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FedLens: federated learning-based privacy-preserving mobile crowdsensing for virtual tourism

  • S.I. : Multifaceted Intelligent Computing Systems (MICS)
  • Published:
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Abstract

FedLens is the federated learning model based on viewing world tourist spots virtually in a privacy-preserving manner. We define Virtual Tourism as enjoying the natural beauty, other related activities online, using AR/VR/MR technology-based ‘virtual eye’, to interact actively with nature and people at tourist spots. Federated learning-based mobile crowdsensing is an emerging collaborative distributed learning paradigm for privacy-preserving, energy-efficient, and scalable networks. Edge intelligent mobile crowdsensing uses geotagged tourist attractions. The purpose of this study is to explore the geo-statistics of tourist areas. The proposed ‘FedLens’ brings tourists closer to the interests using augmented reality through the virtual guide. ArcGIS software maps a tourist area. 5G mobile crowdsensing helps to explore unknown tourist spots in real time. ‘FedLens’ provides a privacy-preserving incentive mechanism to encourage reliable contributors to get better Quality of Information. The average global data aggregation time is approximately 12%. The contributor’s collection time is 88% of the total processing time. The contributors use multifaceted intelligent federated computing to provide detailed geospatial information and promote sustainable ecotourism. Augmented reality-based virtual tourism ecosystem development is the ultimate goal of this work to attract more virtual tourists for a sustainable environment. Future physical tour-planning recommendation systems are incorporated in the proposed model.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Centre of Mobile Cloud Computing, Maulana Abul Kalam Azad University of Technology, West Bengal, Kalyani, 741249, West Bengal, India

    Debashis De

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De, D. FedLens: federated learning-based privacy-preserving mobile crowdsensing for virtual tourism. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-021-00430-6

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