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Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Saeed Ahmed1,2, Noor Gul1,3, Jahangir Khan4, Junsu Kim1, Su Min Kim1,*

1 Department of Electronics Engineering, Korea Polytechnic University, Siheung, 15073, Korea
2 Mirpur University of Science and Technology, Mirpur, 10250, Pakistan
3 Department of Electronics, University of Peshawar, Peshawar, 25120, Pakistan
4 Sarhad University of Science and Information Technology, Peshawar, 25000, Pakistan

* Corresponding Author: Su Min Kim. Email: email

(This article belongs to the Special Issue: Artificial Intelligence Convergence Healthcare System Leveraging Blockchain Networks)

Computers, Materials & Continua 2022, 71(3), 5061-5082. https://doi.org/10.32604/cmc.2022.023639

Abstract

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response. Cognitive Internet of things (CIoT) technologies including inherent characteristics of cognitive radio (CR) are potential candidates to develop a monitoring and early warning system (MEWS) that helps in efficiently utilizing the short response time to save lives during flash floods. However, most CIoT devices are battery-limited and thus, it reduces the lifetime of the MEWS. To tackle these problems, we propose a CIoT-based MEWS to slash the fatalities of flash floods. To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors, we formulate a resource assignment problem for maximizing energy efficiency. To solve the problem, at first, we devise a polynomial-time heuristic energy-efficient scheduler (EES-1). However, its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency. To enhance the energy efficiency of the proposed EES-1 scheme, we additionally formulate an optimization problem based on a maximum weight matching bipartite graph. Then, we additionally propose a Hungarian algorithm-based energy-efficient scheduler (EES-2), solvable in polynomial time. The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS, leading to the extended lifetime of the MEWS without loss of throughput performance.

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APA Style
Ahmed, S., Gul, N., Khan, J., Kim, J., Kim, S.M. (2022). Energy-efficient scheduling for a cognitive iot-based early warning system. Computers, Materials & Continua, 71(3), 5061-5082. https://doi.org/10.32604/cmc.2022.023639
Vancouver Style
Ahmed S, Gul N, Khan J, Kim J, Kim SM. Energy-efficient scheduling for a cognitive iot-based early warning system. Comput Mater Contin. 2022;71(3):5061-5082 https://doi.org/10.32604/cmc.2022.023639
IEEE Style
S. Ahmed, N. Gul, J. Khan, J. Kim, and S.M. Kim "Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System," Comput. Mater. Contin., vol. 71, no. 3, pp. 5061-5082. 2022. https://doi.org/10.32604/cmc.2022.023639



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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