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An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing

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Proceedings of Second International Conference on Sustainable Expert Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 351))

Abstract

Crowdsourcing is a procedure of outsourcing the data to an abundant range of individual workers rather than considering an exclusive entity or a company. It has made various types of chances for some difficult issues by utilizing human knowledge. To acquire a worldwide optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this procedure, there is a major security concern; i.e., the platform may not be trustworthy, and so, it brings about a threat to workers location privacy. Recently, many distinguished research papers are published to address the security and privacy issues in mobile crowdsourcing. According to our knowledge, the security issues that occur in terms of data reporting were not addressed. Secure and efficient data aggregation and data reporting are the critical issue in Mobile Crowdsourcing (MCS). Cluster-based mobile crowdsourcing (CMCS) is the efficient way for data aggregation and data reporting. In this paper, we propose a novel procedure, the efficient fuzzy logic cluster formation protocol (EFLCFP) for cluster formation, and use cluster cranium (CC) for data aggregation and data reporting. We recommend a couple of secure and efficient data transmission (SET) protocols for CMCS, (i) SET-IBE uses additively homomorphic identity-based encryption system and (ii) SET-IBOOS uses the identity-based online/offline digital signature system, respectively. Then, we have widen the features of cluster cranium by increasing the propensity to achieve aggregation and reporting on the data yielded by the requesters without scarifying their privacy. Also, considering query optimization using cost and latency.

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Author information

Authors and Affiliations

  1. Department of CSE, S.A. Engineering College, Chennai, India

    M. Renukadevi

  2. Department of CSE, School of Engineering and Technology, CHIRST University, Bengaluru, India

    E. A. Mary Anita

  3. Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, India

    D. Mohana Geetha

Authors
  1. M. Renukadevi
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  2. E. A. Mary Anita
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  3. D. Mohana Geetha
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Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Kirtipur, Nepal

    Subarna Shakya

  2. Department of Electrical and Computer Engineering, Concordia University, Montréal, QC, Canada

    Ke-Lin Du

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Wang Haoxiang

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Renukadevi, M., Mary Anita, E.A., Mohana Geetha, D. (2022). An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing. In: Shakya, S., Du, KL., Haoxiang, W. (eds) Proceedings of Second International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 351. Springer, Singapore. https://doi.org/10.1007/978-981-16-7657-4_35

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