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Hybrid optimized task scheduling with multi-objective framework for crowd sensing in mobile social networks

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Abstract

Mobile Crowd Sensing (MCS) has become a new archetype allowing individuals to effectively perform their sensing tasks by employing interested mobile users. This paradigm satisfies the requirements of the task requester, along with providing the willing participants with a way to generate profit by performing the specific tasks. Normally, an incentive is provided to the participants by the requester for processing the requested tasks. However, the requester may normally have a limited budget, so they prefer to make payments to the user providing good quality data instead of all the users participating in the process. Thus, selecting the most suitable user among the participant pool is required for executing the tasks efficiently in a short time. This paper presents an efficient online task allocation technique using a hybrid optimization approach. A novel Crow COOT Foraging Optimization (CCFO) algorithm is proposed for allocating tasks in MCS. The optimal user is chosen based on the fitness function devised using various aspects, like finish time, time of receiving task, time of sending task, makespan, monetary cost, ready time, and energy consumption. The CCFO algorithm is developed by modifying the C-BFO algorithm to the COOT algorithm to enhance the performance of the task allocation process. The presented CCFO technique for task allocation based on the fitness function evaluates makespan with the lowest value of 0.482.

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Data availability

The data underlying this article are available in Cambridge Haggle repository, at “https://crawdad.org/cambridge/haggle/20090529/” and UMass Diesel Net repository, at “https://crawdad.org/umass/diesel/20080914/”.

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Authors and Affiliations

  1. Oxford College of Engineering, Venmani, Polur, 606803, India

    Sasireka V

  2. Anna University, Chennai, Tamil Nadu, 600025, India

    Shyamala Ramachandran

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  1. Sasireka V
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  2. Shyamala Ramachandran
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Contributions

Sasi reka V conceived the presented idea and designed the analysis. Also, he carried out the experiment and wrote the manuscript with support from R. Shyamala Ramachandran All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Sasireka V.

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V, S., Ramachandran, S. Hybrid optimized task scheduling with multi-objective framework for crowd sensing in mobile social networks. Peer-to-Peer Netw. Appl. 17, 722–738 (2024). https://doi.org/10.1007/s12083-023-01608-4

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