- Title
- P2DCA: A Privacy-preserving-based data collection and analysis framework for IoMT applications
- Creator
- Usman, Muhammad; Jan, Mian Ahmad; He, Xiangjian; Chen, Jinjun
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185641
- Identifier
- vital:16724
- Identifier
-
https://doi.org/10.1109/JSAC.2019.2904349
- Identifier
- ISBN:0733-8716
- Abstract
- The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.
- Publisher
- New York: IEEE
- Relation
- IEEE journal on selected areas in communications Vol. 37, no. 6 (2019), p. 1222-1230
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright IEEE
- Subject
- Aggregation; Artificial neural networks; Cloud computing; Clusters; Counter-propagation artificial neural network; Data analysis; Data collection; Data privacy; Internet; IoMT; Machine learning algorithms; MSNs; Multimedia; Privacy; Security; Segmentation; Servers; Traffic management; Traffic surveillance; Wireless networks; 4006 communications engineering; 4606 Distributed computing and systems software
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