Abstract

The Internet of Things paradigm has witnessed an unprecedented growth pervading every sector of the societal fabric from homes, cars, health, industry, and business. Given the technological advances witnessed in all the enabling technologies of IoT, it is now possible to connect an increasing number of devices to the internet. The data value chain has slowly risen in prominence. Moving from the vertical solutions to a horizontal architecture has resulted in the development of services which is the culmination of multiple sources of data. The complexity of handling the growing number of connected devices has resulted in active research of architectures and platforms which enable the service providers and data providers to navigate through the maze of technologies. We also look at the data generated by the real world, which is dynamic and non-stationary in nature. The always connected virtual representations of the devices facilitates applications to proactively perceive, comprehend and adapt to the real world situations. Paving the way to integrate learning algorithms, this thesis presents a modular architecture with elements to detect, respond and adapt to the changing data. Given the scope of IoT in different applications, we explore the implementation challenges both the advantages and limitations in two different domains. It includes (i) Smart Asset Management Framework: To provide real time localization of movable medical objects in a hospital. Additionally the movement patterns of the objects in studied and modeled to facilitate predictions. This helps to improve the energy savings of the localization technology. It helps the hospital authorities to understand the usage of the objects for efficient resource planning. (ii)Transitioning to a Industrial4.0 application: To facilitate in the digital transformation of a solar cell research center. With the similar concepts of virtualization (digital twins) and real world knowledge generation, the digital factory vision is conceptualized and implemented in phases.The supporting work including prototypes for smart home environment control, people activity detection and presence detection using bluetooth beacons leading to the development of the architectural components is presented. The implementation details along with results and observations in each of the sections is presented.