Faculty of Engineering and Information Technologies, School of Information Technologies
Abstract
Online data stream processing in Internet of Things (IoT) systems is an emerging paradigm that allows users to use resource-constrained IoT devices with the back- end of resourceful machines to process the data collected from the physical world in a real-time manner. The huge amount of generated sensor data can produce value- added information with different purposes for several applications. Techniques to pro- mote knowledge discovery from the raw data allow fully exploiting the potential usage of wide spread sensors in the IoT. In this context, using the energy of the resource- constrained IoT devices in an efficient way is a major concern. However, the appli- cation of QoS requirements should not be ignored to achieve the purpose of energy saving at any cost. In this thesis, we propose a framework that combines online stream data processing with adaptive system control to address both needs. The online algo- rithms are based on statistical methods to meet the needs of stream data processing. The result of the algorithms are then used to dynamically control the system behaviour to meet the needs of energy-saving. Simulation results show the effectiveness of our proposed framework
