Abstract
Internet of Things (IoT) concepts constitute a predominant area of research in e-healthcare applications, owing to the plethora of opportunities in medical diagnosis. In this work, a ubiquitous computing and communication architecture is proposed through the amalgamation of Internet of Healthcare and Internet of Drone things by leveraging a 5G/6G communication framework. Gait information is aggregated via a smart shoe and the processing is carried out on a set of edge-enabled Unmanned Aerial Vehicles (UAVs). To transfer the data within the edge and cloud layers, a Software Defined Network (SDN) is modeled. Further, a classifier is designed to analyze the records and make predictions on possible neurological disorders at the edge level. Experimental results suggest a 98% classification accuracy for abnormal gait diagnosis at 20% CPU utilization. The findings further reveal a latency of 335 ms. at QoS 2, and 50 msg/s bandwidth utilization with a Connected Client Ratio and SDN Coverage Ratio of 0.99 and 0.95, respectively.
Published Version
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