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.

Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats. In this article, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-to-end proof of concept of our model. Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.

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.

Blockchains are being recently used as a supporting technology framework for decentralized applications requiring functionalities such as exchange of value through tokens, cryptocurrency and smart contracts. In this paper, we have developed a decentralized application model in Python, where blockchain data are stored in a Neo4j graph database. Following the basic principles of Ethereum blockchain network, we implemented a Casper-like consensus mechanism and tested its effectiveness in achieving finality. For block proposing, we employed both Proof of Work and Proof of Stake protocols and examined how participants’ incentives and consensus criteria differ according to each one. A major part of this work is to incorporate the graph model in the functionality of the blockchain and its components, while also exploiting its benefits in data analysis by finding relationships between data and extracting their true value. Through this approach, we were able to monitor and visualize changes in blockchain data in various use case scenarios. Lastly, we ran a series of simulated experiments to test the efficiency of the implemented technologies and mechanisms in preventing the most common blockchain attacks such as the 51% Attack, Catastrophic Crashes and Attack from dynamic validator sets. We show how the modelling of the blockchain data as a distributed graph can assist protocols operations, enhance their security, and facilitate the application of analytical methods to the stored information through path-dependent queries.

Lately, the deployment of heterogeneous wireless networks has emerged, as part of the 5G vision, to cope with the users’ exaggerated service demands. In this context, the application of Non-Orthogonal Multiple Access (NOMA) technique constitutes a promising solution to facilitate a balance between spectral efficiency and system complexity. In this article, we consider the problem of joint user association and uplink power allocation, in heterogeneous 5G wireless networks, employing NOMA technology. The coupled problem is treated under an incomplete information scenario, where the Base Stations (BSs) have statistical only knowledge of the users’ channel conditions. To deal with the incompleteness of Channel State Information (CSI), a Contract Theory (CT) based approach is introduced. A Reinforcement Learning (RL) based methodology, capitalizing on the provided feedback from the communication environment is initially adopted, in order to achieve the users to BS association in an iterative and distributed manner. The problem of uplink power allocation is subsequently formulated as a contract between each BS and its corresponding users. The optimal power is thus obtained as the solution of the optimization of each BS’s utility function, while ensuring the optimality of the utility function of each associated user, given the unique communications characteristics and type of each user. Detailed numerical evaluation of the performance of the proposed unified user association and power allocation framework is provided, via modeling and simulation, illustrating its operation, features and benefits, under densely deployed heterogeneous environments.

Natural disasters and terrorist attacks pose a significant threat to human society, and have stressed an urgent need for the development of comprehensive and efficient evacuation strategies. In this paper, a novel evacuation-planning mechanism is introduced to support the distributed and autonomous evacuation process within the operation of a public safety system, where the evacuees exploit the capabilities of the proposed ESCAPE service, towards making the most beneficial actions for themselves. The ESCAPE service was developed based on the principles of reinforcement learning and game theory, and is executed at two decision-making layers. Initially, evacuees are modeled as stochastic learning automata that select an evacuation route that they want to go based on its physical characteristics and past decisions during the current evacuation. Consequently, a cluster of evacuees is created per evacuation route, and the evacuees decide if they will finally evacuate through the specific evacuation route at the current time slot or not. The evacuees’ competitive behavior is modeled as a non-co-operative minority game per each specific evacuation route. A distributed and low-complexity evacuation-planning algorithm (i.e., ESCAPE) is introduced to implement both the aforementioned evacuee decision-making layers. Finally, the proposed framework is evaluated through modeling and simulation under several scenarios, and its superiority and benefits are revealed and demonstrated.