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.

This paper presents the innovative ground-based Positioning, Navigation, and Timing (PNT) solution, named Dead-on-Target (DoT), which addresses the challenge of secure navigation support for search and rescue and military operations in GPS-denied environments. DoT leverages the matching theory and coalition games to optimize PNT services. Initially, a realistic operational field is designed, with anchor nodes and targets, operating in GPS-denied conditions. Then, the Approximate Dead-on-Target (ADoT) framework is introduced, based on the theory of matching games, to facilitate the selection of optimal anchor nodes for the targets, while the latter ones reside in the Forward Operating Base (FOB). Also, the Accurate Dead-on-Target (AccDoT) solution, grounded in coalition games, enables the collaborative selection of anchor nodes during rescue operations. The paper provides comprehensive numerical results and real-life field testing, demonstrating the effectiveness and scalability of the ADoT and AccDoT algorithms, and offering insights into various operational formations during real-life rescue operations. © 2024 IEEE.

Crowdsourcing has been widely employed to collect information, either at regional or global scales, about different phenomena, by engaging user communities, in order to complement or even substitute other specialized and expensive means and sources of data. In such a setting, the design of crowdsourcing models that can jointly provide appropriate rewards to the users in order to incentivize them to participate in the crowdsourcing process, while at the same time provide the necessary information to potentially various tasks (mapped to different geographical areas) announced by a requester is of high research and practical importance. In this paper, a novel dynamic crowdmapping framework is introduced, to enable the users autonomously select the geographical area, and thus corresponding task, where they will contribute their available information based on a hedonic coalition formation game. Based on the proposed hedonic coalition formation game, the requester also allocates appropriate rewards to the users considering their quality and quantity of information. The existence of a Nash-stable and individual-stable coalition formation is proven and a hedonic coalition formation algorithm is introduced to determine the stable coalition formation. The performance evaluation of the proposed framework is achieved via modeling and simulation. © 2023 IEEE.

The continuous growth of mobile data traffic with strict time requirements calls for the development of sustainable edge content caching solutions. This becomes more prominent in the setting of Information-Centric Networking (ICN) architectures, where different ICNs are leasing caching slices to the available content providers (CPs), who in turn offer their services to the users. In this paper, we introduce a novel symbiotic content caching framework that provides the necessary economic benefits to the ICNs and the CPs to mutualistically collaborate among each other. The corresponding mutualistic interactions among the ICNs and CPs are studied under the prism of two different market-based models, namely the competitive market and oligopoly market models. A game-theoretic approach is followed in order to determine the optimal leasing prices of the ICNs' content caching slices and the CPs' optimal amount of leased caching memory from the ICNs. The performance evaluation of the proposed new symbiotic content caching paradigm is achieved via modeling and simulation, while the benefits and drawbacks of the competitive and oligopoly market modeling approaches are revealed and discussed. © 2023 IEEE.

In this paper, the challenge of dynamic spectrum management is treated following an economic-based perspective. In particular, a novel bioinspired spectrum allocation from the Network Service Providers (NSPs) to the users is introduced, exploiting the theory of symbiosis under the free competition. The overall objective is to determine the NSPs optimal band-width prices to maximize their profit, while satisfying the users bandwidth needs. The aforementioned spectrum management framework is analyzed while exploiting a novel 3D network architecture consisting of High Altitude Platforms (HAPs), Unmanned Aerial Vehicles (UAVs), and ground gNBs NSPs, each one of them presenting different operational characteristics and capabilities in terms of coverage. Specifically, various NSPs owning the ground gNBs, UAVs, HAPs, respectively, engage in a symbiotic relationship with the users, by offering licensed and unlicensed spectrum bands and receiving payment from them in return. The Nash equilibrium is determined for the free market modeling, where the optimal unlicensed bandwidth slices' prices for each NSP are derived. The performance evaluation of the proposed approach is achieved via modeling and simulation. © 2023 IEEE.