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Privacy-preserving for the internet of things in multi-objective task scheduling in cloud-fog computing using goal programming approach

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Abstract

For the Internet of Things (IoT) that have low processing power, fog computing can play an essential role as a processing resource to execute their tasks. Timely execution of the service and the optimal service cost are two major challenges of task scheduling in fog computing. In this article, a privacy architecture is proposed for task scheduling in IoT, and based on this architecture, a multi-objective algorithm is presented to minimize the service time and service cost. We simulate the problem with four scenarios: easy, medium, semi-hard, and hard, and then compare it with other multi-objective algorithms. Since the proposed algorithm is multi-objective, the goal programming approach (GPA) is used to choose one possible solution. The simulation results show that our proposed algorithm has better performance and higher convergence speed to the optimal solution than other algorithms while considering the privacy requirements of IoT devices.

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Notes

  1. Multi-Objective Particle Swarm Optimization.

  2. Multi-Objective Tabu Search.

  3. Multi-Objective Moth-Flame optimization.

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Authors and Affiliations

  1. Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Abbas Najafizadeh, Afshin Salajegheh & Amir Sahafi

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    Amir Masoud Rahmani

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Correspondence to Afshin Salajegheh.

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Najafizadeh, A., Salajegheh, A., Rahmani, A.M. et al. Privacy-preserving for the internet of things in multi-objective task scheduling in cloud-fog computing using goal programming approach. Peer-to-Peer Netw. Appl. 14, 3865–3890 (2021). https://doi.org/10.1007/s12083-021-01222-2

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