A Multi-objective Optimization Algorithm of Task Scheduling in WSN

  • Liang Dai University of Oradea
  • Hongke Xu School of Electronic and Control Engineering Chang’an University
  • Ting Chen
  • Qian Chao School of Electronic and Control Engineering Chang’an University
  • Lijing Xie Information and Navigation College Air Force Engineering University


Sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving. The above needs form a contrast to the lower efficiency of task-performing caused by the  ailureprone sensor. To solve this problem, a multi-objective optimization algorithm of task scheduling is proposed for wireless sensor networks (MTWSN). This algorithm tries its best to make less makespan, but meanwhile, it also pay much more attention to the probability of task-performing and the lifetime of network. MTWSN avoids the task assigned to the failure-prone sensor, which effectively reducing the effect of failed nodes on task-performing. Simulation results show that the proposed algorithm can trade off these three objectives well. Compared with the traditional task scheduling algorithms, simulation experiments obtain better results.


[1] Z. Zeng, A. Liu, D. Li (2008), A Highly Efficient DAG Task Scheduling Algorithm for Wireless Networks, In Proc. of ICYCS 2008,Zhang Jia Jie, Hunan, China, 570-575.

[2] J. Lin, W. Xiao, F. L. Lewis (2009), Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks. IEEE Transactions on Instrumentation and Measurement, 58(6):1886 - 1896.

[3] L. Dai, Y. Chang, Z. Shen (2011), An Optimal Task Scheduling Algorithm in Wireless Sensor Networks, Int J Comput Commun, ISSN 1841-9836, 6(1):101-112.

[4] K. Deb, A. Pratap, S. Agarwal (2002), A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197.

[5] J. Mao, C. G. Cassandras, Q. Zhao (2007), Optimal Dynamic Voltage Scaling in Energy- Limited Nonpreemptive Systems with Real-Time Constraints. IEEE Transactions on Mobile Computing, 6(6):678 - 688.

[6] P. Heemin, B. S. Mani (2003), Energy-efficient Task Assignment Framework for Wireless Sensor Netwoks, UC Los Angeles: The Berkeley Electronic Press.

[7] M. Younis, K. Akkaya, A. Kunjithapatham (2003), Optimization of Task Allocation in a Cluster-based Sensor Network. In Proc. of the Eighth IEEE International Symposium on Computers and Communication, Netherlands: IEEE Computer Press, 329-334.

[8] A. Wang, A. Chandrakasan (2002), Energy-efficient DSPs for Wireless Sensor Networks. IEEE Signal Processing Magazine, 19(4):68-78.

[9] Y. Tian, J. Boangoat, E. Ekici (2006), Real-time Task Mapping and Scheduling for Collaborative In-network. In Proc. of the 20th International Parallel and Distributed Processing Symposium. Rhodes Island: IEEE Computer Press, 1-10.

[10] Y. Tian, E. Ekici (2005), Energy-constrained Task Mapping and Scheduling in Wireless Sensor Networks. In Proc. of the Workshop on Resource Provisioning and Management in Sensor Networks. Washington, D C: IEEE Computer Society, 211-218, 2005.

[11] Y. Yu, V. K. Prasanna (2005), Energy-balanced Task Allocation for Collaborative Processing in Wireless Sensor Networks. ACM/Kluwer Mobile Networks and Applications Journal, 10(1):115-131.

[12] Y. Gu (2007), Real-time Multimedia Processing in Video Sensor Networks, Signal Processing: Image Communication, 22(3):237-251.

[13] T. Xie, X. Qin (2008), An Energy-Delay Tunable Task Allocation Strategy for Collaborative Applications in Networked Embedded Systems. IEEE Transactions on Computers, 57(3):329-343.

[14] I. S. Kulkarni, D. Pompili (2010), Task Allocation for Networked Autonomous Underwater Vehicles in Critical Missions. IEEE Journal on Selected Areas in Communications, 28(5):716- 727.

[15] C. M. Fonseca, P. J. Fleming (1993), Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization, In Genetic Algorithms: Proceedings of the Fifth International Conference, San Mateo, CA: Morgan Kaufmann, 416-423.

[16] R. L. Haupt, S. E. Haupt (2004), Practical Genetic Algorithms. John Wiley & Sons.

[17] N. Srinivas, K. Deb (1995), Multiobjective optimization using nondominated sorting in genetic algorithms. Journal of Evolutionary Computation, 2(3):221-248.
How to Cite
DAI, Liang et al. A Multi-objective Optimization Algorithm of Task Scheduling in WSN. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 9, n. 2, p. 160-171, feb. 2014. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1016>. Date accessed: 06 july 2020. doi: https://doi.org/10.15837/ijccc.2014.2.1016.


Wireless Sensor Networks (WSN); task scheduling; multi-objective optimization; improved NSGA-II