Wearable IoT data stream traceability in a distributed health information system |
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Affiliation: | 1. Federal Rural University of Rio de Janeiro (UFRRJ), Department of Computer Sciences, BR-465, Km 7 – Room 80 – P1, CEP. 23.897-000, Seropédica, RJ, Brazil;2. Brazilian Agricultural Research Corporation (Embrapa), BR-465, Km 7 – Bairro Ecologia, CEP: 23891-000, Seropédica, RJ, Brazil;1. University of Massachusetts, Dartmouth, MA 02747, USA;2. The Israel Electric Corporation, PO Box 10, Haifa 31000, Israel;1. Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain;2. Universidade de Aveiro, Instituto de Telecomunicações, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal;1. School of Information Science and Engineering, Yunnan University, No. 2 North Green Lake Road, Kunming 650091, China;2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;3. School of Mathematics and Big Data, Foshan University, China;4. Department of Computer Science and Information Engineering, Chung Hua University, Taiwan |
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Abstract: | With the soaring interest in the Internet of Things (IoT), some healthcare providers are facilitating remote care delivery through the use of wearable devices. These devices are employed for continuous streaming of personal medical data (e.g., vitals, medications, allergies, etc.) into healthcare information systems for the purposes of health monitoring and efficient diagnosis. However, a challenge from the perspective of the physicians is the inability to reliably determine which data belongs to who in real-time. This challenge emanates from the fact that healthcare facilities have numerous users who own multiple devices; thereby creating an N x M data source heterogeneity and complexities for the streaming process. As part of this research, we seek to streamline the process by proposing a wearable IoT data streaming architecture that offers traceability of data routes from the originating source to the health information system. To overcome the complexities of mapping and matching device data to users, we put forward an enhanced Petri Nets service model that aids with a transparent data trace route generation, tracking and the possible detection of medical data compromises. The results from several empirical evaluations conducted in a real-world wearable IoT ecosystem prove that: 1) the proposed system’s choice of Petri Net is best suited for linkability, unlinkability, and transparency of the medical IoT data traceability, 2) under peak load conditions, the IoT architecture exhibits high scalability, and 3) distributed health information system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected. |
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Keywords: | Internet of Things (IoT) Sensors Mobile devices Middleware Wearables Petri Nets Privacy Health Information System |
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