The growing demand for cost-effective and efficient renewable energy solutions has driven the adoption of Concentrated Solar Power (CSP) systems. However, the high installation and maintenance costs of wired heliostat control systems remain a significant barrier. This paper introduces SOLARNET, a novel wireless communication framework designed to optimize CSP field operations through intelligent clustering and interference management. SOLARNET leverages a dynamic clustering mechanism, where heliostats autonomously form clusters to minimize interference and ensure reliable communication, even in densely populated CSP fields. A key contribution of this work is the development of a path loss model specifically tailored for CSP environments, derived from real-world data collected at the National Solar Thermal Test Facility. The proposed system supports both Closed-Loop Autocalibration (CLA) and Non-Closed-Loop Autocalibration (NCLA) operations, and guarantees low-latency communication and high reliability under varying solar conditions. Extensive simulations demonstrate SOLARNET’s ability to maintain optimal performance, even in worst-case scenarios, with end-to-end latency and signal-to-interference-plus-noise ratio (SINR) consistently meeting operational constraints. By reducing costs and improving efficiency, SOLARNET paves the way for scalable and sustainable CSP systems, offering a robust solution for the future of solar energy management.

Global Navigation Satellite Systems (GNSS) are critical for enhancing the precise Positioning, Navigation, and Timing (PNT). However, GNSS signals are often disrupted by interference, jamming, or spoofing, especially in harsh environments blocked by obstacles or by intentional intervention of malicious users. To address these challenges, we introduce GRAPPE solution, which is a game-theoretic approach for precise target positioning in GNSS-denied environments. GRAPPE leverages the Potential Game Theory to enable distributed nodes to collaboratively and autonomously determine the position of a target, such as an enemy in military scenarios or a victim in civilian search-and-rescue operations. We model the interactions among the nodes and provide the existence of a Pure Nash Equilibrium (PNE). Two learning-based algorithms, Strategic Optimization Dynamics (SOD) and Comprehensive Exploration Dynamics (CED), are designed to determine the PNE by mainly focusing on exploitation and exploration, respectively. Detailed simulation results demonstrate that GRAPPE outperforms existing PNT solutions in terms of superior accuracy and scalability.

Integrated Sensing and Communication (ISAC) has become critical in public safety and disaster response operations given its ability to jointly support real-time data collection and efficient communication. In this paper, the DRAGON framework is introduced to address existing research gaps by introducing a reinforcement learning-based approach that enables the users to autonomously select UAVs for communication and optimize the resource allocation and task incentives allocation. A Stackelberg game-theoretic approach enables the DRAGON framework to ensure the users' efficient sensing and communication in disaster areas by prioritizing both the users' utility and the UAVs' operational energy efficiency. Comprehensive experiments show the superior performance of DRAGON over other existing models and also validate its scalability and real-world applicability.

Ground-based positioning solutions are critical in featureless, Global Positioning System or GPS-denied environments, where traditional infrastructure-based methods are unavailable. This paper introduces PIONEER a novel ground-based positioning solution that enables the targets to collaboratively determine their positions without reliance on external infrastructure. PIONEER operates by utilizing the received signal strength among targets to minimize their positioning errors and reduce the pseudorange measurement errors, thus enhancing the overall positioning accuracy. PIONEER models the targets' interactions as a potential game with a proven Pure Nash Equilibrium (PNE), guiding the optimal transmission power levels and the peer-targets selection. Two learning-based distributed algorithms, Best Response and Better Reply Dynamics, are introduced to determine the PNE based on the exploitation and exploration processes, respectively. Experimental results conducted in featureless terrains demonstrate the PIONEER's operational advantages, outperforming existing alternatives in reducing the positioning errors and proving its real-world applicability.

Wireless communication systems contribute to the design of next-generation Concentrated Solar Power (CSP) fields by enabling cost-effective real-time control of the heliostats. However, their performance remains vulnerable to the seasonal environmental dynamics which can impact the real-time communication of the heliostats with the central station. This paper investigates how the changing solar geometries across the seasons impact the wireless communication channel characteristics, the communication latency, and the CSP system’s reliability in large-scale deployments. We propose a novel wireless communication architecture leveraging the Integrated Access and Backhaul (IAB) technology along with a dynamic bandwidth splitting and an adaptive clustering mechanism in order to address the impact of the seasonal dynamics on the wireless communication in the CSP fields. Detailed emulation-based experiments on a 7,683-heliostat field demonstrate significant seasonal performance variations with less than 1% higher communication latency during the winter period and 40% longer total autocalibration durations compared to the summer operations. The experiments reveal that these effects stem primarily from the degraded Line-of-Sight (Los) wireless communications conditions during the winter period and the reduced Direct Normal Irradiance availability.

The establishment of Peer-to-Peer (P2P) Electric Vehicles (EVs) energy trading markets holds significant importance for optimizing energy exchange among EVs. Despite previous research, challenges remain in enabling distributed pairing of EVs and determining the optimal energy exchange among them. This paper introduces a reinforcement learning mechanism for Charging EVs (CEVs) to autonomously select optimal Discharging EVs (DEVs) and a multilateral bargaining game-theoretic model for determining the optimal procured energy amounts. These innovations enable DEVs to optimize the service provision and profit, thus, contributing to the advancement of P2P EVs energy trading markets. Experimental validation demonstrates the operational characteristics, scalability, and adaptability of the proposed P2P energy trading market model.

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE’s operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation.

This paper introduces an interactive model and architectural framework for a smart grid system, emphasizing the prosumers' dual roles as energy buyers and sellers within localized energy trading markets, i.e., coalitions. Initially, the utilities of both buyers and sellers are formulated to capture the benefits derived from their energy-related activities. Then, the paper presents the Approximate SMARTFORM (ASMARTFORM) mechanism, based on the principles of matching theory, to enhance the prosumers' engagement in coalitions by addressing externalities influencing their decisions. To further improve the coalition formation process, the Accurate SMARTFORM (AccSMARTFORM) mechanism is formulated based on the theory of coalition games, utilizing the suboptimal output of ASMARTFORM as input. The paper analyzes the existence of a Nash-Individually stable partition of prosumers in coalitions. Simulation results, utilizing real data from the U.S. Energy Information Administration, validate the operational effectiveness and scalability of both ASMARTFORM and AccSMARTFORM mechanisms. A comprehensive comparison with alternative coalition formation methods, including minimum energy prices, demonstrates the superior performance of AccSMARTFORM in meeting buyers' energy demand and maximizing sellers' profit.

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.

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.

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.

Community-driven energy initiatives have recently emerged as key enablers in the realization of efficient energy management systems, focusing in particular on trading and management of energy. In this article, we aim at introducing a novel community-based load balancing and prosumers incentivization framework in smart grid systems, based on the theory of hedonic community formation games. Such an approach enables the prosumers to autonomously select the most beneficial partition (community) they should join, in terms of optimizing their achieved payoff, while accounting for both the discounts offered by the provider and the load balancing characteristics of each community. Following such modeling, the existence of a Nash-stable and individually-stable partition is mathematically proven and a distributed hedonic community formation algorithm is designed, that converges to the stable solution. The performance of the proposed approach is achieved via modeling and simulation, and detailed numerical results demonstrate its operational characteristics and its benefits when compared to alternative strategies.

Increasing the security and robustness of Positioning, Navigation, and Timing (PNT) systems is a critical issue towards exploiting the PNT service at its full capacity. In this paper, we treat the joint problem of designing a secure and robust alternative PNT solution that supports the targets' PNT services, while simultaneously detecting and ejecting malicious nodes from the system that aim at deteriorating the proposed PNT solution's accuracy. The overall problem is formulated as a non-cooperative game among the targets and other collaborator nodes in order to jointly minimize their personal experienced positioning and timing error, as well as the overall system's error. The theory of potential games is adopted to prove the existence of at least one Pure Nash Equilibrium (PNE), while a log-linear-based reinforcement learning (RL) algorithm is proposed to enable the targets and collaborators to determine such a PNE. A detailed analysis for attack detection is presented considering both single-attack and distributed denial of service (DDoS) attack scenarios, where the malicious collaborators can fake their coordinates and their transmission power, while an overall PNT methodology including their ejection from the system is outlined. The performance evaluation of the proposed approach is achieved via modeling and simulation.

Online Social Networks (OSNs) have become part of our everyday life, as a means of interacting with people, sharing content, attending events, etc. Influencers are professional OSN users, who have a large loyal audience and use the OSNs to market various goods or services based on brand partnerships. In this paper, we introduce a novel mechanism to enable OSN users to become influencers by strategically deciding their activity within the OSN. Initially, the concepts of social coordinates and social communities are proposed. Then, a non-cooperative game is introduced to enable the users to make optimal decisions regarding their activity in the OSN in order to become part of an influencers' social community and enjoy the benefit of additional followers. We show that the non-cooperative game is an exact potential game with at least one Nash Equilibrium and we introduce a distributed algorithm to determine its Nash Equilibrium. A detailed set of numerical results, based on real data extracted from Instagram, show the pure operation and performance of the proposed framework, as well as the impact of the social coordinates on the users' decisions. Also, a real-life case study is presented to show the applicability of the proposed framework in Instagram.

In this paper, the problem of user recruitment by different competing marketing agencies (MAs) is treated, towards contributing to the development of an effective crowdsourcing framework applicable in Online Social Networks. Initially, a labor economics approach, following the principles of contract theory, is devised to enable the marketing agencies to reveal the potential of each participating user to contribute a personalized level of quality and quantity of information to the crowdsourcing process. The overall objective of each MA in the aforementioned competitive environment is to maximize their personal benefit, i.e., total utility obtained by the user recruitment process, given its available total budget. The latter optimization problem is formulated and solved as a Generalized Colonel Blotto (GCB) game among the MAs, where each MA aims at properly incen-tivizing each user to report its information to this agency. A Pure Nash Equilibrium (PNE) is determined resulting in the optimal rewards that each marketing agency should provide to each user. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented to reveal the benefits of the proposed crowdsourcing model under different scenarios.