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This thesis investigates the application of different types of intelligence, such as rule-based and machine learning, to implementing distributed intelligence at the edge of the network. A novel and generalizable distributed intelligence architecture is presented that leverages edge computing to enable the intelligence of things by utilizing information closer to IoT devices. The architecture is comprised of two tiers, which address the heterogeneity and constraints of IoT devices. Additionally, the thesis identifies a suitable reasoner for two-level distributed intelligence and an efficient way of applying it in the architecture via an IoT gateway. To mitigate communication challenges in edge computing, two-level mechanisms are proposed.
