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On the Handling of Node Failures: Energy-Efficient Job Allocation Algorithm for Real-time Sensor Networks

  • Karimi, Hamid (School of Electrical and Computer Engineering, College of Engineering, University of Tehran) ;
  • Kargahi, Mehdi (School of Electrical and Computer Engineering, College of Engineering, University of Tehran) ;
  • Yazdani, Nasser (School of Electrical and Computer Engineering, College of Engineering, University of Tehran)
  • 투고 : 2010.06.16
  • 심사 : 2010.08.23
  • 발행 : 2010.09.30

초록

Wireless sensor networks are usually characterized by dense deployment of energy constrained nodes. Due to the usage of a large number of sensor nodes in uncontrolled hostile or harsh environments, node failure is a common event in these systems. Another common reason for node failure is the exhaustion of their energy resources and node inactivation. Such failures can have adverse effects on the quality of the real-time services in Wireless Sensor Networks (WSNs). To avoid such degradations, it is necessary that the failures be recovered in a proper manner to sustain network operation. In this paper we present a dynamic Energy efficient Real-Time Job Allocation (ERTJA) algorithm for handling node failures in a cluster of sensor nodes with the consideration of communication energy and time overheads besides the nodes' characteristics. ERTJA relies on the computation power of cluster members for handling a node failure. It also tries to minimize the energy consumption of the cluster by minimum activation of the sleeping nodes. The resulting system can then guarantee the Quality of Service (QoS) of the cluster application. Further, when the number of sleeping nodes is limited, the proposed algorithm uses the idle times of the active nodes to engage a graceful QoS degradation in the cluster. Simulation results show significant performance improvements of ERTJA in terms of the energy conservation and the probability of meeting deadlines compared with the other studied algorithms.

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참고문헌

  1. W. Alsalih, S. Akl, and H. Hassancin, "Energy-aware task scheduling: towards enabling mobile computting over MANETs," in Proc.19th IEEE International Conference on Parallel and Distributed Processing, pp.51-59, 2005. https://doi.org/10.1109/IPDPS.2005.199
  2. M. Alghamdi, T. Xie, and X. Qin, "PARM: A Power-Aware Message Scheduling Algorithm for Real-Time Wireless Networks," in Proc. 1th ACM Workshop Wireless Multimedia Networking and Performance Modeling, pp.86-92, 2005.
  3. S. Baruah, G. Koren, B. Mishra, A. Raghunathan, L. Rosier, and D. Shasha, "On-line scheduling in the presence of overload," in Proc. 32th Annual Symposium on Foundations of Computer Science, pp.100-110, 1991.
  4. P. Basu, W. Ke, and T. Little, "Dynamic task-based anycasting in mobile ad hoc network," Mobile Network & Application, vol.8, pp.593-612, 2003. https://doi.org/10.1023/A:1025198129990
  5. D. Estrin, L. Girod, G. Pottie, and M. Srivastava, "Instrumenting the world with wireless sensor networks," in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.4, pp.2033-2036, 2001.
  6. S. Giannecchini, M. Caccamo, and C. Shih, "Collaborative Resource Allocation in Wireless Sensor Networks," Urbana, vol.51, pp.35-45, 2004.
  7. H. Jingcao and R. Marculescu, "Energy-aware communication and task scheduling for network-onchip architectures under real-time constraints," in Proc. Europe Conference and Exhibition on Design, Automation and Test, pp.234-239, 2004.
  8. Z. Jinghua, L. Jianzhong, and G. Hong, "Tasks allocation for real-time applications in heterogeneous sensor networks for energy minimization," in Proc. 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp.20-25, 2007.
  9. B. Kalyanasundaram and K. Pruhs, "Speed is as powerful as clairvoyance," Journal of the ACM, vol.47, pp.617-643, 2000. https://doi.org/10.1145/347476.347479
  10. C. Koo, T. Lam, T. Ngan and K. To, "Extra processors versus future information in optimal deadline scheduling," Theory of Computing Systems, vol.37, pp.323-341, 2004. https://doi.org/10.1007/s00224-004-1116-z
  11. R. Kumar, M. Wolenetz, B. Agarwalla, J. Shin, P. Hutto, A. Paul, and U. Ramachandran, "DFuse: a framework for distributed data fusion," in Proc. 1th international conference on Embedded networked sensor systems, pp.114-125, 2003.
  12. T. Lam and K. To, "Performance guarantee for online deadline scheduling in the presence of overload," in Proc. 12th Annual ACM-SIAM Symposium on Discrete Algorithms, pp.755-764, 2001.
  13. J. P. Lehoczky and S. Ramos-Thuel, "An optimal algorithm for scheduling soft-aperiodic tasks in fixed-priority preemptive systems," in Proc. Real-Time Systems Symposium, pp.110-123, 1992.
  14. J. Luo and N. K. Jha, "Power-Conscious Joint Scheduling of Periodic Task Graphs and Aperiodic Tasks in Distributed Real-Time Embedded Systems," in Proc. IEEE/ACM International Conference on Computer-aided Design, pp.357-364, 2000.
  15. C. Margi, R. Manduchi, and K. Obraczka, "Energy consumption tradeoffs in visual sensor networks," in Proc. 24th Brazilian Symposium on Computer Networks, 2006.
  16. C. Meesookho, S. Narayanan, and C. Raghavendra, "Collaborative classification applications in sensor networks," in Proc. 2th IEEE Workshop on Sensor Array and Multichannel Signal Processing, pp.370-374, 2002.
  17. C. Phillips, C. Stein, E. Torng, and J. Wein, "Optimal time-critical scheduling via resource augmentation," Algorithmica, vol.32, pp.163-200, 2008. https://doi.org/10.1007/s00453-001-0068-9
  18. X. Qin, Z. Han, H. Jin, L. Pang, and S. Li, "Real-time fault-tolerant scheduling in heterogeneous distributed systems," in Proc. Workshop on Cluster Computing-Technologies, Environments and Application, 2000.
  19. S. Shivle, R. Castain, H. Siegel, A. Maciejewski, T. Banka, K. Chindam, S. Dussinger, P. Pichumani, P. Satyasekaran, and W. Saylor, "Static mapping of subtasks in a heterogeneous ad hoc grid environment," in Proc. 13th IEEE Workshop on Heterogeneous Computing, pp.21-32. 2004.
  20. J. Stankovic, K. Ramamritham, M. Spuri, and G. Buttazzo, "Deadline scheduling for real-time systems: EDF and related algorithms," Springer, 1998.
  21. S. R. Thuel and J. P. Lehoczky, "On-line scheduling of hard deadline aperiodic tasks in fixed-priority systems," in Proc. Real-Time Systems Symposium, pp.160-171, 1993.
  22. S. R. Thuel and J. P. Lehoczky, "Algorithms for scheduling hard aperiodic tasks in fixed-priority systems using slack stealing," in Proc. Real-Time Systems Symposium, pp.22-33, 1994.
  23. Y. Tian, E. Ekici, and F. Ozguner, "Cluster-based information processing in wireless sensor networks: an energy-aware approach," Wireless Communications and Mobile Computing, vol.7, pp.893-907, 2007. https://doi.org/10.1002/wcm.502
  24. T. Xie and X. Qin, "An energy-delay tunable task allocation strategy for collaborative applications in networked embedded systems," IEEE Transactions on Computers, vol.57, pp.329-343, 2008. https://doi.org/10.1109/TC.2007.70809
  25. C. Yang, G. Deconinck, and W. Gui, "Fault-tolerant scheduling for real-time embedded control systems," Journal of Computer Science and Technology, vol.19, pp.191-202, 2004. https://doi.org/10.1007/BF02944797
  26. Y. Yu and V. Prasanna, "Energy-balanced task allocation for collaborative processing in wireless sensor networks," Mobile Networks and Applications, vol.10, pp.115-131, 2005. https://doi.org/10.1023/B:MONE.0000048550.31717.c5