Improving the Mean Time to Failure of the System with the New Architecture of the Main Node with the Replacement Node of Industrial Wireless Sensor Networks for Monitoring and Control using Markov Model
محورهای موضوعی : Majlesi Journal of Telecommunication Devices
Ahmadreza Zamani
1
(Department of Mechanical, Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Mohammad Ali Pourmina
2
(Department of Mechanical, Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.)
Ramin Shaghaghi Kandovan
3
(Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran)
کلید واژه: spare sensor, industrial sensor networks, Failure Rate, redundancy, mean time to failure, backup node, Fault tolerance, Markov Model,
چکیده مقاله :
Industrial and physical site information is sent to the monitoring center by sensors in wireless sensor networks so that they can easily control the process of a company in order to improve the optimal performance of the system until the failure occurs to monitor and control in wireless sensor networks. Sensors are exposed to a wide range of failures, possible hardware and software problems in normal conditions, extreme weather conditions or other conditions caused by harsh physical environment in the field of sensors. Therefore, there is a possibility of unpredictable failure for all types of sensors and with Industrial process monitoring, preventive status monitoring, prevented error and fault and failures. The focus of this article is to present a new architecture in improving the correct performance of the system, the replacement rate of more damaged nodes and timely replacement, at the time of the starting point of the failure, the main sensor with spare ones or healthy sensors with faulty ones. The proposed network structure is such that the spare node is placed in parallel with the main node; this method makes it possible for the spare node to be replaced in case of failure of the main node, and the failed node can be quickly repaired and put in a standby mode. Our proposed model is analyzed in terms of the average time of correct system operation until failure known as mean time to failure. In this article is presented and studied and evaluated, a new architecture to improve network performance against failure using Markov model and state probability, and mean failure rate for node fault tolerance, before failure with timely replacement in wireless sensor network. In the proposed architecture, the results show a better improvement of the system's correct performance in order to reduce the adverse effects of errors and failures and improve fault tolerance. The simulation results show that the advantage of using this method reduces the adverse effects of errors and failures and improves the optimal performance of the system in the industrial site.
1. Munir, A., J. Antoon, and A. Gordon-Ross, Modeling and analysis of fault detection and fault tolerance in wireless sensor networks. ACM Transactions on Embedded Computing Systems (TECS), 2015. 14(1): p. 1-43.
2. Jiang, W., Z. Ma, and X. Deng, An attack-defense game based reliability analysis approach for wireless sensor networks. International Journal of Distributed Sensor Networks, 2019. 15(4): p. 1550147719841293.
3. Zhou, P., et al., A comprehensive technological survey on dependable self-managing CPS: The decade of researches on correctness and dependability. 2017.
4. Vatinvises, T., Monitoring Mechanism Design for a Distributed Reconfigurable Onboard Computer. 2016, Georg-August-Universität Göttingen.
5. Ghobaei-Arani, M., S. Jabbehdari, and M.A. Pourmina, An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach. Future Generation Computer Systems, 2018. 78: p. 191-210.
6. Power, A., A predictive fault-tolerance framework for IoT systems. 2020: Lancaster University (United Kingdom).
7. Venkatesan, L., S. Shanmugavel, and C. Subramaniam, A survey on modeling and enhancing reliability of wireless sensor network. Wireless Sensor Network, 2013. 5(03): p. 41.
8. Mosharmovahed, B., M.A. Pourmina, and S. Jabbehdari, Clustering Methods Survey in VANET on Roads and Mountain Trails with High Reliability. Majlesi Journal of Telecommunication Devices, 2021(1).
9. Kumar, V., et al., Reliability analysis in wireless sensor networks. International Journal of Engineering and Technology, 2011. 3(2): p. 74-79.
10. Atif, M., et al., Soft computing techniques for dependable cyber-physical systems. IEEE Access, 2019. 7: p. 72030-72049.
11. Silva, I., et al., Reliability and availability evaluation of wireless sensor networks for industrial applications. Sensors, 2012. 12(1): p. 806-838.
12. Catelani, M., et al., Reliability Analysis of Wireless Sensor Network for Smart Farming Applications. Sensors, 2021. 21(22): p. 7683.
13. Soltanali, H., et al., Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 2019. 233(4): p. 682-697.
14. Alsheikh, M.A., et al., Markov decision processes with applications in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 2015. 17(3): p. 1239-1267.
15. Munir, A. and A. Gordon-Ross. Markov modeling of fault-tolerant wireless sensor networks. in 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN). 2011. IEEE.
16. Bein, D., et al., Reliability modeling in wireless sensor networks. International Journal of Information Technology, 2005. 11(2): p. 1-8.
17. Kumar, V., et al., Markov model for reliable packet delivery in Wireless Sensor Networks. International Journal of Computer Science Issues (IJCSI), 2011. 8(3): p. 429.
