Description

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The rapid introduction of Internet of Things (IoT) technology has boosted the service deliverance aspects of industrial sectors like Healthcare and Defence-security. IoT technology backed by Fog-Cloud computing and Big Data analytics is not only capable of sensing the acute details of sensitive events from wider perspectives, but it also provides a means to deliver time-sensitive services in efficient manner. With utilization of Fog-Cloud computing technology and Big Data analytics, this thesis explores temporal analytics to present numerous IoT frameworks for provisioning real-time decision-making services in Healthcare and Defence-security industry.
IoT technology has been efficiently adopted in different fields of the healthcare domain. A framework for IoT based patient monitoring in Intensive Care Unit (ICU) is presented to enhance the deliverance of curative services. Cloud Computing has been used as a platform for provisioning necessary computational resources. Though ICUs remained a center of attraction for high quality care among researchers, still number of studies have depicted the vulnerability to a patient's life during ICU stay. The presented work addresses numerous health-oriented concerns in terms of efficient monitoring of various events (and anomalies) with temporal associations, temporal associative granulations over different abstractions levels using temporal big data analytics. This is followed by time-sensitive warning and emergency alert generation procedures to provide an effective mobile healthcare environment.
Incorporation of IoT in healthcare industry has led researchers around the world to develop smart applications like mobile healthcare, health-aware recommendations, and intelligent healthcare systems. Inspired from these aspects, an intelligent healthcare framework based on IoT technology is presented to provide ubiquitous healthcare to person during his/her workout sessions. The intelligence of the presented framework lies with its ability to analyze real-time health conditions during workouts and predict probabilistic health state vulnerability. For predictive purpose, the proposed framework incorporates Artificial Neural Network (ANN) model, which is comprised of three phases namely, monitor, learn, and predict. In addition to this, the presented framework is supported by a mathematical foundation to predict probabilistic vulnerability, in terms of Probabilistic State of Vulnerability (PSoV). Also, the probabilistic vulnerability is represented in real-time using color-coded technique, depicting the health state of the person during workouts.