An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing
- Title
- An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing
- Creator
- Renukadevi M.; Mary Anita E.A.; Mohana Geetha D.
- Description
- 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. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-351, pp. 427-446.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Cluster cranium; Data aggregation; Data reporting; Mobile crowdsourcing; Query optimization; Security
- Coverage
- Renukadevi M., Department of CSE, S.A. Engineering College, Chennai, India; Mary Anita E.A., Department of CSE, School of Engineering and Technology, CHIRST University, Bengaluru, India; Mohana Geetha D., Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981167656-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Renukadevi M.; Mary Anita E.A.; Mohana Geetha D., “An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/20427.