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Decentralized Intelligent Decision Making In Cyber Physical Social Systems, Nathan Patrizi
Decentralized Intelligent Decision Making In Cyber Physical Social Systems, Nathan Patrizi
Electrical and Computer Engineering ETDs
The accelerated evolution towards jointly considering the physical, cyber, and social space is expected to dramatically increase the interest of the research and industrial community to build efficient, resilient, and secure Cyber Physical Social Systems. In this dissertation, we focus our research activities on devising decentralized intelligent decision making models, frameworks, and algorithms to support the smooth operation of Cyber Physical Social Systems. The proposed decentralized intelligent decision making models are jointly exploiting theories from the field of Economics, such as Game Theory and Contract Theory, and from the field of Computer Science, such as Reinforcement Learning concepts. Reinforcement learning …
Machine Learning Based Dynamic Power Dispatching And Smoothing Using Hybrid Energy Storage System For Renewable Energy Systems, Bhuvaneshwarr Ramalingam
Machine Learning Based Dynamic Power Dispatching And Smoothing Using Hybrid Energy Storage System For Renewable Energy Systems, Bhuvaneshwarr Ramalingam
Electrical and Computer Engineering ETDs
The stochastic fluctuations from Renewable Energy Resources (RER) have a great influence on power quality and off-grid communities. A combination of the different storage systems is accessible for RER generation intermittency and to bring about finest smoothing operating cycle compared to sole Energy Storage System (ESS). Additionally, energy management in Hybrid Energy Storage System (HESS) creates an uncertainty during power smoothing operation. This research materializes, an intelligent mechanism for power smoothing and dispatch with the introduction of hybridized storage that can accommodate the unpredictable behavior of RER under dynamic load. A feed-forward neural network is proposed as a power smoothing …
Artificial Intelligent Risk-Aware Autonomous Decision-Making In Resource-Constrained Computing Systems, Pavlos Athanasios Apostolopoulos
Artificial Intelligent Risk-Aware Autonomous Decision-Making In Resource-Constrained Computing Systems, Pavlos Athanasios Apostolopoulos
Electrical and Computer Engineering ETDs
Artificial Intelligent autonomous systems are becoming increasingly ubiquitous in daily life. Mobile devices for example provide mechanical-generated intelligent support to humans, with various degrees of autonomy, and are a key part of the recent autonomous revolution. Autonomous intelligent systems aim to understand and interact with their users in a timely manner, while many of them are characterized by constrained resources. Despite that, the average person does not act in a formulaic and risk-neutral manner but instead exhibits risk-aware attitudes when performing a task that includes sources of uncertainties. When humans make decisions, they explore their surroundings, understand the emerging risks, …
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 …
Satisfaction-Aware Data Offloading In Surveillance Systems, Marcos Paul Torres
Satisfaction-Aware Data Offloading In Surveillance Systems, Marcos Paul Torres
Electrical and Computer Engineering ETDs
In this thesis, exploiting Fully Autonomous Aerial Systems' (FAAS) and Mobile Edge Computing (MEC) servers' computing capabilities to introduce a novel data offloading framework to support the energy and time-efficient video processing in surveillance systems based on satisfaction games. A surveillance system is introduced consisting of Areas of Interest (AoIs), where a MEC server is associated with each AoI, and a FAAS is flying above the AoIs to support the IP cameras' computing demands. Each IP camera adopts a utility function capturing its Quality of Service (QoS) considering the experienced time and energy overhead to offload and process remotely or …
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 …