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Multiple heterogeneous cluster-head-based secure data collection in mobile crowdsensing environment

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Abstract

The security and privacy of data are major concerns in the mobile crowdsensing (MCS) environment due to the huge amount of heterogeneous data received from various users and devices automatically or manually regarding their surrounding environment. User participation in the MCS approach is highly essential to have a vast dataset for analysis that will provide the required information or beneficial solution for society. However, it is difficult to achieve due to huge energy consumption, the need for internet connectivity for data transmission, and the security and privacy of data. Therefore, it is essential to have a network coverage model in which data transmission can be done with minimal energy consumption and the need for internet connectivity can be removed from the user’s side. The user’s sensitive data needs to be protected from internal and external attackers to improve the efficiency of the solution provided by the MCS environment with genuine data. This work is based on data collection from users based on their experience for a certain location using the hybrid network coverage model based on clustering, in which each location may have just one or multiple heterogeneous cluster heads. Discrete event-based CrowdSenSim Simulator has been used to design a simulation environment in urban spaces in which 2000 users will move to any location randomly among considered 40 locations and provide feedback data for the location. In this paper, a novel security mechanism based on multiple heterogeneous cluster heads per location has been presented, and it provides better security against attackers than the security model with one cluster head per location. The proposed multiple-cluster heads per location (MCHL)-based mechanism has been compared with the vulnerable one-cluster head per location (OCHL)-based mechanism on the basis of the average number of rounds attackers attacked, average number of locations attackers attacked, average coverage and average efficiency of attackers, and average efficiency of system security.

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Acknowledgements

I, Ramesh K. Sahoo, am thankful to my supervisor Dr. Sateesh Kumar Pradhan, and my mentors, Dr. Srinivas Sethi and Dr. Siba K. Udgata, for their proper guidance.

Funding

No funding was received to assist with the preparation of this manuscript.

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Author notes

  1. Sateesh Kumar Pradhan, Srinivas Sethi and Siba K. Udgata have contributed equally to this work.

Authors and Affiliations

  1. P.G. Department of CS, Utkal University, Vani Vihar, Bhubaneswar, Odisha, 751004, India

    Ramesh K. Sahoo & Sateesh Kumar Pradhan

  2. Department of CSEA, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, 759146, India

    Srinivas Sethi

  3. School of Computer and Information Sciences, University of Hyderabad, Gachibowli, Hyderabad, Telangana, 500046, India

    Siba K. Udgata

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  1. Ramesh K. Sahoo
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  4. Siba K. Udgata
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Contributions

All authors contributed to the study’s conception and design. Ramesh Kumar Sahoo performed material preparation, data collection, and analysis under the guidance of Dr. Sateesh Kumar Pradhan, Dr. Srinivas Sethi, and Dr. Siba K. Udgata. Ramesh Kumar Sahoo wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ramesh K. Sahoo.

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Sahoo, R.K., Pradhan, S.K., Sethi, S. et al. Multiple heterogeneous cluster-head-based secure data collection in mobile crowdsensing environment. J Supercomput 80, 25118–25154 (2024). https://doi.org/10.1007/s11227-024-06395-0

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