- Title
- A distributed and anonymous data collection framework based on multilevel edge computing architecture
- Creator
- Usman, Muhammad; Jan, Mian; Jolfaei, Alireza; Xu, Min; He, Xiangjian; Chen, Jinjun
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173327
- Identifier
- vital:14681
- Identifier
-
https://doi.org/10.1109/TII.2019.2952645
- Identifier
- ISBN:1551-3203 (ISSN)
- Abstract
- 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. © 2005-2012 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6114-6123
- Rights
- Copyright @ 2020 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 10 Technology; Distributed; Edge computing; Privacy; Quality of service (QoS); Security
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