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.

This paper introduces the innovative concept of Aerial Integrated Access and Backhaul (AIAB) networks and proposes an optimal bandwidth splitting and power control mechanism to enhance the energy efficiency for the UAVs and ground users equipment. The key contributions of this research include the introduction of AIAB networks based on the 3GPP principles, along with a comprehensive system model detailing channel and communication aspects. Additionally, a novel twostage optimization problem is formulated to address the resource allocation challenges, particularly focusing on optimal bandwidth splitting and uplink transmission power optimization. A Stackelberg game-theoretic approach is proposed to tackle this problem, facilitating the efficient resource management. Through detailed simulation-based experiments, the effectiveness and scalability of the proposed AIAB architecture and resource allocation mechanism are demonstrated, highlighting its superiority over centralized and semi-centralized approaches.

The need to maintain efficient and environmentally responsible data processing at the network edge has introduced a new research field in the area of edge computing sustainability. This paper introduces a novel social-aware, trust-based data offloading framework, named TANDEM, in Multi-access Edge Computing (MEC) environments. TANDEM is designed to jointly optimize the user' data offloading strategies and the MEC providers' dynamic pricing policies. TANDEM incorporates a social-aware trust model based on direct and indirect interactions of the users with the MEC servers, and is based on a Stackelberg game-theoretic approach to optimize the data offloading and pricing. TANDEM significantly outperforms existing methods by reducing carbon emissions in MEC systems and ensuring a sustainable edge computing environment.

The rapid growth of data and computing needs in the Internet of Medical Things (IoMT) necessitates efficient mechanisms for optimizing the resource management in e-health applications. This paper presents the NEMESIS framework, which enables the users to determine their optimal Multi-Access Edge Computing (MEC) server selection and data offloading strategies by considering the reliability of the MEC servers based on individual interactions and shared user experiences. A comprehensive system model is introduced that defines the users’ interactions, the data offloading processes, and the impact of various IoMT devices, along with a novel utility function that evaluates the tradeoffs in the MEC server selection and task offloading. Additionally, a reliability model is proposed that incorporates the direct user interactions and their peers evaluations of the MEC servers’ computing services, while a regret learning mechanism is designed to optimize the users’ strategies under varying information scenarios. The results demonstrate that the NEMESIS framework operates efficiently in real-time and outperforms state-of-the-art scheduling and offloading schemes in terms of latency and energy consumption.

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.

Though extensive research efforts have been devoted to the problem of computing resource pricing, they mainly focus on single computing paradigms. In this paper, we provide a holistic approach to this problem, by treating the whole computing continuum, consisting of cloud, edge, and fog computing providers, simultaneously offering their resources to the users. Within such a complex setting, we establish the concept of symbiotic computing resource pricing and sharing, where the computing providers and the users coexist within a mutually beneficial ecosystem, sharing services and resources as a means of ensuring their business survival and service satisfaction. Under this prism, we introduce two key pricing families, namely the non-cooperative one which involves competition and is treated through game theoretic approaches, and the cooperative resource pricing (full or partial), which addresses complex scenarios through optimization and coalition. A thorough performance assessment is provided, through modeling and simulation, in order to highlight and quantify the key characteristics and tradeoffs of the various resource pricing approaches introduced.

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.

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.

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.

Information Centric Networking (ICN) paradigm exploits the in-network caching capacity to support the process of fast and efficient content distribution. In addition to the algorithmic and implementation challenges associated with the decision-making of content placement, the sustainability of content caching frameworks heavily depends on the design of appropriate network economics models to define and support the interactions among the involved players. In order to treat this need, in this paper, considering multiple Content Providers (CPs) while exploiting the in-network caching model, we particularly examine the joint problem of maximizing the CPs profit and their market penetration in terms of attracting a large portion of customers. The problem is formulated as a non-cooperative game among the CPs and the existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven. The performance evaluation of the proposed network economics-based approach is achieved via modeling and simulation, while its superiority against other alternatives is demonstrated.

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.