Abstract
Processing and analysis of the expedited volume of data are considered significant challenges for the Internet of Things (IoT) systems in which devices are constantly generating data. The Fog architecture will allow delay-sensitive applications to run everywhere. In IoT, users with smart devices can sense tasks and contribute to their performance. Mobile Crowdsensing (MCS) utilizes users’ crowds as leverage to collect information from the surroundings through their mobile sensors in Fog Computing networks. An efficient reward mechanism to attract the optimal users’ participation in different regions that take into account the sensing cost and, on the other hand, the quality of data collected is at an acceptable level, and the need to balance these two factors are significant for MCS. This research considers the Mobile Crowdsensing in Fog-Based IoT (FITMCS) to allocate optimal user rewards. For this purpose, improving the Coverage Factor (CF) and rewarding system (sensing cost) in MCS is regarded as an optimization problem. The Harris hawks optimization (HHO) tries to provide an optimal solution to solve it. FITMCS was simulated in a MATLAB environment, and two different scenarios, CF and sensing cost metrics, were used to measure its efficiency. The first scenario considers the number of users (50, 100, and 150 users), and the second scenario assumes the number of tasks (20, 25, and 30 sensing tasks) in the sensing environment as a variable. The results revealed that FITMCS improved the sensing cost by an average of 11.59% and CF by 25.1% compared to the previous scheme.
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Seyfollahi, A., Abeshloo, H. & Ghaffari, A. Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimization. J Ambient Intell Human Comput 13, 4543–4558 (2022). https://doi.org/10.1007/s12652-021-03344-0
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DOI: https://doi.org/10.1007/s12652-021-03344-0