The pervasive application of Internet of Things (IoT) has been seen in many aspects in human daily life and industrial production. The concept of IoT originates from traditional machine-to-machine (M2M) communications which aimed at solving domain-specific and applicationspecific problems. Today, the rapid progress of communication technologies, the maturation of Internet infrastructures, the continuously reduced cost of sensors, and emergence of more open standards, have witnessed the approaching of the expected IoT era, which envisions full connectivity between the physical world and the digital world via the Internet protocol. The popularity of cloud computing technology has enhanced this IoT transform, benefiting from the superior computing capability and flexible data storage, let alone the security, reliability and scalability advantages.

However, there are still a series of obstacles confronted by the industry in deployment of IoT services. First, due to the heterogeneity of hardware devices and application scenarios, the interoperability and compatibility between link-layer protocols, sub-systems and back-end services are significantly challenging. Second, the device management requires a uniform scheme to implement the commissioning, communication, authorization and identity management to guarantee security. Last, the heterogeneity of data format, speed and storage mechanism for different services pose a challenge to further data mining.

This thesis aims to solve these aforementioned challenges by proposing a data-centric IoT framework based on public cloud platforms. It targets at providing a universal architecture to facilitate the deployment of IoT services in massive IoT and broadband IoT categories. The framework involves three representative communication protocols, namely WiFi, Thread and Lo-RaWAN, to enable support for local, personal, and wide area networks. A security assessment taxonomy for wireless communications in building automation networks is proposed as a tool to evaluate the security performance of adopted protocols, so as to mitigate potential network flaws and guarantee the security. Azure cloud platform is adopted in the framework to provide device management, data processing and storage, visualization, and intelligent services, thanks to the mature cloud infrastructure and the uniform device model and data model. We also exhibit the value of the study by applying the framework into the digitalization procedure of the green plant wall industry. Based on the framework, a remote monitoring and management system for green plant wall is developed as a showcase to validate the feasibility. Furthermore, three specialized visualization methods are proposed and a neuron network-based anomaly detection method is deployed in the project, showing the potential of the framework in terms of data analytics and intelligence.

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.

Visionen med Sakernas internet (IoT) är en värld där fysiska apparater är uppkopplade i en sådan grad att dess digitaliseringslösningar ger verklig samhällsnytta. Framsteg inom informations och kommunikationsteknologi har lett till utveckling av IoT-applikationer för olika ändamål. Dessa applikationer förväntas att få en betydande roll för samhället i stort såväl som i industriella sammanhang. Framväxten av molntjänster med kraftfulla lagrings, beräknings, analys och visualiseringsmöjligheter har accelererat användningen av IoT-teknologi. Trots den snabba utvecklingen så finns det flera utmaningar kvar, som exempelvis kompatibilitet mellan olika protokoll, delsystem och olika underliggande tjänster. Ett annat exempel är heterogenitet vad gäller datainsamling och kommunikation, vilket det ännu inte finns någon lösning för hos molntjänsteleverantörerna. I den här avhandlingen presenteras ett IoT-ramverk baserat på publika molntjänster som adresserar dessa utmaningar. Ramverket inkluderar stöd för WiFi, Thread och LoRaWAN nätverk för att möjliggöra ett brett utbud av IoT-applikationer. En taxonomi för säkerhetsbedömning av olika delar i berörda IoT-nätverk ingår också. Som en följd av att allt mer av beräkningarna i molnet decentraliserats ut i ändnoderna så har IoT-ramverket designats med hänsyn till det. Inkluderat är också en omfattande prestandaanalys av IoT-ramverkets stack för beräkningar i ändnoderna, i vilken kommunikation, beräkning, samt maskinlärning har utvärderats. En plattform med demonstratörer baserad på IoT-ramverket har designats och realiserats för växtväggsindustrin. Plattformen har även använts för att demonstrera hur maskininlärning kan tillämpas för att ge växtväggar intelligens att upptäcka anomalier. Baserat på detta forskningsarbete och erfarenheter från pilotinstallationer, så har en digitaliseringsmetodik utarbetats för att guida små och medelstora företag i den digitala transformation som IoT-teknologin medför. På detta sätt har en sann landning av IoT-tekniken i samhället demonstrerats.