Enable the landing of Internet of Things: a holistic approach

Sammanfattning: Internet of Things (IoT) envisions a world where physical assets are fully connected with the Internet infrastructure to provide digital services.  With the advancement of information and communication technologies, IoT applications have experienced a growth in many industries and are anticipated to reshape the landscape of social life and industry production. The emergence of cloud computing has accelerated the widespread employment of IoT technologies, benefiting from superb computation, storage, analytics and visualization capabilities. However, the landing of IoT still encounters several open challenges, i.e., interoperability and compatibility between link layer protocols, subsystems, and back-end services. Moreover, a uniform scheme for device management and the heterogeneity of data have not been tackled by cloud suppliers. In this dissertation, a data-centric IoT framework based on public cloud is presented to address these challenges. It features WiFi, Thread, and LoRaWAN networks to provide support for personal, local and wide area networks so as to enable wide coverage of IoT applications. A security analysis taxonomy is proposed to perform security assessment of IoT field networks and enhance security considerations. In light of the recent industrial tendency that cloud computing is evolving towards edge-cloud computing, further reinforcement of the IoT framework is proposed with the novel edge-cloud computing paradigm. A comprehensive performance evaluation of the edge-cloud computing stack is conducted, while the communication, computing and intelligence capabilities are thoroughly studied for future cloud and edge computing enabled IoT applications. Furthermore, the cloud and edge computing enabled IoT landing with a digitalization practice is showcased in the vertical plant wall industry. A remote monitoring and management system for indoor climate control has been developed based on the IoT framework. As a further step, it is also demonstrated how machine learning can be leveraged to achieve artificial intelligence in IoT with a case study, i.e., anomaly detection for indoor climate. Based on the expertise we accumulated from the industry digitalization practice, a reference framework that intends to guide small and medium sized enterprises to perform IoT enabled digital transformation is proposed. In this way, a true landing of the IoT technology in the society has been demonstrated.