networks," in Proc. of 2011 IEEE Congress on Evolutionary Computation, pp. 1950-1957, Jun 2011.
  12. A. Sridharan and B. Krishnamachari, "Max-Min Fair Collision-Free Scheduling for Wireless Sensor Networks," in Proc. of 2004 IEEE International Conference on Performance, Computing and Communications, pp. 585-590, Apr 2004.
  13. S. G. Cui, R. Madan, A. Goldsmith and S. Lall, "Energy-Delay Tradeoffs for Data Collection in TDMA-based Sensor Networks," in Proc. of 2005 IEEE International Conference on Communications, vol. 5, pp. 3278-3284, May 2005.
  14. P. Djukic and S. Valaee, "Delay Aware Link Scheduling for Multi-Hop TDMA Wireless Networks," IEEE/ACM Transactions on Networking, vol. 17, no. 3, pp. 870-883, Jun 2009. https://doi.org/10.1109/TNET.2008.2005219
  15. N. A. Pantazis, D. J. Vergados, D. D. Vergados and C. Douligeris, "Energy Efficiency in Wireless Sensor Networks Using Sleep Mode TDMA Scheduling," Ad Hoc Networks, vol. 7 no. 2, pp. 322-343, Mar 2009. https://doi.org/10.1016/j.adhoc.2008.03.006
  16. HART Communication Foundation, "Network Management Specification," http://WirelessHART.hartcomm.org/, Apr 2007.
  17. E. Shih, S. H. Cho and N. Ickes et al., "Physical Layer Driven Protocol and Algorithm Design for Energy-Efficient Wireless Sensor Networks," in Proc. of the 7th Annual International Conference on Mobile Computing and Networking, pp. 272-287, Jul 2001.
  18. R. Jurdak, A. G. Ruzzelli and G. M. P. O'Hare, "Radio Sleep Mode Optimization in Wireless Sensor Networks, " IEEE Transactions on Mobile Computing, vol. 9, no. 7, pp. 955-968, Jul 2010. https://doi.org/10.1109/TMC.2010.35
  19. A. N. Kim, F. Hekland, S. Petersen and P. Doyle, "When HART Goes Wireless: Understanding and Implementing the WirelessHART Standard," in Proc. of IEEE International Conference on Emerging Technologies and Factory Automation, pp. 899-907, Sep 2008.
  20. J. Kim, S. Kim and J. Lee, "Adaptive Duty Cycling MAC Protocols Using Closed-Loop Control for Wireless Sensor Networks," KSII Transactions on Internet and Information Systems, vol. 5, no. 1, pp. 105-122, Jan 2011.
  21. L. Wang and D. Z. Zheng, "An Effective Hybrid Optimization Strategy for Job-Shop Scheduling Problems," Computers and Operations Research, vol. 28, no. 6, pp. 585-596, May 2001. https://doi.org/10.1016/S0305-0548(99)00137-9
  22. X. Z. Cao and Z. H. Yang, "An Improved Genetic Algorithm for Dual-Resource Constrained Flexible Job Shop Scheduling," in Proc. of 2011 International Conference on Intelligent Computation Technology and Automation, pp. 42-45, Mar 2011.
  23. G. Ritchie, "Static Multi-processor Scheduling with Ant Colony Optimization and Local Search," Master of Science thesis, University of Edinburgh, 2003.
  24. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, Ann Arbor, 1975.
  25. S. Kirkpatrick, C. D. Gelatt Jr. and M. P. Vecchi, "Optimization by Simulated Annealing," Science, vol. 220, no. 4958, pp. 671-680, May 1983. https://doi.org/10.1126/science.220.4598.671
  26. Texas Instruments CC2420 Radio Transceiver, http://focus.ti.com/docs/prod/folders/print/cc2420.html, 2007.
  27. X. Yang and N. H. Vaidya, "A Wakeup Scheme for Sensor Networks: Achieving Balance between Energy Saving and End-to-End Delay," in Proc. of 10th IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 19-26, May 2004.
  28. S. G. Ponnambalam and M. Mohan Reddy, "A GA-SA Multiobjective Hybrid Search Algorithm for Integrating Lot Sizing and Sequencing in Flow-Line Scheduling," The International Journal of Advanced Manufacturing Technology, vol. 21, no. 2, pp. 126-137, Jan 2003. https://doi.org/10.1007/s001700300015
  29. K. P. Dahal and N. Chakpitak, "Generator Maintenance Scheduling in Power Systems Using Metaheuristic-based Hybrid Approaches," Electric Power Systems Research, vol. 77, no. 7, pp. 771-779, May 2007. https://doi.org/10.1016/j.epsr.2006.06.012
  30. A. Norozi, M. K. A. Ariffin, N. Ismail and F. Mustapha, "An Optimization Technique Using Hybrid GA-SA Algorithm for Multi-Objective Scheduling Problem," Scientific Research and Essays, Vol. 6, no. 8, pp. 1720-1731, Apr 2011.