Application of Genetic Algorithm in Time-Based Wireless Sensor Network Schedule Optimization
Main Article Content
Abstract
In recent years, wireless sensor networks (WSN) have been particularly interested, studied and applied very strongly. A sensor network is generally limited in resources and energy, which greatly restrict its applicability. Sensor network optimization in practice is a very diverse with a wide range of applications, whereas sensor network scheduling is important in lowering energy consumption and maximizing network lifetime. However, optimization of sensor network schedule a very complex problem with many constraints that is not trivial to solve by analytical methods. This article discusses a heuristical approach using a genetic algorithm to find an optimal solution for network scheduling. The evaluation of fitness function, as well as selection with crossover and mutation operations help to evolve individuals in the population through generations in an optimal direction.
Keywords
sensor network, schedule optimization, genetic algorithm, energy efficiency
Article Details
References
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Multimedia Analysis and Pattern Recognition
(MAPR). Hanoi, Vietnam, Oct.
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November) A wireless sensor network design tool to
support building energy management. In Proceedings
of the First ACM Workshop on Embedded Sensing
Systems for Energy-Efficiency in Buildings (pp. 25-
30). ACM.
[3] Ma, J., Lou, W., Wu, Y., Li, X. Y., & Chen, G. (2009,
April). Energy efficient TDMA sleep scheduling in
wireless sensor networks. In IEEE INFOCOM 2009
(pp. 630-638). IEEE.
[4] L. Wang, and X. Yang. “A survey of energy-efficient
scheduling mechanisms in sensor networks,” Mobile
Networks and Applications, vol. 11, no. 5, pp. 723-
740, 2006.
[5] Berman, P., Calinescu, G., Shah, C., & Zelikovsly, A.
(2005). Efficient energy management in sensor
networks. In Y. Xiao & Y. Pan (Eds.), Ad hoc and
sensor networks. Nova Science.
[6] Tian, D., & Georganas, N. D. (2002). A coveragepreserving node scheduling scheme for large wireless
sensor networks. In Proceedings of the 1st ACM
International Workshop on Wireless Sensor Networks
and Applications (WSNA ’02) (pp. 32–41), Atlanta,
Georgia.
[7] Heinzelman, W. R., Chandrakasan, A., &
Balakrishnan, H. (2000, January). Energy-efficient
communication protocol for wireless microsensor
networks. In Proceedings of the 33rd annual Hawaii
international conference on system sciences (pp. 10-
pp). IEEE.
[8] He, T., Krishnamurthy, S., Stankovic, J. A.,
Abdelzaher, T., Luo, L., Stoleru, R. et al. (2004).
Energy-efficient surveillance system using wireless
sensor networks. In Proceedings of the 2nd
International Conference on Mobile Systems,
Applications, and Services (MobiSys ’04) (pp. 270–
283), Boston, Massachusetts.
[9] J. H. Holland, Adaptation in Natural and Artificial
Systems, The University of Michigan Press,
Michigan, 1975.
[10] Lee, S. C., Tseng, H. E., Chang, C. C., & Huang, Y.
M. (2019). Applying Interactive Genetic Algorithms
to Disassembly Sequence Planning. International
Journal of Precision Engineering and Manufacturing,
1-17.
[11] Liu, T. K., Lin, S. S., & Hsueh, P. W. (2019).
Optimal design for transport and logistics of steel mill
by-product based on double-layer genetic algorithms.
Journal of Low Frequency Noise, Vibration and
Active Control, 1461348419872368.
[12] Al-Furhud, M. A., & Ahmed, Z. H. (2020). Genetic
Algorithms for the Multiple Travelling Salesman
Problem. International Journal of Advanced
Computer Science and Applications (IJACSA), 11(7),
553-560.
[13] Nguyễn, T. H., Lê, M. H., Đào, T. K., Hà, V. P., &
Phạm, T. N. Y. (2020). Thiết kế, chế tạo nút cảm biến
có khả năng tùy biến phục vụ nghiên cứu, phát triển
nền tảng mô phỏng mạng cảm biến. Tạp chí Khoa học
và công nghệ, 56(4), 26-30.
[14] Ha, V.P., Dao, T.K., Le, M.H., Nguyen, T.H., and
Nguyen, V.T. 2020. Design and implementation of
an energy simulation platform for wireless sensor
networks. 2020 IEEE International Conference on
Multimedia Analysis and Pattern Recognition
(MAPR). Hanoi, Vietnam, Oct.