Advertisement

Wireless Networks

, Volume 19, Issue 6, pp 1155–1170 | Cite as

SAS-TDMA: a source aware scheduling algorithm for real-time communication in industrial wireless sensor networks

  • Wei ShenEmail author
  • Tingting Zhang
  • Mikael Gidlund
  • Felix Dobslaw
Article

Abstract

Scheduling algorithms play an important role for TDMA-based wireless sensor networks. Existing TDMA scheduling algorithms address a multitude of objectives. However, their adaptation to the dynamics of a realistic wireless sensor network has not been investigated in a satisfactory manner. This is a key issue considering the challenges within industrial applications for wireless sensor networks, given the time-constraints and harsh environments. In response to those challenges, we present SAS-TDMA, a source-aware scheduling algorithm. It is a cross-layer solution which adapts itself to network dynamics. It realizes a trade-off between scheduling length and its configurational overhead incurred by rapid responses to routes changes. We implemented a TDMA stack instead of the default CSMA stack and introduced a cross-layer for scheduling in TOSSIM, the TinyOS simulator. Numerical results show that SAS-TDMA improves the quality of service for the entire network. It achieves significant improvements for realistic dynamic wireless sensor networks when compared to existing scheduling algorithms with the aim to minimize latency for real-time communication.

Keywords

Real-time communication TDMA scheduling algorithms Wireless sensor networks Cross-layer protocol 

Notes

Acknowledgments

We would like to thank the anonymous reviewers for their valuable advices and suggestions for improvement. We would also like to express our appreciation to Prof. Youzhi Xu for his assistance. This work has been supported by Swedish Knowledge Foundation (KK-stiftelsen) and Sensible Things That Communicate (STC) research program at Mid Sweden University.

References

  1. 1.
    Song, J., Han, S., Mok, A., Chen, D., Lucas, M., & Nixon, M. (2008). WirelessHART: Applying wireless technology in real-time industrial process control. In Proceedings of the IEEE real-time and embedded technology and applications symposium (RTAS) 2008 (pp. 377–386).Google Scholar
  2. 2.
    Industrial communication networks—Wireless communication network and communication profiles—WirelessHARTTM. (2010). International electrotechnical commission (IEC) (p. 62591).Google Scholar
  3. 3.
    Wireless Systems for Industrial Automation: Process Control and Related Applications. (2009). ISA100.11a Standard.Google Scholar
  4. 4.
    Industrial communication networks—Fieldbus specifications—WIA-PA communication network and communication profile. (2011). International electrotechnical commission (IEC) (p. 62061).Google Scholar
  5. 5.
    Willig, A., Matheus, K., & Wolisz, A. (2005). Wireless technology in industrial networks. Proceedings of the IEEE, 93(6), 1130–1151.CrossRefGoogle Scholar
  6. 6.
    Wang, W., Wang, Y., Li, X.-Y., Song, W.-Z., & Frieder, O. (2006). Efficient interference-aware TDMA link scheduling for static wireless networks. in Proceedings of ACM MobiCom (pp. 262–273).Google Scholar
  7. 7.
    Song, W.-Z., Yuan, F., & Lahusen, R. (2006). Time-optimum packet scheduling for many-to-one routing in wireless sensor networks. In Proceedings of the 3rd IEEE international conference on mobile ad-hoc and sensor systems (MASS) (pp. 81–90). Vancouver BC, Canada.Google Scholar
  8. 8.
    Chakraborty, G. (2004). Genetic algorithm to solve optimum TDMA transmission schedule in broadcast packet radio networks. IEEE Transactions on Communications, 52, 765–777.CrossRefGoogle Scholar
  9. 9.
    Gandham, S., Dawande, M., & Prakash, R. (2005). Link scheduling in sensor networks: Distributed edge coloring revisited. in Proceedings of the IEEE INFOCOM, 4, 2492–2501.Google Scholar
  10. 10.
    Zhang, H., Soldati, P., & Johansson, M. (2009). Optimal link scheduling and channel assignment for convergecast in linear WirelessHART networks. In Proceedings of the 7th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOPT) (pp. 1–8).Google Scholar
  11. 11.
    Djukic, P., & Valaee, S. (2009). Delay aware link scheduling for multi-hop TDMA wireless networks. IEEE/ACM Transactions Network, 17(3), 870–883.Google Scholar
  12. 12.
    Ergen, S. C., & Varaiya, P. (2010). TDMA scheduling algorithms for wireless sensor networks. Wireless Networks, 16(4), 985–997.CrossRefGoogle Scholar
  13. 13.
    Shi, L., & Fapojuwo, A. O. (2010). TDMA scheduling with optimized energy efficiency and minimum delay in clustered wireless sensor networks. IEEE Transactions on Mobile Computing, 9(7), 927–940.CrossRefGoogle Scholar
  14. 14.
    Cui, S., Madan, R., Goldsmith, A., & Lall, S. (2005). Energy-delay tradeoffs for data collection in TDMA-based sensor networks. Proceedings of IEEE International Conference on Communications (ICC 2005), 5, 3278–3284, 16–20.Google Scholar
  15. 15.
    Ngo, C. Y., & Li, V. O. K. (2003). Centralized broadcast scheduling in packet radio networks via genetic-fix algorithms. IEEE Transactions on Communication, 51(9), 1439–1441.CrossRefGoogle Scholar
  16. 16.
    Lu, G., & Krishnamachari, B. (2007). Minimum latency joint scheduling and routing in wireless sensor networks. Ad Hoc Network, 5(6), 832–843.CrossRefGoogle Scholar
  17. 17.
    Srinivasan, K., Kazandijeva, M. A., Agarwal, S., & Levis, P. (2008). The -factor: Measuring wireless link burstiness. In Proceeding of 6th ACM conference on embedded networked sensor systems (SenSys) (pp. 29–42).Google Scholar
  18. 18.
    Srinivasan, K., Dutta, P., Tavakoli, A., & Levis, P. (2010). An empirical study of low power wireless. ACM Transaction on Sensor Networks (TOSN), 6(2). http://dl.acm.org/citation.cfm?id=1689239.1689246&coll=DL&dl=ACM&CFID=203248284&CFTOKEN=93256816.
  19. 19.
    Watteyne, T., Mehta, A., & Pister, K. (2009). Reliability through frequency diversity: Why channel hopping makes sense. In Proceedings of the 6th ACM symposium on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks (PE-WASUN) (pp. 116–123).Google Scholar
  20. 20.
    IEEE Standard for Information Technology Telecommunications and information exchange between systemsLocal and metropolitan area networks Specific requirements Part 15.4: Wireless medium access control (MAC) and physical layer (PHY) specifications for low rate wireless personal area networks (WPANs), September 2006.Google Scholar
  21. 21.
    Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., & Levis, P. (2009). Collection tree protocol. In Proceedings of the 7th ACM conference on embedded networked sensor systems (SenSys) (pp. 1–14).Google Scholar
  22. 22.
    Levis, P., Patel, N., Culler, D., & Shenker, S. (2004). Trickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks. In Proceedings of the 1st USENIX/ACM symposium on networked systems design and implementation (NSDI) (pp. 2–2).Google Scholar
  23. 23.
    Ramanathan, S., & Lloyd, E. L. (1993). Scheduling algorithms for multihop radio networks. IEEE/ACM Transaction on Networks, 1(2), 166–177.CrossRefGoogle Scholar
  24. 24.
    Watteyne, T., Lanzisera, S., Mehta, A., & Pister, K. (2010). Mitigating multipath fading through channel hopping in wireless sensor networks. In Proceedings of the 2010 IEEE international conference on communication (ICC) (pp. 23–27).Google Scholar
  25. 25.
    Nikoletseas, S., & Rolim, J. D. P. (2011). Theoretical aspects of distributed computing in sensor networks, part 4 (pp. 407–445). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  26. 26.
    Levis, P., Lee, N., Welsh, M., & Culler, D. (2003). TOSSIM: Accurate and scalable simulation of entire TinyOS applications. In Proceedings of the first ACM conference on embedded networked sensor systems (SenSys) (pp. 126–137).Google Scholar
  27. 27.
    Lee, H. J., Cerpa, A., & Levis, P. (2007). Improving wireless simulation through noise modeling. In Proceedings of the 6th international conference on information processing in wireless sensor networks (IPSN) (pp. 21–30).Google Scholar
  28. 28.
    Zuniga, M., & Krishnamachari, B. (2007). An analysis of unreliability and asymmetry in low-power wireless links. ACM Transaction on Sensor Networks (TOSN), 3(2). http://dl.acm.org/citation.cfm?id=1240226.1240227&coll=DL&dl=ACM&CFID=203248284&CFTOKEN=93256816.

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Wei Shen
    • 1
    Email author
  • Tingting Zhang
    • 1
  • Mikael Gidlund
    • 2
  • Felix Dobslaw
    • 1
  1. 1.Department of Information Technology and Media (ITM)Mid Sweden UniversitySundsvallSweden
  2. 2.ABB Corporate ResearchVasteras Sweden

Personalised recommendations