A Classical Machine Learning Model Scheduling in Industrial Wireless Sensor Networks

Saleh Al-Sharaeh, Nancy Shaar, Lara Shboul


Time synchronization is a primary issue in industrial wireless sensor networks (IWSNs). It helps to optimize the connection and preserve battery consumption, and thus increase the network lifetime. This study aims to identify the most effective factors that decrease the battery consumption and monitor the critical targets in wireless sensor networks (WSNs) through addressing the coverage and connectivity aware scheduling of sensor nodes (SNs). On the other hand, this paper aims to get a scheduling algorithm for industrial wireless sensor networks of SNs by using classical machine learning in the proposed model like support vector machine, decision tree, and RProp (resilient back-propagation) algorithms. In this paper, classical machine learning methods are applied for testing the extracted features and the affected degree for network configurations. An extensive simulation run showed high accuracy for machine learning measurements and extracted the most affected features that play a big role in the sensor node scheduling in industrial wireless sensor networks. For testing, we used the KNIME (KoNstanz Information MinEr ) model that gives a result with high accuracy. The SVM (Support Vector Mashine), Decision Tree, and RProp classifiers give an accuracy of 92.489%, 97.979%, and 98.335%, respectively.


Keywords: resilient back-propagation algorithm, wireless sensor networks, classical machine learning.




Full Text:



QUEIROZ D.V., ALENCAR M.S., GOMES R.D., FONSECA I.E., and BENAVENTE-PECES C. Survey and systematic mapping of Industrial Wireless Sensor Networks. Journal of Network and Computer Applications, 2017, 97: 96-125.‏ https://doi.org/10.1016/j.jnca.2017.08.019

LI K., NI W., DUAN L., ABOLHASAN M., and NIU J. Wireless power transfer and data collection in wireless sensor networks. IEEE Transactions on Vehicular Technology, 2017, 67(3): 2686-2697.‏ ‏https://doi.org/10.1109/TVT.2017.2772895

KADDI M., KHALILI Z., and BOUCHRA M. A Differential Evolution Based Clustering and Routing Protocol for WSN. In: 2020 2nd International Conference on Mathematics and Information Technology (ICMIT). IEEE, 2020. p. 190-195. https://doi.org/10.1109/ICMIT47780.2020.9047006

HARIZAN S., and KUILA P. Coverage and connectivity aware energy-efficient scheduling in target-based wireless sensor networks: an improved genetic algorithm-based approach. Wireless Networks, 2019, 25(4): 1995-2011. https://doi.org/10.1007/s11276-018-1792-2

WANG Q., LIU W., WANG T., ZHAO M., LI X., XIE M., MA M., ZHANG G., and LIU A. Reducing delay and maximizing lifetime for wireless sensor networks with dynamic traffic patterns. IEEE Access, 2019, 7: 70212-70236.‏ https://doi.org/10.1109/ACCESS.2019.2918928

XU Y., JIAO W., and TIAN M. Energy-efficient connected-coverage scheme in wireless sensor networks. Sensors, 2020, 20(21): 6127.‏ https://doi.org/10.3390/s20216127

SINGH S.P., and SHARMA S.C. A particle swarm optimization approach for energy-efficient clustering in wireless sensor networks. International Journal of Intelligent Systems and Applications, 2017, 11(6): 66.‏ https://doi.org/10.5815/ijisa.2017.06.07

AL-AZZAM S., and SHARIEH A. A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks. International Journal of Electrical and Computer Engineering, 2020, 10(4): 3623-3634. https://doi.org/10.11591/ijece.v10i4.pp3623-3634

HARIZAN S., and KUILA P. Coverage and connectivity aware critical target monitoring for wireless sensor networks: Novel NSGA‐II-based approach. International Journal of Communication Systems, 2020, 33(4): e4212. ‏https://doi.org/10.1002/dac.4212

CARLSON J., DAEHLER K.R., ALONZA A.C., BARENDSEN E., BERRY A., BOROWSKI A., CARPENDALE J., CHAN K.K.H., COOPER R., FRIEDRICHSEN P., GESS-NEWSOME J., INEKE H.-R., HUME A., KIRSCHNER S., LIEPERTZ S., LOUGHRAN J., MAVHUNGA E., NEUMANN K., NILSSON P., PARK S., ROLLNICK M., SICKEL A., SUH J.K., SCHNEIDER R., VAN DRIEL J., and WILSON C. The refined consensus model of pedagogical content knowledge in science education. In: Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science. Springer, Singapore, 2019: 77-94. https://doi.org/10.1007/978-981-13-9574-1_11

YARINEZHAD R., and HASHEMI S.N. A sensor deployment approach for target coverage problem in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 2020, 1-16.‏ https://doi.org/10.1007/s12652-020-02195-5

ELHABYAN R., SHI W., and ST-HILAIRE M. Coverage protocols for wireless sensor networks: Review and future directions. Journal of Communications and Networks, 2019, 21(1): 45-60.‏ https://doi.org/10.1109/JCN.2019.000005

FAYAZIBARJINI E., GHARAVIAN D., and SHAHGHOLIAN M. Target tracking in wireless sensor networks using NGEKF algorithm. Journal of Ambient Intelligence and Humanized Computing, 2019, 1-13. ‏https://doi.org/10.1007/s12652-019-01536-3

ELSHARIEF M., MOHAMED A., EL-GAWAD A., KO H., and PACK S. EERS: Energy-Efficient Reference Node Selection Algorithm for Synchronization in Industrial Wireless Sensor Networks. Sensors, 2020, 20(15): 4095.‏ https://doi.org/10.3390/s20154095

MUKHERJEE M., SHU L., HU L., HANCKE G.P., and ZHU C. Sleep scheduling in industrial wireless sensor networks for toxic gas monitoring. IEEE Wireless Communications, 2017, 24(4): 106-112.‏ https://doi.org/10.1109/MWC.2017.1600072WC

TOMAR V., and SINGH D. Coverage and connectivity aware data gathering protocol for wireless sensor networks. In: 2016 2nd international conference on next-generation computing technologies (NGCT). IEEE, 2016. p. 432-438. ‏https://doi.org/10.1109/NGCT.2016.7877455

SUNDARARAJ V., MUTHUKUMAR S., and KUMAR R.S. An optimal cluster formation based energy-efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers & Security, 2018, 77: 277-288.‏ https://doi.org/10.1016/j.cose.2018.04.009


  • There are currently no refbacks.