KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Reporting Interval Adaptive, Sensor Control Platform for Energy-saving Data Gathering in Wireless Sensor Networks

  • Choi, Wook (Department of Computer Science and Engineering, Hankuk University of Foreign Studies) ;
  • Lee, Yong (Department of Information and Communications Engineering, ChungJu National University) ;
  • Kim, Sang-Chul (Department of Computer Science and Engineering, Hankuk University of Foreign Studies)
  • Received : 2010.12.01
  • Accepted : 2011.02.24
  • Published : 2011.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Due to the application-specific nature of wireless sensor networks, the sensitivity to such a requirement as data reporting interval varies according to the type of application. Such considerations require an application-specific, parameter tuning paradigm allowing us to maximize energy conservation prolonging the operational network lifetime. In this paper, we propose a reporting interval adaptive, sensor control platform for energy-saving data gathering in wireless sensor networks. The ultimate goal is to extend the network lifetime by providing sensors with high adaptability to application-dependent or time-varying, reporting interval requirements. The proposed sensor control platform is based upon a two phase clustering (TPC) scheme which constructs two types of links within each cluster - namely, direct link and relay link. The direct links are used for control and time-critical, sensed data forwarding while the relay links are used only for multi-hop data reporting. Sensors opportunistically use the energy-saving relay link depending on the user reporting, interval constraint. We present factors that should be considered in deciding the total number of relay links and how sensors are scheduled for sensed data forwarding within a cluster for a given reporting interval and link quality. Simulation and implementation studies demonstrate that the proposed sensor control platform can help individual sensors save a significant amount of energy in reporting data, particularly in dense sensor networks. Such saving can be realized by the adaptability of the sensor to the reporting interval requirements.

Keywords

References

  1. A. Ipakchi and F. Albuyeh, "Grid of the Future," IEEE Power and Energy Magazine, vol. 7, no. 2, pp. 52-62, 2009. https://doi.org/10.1109/MPE.2008.931384
  2. E. A. Lee, "Cyber Physical Systems: Design Challenges," in Proc. of Int'l Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), pp. 363-369, 2008.
  3. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, "Wireless Sensor Networks: A Survey," Computer Networks, vol. 38, no. 4, pp.393-422, 2002. https://doi.org/10.1016/S1389-1286(01)00302-4
  4. A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and J. Anderson, "Wireless Sensor Networks for Habitat Monitoring," in Proc. of ACM Workshop on Wireless Sensor Networks and Applications (WSNA), pp. 88-97, 2002.
  5. G. J. Pottie and W. J. Kaiser, "Wireless Integrated Network Sensors," Communications of the ACM, vol. 43, no. 5, pp.51-58, 2000. https://doi.org/10.1145/332833.332838
  6. Y. Xu, J. Winter and W. Lee, "Dual Prediction-based Reporting for Object Tracking Sensor Networks," in Proc. of Int'l Conference on Mobile and Ubiquitous Systems: Networking and Services (MOBIQUITOUS), pp.154-163, 2004.
  7. S. Ghiasi, A. Srivastava, X. Yang and M. Sarrafzadeh, "Optimal Energy Aware Clustering in Sensor Networks," Sensors, vol. 2, no. 7, pp. 258-269, 2002. https://doi.org/10.3390/s20700258
  8. W. B. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, "An Application-specific Protocol Architecture for Wireless Microsensor Networks," IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp.660-670, 2002. https://doi.org/10.1109/TWC.2002.804190
  9. C. Intanagonwiwat, R. Govindan and D. Estrin, "Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks," in Proc. of ACM Mobile Computing and Networking (MOBICOM), pp. 56-67, 2000.
  10. L. Krishnamachari, D. Estrin and S. Wicker, "The Impact of Data Aggregation in Wireless Sensor Networks," in Proc. of IEEE Int'l Conference on Distributed Computing Systems Workshops (ICDCSW), pp. 575-578, 2002.
  11. W. Choi, S. K. Das and K. Basu, "Angle-based Dynamic Path Construction for Route Load Balancing in Wireless Sensor Networks," in Proc. of IEEE Wireless Communications and Networking Conference (WCNC), pp. 2474-2479, 2004.
  12. X. Hong, M. Gerla, H. Wang and L. Clare, "Load Balanced, Energy-Aware Communications for Mars Sensor Networks," IEEE Aerospace, vol. 3, pp. 1109-1115, 2002.
  13. "Ubiquitous Sensor Network System using ZigbeX," 3rd Edition. Hanback Electronics Research Center, Hanback. Electronics. Co., 2008.
  14. O. Younis and S. Fahmy, "HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks," IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp.366-379, 2004. https://doi.org/10.1109/TMC.2004.41
  15. R. Virrankoski and A. Savvides, "TACS: Topology Adaptive Spatial Clustering for Sensor Networks," in Proc. of IEEE Int'l Conference on Mobile Ad hoc and Sensor Systems (MASS), pp. 1-10, 2005.
  16. M. Lotfinezhad, B. Liang and E. S. Sousa, "Adaptive Cluster-Based Data Collection in Sensor Networks with Direct Sink Access," IEEE Transactions on Mobile Computing, vol. 7, no. 7, pp.884-897, 2007.
  17. J. Baek, S. K. An, P. S. Fisher and E. J. Jones, "Dynamic Cluster Header Selection and with Self-incentive for Wireless Sensor Networks," in Proc. of IEEE Sarnoff Symposium, pp. 1-5, 2009.
  18. J. Baek, S. K. An and P. S. Fisher, "Dynamic Cluster Header Selection and Conditional Re-clustering for Wireless Sensor Networks," IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp.2249-2257, 2010. https://doi.org/10.1109/TCE.2010.5681097
  19. Y.Wu, X. Li, Y. Liu and W. Lou, "Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation," IEEE Transactions on Parallel and Distributed Systems, vol. 21, no. 2, pp.275-287, 2010. https://doi.org/10.1109/TPDS.2009.45
  20. H. J. Choe, P. Ghosh and S. K. Das, "Cross-Layer Design for Adaptive Data Reporting in Wireless Sensor Networks," in Proc. of IEEE Pervasive Computing (PERCOM), 2009.
  21. N. Bulusu, J. Heidemann and D. Estrin, "GPS-less Low Cost Outdoor Localization for Very Small Devices," IEEE Personal Communications, vol. 7, no. 5, pp.28-34, 2000.
  22. A. Nasipuri and K. Li, "A Directionality based Location Discovery Scheme for Wireless Sensor Networks," in Proc. of ACM Workshop on Wireless Sensor Networks and Applications (WSNA), pp. 105-111, 2002.
  23. I. Stojmenovic, "Position-Based Routing in Ad Hoc Networks," IEEE Communications, vol. 40, no. 7, pp.128-134, 2002.
  24. F. Zhao and L. Guibas, "Wireless Sensor Networks: An Information Processing Approach," Morgan Kaufmann, 2004.
  25. S. Banerjee and A. Misra, "Minimum Energy Paths for Reliable Communication in Multi-hop Wireless Networks," in Proc. of ACM Mobile Ad-hoc Network Symposium (MOBIHOC), pp.145-156, Jun 2002.
  26. W. Choi, P. Shah and S. Das, "A Framework for Energy-Saving Data Gathering Using Two-Phase Clustering in Wireless Sensor Networks," in Proc. of Mobile and Ubiquitous Systems: Networking and Services (Mobiquitous), 2004.
  27. M. Z. Zamalloa, K. Seada, B. Krishnamchari and A. Helmy, "Efficient Geographic Routing over Lossy Links in Wireless Sensor Networks," ACM Transactions on Sensor Networks, vol. 4, no. 12, pp.1-32, 2008.
  28. M. K. Molloy, "Fundamentals of Performance Modeling," Macmillan, 1989.
  29. Hanback. Electronics. Co. http://www.hanback.co.kr.