Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm

Tahere MotedayenDepartment of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, IranView further author information

Mahdi YaghoobiDepartment of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, IranCorrespondence[email protected]
View further author information

Maryam KheirabadiDepartment of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, IranView further author information

Abstract

In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices. Users monitor their data in the form of fog computing (mobile mass monitoring). Service providers are required to pay user rewards without increasing platform costs. One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs (reward) is the Cooperative Based Method for Smart Sensing Tasks (CMST). This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm. The proposed method is implemented with MATLAB. The average findings show that the network coverage rate is 31% and the monitoring cost is 11% optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms. And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.

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Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Tahere Motedayen

Tahere Motedayen received the bachelor degree and Master Degree in Computer Engineering at Islamic Azad university of Mashhad, Iran. She is graduating with a Ph.D. degree in Computer Engineering from Islamic Azad Univercity of Mashhad, Iran. Her area of research is fractional calculus and evolutionary algorithm.

Mahdi Yaghoobi

Mahdi Yaghoobi is working as an Associate professor in the Departmant of Electrical Engineering Islamic Azad University of Mashhad, Iran. He has taken PhD from Science Reserch Unit of Islamic Azad Univercity of Tehran, Iran. His area of research is chaos application, predictive control, fuzzy control and evolutionary algorithm.

Maryam Kheirabadi

Maryam Kheirabadi, PhD in Computer Science from Kingston University, London, UK 2012. Msc in Business Information Systems, Royal Holloway, London, UK 2005. Bs in Computer Engineering, Azad University of Mashhad, Iran 2002. My specialty is on Software Engineering, also worked and researched on Cloud Computing, Data Mining and etc.

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