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1. Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

   https://doi.org/10.3390/s20082434  

   Mitsis, Giorgos ; Tsiropoulou, Eirini Eleni ; Papavassiliou, Symeon ( April 2020 , Sensors)

   null (Ed.)

   Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits. 

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2. Risk-Aware Resource Management in Public Safety Networks

   https://doi.org/10.3390/s19183853  

   Vamvakas, Panagiotis ; Tsiropoulou, Eirini Eleni ; Papavassiliou, Symeon ( September 2019 , Sensors)

   null (Ed.)

   Modern Public Safety Networks (PSNs) are assisted by Unmanned Aerial Vehicles (UAVs) to provide a resilient communication paradigm during catastrophic events. In this context, we propose a distributed user-centric risk-aware resource management framework in UAV-assisted PSNs supported by both a static UAV and a mobile UAV. The mobile UAV is entitled to a larger portion of the available spectrum due to its capability and flexibility to re-position itself, and therefore establish better communication channel conditions to the users, compared to the static UAV. However, the potential over-exploitation of the mobile UAV-based communication by the users may lead to the mobile UAV’s failure to serve the users due to the increased levels of interference, consequently introducing risk in the user decisions. To capture this uncertainty, we follow the principles of Prospect Theory and design a user’s prospect-theoretic utility function that reflects user’s risk-aware behavior regarding its transmission power investment to the static and/or mobile UAV-based communication option. A non-cooperative game among the users is formulated, where each user determines its power investment strategy to the two available communication choices in order to maximize its expected prospect-theoretic utility. The existence and uniqueness of a Pure Nash Equilibrium (PNE) is proven and the convergence of the users’ strategies to it is shown. An iterative distributed and low-complexity algorithm is introduced to determine the PNE. The performance of the proposed user-centric risk-aware resource management framework in terms of users’ achievable data rate and spectrum utilization, is achieved via modeling and simulation. Furthermore, its superiority and benefits are demonstrated, by comparing its performance against other existing approaches with regards to UAV selection and spectrum utilization. 

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3. Data Offloading in UAV-assisted Multi-access Edge Computing Systems under Resource Uncertainty

   https://doi.org/10.1109/TMC.2021.3069911  

   Apostolopoulos, Pavlos Athanasios ; Fragkos, Georgios ; Tsiropoulou, Eirini Eleni ; Papavassiliou, Symeon ( January 2021 , IEEE Transactions on Mobile Computing)

   null (Ed.)

   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. 

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4. Artificial Intelligence Enabled Distributed Edge Computing for Internet of Things Applications

   https://doi.org/10.1109/DCOSS49796.2020.00077  

   Fragkos, Georgios ; Tsiropoulou, Eirini Eleni ; Papavassiliou, Symeon ( May 2020 , 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS))

   null (Ed.)

   Artificial Intelligence (AI) based techniques are typically used to model decision making in terms of strategies and mechanisms that can result in optimal payoffs for a number of interacting entities, often presenting antagonistic behaviors. In this paper, we propose an AI-enabled multi-access edge computing (MEC) framework, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate 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 Nash Equilibrium (PNE) point is shown by proving that the game is submodular. Furthermore, different operation points (i.e. offloading strategies) are obtained and studied, based either on the outcome of Best Response Dynamics (BRD) algorithm, or via alternative reinforcement learning approaches (i.e. gradient ascent, log-linear, and Q-learning algorithms), which explore and learn the environment towards determining the users' stable data offloading strategies. The corresponding outcomes and inherent features of these approaches are critically compared against each other, via modeling and simulation. 

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5. P2P Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning

   https://doi.org/10.1145/3603148  

   Timilsina, Ashutosh ; Silvestri, Simone ( May 2023 , ACM Transactions on Evolutionary Learning and Optimization)

   Peer-to-peer (P2P) energy trading is a decentralized energy market where local energy prosumers act as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling, assume users’ sustained active participation, and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this paper we propose an automated P2P energy trading framework that specifically considers the users’ perception by exploiting prospect theory . We formalize an optimization problem that maximizes the buyers’ perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm for Trading Energy ( DEbATE ) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers’ profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity ( PQR ), is based on Q-learning. Additionally, the given scalability issues of PQR , we propose a Deep Q-Network-based algorithm called ProDQN that exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve \(26\% \) higher perceived value for buyers and generate \(7\% \) more reward for sellers, compared to recent state-of-the-art approaches. 

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