The electrical grid is experiencing a significant restructuring to face the challenges posed by the green transition. A key aspect in this restructuring process is the increased engagement of consumers as active players, who, through the participation in demand side management schemes, can deliver services to the grid. In this paper, a novel pricing mechanism composed of two dynamic volumetric parts is introduced which can be employed by a central coordinator (e.g., a DSO) to simultaneously: i) prevent congestions in the grid, and ii) recuperate its investment costs. To capture the interconnected objective functions, and coupling constraints between the central coordinator and the consumers, the problem is formulated as a generalized Nash equilibrium problem, which is solved using a distributed and privacy-friendly hybrid best-response plus dual-ascent algorithm with convergence guarantees. Moreover, the work considers that the consumers’ decision-making processes can deviate from full economic rationality and be driven by personal preferences about the tradeoff between their induced comfort and their economic savings. Numerical simulations corroborate the convergence of the proposed algorithm, and show the effectiveness of the proposed two-part tariff in eliminating grid congestions. The results highlight the negative impact of a reduced user price sensitivity on the collective costs of all consumers.

In this paper, a novel mathematical framework is introduced for modeling and analyzing the cyber-physical security of time-critical UAV applications. A general UAV security network interdiction game is formulated to model interactions between a UAV operator and an interdictor, each of which can be benign or malicious. In this game, the interdictor chooses the optimal location(s) from which to target the drone system by interdicting the potential paths of the UAVs. Meanwhile, the UAV operator responds by finding an optimal path selection policy that enables its UAVs to evade attacks and minimize their mission completion time. New notions from cumulative prospect theory (PT) are incorporated into the game to capture the operator's and the interdictor's subjective valuations of mission completion times and perceptions of the risk levels facing the UAVs. The equilibrium of the game, with and without PT, is then analytically characterized and studied, while providing detailed derivations of mission completion times (and their expected values), PT valuations of both players (along with proofs of their convergence), and equilibrium points under different studied security regimes. Novel algorithms are then proposed to reach the game's equilibria under both PT and classical game theory. Simulation results show the properties of the equilibrium for both the rational and PT cases, highlighting the effects of bounded rationality on the interdictor's and the operator's strategies as well as mission completion times, and the way it can be exploited by a fully rational opponent.

In this paper, a novel anti-jamming mechanism is proposed to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, the problem is formulated as a dynamic psychological game between a soldier and an attacker. In this game, the soldier seeks to accomplish a time-critical mission by traversing a battlefield within a certain amount of time, while maintaining its connectivity with an IoBT network. The attacker, on the other hand, seeks to find the optimal opportunity to compromise the IoBT network and maximize the delay of the soldier's IoBT transmission link. The soldier and the attacker's psychological behavior are captured using tools from psychological game theory, with which the soldier's and attacker's intentions to harm one another are considered in their utilities. To solve this game, a novel learning algorithm based on Bayesian updating is proposed to find an ∈ -like psychological self-confirming equilibrium of the game.

The use of unmanned aerial vehicles (UAVs) as delivery systems of online goods is rapidly becoming a global norm, as corroborated by Amazon's “Prime Air” and Google's “Project Wing” projects. However, the real-world deployment of such drone delivery systems faces many cyber-physical security challenges. In this paper, a novel mathematical framework for analyzing and enhancing the security of drone delivery systems is introduced. In this regard, a zero-sum network interdiction game is formulated between a vendor, operating a drone delivery system, and a malicious attacker. In this game, the vendor seeks to find the optimal path that its UAV should follow, to deliver a purchase from the vendor's warehouse to a customer location, to minimize the delivery time. Meanwhile, an attacker seeks to choose an optimal location to interdict the potential paths of the UAVs, so as to inflict cyber or physical damage to it, thus, maximizing its delivery time. First, the Nash equilibrium point of this game is characterized. Then, to capture the subjective behavior of both the vendor and attacker, new notions from prospect theory are incorporated into the game. These notions allow capturing the vendor's and attacker's (i) subjective perception of attack success probabilities, and (ii) their disparate subjective valuations of the achieved delivery times relative to a certain target delivery time. Simulation results have shown that the subjective decision making of the vendor and attacker leads to adopting risky path selection strategies which inflict delays to the delivery, thus, yielding unexpected delivery times which surpass the target delivery time set by the vendor.