The effective evacuation during disaster scenarios, either physical or man-made, is critical in order to ensure the safety and survival of the victims, minimize the casualties, and facilitate the rapid and organized movement of people to safety. In this paper, the EVACUSCAPE model is introduced to optimize the matching between victims and evacuation routes during an evacuation process. Initially, the characteristics of both the victims and the evacuation routes are analyzed to establish a foundational matching mechanism among them. The Approximate EVACUSCAPE algorithm is developed to perform an initial matching between the victims and the evacuation routes by disregarding externalities that influence the victims' decisions, such as the actions of other evacuees. Then, the Accurate EVACUSCAPE algorithm refines the matching process by incorporating the principles of coalition games to account for these externalities and ultimately derive an optimal and stable evacuation strategy. A comprehensive evaluation using real-world datasets demonstrates the effectiveness and robustness of the EVACUSCAPE model, which significantly outperforms conventional evacuation strategies where the victims select routes based solely on proximity or time-optimization in a selfish manner.

Edge computing has emerged as a transformative technology in public safety and has the potential to support the rapid data processing and real-time decision-making during critical events. This paper introduces the BRAVE framework, a cutting-edge solution where the UAVs act as Mobile Edge Computing (MEC) servers, addressing users’ computational demands across disaster-stricken areas. An accurate UAV energy consumption model is introduced, including the UAV's travel, processing, and hover energy. BRAVE accounts for both the users’ Quality of Service (QoS) requirements, such as latency and energy constraints, and UAV energy limitations in order to determine the UAVs’ optimal path planning. The BRAVE framework consists of a two-level decision-making mechanism: a submodular game-based model ensuring the users’ optimal data offloading strategies, with provable Pure Nash Equilibrium properties, and a reinforcement learning-driven UAV path planning mechanism maximizing the data collection efficiency. Furthermore, the framework extends to collaborative multi-agent reinforcement learning (BRAVE-MARL), enabling the UAVs’ coordination for enhanced service delivery. Extensive experiments validate the BRAVE framework's adaptability and effectiveness and provide tailored solutions for diverse public safety scenarios. © 2025

Precise positioning, navigation, and timing (PNT) capabilities are essential for numerous critical infrastructure systems and advanced location-dependent applications. The challenges related to the reliability and accuracy of traditional Global Navigation Satellite Systems (GNSS) have driven the pursuit of innovative alternative PNT methodologies. This paper presents a novel approach inspired by the concept of biological symbiosis and leveraging the advanced capabilities of Reconfigurable Intelligent Surfaces (RISs). The proposed framework establishes a cooperative interaction between targets with unknown positions and collaborator nodes with approximate location estimates. These interactions are supported by the RISs and the anchor nodes with known positions. The objective is to minimize errors in positioning and timing for both the targets and the collaborators. This challenge is modeled as a non-cooperative game, and the existence of a Nash Equilibrium is demonstrated using potential game theory. To solve the game, Best Response Dynamics and a log-linear Reinforcement Learning (RL)-based approach are developed to identify the equilibrium state. The proposed system is thoroughly evaluated through simulations, in order to demonstrate its performance and the key trade-offs between game-theoretic strategies and the RL-based solutions.

This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection. © 2024 by the authors.

Multi-provider multi-user multi-access edge computing provides a recent market-driven networking paradigm facilitating the user data offloading process. In this paper we introduce the AGORA framework, which employs a sophisticated multi-leader multi-follower Stackelberg game that jointly optimizes the data offloading, computing resource allocation, and computing resource pricing, all facilitated through a non-cooperative game-theoretic approach. In order to support the aforementioned modeling and approach, a novel utility function that quantifies the users satisfaction, factoring in the computing service cost, and an innovative profit function for the MEC providers is introduced, emphasizing the market penetration and the computing service provision costs. Numerical results, obtained via modeling and simulation, demonstrate AGORA's remarkable adaptability, accommodating homogeneous and heterogeneous user computing demands, while simultaneously outperforming proportional fairness resource allocation approaches, and significantly enhancing the MEC providers' profitability and the users' satisfaction from the edge computing services. © 2024 IEEE.

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms' trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers' monetary incentives, profit, and the platforms' trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms' monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms' reputation. Simulation results demonstrate TOPMG's operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies. © 2024 IEEE.

The efficient operation of the unified Integrated Sensing and Communication (ISAC) - Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices' externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices' externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia's operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability. © 2024 IEEE.

In the emerging landscape of Integrated Sensing and Communication (ISAC) networks, achieving energy efficiency while concurrently performing sensing and communication tasks remains challenging. This paper introduces the GENESIS framework, a novel solution that empowers User Equipment (UEs) to make informed decisions regarding their transmission power allocation, optimizing the energy efficiency of sensing, communication, and data reporting to the gNB (gNodeB) functions. Initially, a novel ISAC network paradigm is proposed, where the gNB employs rewards, such as monetary incentives, to motivate UEs to engage in sensing, data collection, and reporting within its coverage area based on the principles of Contract Theory. The proposed GENESIS framework integrates the incentive mechanism with an optimal resource management technique which facilitates UEs to make energy-efficient decisions that balance their dual roles of sensing and communication, distributedly, while maximizing overall energy efficiency. The resulting multi-variable resource management problem is formulated as a non-cooperative game, establishing the existence and uniqueness of a Nash Equilibrium. Through modeling and simulation, we demonstrate GENESIS benefits, showcasing its energy-efficient operation and rapid convergence to optimal operational points. © 2024 IEEE.

Integrated Access and Backhaul (IAB) networking paradigm and the use of mm-wave technology have emerged as key enablers for the deployment of B5G/6G systems. In this paper we introduce the SynergyWave framework that empowers the IAB nodes and the users to independently optimize their transmission power levels, while simultaneously the IAB nodes perform optimal bandwidth splitting across the access and backhaul links. The key objective of SynergyWave framework is the enhancement of the energy efficiency of each participating entity in a decentralized and autonomous manner. Exploiting the channel modeling framework established by the 3rd Generation Partnership Project (3GPP) for mm-wave networks, we initially model the achievable data rate for both the access and backhaul links in the IAB network. Subsequently, a two-stage energy efficiency optimization problem is formulated and treated based on a Stackelberg game theoretic approach. In particular, it models and optimizes resource allocation in mm-wave IAB networks, determining optimal bandwidth splitting and uplink transmission power levels for IAB nodes and their users. The SynergyWave framework is assessed via modeling and simulation, and the obtained numerical results demonstrate that substantial energy efficiency improvements can be achieved for both users and IAB nodes. © 2024 IEEE.

Alternative Positioning, Navigation, and Timing (PNT) solutions are important for ensuring robust and reliable communication, navigation, and timing services, especially in environments where traditional Global Navigation Satellite Systems (GNSS) may be unavailable or compromised. In this paper, we introduce the MERCURY mechanism, leveraging reinforcement learning and multilateral bargaining game theory, alongside the Reconfigurable Intelligent Surface (RIS) technology, to optimize RIS selection, allocation of RIS elements, and phase shifts to develop a novel alternative PNT solution. These optimizations aim to minimize the PNT error and accurately determine the targets, positions. Our contributions include designing an Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm for autonomous RIS selection, proposing a game-theoretic model for distributed RIS elements allocation, and developing a phase shift optimization model. Through simulation-based experiments, we demonstrate the real-time applicability and superiority of the MERCURY mechanism in improving PNT system performance and mitigating targets' PNT error. © 2024 IEEE.