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Artificial Intelligence Enabled Distributed Edge Computing For Internet Of Things Applications, Georgios Fragkos
Artificial Intelligence Enabled Distributed Edge Computing For Internet Of Things Applications, Georgios Fragkos
Electrical and Computer Engineering ETDs
Artificial Intelligence (AI) based techniques are typically used to model decision-making in terms of strategies and mechanisms that can conclude to optimal payoffs for a number of interacting entities, often presenting competitive behaviors. In this thesis, an AI-enabled multi-access edge computing (MEC) framework is proposed, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate Internet of Things (IoT) applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes’ communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure …
Artificial Intelligence Empowered Uavs Data Offloading In Mobile Edge Computing, Nicholas Alexander Kemp
Artificial Intelligence Empowered Uavs Data Offloading In Mobile Edge Computing, Nicholas Alexander Kemp
Electrical and Computer Engineering ETDs
The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs' data to be …