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In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex Multi-access Edge Computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user's satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework's consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users' experienced performance compared to alternative approaches. 

In this paper, a flexible resource sharing paradigm is introduced, to enable the allocation of users’ computing tasks in a social cloud computing system offering both Virtual Machines (VMs) and Serverless Computing (SC) functions. VMs are treated as a safe computing resource, while SC due to the uncertainty introduced by its shared nature, is treated as a common pool resource, being susceptible to potential over-exploitation. These computing options are differentiated based on the potential satisfaction perceived by the user, as well as their corresponding pricing, while taking into account the social interactions among the users. Considering the inherent uncertainty of the considered computing environment, Prospect Theory and the theory of the Tragedy of the Commons are adopted to properly reflect the users’ behavioral characteristics, i.e., gain-seeking or loss-averse behavior, as well as to formulate appropriate prospect-theoretic utility functions, embodying the social-aware and risk-aware user’s perceived satisfaction. A distributed maximization problem of each user’s expected prospect-theoretic utility is formulated as a non-cooperative game among the users and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal computing jobs offloading to the VMs and the SC, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation. 

In this paper, we exploit the capabilities of Fully Autonomous Aerial Systems' (FAAS) and the Mobile Edge Computing (MEC) to introduce a novel data offloading framework and support the energy and time efficient video processing in surveillance systems based on game theory in satisfaction form. 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 collectively 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 its data either remotely or locally. A non-cooperative game among the cameras is formulated to determine the amount of offloading data to the MEC server and/or the FAAS. The novel concept of Satisfaction Equilibrium (SE) is introduced where the IP cameras satisfy their minimum QoS prerequisites instead of maximizing their performance by wasting additional system resources. A distributed learning algorithm determines the IP cameras' stable data offloading, while a reinforcement learning algorithm determines the FAAS's movement among the AoIs exploiting the accuracy, timeliness, and certainty of the collected data by the IP cameras per AoI. The performance evaluation of the proposed framework is achieved via modeling and simulation.