Dynamic task scheduling strategy with game theory in wireless sensor networks. (English) Zbl 1376.68030
Summary: Task allocation and scheduling is an important typical problem in the area of high performance computing. Unfortunately, the existing traditional solutions to this problem in high performance computing cannot be directly implemented in wireless sensor networks (WSNs) due to the limitations of WSNs such as resource availability and shared communication medium. In this paper, a dynamic task scheduling strategy with the application of the game theory in WSNs is presented. First, an effective parallel alliance generating algorithm is proposed to process the multi-tasks environment. A task allocation algorithm based on the game theory is used to enhance the performance of the network. A novel resource conflict eliminating algorithm is also developed to eliminate the conflicting issues. Finally, the simulation results confirm and reassure the effectiveness of our proposed scheme as we compare with that of the other schema’s available in the public domain.
MSC:
| 68M20 | Performance evaluation, queueing, and scheduling in the context of computer systems |
| 91A80 | Applications of game theory |
References:
| [1] | I. F. Akyildiz, W. Su and M. Sankarasubramaniam et al., A survey on sensor networks, IEEE Transactions on Communications Magazine 40(8) (2002) 102-114. |
| [2] | Gorji-Ara, B., Fast and efficient voltage scheduling by evolutionary slack distribution, Proc. Asia and South Pacific Design Automation Conference, 659-662 (2004) |
| [3] | Zhu, D.Melhem, R.Childers, B. R., IEEE Transactions on Parallel and Distributed Systems14(7), 686 (2003). |
| [4] | Aydin, H.; Yang, Q., Energy-aware partitioning for multiprocessor real-time systems, Proc. 17th Int. Parallel and Distributed Processing Symp., 113-121 (2003) |
| [5] | Giannecchini, S.; Caccamo, M.; Shih, C. S., Collaborative resource allocation in wireless sensor networks, Proc. 16th Euromicro Conf. Real-Time Systems, 35-44 (2004) |
| [6] | Tian, Y.; Ekici, E.; Ozgner, F., Energy-constrained task mapping and scheduling in wireless sensor networks, Proc. IEEE Int. Conf. Mobile Adhoc and Sensor Systems, 211-218 (2005) |
| [7] | Tian, Y., Dynamic critical-path task mapping and scheduling for collaborative in-network processing in multi-hop wireless sensor networks, Proc. 2006 Int. Conf. Parallel Processing Workshops, 215-222 (2006) |
| [8] | Goh, L. K.; Bharadwaj, V., An energy-balanced task scheduling heuristic for heterogeneous wireless sensor networks, Proc. 15th IEEE Int. Conf. High Performance Computing, 257-268 (2008) |
| [9] | Xie, T.Qin, X., IEEE Transactions on Computers57(3), 329 (2008). · Zbl 1373.68165 |
| [10] | Lin, J. Y.et al., IEEE Transaction on Instrumentation and Measurement58(6), 1886 (2009). |
| [11] | Neumman, Von; Morgenstern, O., Theory of Games and Economic Behavior (1944) · Zbl 0063.05930 |
| [12] | Kennedy, J.; Eberhart, R. C., Particle swarm optimization, Proc. IEEE Int. Conf. Neural Networks, 1942-1948 (1995) |
| [13] | Kennedy, J.; Eberhart, R. C., A discrete binary version of the particle swarm algorithm, Proc. World Multiconference on Systemics, Cybernetics and Informatics, 4104-4109 (1997) |
| [14] | M. Clerc, Discrete particle swarm optimization-illustrated by the traveling salesman problem, D-lib Magazine (2000), http://www.mauriceclerc.net. · Zbl 1139.90415 |
| [15] | Pan, Q. K.; Tasgetiren, M. F.; Liang, Y. C., A discrete particle swarm optimization algorithm for the permutation flowshop sequecing problem with makespan criteria, Proc. Twenty-sixth SGAI Int. Conf. Innovative Techniques and Applications of Artificial Intelligence, 19-31 (2006) |
| [16] | Soh, L. K.; Tsatsouli, C., Satisficing coalition formation among agents, Proc. 5th Int. Conf. Autonomous Agents and Multiagent Systems, 1062-1063 (2001) |
| [17] | Soh, L. K.; Tsatsouli, C., Reflective negotiating agents for real-time multi-sensor target tracking, Proc. Seventeenth Int. Joint Conf. Artificial Intelligence, 1121-1127 (2001) |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
