Vol.71, No.3, 2022, pp.5061-5082, doi:10.32604/cmc.2022.023639
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:
(This article belongs to this Special Issue: Artificial Intelligence Convergence Healthcare System Leveraging Blockchain Networks)
Received 15 September 2021; Accepted 05 November 2021; Issue published 14 January 2022
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.
Flash floods; internet of things; cognitive radio; early warning system; network lifetime; energy efficiency
Cite This Article
Ahmed, S., Gul, N., Khan, J., Kim, J., Kim, S. M. (2022). Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System. CMC-Computers, Materials & Continua, 71(3), 5061–5082.
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