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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. 

Recent technological advances in the use of Unmanned Aerial Vehicles (UAVs) and Wireless Powered Communications (WPC) have enabled the energy efficient operation of the Public Safety Networks (PSN) during disaster scenarios. In this paper, an energy efficient information flow and energy harvesting framework capturing users' risk-aware characteristics is introduced based on the principles of Contract Theory. To better support the operational effectiveness of the proposed framework, users are clustered in rescue groups following a socio-physical-aware group formation mechanism, while rescue leaders for each group are selected. A reinforcement learning approach is applied to enable the optimal matching between the UAVs and the rescue leaders in a distributed and efficient manner. The proposed contract-theoretic framework models the UAVs-victims relation based on a labor market setting via offering rewards to the users (incentives) in order to compensate them for their invested labor (reporting information). Detailed numerical results demonstrate the benefits and superiority of the proposed framework under different settings. 

In this paper an Unmanned Aerial Vehicles (UAVs) - enabled dynamic multi-target tracking and data collection framework is presented. Initially, a holistic reputation model is introduced to evaluate the targets' potential in offloading useful data to the UAVs. Based on this model, and taking into account UAVs and targets tracking and sensing characteristics, a dynamic intelligent matching between the UAVs and the targets is performed. In such a setting, the incentivization of the targets to perform the data offloading is based on an effort-based pricing that the UAVs offer to the targets. The emerging optimization problem towards determining each target's optimal amount of offloaded data and the corresponding effort-based price that the UAV offers to the target, is treated as a Stackelberg game between each target and the associated UAV. The properties of existence, uniqueness and convergence to the Stackelberg Equilibrium are proven. Detailed numerical results are presented highlighting the key operational features and the performance benefits of the proposed framework. 

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. 

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 offloaded to the selected MEC servers, while the existence of at least one Nash Equilibrium (NE) is proven by exploiting the power of submodular games. A best response dynamics framework and two alternative reinforcement learning algorithms are introduced that converge to an NE, and their tradeoffs are discussed. The overall framework performance evaluation is achieved via modeling and simulation, in terms of its efficiency and effectiveness, under different operation approaches and scenarios. 

Given the substantial penetration of social networks in citizens' everyday life activities, the success of a public safety system depends on the citizens' incentivization by the Emergency Control Center (ECC), and their effective effort contribution in the overall disaster management operation. In this paper, we introduce a formal method based on the principles of Contract Theory, to identify the optimal rewards to the citizens from the ECC's perspective, and the optimal invested effort from the citizens' side, referred to as contract pairs. The identification of these contract pairs (i.e., rewards and respective efforts) between the ECC and each citizen, depend on each citizen's social and communication characteristics that are used to define their specific type and profile, while they are properly reflected in the corresponding designed utility functions to be optimized. The problem under consideration is treated for both cases of complete (ideal) and incomplete (realistic) information availability, with respect to the level of knowledge of the ECC about the exact type of each citizen. The overall framework was evaluated via modeling and simulation, in terms of its efficiency and effectiveness, by studying multiple operation approaches and scenarios. 

Electronic money is the digital representation of physical banknotes enabling offline and online payments. An electronic e-Cash scheme, termed PUF- Cash was proposed in prior work. PUF-Cash preserves user anonymity by leveraging the random and unique statistical properties of physically unclonable functions (PUFs). PUF-Cash is extended meaningfully in this work by the introduction of multiple trusted third parties (TTPs) for token blinding and a fractional scheme to diversify and mask Alice's spending habits from the Bank. A reinforcement learning (RL) framework based on stochastic learning automata (SLA) is proposed to efficiently select a subset of TTPs as well as the fractional amounts for blinding per TTP, based on the set of available TTPs, the computational load per TTP and network conditions. An experimental model was constructed in MATLAB with multiple TTPs to verify the learning framework. Results indicate that the RL approach guarantees fast convergence to an efficient selection of TTPs and allocation of fractional amounts in terms of perceived reward for the end-users. 

In this paper, an Unmanned Aerial Vehicles (UAVs) - enabled human Internet of Things (IoT) architecture is introduced to enable the rescue operations in public safety systems (PSSs). Initially, the first responders select in an autonomous manner the disaster area that they will support by considering the dynamic socio-physical changes of the surrounding environment and following a set of gradient ascent reinforcement learning algorithms. Then, the victims create coalitions among each other and the first responders at each disaster area based on the expected- maximization approach. Finally, the first responders select the UAVs that communicate with the Emergency Control Center (ECC), to which they will report the collected data from the disaster areas by adopting a set of log-linear reinforcement learning algorithms. The overall distributed UAV-enabled human Internet of Things architecture is evaluated via detailed numerical results that highlight its key operational features and the performance benefits of the proposed framework. 

This paper introduces a Multi-Agency DisAster Management (MADAM) framework for Unmanned Aerial Vehicle (UAV)-assisted public safety systems, based on the principles of game theory and reinforcement learning. Initially, the information quality and criticality (IQC) provided by each agency to an UAV-assisted public safety network is introduced and quantified, and the concept of Value of Information (VoI) that measures each agency’s positive contribution to the overall disaster management process is defined. Based on these, a holistic cost function is adopted by each agency, reflecting its relative abstention from the information provisioning process. Each agency aims at minimizing its personal cost function in order to better contribute to the disaster management. This optimization problem is formulated as a non-cooperative game among the agencies and it is proven to be an exact potential game, thus guaranteeing the existence of at least one Pure Nash Equilibrium (PNE). We propose a binary log-linear reinforcement learning algorithm that converges to the optimal PNE. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority compared to other approaches is demonstrated. 

Electronic money or e-Cash is becoming increasingly popular as the preferred strategy for making purchases, both on- and off-line. Several unique attributes of e-Cash are appealing to customers, including the convenience of always having "cash-on-hand" without the need to periodically visit the ATM, the ability to perform peer-to-peer transactions without an intermediary, and the peace of mind associated in conducting those transactions privately. Equally important is that paper money provides customers with an anonymous method of payment, which is highly valued by many individuals. Although anonymity is implicit with fiat money, it is a difficult property to preserve within e-Cash schemes. In this paper, we investigate several artificial intelligence (AI) approaches for improving performance and privacy within a previously proposed e-Cash scheme called PUF-Cash. PUF-Cash utilizes physical unclonable functions (PUFs) for authentication and encryption operations between Alice, the Bank and multiple trusted third parties (mTTPs). The AI methods select a subset of the TTPs and distribute withdrawal amounts to maximize the performance and privacy associated with Alice's e-Cash tokens. Simulation results show the effectiveness of the various AI approaches using a large test-bed architecture.