Efficient data management for the Internet of Things

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Αμαξηλάτης, Δημήτριος
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Technological developments in recent years in both hardware and software have led to an explosion of devices and services in what is often called the Internet of Things. Today in every home in the US there are 11 "smart" devices with sensors capable of accurately describing and watching their environment. This trend is expected to continue further in the future, with this figure rising to 500 in 2022, sharply increasing the volume of information that can be exported from human activity. This plethora of data creates enormous potential for developing applications and services for users to improve their life parameters (from personal comfort to health or transport). The secure and effective processing of these data is a major problem, for which no specific solution has been widely adopted so far to address the significant problems of personal data protection and the efficient management of such data. Technologies such as IPv6 promote cloud management by directly exposing all devices over the Internet and thus allowing easy interaction and remote control, as opposed to Bluetooth LE, where local device management is the only available solution and selected information is published on the Internet using gateway devices. Finally, it is particularly important to enrich the data with external data such as meteorological forecasts or data on traffic and public transport routes. In this thesis, we are studying the real-time monitoring and organization of sensing infrastructures according to the changing requirements of applications and users. The aim is to design, implement and evaluate experimental self-organizing mechanisms using semantic information to improve the quality of data flows provided at the Internet level. As part of this process, we also seek to combine flows to more efficiently share and manage information. The mechanisms we implement are based on the "semantic entities" model, either as a part of the device network or as part of a web service aiming at balancing computing and storage requirements at the various levels of the network hierarchy. The goal is also to study new data processing and tracking techniques to draw appropriate conclusions, predictions and decisions. In this thesis, we studied Internet of Things sensing infrastructures of multiple sizes in terms of the technologies and methods that are needed to appropriately organize and analyze data the data streams that are form from the installed devices to the cloud level. The aim was to design, implement and evaluate by experimental methods flexible mechanisms for representing the semantic information of the installations, generating statistical analysis on the data flows and finally provide a better understanding on the conditions of the environments monitored. As part of this process, the analysis of the data flows is used to generate “knowledge” and find problematic and defective devices or calculate predictions on their future values.
Internet of Things, Smart devices, Cloud