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Improving mobile mass monitoring in the IoT environment based on Chaotic Fog Algorithm | ||
| پدافند الکترونیکی و سایبری | ||
| Article 8, Volume 11, Issue 3 - Serial Number 43, January 2024, Pages 77-88 PDF (723.41 K) | ||
| Document Type: Original Article | ||
| Authors | ||
| Tahere Motedaien1; Mahdi Yaghoobi* 2; Maryam kheirabadi3 | ||
| 1PhD student. Computer Engineering Dep., Neyshabour Branch, Islamic Azad University, Neyshabour, Iran | ||
| 2Associate Professor, Computer Department, Islamic Azad University, Mashhad Branch, Mashhad, Iran. | ||
| 3Assistant Professor, Computer Department, Islamic Azad University, Neyshabor Branch, Neyshabor, Iran | ||
| Receive Date: 26 March 2023, Revise Date: 26 July 2023, Accept Date: 20 August 2023 | ||
| Abstract | ||
| In the IoT-based IoT environment, users can monitor tasks in the network environment and participate in the data collection process by smart devices. Users monitor their environment data in the form of fog computing during this process, also called mobile mass monitoring, and service providers are required to pay user rewards. But rewards should not be such as to increase platform costs. At the same time, maximizing the maximization rate is one of the main goals of service providers. Increasing network coverage rates and reducing platform costs can be considered as an optimization problem. But providing an algorithm that is less involved in local optimizations and can always provide good results is a challenge in itself. This article is tried to present an efficient approach based on the improved forest optimization algorithm using chaos theory and fuzzy parameter adjustment to reduce platform costs and maximize mobile mass monitoring coverage rate. The proposed method is implemented in MATLAB software and the analysis of the findings shows that the proposed method can optimize the network coverage rate by 31% (average) and the monitoring cost by 11% (average) compared to the CMST plan. | ||
| Keywords | ||
| Internet of Things; mobile mass monitoring; Forest Optimization Algorithm; Chaos Theory; Fuzzy System | ||
| References | ||
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