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Wearable IoT data stream traceability in a distributed health information system
Lomotey R., Pry J., Sriramoju S.  Pervasive and Mobile Computing 40  692-707, 2017. Type: Article
Date Reviewed: Apr 5 2018

Personal use of wearable Internet of Things (IoT) devices for health monitoring is rapidly expanding. For medical use, routing continuous streams of this data raises new security, safety, and complexity concerns. This paper explores traceability between various dynamic individual sources all the way to backend health management systems. Built on top of various distributed communication protocols, these new IoT systems must deal with dynamic users, mobility, and new data generation requirements. This is where Petri nets are used by the authors to manage concurrency, synchronization, and event-based processes. A Petri net models the interaction between humans and their IoT devices, and then to the personal servers and finally to the backend hospital facilities. The Petri net network graph is then used to control and visualize reachability between the sensors and the processing nodes. These reachability assertions are shown through their proofs and an extensive evaluation section.

For testing, users with wearable IoT devices, like SensorTags, were used in combination with various cloud machines. The scalability and system load of the distributed IoT scheme are evaluated through use of the Petri net. A vast number of concurrent data requests and generation were captured by users, each with a multitude of IoT wearable broadcasting devices.

For performance and security, the authors show how a Petri net can manage peak load conditions for IoT architecture scalability. They also address how system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected. The Petri net provides a valuable way to collect and visualize all the data as it routes to the desired endpoints. With potential cross product of M user devices with N users, the complexity is rapidly becoming unmanageable. This empirical evaluation helps show the feasibility of the authors’ proposed approach to manage this complexity.

It will be interesting to see how personal health records being gathered by new user IoT devices (like smart watches with biometrics) could also be integrated into these new health trust models. Expanding to support telemedicine with remote diagnostics will also be exciting.

Reviewer:  Scott Moody Review #: CR145950
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Internet (C.2.1 ... )
 
 
Internet (C.2.5 ... )
 
 
Medical Information Systems (J.3 ... )
 
 
Petri Nets (D.2.2 ... )
 
 
Portable Devices (C.5.3 ... )
 
 
Sensors (I.2.9 ... )
 
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