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Location Aware Task Offloading Framework For Edge Computing Empowered By Reconfigurable Intelligent Surfaces, Md Sahabul Hossain
Location Aware Task Offloading Framework For Edge Computing Empowered By Reconfigurable Intelligent Surfaces, Md Sahabul Hossain
Electrical and Computer Engineering ETDs
In this thesis, an energy efficient task offloading mechanism in a multi-access edge computing (MEC) environment is introduced, based on the principles of Contract Theory. The technology of Reconfigurable Intelligent Surfaces (RIS) is adopted and serves as the enabler for the energy efficient task offloading, from the perspective of location-awareness and improved communication environment. Initially a novel positioning, navigation, and timing solution is designed, based on the RIS technology and an artificial intelligent method that selects a set of RISs to perform the multilateration technique and determine the Internet of Things (IoT) nodes’ positions in an efficient and accurate manner …
Smart Energy Harvesting For Internet Of Things Networks, Fisayo Sangoleye
Smart Energy Harvesting For Internet Of Things Networks, Fisayo Sangoleye
Electrical and Computer Engineering ETDs
In this thesis, we address the problem of prolonging the battery life of Internet of Things (IoT) nodes by introducing a smart energy harvesting framework for IoT networks supported by femtocell access points (FAPs) based on the principles of Contract Theory and Reinforcement Learning. Initially, the IoT nodes’ social and physical characteristics are identified and captured through the concept of IoT node types. Then, Contract Theory is adopted to capture the interactions among the FAPs, who provide personalized rewards, i.e., charging power, to the IoT nodes to incentivize them to invest their effort, i.e., transmission power, to report their data …
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 …