A Distributed and Anonymous Data Collection Framework Based on Multilevel Edge Computing Architecture

Muhammad Usman, Mian Ahmad Jan, Alireza Jolfaei, Min Xu, Xiangjian He, Jinjun Chen

Research output: Journal PublicationArticlepeer-review

20 Citations (Scopus)


Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests.

Original languageEnglish
Article number8894832
Pages (from-to)6114-6123
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number9
Publication statusPublished - Sept 2020
Externally publishedYes


  • Distributed
  • edge computing
  • privacy
  • quality of service (QoS)
  • security

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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