P2DCA: A Privacy-Preserving-Based Data Collection and Analysis Framework for IoMT Applications

Abstract

The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays, and can be used in various smart city applications, such as traffic management, ehealth, and surveillance. In IoMT applications, multimedia data is generated from devices with sensing capabilities, e.g., Multimedia Sensor Nodes (MSNs). These devices come with limited computational and storage resources, and cannot hold captured multimedia data for a long time if the connection between a base station and cloud server is down. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, such a data collection raise privacy issues, such as revealing identities and location information of MSNs to mobile sinks. 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 framework for IoMT applications. The proposed framework distributes an underlying Wireless Multimedia Sensor Networks (WMSNs) into multiple clusters. Each cluster is represented by a cluster head. The cluster heads are responsible to protect the privacy of member nodes through data and location aggregation. Later, aggregated multimedia data is analyzed at cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that our proposed framework outperforms other stateof-the-art techniques, and can be used to collect multimedia data in various IoMT applications.

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

“…The performance analysis indicates that the proposed framework is secured and effective. Another privacy-based data collection framework was proposed by Usman et al [97]. The result reveals that the proposed framework can be beneficial in collecting multimedia data in applications.…”

Data Collection in Sensor-Cloud: A Systematic Literature Review

“…The result shows some promise. A framework named an efficient privacy-preserving-based data collection and analysis (P2DCA) for IoMT applications was proposed by Usman et al [44]. Hence, the proposed framework is aimed to protect against privacy issues when collecting data to the cloud.…”

Toward configurable modeling for artifact‐centric business processes

“…Later, we may use artifact to denote artifact class since we focus on the model level in this work. In the age of cloud computing, workflows are often deployed in cloud, in which data security is especially significant. Thus, the privacy property of data is considered in the specification.…”

“…An artifact object O = (id, , q) is initial if q = s 0 (initial state) and is undefined for every data attribute and final if q ∈ F. Later, we may use artifact to denote artifact class since we focus on the model level in this work. In the age of cloud computing, workflows are often deployed in cloud, [19][20][21][22][23][24][25][26] in which data security is especially significant.…”