The integration of the Over-the-Air Computation (AirComp) in Concentrated Solar Power (CSP) systems offers a transformative potential for real-time data aggregation in 6G-IoT networks. However, such an innovative deployment introduces critical vulnerabilities to jamming attacks that distort the aggregated signals without detection. This paper introduces a robust security framework that leverages an artificial intelligence (AI) statistical signal analysis, adaptive detection thresholds, and spatially-aware mitigation to detect and eject both simple and coordinated jamming attacks in real-world AirComp CSP systems. Detailed experimental validation at the National Solar Thermal Test Facility demonstrates significant improvements in the data aggregation and attack detection rates. It also ensures trustworthy data aggregation from commercial CSP fields, while maintaining their operational resilience.

This article demonstrates an unexplored relay operating mode in Bluetooth Low Energy (BLE)-based Wireless Sensor Networks, enabled by the link-layer synchronization inherent to BLE periodic advertisements. This synchronization allows relay nodes to avoid the power-hungry 100% duty primary-channel scanning that conventional passive BLE receivers rely on for reliable packet capture. To translate this insight into practice, we present the design and implementation of Dynamic Primary-Channel Scanning (DPCS), a practical strategy that disables primary-channel scanning after periodic synchronization and autonomously re-enables it upon synchronization loss, thereby allowing relay nodes to remain asleep for most of their lifetime while still forwarding data on scheduled secondary-channel receptions. We integrate DPCS into a multi-hop, chain-based data propagation framework for linear BLE-based WSNs, where sensor nodes themselves act as relays and forward concatenated data hop-by-hop toward a sink. Enabled by the innate properties of the periodic advertising mechanism, DPCS is directly deployable on commercial off-the-shelf (COTS) BLE 5.x devices as an application-level control logic using the provisions of the BLE stack. Experiments with nRF52832-based nodes in five-node chains demonstrate over 99% per-hop data reception reliability indoors across 50–60 m with modest retransmissions. In outdoor deployments, the per-hop reliability consistently remains above 99% without retransmissions across a 180–200 m span, with rapid automatic re-synchronization after interruptions. Power profiling shows consistently lower energy consumption than both continuous and duty-cycled primary-channel scanning baselines, with up to two to three orders of magnitude savings over continuous scanning at a sensing interval of 60 s. This chain-based analysis provides a baseline for extending the proposed periodic-advertising-based relay operation to more complex network topologies.

In Wireless Sensor Networks (WSNs) involving frequent transmission of large sensor data, packet collisions degrade the performance and add to the energy consumption of the sensor nodes since they require additional packet retransmissions to compensate data loss. In this paper, applications like livestock health monitoring systems have been targeted that involve frequent transmission of large sized motion data sampled along multiple axes. We choose low power non-connectable Bluetooth Low Energy (BLE) extended advertisements for quick transmission of large sensor data, and propose a non-connectable BLE advertising based polling algorithm for eliminating packet collision. Contrary to the existing polling-based schemes that are implemented at the link layer, this is the first work to propose a broadcast-based polling algorithm for the "application" layer. This facilitates its adaptability with commercially available BLE chips in practical deployments, since it runs on top of the standardized BLE link layer that does not implement carrier sensing. It has been shown through real-world experimentation carried out using nRF52832 and nRF52840 SoCs for 5 days, that the proposed polling technique is more effective in networks with higher transmission rates. In a network of 25 nodes transmitting 3,600 bytes every 30 s, with two packet re-transmissions, polling achieves a consistent data delivery success rate between 98-99%, as opposed to nearly 95% achieved without polling in higher (three) re-transmissions. Higher packet delivery rates are achievable in fewer re-transmissions with polling, thus saving power of the sensor nodes.

Federated Learning (FL) enables distributed model training across multiple wireless edge devices without sharing raw data, but the communication overhead remains a significant bottleneck. Over-the-Air (OTA) computation has emerged as a promising solution by leveraging the superposition property of the multi-access channels to simultaneously aggregate updates and reduce the transmission costs. Existing approaches often neglect the challenge of the edge devices’ incentivization to adhere to the optimal transmission patterns. This paper introduces the CLEOS model, which jointly optimizes the transmit-receive beamforming by dynamically determining the optimal scaling factor and the edge devices’ transmission power. A contract-theoretic framework is integrated to incentivize the devices to adopt optimal transmission strategies in order to enhance the efficiency of the overall OTA-FL process. The effectiveness of CLEOS is validated through practical experiments and numerical evaluations, which demonstrate significant improvements in the global model’s accuracy and resource allocation.

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.

The rapid growth of social media activity has generated vast amounts of fragmented data resulting in substantial challenges in the social event analysis while preserving user privacy. Federated Learning (FL) offers a decentralized solution and enables the local model training without sharing raw data. However, user participation requires appropriate incentives and trust in Online Social Network (OSN) providers. This paper presents TAMIS, a trust-driven FL framework enhanced by monetary incentives for social event analysis. TAMIS introduces a novel trust-based reinforcement learning (RL) mechanism for OSN provider selection that enables the users to identify the most reliable provider based on analysis quality and incentives. Additionally, a game-theoretic bargaining model ensures the fair incentive distribution among the users. Detailed simulations show TAMIS’s superior performance, scalability, and adaptability compared to centralized models, while effectively balancing trust, fairness, and efficiency. The framework’s ability to prioritize high-accuracy users and dynamically adapt to the users’ charactestics makes it a robust solution for decentralized, trust-aware FL systems, with significant potential for real-world applications.

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.

Approximate and delayed computing have emerged as promising paradigms to offer flexibility in computational accuracy and strategically differentiate tasks between edge and cloud execution to enhance resource utilization. However, these approaches introduce tradeoffs, potentially compromising accuracy on one hand and increasing latency on the other. In this paper, we explore the integration of approximate and delayed computing paradigms within the edge-cloud computing continuum. Users can either offload tasks for approximate computing at the edge or opt for exact but potentially delayed computing at the cloud. In this context, the joint problem of computation task offloading and data compression is formulated and solved as a non-cooperative Game in Satisfaction Form. Each user autonomously determines the amount of task to offload for either computing option and the percentage of data compression for approximate computing, aiming to achieve an acceptable accuracy-latency tradeoff. The formulated game admits a Satisfaction Equilibrium (SE) point, which is concluded using a Reinforcement Learning (RL)-based algorithm. Simulation results demonstrate the performance of the proposed task offloading framework in the achieved accuracylatency tradeoff compared against different offloading strategies.

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.

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.

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 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.

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.

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.

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.

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.

Building on prior concepts of electronic money (eCash), we introduce a digital currency where a physical unclonable function (PUF) engenders devices with the twin properties of being verifiably enrolled as a member of a legitimate set of eCash devices and of possessing a hardware-based root-of-trust. A hardware-obfuscated secure enclave (HOSE) is proposed as a means of enabling a PUF-based propagation-of-provenance (POP) mechanism, which allows eCash tokens ( eCt ) to be securely signed and validated by recipients without incurring any third-party dependencies at transfer time. The POP scheme establishes a chain of custody starting with token creation, extending through multiple bilateral in-field transactions, and culminating in redemption at the token-issuing authority. A lightweight mutual-zero-trust (MZT) authentication protocol establishes a secure channel between any two fielded devices. The POP and MZT protocols, in combination with the HOSE, enable transitivity and anonymity of eCt transfers between online and offline devices.

Rate-Splitting Multiple Access (RSMA) has been recently recognized as a more general multiple access technique that overcomes the limiting factors of its predecessors related to the signal decoding complexity and interference management tradeoff. In this paper, we investigate the application of the RSMA technique to facilitate the users’ concurrent offloading to multiple servers in a multi-server Multi-Access Edge Computing (MEC) system. Each user fully offloads different parts of its computation task at the available MEC servers (or a combination of them) using the same frequency band. We aim to minimize the sum of users’ maximum experienced delay among the different MEC servers, stemming from both the offloading and processing, by jointly optimizing their computation task assignment ratios to the servers, their allocated common-message rates, common and private-message transmission powers, and computing resources related to each server. The formulated min-max-sum problem is non-convex, and its optimization variables are highly coupled. By examining its structure, we equivalently transform the problem and further decompose it into two independent sub-problems that separately provide solutions to the radio and computing resource allocation problems. Numerical results show the effectiveness of the proposed solution in terms of the users’ experienced delay and the proposed algorithm’s real execution time.

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.

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.

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.

Integrated Sensing and Communication (ISAC) has recently emerged as an additional communication service within the Internet of Things (IoT) and Cyber-Physical Systems (CPS) era, through which distributed nodes are able to communicate their sensing information to a Base Station (BS) using integrated signals. In this paper, we study the coexistence of ISAC with other communication types of the nodes, by introducing a software-defined framework to control the nodes' uplink transmission powers related to each service. Each node is simultaneously engaged in two types of communications with different BSs for ISAC and generic data transmission to the cloud via dual connectivity. The uplink power splitting/control problem between the BSs is formulated as a non-cooperative game in satisfaction form, through which each node autonomously concludes to a Satisfaction Equilibrium (SE) point that meets its minimum ISAC and pure communication-oriented requirements. Different achievable SE points are analyzed, while an Reinforcement Learning (RL) and a searching-based algorithm are introduced to conclude to the SE and Minimum Efficient SE (MESE) of the studied problem. Simulation results demonstrate the operation of the algorithms and the overall proposed framework in achieving an efficient share of the resources to the different services.

In this article, we address the problem of modeling and orchestrating the interactions between a museum and its visitors, viewing the system as a cyber–physical–social system (CPSS). In particular, the museum operator provides monetary rewards to the visitors in exchange for their contributions, which are expressed as their total number of provided feedback evaluations of visited exhibits over their touring time. The interactions among the museum operator and visitors are captured in appropriately designed utility functions following the principles of labor economics, while the visitors’ behavioral characteristics are utilized to define their unique types. Under such a setting and formulation, the goal of the museum operator is to optimize their profit and benefits while jointly satisfying the visitors’ quality of experience prerequisites as reflected via their utility functions. The corresponding optimization problem is treated and solved under the general and realistic cases of incomplete information, wherein the museum operator estimates the visitors’ types probabilistically. The resulting outcome, referred to as the “optimal contract,” jointly determines the visitors’ optimal contributions, as well as the museum operator’s optimal amount of personalized rewards provided to each visitor. The performance of the proposed approach is evaluated through modeling and simulation, and detailed numerical results are presented to demonstrate the key benefits of the proposed optimization approach versus either type-agnostic or heuristic alternatives.

In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex Multi-access Edge Computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user’s satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework’s consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users’ experienced performance compared to alternative approaches.

In this paper, the joint problem of agents, e.g., police officers, firefighters, etc., to Unmanned Aerial Vehicles (UAVs) association and optimal partial task offloading is addressed based on the principles of reinforcement learning and contract theory, respectively, in public safety scenarios. A two-layers approach is followed. At the first layer, the agents act as learning automata in order to learn their most beneficial UAV selection to optimize their long-term reward in terms of processing their offloaded data, while respecting their delay constraints and tolerance stemming from their requested computing service and the public safety scenario that they serve. At the second layer, a contract-theoretic model is proposed to determine the agents’ optimal amount of offloaded data to the selected UAV and the UAV’s optimal portion of allocated computing capacity to each agent’s computing tasks, while considering the urgency of the agents’ requested service. A detailed set of numerical and comparative simulation results demonstrates the drawbacks and benefits of the proposed framework under rea-life public safety scenarios.

In this paper, a novel crowdsourcing mechanism is introduced in Unmanned Aerial Vehicles (UAVs)-assisted smart cities environments based on the principles of Contract Theory and Reinforcement Learning. Initially, a contract-theoretic mechanism is proposed to enable the UAVs to incentivize the Internet of Things (IoT) nodes to report their collected data (i.e., effort) via providing personalized rewards to them. This novel mechanism exploits the IoT nodes’ physical and social characteristics in order to design optimal personalized contracts, i.e., pairs of {effort, reward}, while jointly optimizing the benefits of the UAVs and the IoT nodes from the crowdsourcing process. Additionally, a reinforcement learning-based mechanism is designed to enable the IoT nodes to select a UAV to report their data, while considering the received rewards in the crowdsourcing process. A set of detailed numerical and comparative results are presented to demonstrate the operational characteristics of the proposed crowdsourcing framework, as well as its drawbacks and benefits compared to the state of the art.

In this paper, we consider a competitive aerial computation offloading environment, where two edge resource operators provide computing services on a time-slot basis to multiple users, via Unmanned Aerial Vehicles (UAVs), each bearing a mounted edge server. The aim of each UAV is to selfishly maximize the difference between its personal and the opponent UAV's utility, by competitively allocating its energy resources to the different users in the system. The problem is formulated as a Generalized Colonel Blotto (GCB) game, where the UAVs allocate their resources across a number of battlefields, i.e., the users, as competing players, seeking to win the battlefield by increasing the difference of their in-between allocated resources and thus, experienced utility. The overall framework is complemented by a Reinforcement Learning (RL)-empowered algorithm to account for the energy efficient scheduling of the UAVs' overall available energy in the different time slots, where the GCB game is realized. The performance evaluation of the proposed framework is achieved via modeling and simulation. The obtained numerical results demonstrate the operation of the proposed GCB game, under different levels of competitiveness between the UAVs, and assess the effectiveness and efficiency of the proposed RL algorithm against different comparative scenarios.

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.

Capitalizing on the different available computing options across the network, the concept of computing continuum has recently emerged to efficiently manage the exaggerated computation demands of the numerous Internet-of-Things (IoT) users and applications. Nevertheless, the edge computing’s attractiveness to the users, in terms of its reduced incurred time and energy overhead, acts as an impediment in the realization of the envisioned computing continuum. In this paper, recognizing the potential of forwarding delay-tolerant tasks to upper computing layers, we design an incentivization-based mechanism for the offloading users, aiming to shift their preference from the edge to the upper fog computing layer. The corresponding mechanism comprises two stages, in which different models of Contract Theory are adopted. In the first stage, a users-to-edge server contract is formulated to determine the optimal amount of each user’s initially offloaded task at the edge that is allowed to be further forwarded to the fog, based on the user’s delay tolerance. Subsequently, an edge-to-fog server contract is formulated to account for the edge server’s tradeoff between the local execution and transmission overheads, deriving the most beneficial amount of the users’ tasks that ultimately reaches the fog. The overall mechanism is evaluated via modeling and simulation regarding its operation and efficiency under different scenarios.

In this paper we exploit the advances provided by the Reconfigurable Intelligent Surfaces (RIS) technology, which allows for the software-defined control of the electromagnetic properties of the wireless medium, in order to introduce a low-cost and easily deployable ground-based alternative positioning, navigation, and timing (PNT) service. According to the proposed solution, the positioning of the target is performed by one original signal transmitted by one base station (BS), and three additional signals reflected by three RISs (selected from a set of available RISs), thus reducing significantly the required implementation and infrastructure cost. Different reinforcement learning algorithms, based on the gradient ascent and the log-linear learning models, are introduced and investigated to enable the target, to autonomously and dynamically select the optimal set of three RISs to be used for the optimization of its positioning accuracy. Subsequently, an iterative least square algorithm is realized to determine the position of the target. Detailed numerical results are presented that highlight the tradeoffs of the introduced reinforcement learning algorithms in terms of convergence and impact on achieved positioning precision, and demonstrate the superiority of the proposed methodology against existing ground-based PNT solutions.

The Minimum Collisions Assignment in an interdependent networked system is the problem of assigning a finite set of resources over the nodes of the network, such that the number of collisions, i.e., the number of interdependent nodes receiving the same resource, is minimized. It has been shown in the literature that, when the number of resources is larger than the maximum degree of the underlying graph, there exists a randomized algorithm which converges, with high probability, to an assignment of resources having zero collisions. In this work we investigate the case of a resource-constrained networked system, where the number of resources is less than or equal to the maximum degree of the underlying graph. We provide and analyze a distributed, randomized, algorithm that converges in a logarithmic number rounds to an assignment of resources over the network for which every node has at most a certain number of collisions.

Electronic cash ( e-Cash ) is a digital alternative to physical currency such as coins and bank notes. Suitably constructed, e-Cash has the ability to offer an anonymous offline experience much akin to cash, and in direct contrast to traditional forms of payment such as credit and debit cards. Implementing security and privacy within e-Cash, i.e., preserving user anonymity while preventing counterfeiting, fraud, and double spending, is a non-trivial challenge. In this article, we propose major improvements to an e-Cash protocol, termed PUF-Cash, based on physical unclonable functions ( PUFs ). PUF-Cash was created as an offline-first, secure e-Cash scheme that preserved user anonymity in payments. In addition, PUF-Cash supports remote payments; an improvement over traditional currency. In this work, a novel multi-trusted-third-party exchange scheme is introduced, which is responsible for “blinding” Alice’s e-Cash tokens; a feature at the heart of preserving her anonymity. The exchange operations are governed by machine learning techniques which are uniquely applied to optimize user privacy, while remaining resistant to identity-revealing attacks by adversaries and trusted authorities. Federation of the single trusted third party into multiple entities distributes the workload, thereby improving performance and resiliency within the e-Cash system architecture. Experimental results indicate that improvements to PUF-Cash enhance user privacy and scalability.

Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats. In this article, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-to-end proof of concept of our model. Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.

The emergence of Unmanned Aerial Vehicles (UAVs) as part of the safety-critical and traffic alleviation infrastructure in 5G and beyond wireless networks, promotes the rethinking of the conventional resource orchestration management. In this paper, we propose a novel methodology that treats the uplink power allocation problem in UAV-assisted wireless networks, operated under Non-Orthogonal Multiple Access (NOMA), based on the principles of labor economics and Contract Theory (CT). The proposed approach specifically targets the challenge of imperfect Channel State Information (CSI) due to the uncertainties of the wireless links. The users are characterized by types that depend on their experienced channel conditions, which are typically unknown to the UAVs, while the latter probabilistically estimate the users’ types. The users’ transmission powers are iteratively optimized and determined, while an Reinforcement Learning (RL)-empowered user-to-UAV association procedure is realized. The overall framework is evaluated via modeling and simulation regarding its proper operation, effectiveness and efficiency, under different scenarios.

In this paper, we introduce a behavioral and labor economics based approach to address the challenge of citizens’ health data acquisition during a pandemic, in a smart city scenario consisting of the healthcare operator, multiple businesses, and citizens with wearable devices. Initially, a reinforcement learning approach is adopted in order for the citizens to select the business to visit, exploiting both social and physical characteristics of all involved entities. Subsequently, following the principles of behavioral economics, the problem of the citizens’ incentivization by the businesses to provide their health data via offering personalized rewards is studied. The solution of the corresponding optimization problem concludes to a contract between the business and each citizen associated with this business, containing the optimal reward and optimal portion of reported data. The process is completed by introducing an optimization framework, where the healthcare operator incentivizes the businesses to provide the collected health data to it, by providing them tailored rewards. This is founded on the principles of Contract Theory, where the healthcare operator aims at maximizing its benefit from the data acquisition process, while guaranteeing that the optimal determined contracts are acceptable by the respective businesses. Finally, through modeling and simulation, the performance, effectiveness, and robustness of the overall proposed framework is demonstrated, under various realistic scenarios.

Social Internet of Things (SIoT) studies the social and physical relationships among sensor nodes. In this paper, the data acquisition problem from the SIoT nodes is studied following a techno-economics-based approach via exploiting Contract Theory. The SIoT nodes’ social and physical characteristics are analyzed to define the SIoT nodes’ unique types, which are of continuous and dynamically changing nature. The crowdsourcing manager offers personalized rewards to the SIoT nodes in order to incentivize them to provide their collected data, while considering imperfect information of the SIoT nodes’ attributes. A contract-theoretic optimization problem is formulated and solved to determine the optimal contracts, i.e., crowdsourcing manager’s rewards and SIoT nodes’ performance, while jointly optimizing the benefits of all the involved entities. Simulation results show that the proposed framework improves the system’s social welfare by 11% compared to an SIoT nodes’ type agnostic approach.

To support the provisioning of modern services in a smart city environment, future communication networks need to be intelligently designed with respect to the city infrastructure and energy efficient utilization of resources. Unmanned Aerial Vehicles (UAVs) are already being utilized as part of a smart city wireless network infrastructure to provide on-demand connectivity and eliminate the network’s coverage holes, especially when communication conditions are unfavorable. Complementary to this, the adoption of Reconfigurable Intelligent Surface (RIS) technology allows for the creation of a more controllable smart wireless communications environment. In this article, a multi-user Non-Orthogonal Multiple Access (NOMA) communications system aided by a RIS and a UAV is studied. Based on a single-leader multiple-followers Stackelberg Game, we aim to jointly optimize the overall received signal strength at the UAV and maximize the users’ achieved energy efficiency. The UAV – acting as a leader - intelligently steers the RIS-reflected signals in order to enhance the corresponding received signal quality, by determining the RIS elements’ effective phase shifts. This, in turn, is exploited by the users (i.e., followers), which through the formulation of a non-cooperative game, where each user aims at maximizing its achieved energy efficiency, they determine their optimal uplink transmission power. The proposed optimization framework is evaluated via modeling and simulation, demonstrating the significant power savings and the ultimate users’ satisfaction occurring by the introduction of the RIS.

In this paper, to exploit the challenges and potential offered by the simultaneous use of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) transmission options in future 5G wireless systems, we aim at the proper modeling and transformation of the uplink power allocation problem. In particular, in this setting, each user has two degrees of freedom in the decision making process, namely its overall transmission power level, and the corresponding power investment to the OFDMA and/or NOMA based transmissions. The resulting multi-variable power allocation problem is treated and solved under three different perspectives, namely: 1) Games in Normal Form and Nash Equilibrium (NE); 2) Optimization techniques targeting system social welfare through a centralized optimal solution; and 3) Games in Satisfaction Form and Efficient Satisfaction Equilibrium (ESE). Based on these approaches, different solutions and stable operation points are identified and their properties are analyzed. An in depth evaluation and comparison of the various obtained outcomes is achieved, via modeling and simulations. The focus is placed on the impact and the interplay of the NOMA specific features, including the potential over-exploitation of the available bandwidth, the fairness in accessing it, and the interference treatment. It is also shown that, using the satisfaction form games for the users to converge to the ESE, provides an efficient and promising user-centric modeling approach to the power allocation problem, as the system adapts to the users’ application needs, while at the same time eliminates a significant amount of interference.

Artificial Intelligence (AI) based techniques are typically used to model decision making in terms of strategies and mechanisms that can result in optimal payoffs for a number of interacting entities, often presenting antagonistic behaviors. In this paper, we propose an AI-enabled multi-access edge computing (MEC) framework, supported by computing-equipped Unmanned Aerial Vehicles (UAVs) to facilitate IoT applications. Initially, the problem of determining the IoT nodes optimal data offloading strategies to the UAV-mounted MEC servers, while accounting for the IoT nodes' communication and computation overhead, is formulated based on a game-theoretic model. The existence of at least one Pure Nash Equilibrium (PNE) point is shown by proving that the game is submodular. Furthermore, different operation points (i.e. offloading strategies) are obtained and studied, based either on the outcome of Best Response Dynamics (BRD) algorithm, or via alternative reinforcement learning approaches (i.e. gradient ascent, log-linear, and Q-learning algorithms), which explore and learn the environment towards determining the users' stable data offloading strategies. The corresponding outcomes and inherent features of these approaches are critically compared against each other, via modeling and simulation.

In this paper, an Unmanned Aerial Vehicles (UAVs) - enabled human Internet of Things (IoT) architecture is introduced to enable the rescue operations in public safety systems (PSSs). Initially, the first responders select in an autonomous manner the disaster area that they will support by considering the dynamic socio-physical changes of the surrounding environment and following a set of gradient ascent reinforcement learning algorithms. Then, the victims create coalitions among each other and the first responders at each disaster area based on the expected- maximization approach. Finally, the first responders select the UAVs that communicate with the Emergency Control Center (ECC), to which they will report the collected data from the disaster areas by adopting a set of log-linear reinforcement learning algorithms. The overall distributed UAV-enabled human Internet of Things architecture is evaluated via detailed numerical results that highlight its key operational features and the performance benefits of the proposed framework.

In this paper an Unmanned Aerial Vehicles (UAVs) - enabled dynamic multi-target tracking and data collection framework is presented. Initially, a holistic reputation model is introduced to evaluate the targets' potential in offloading useful data to the UAVs. Based on this model, and taking into account UAVs and targets tracking and sensing characteristics, a dynamic intelligent matching between the UAVs and the targets is performed. In such a setting, the incentivization of the targets to perform the data offloading is based on an effort-based pricing that the UAVs offer to the targets. The emerging optimization problem towards determining each target's optimal amount of offloaded data and the corresponding effort-based price that the UAV offers to the target, is treated as a Stackelberg game between each target and the associated UAV. The properties of existence, uniqueness and convergence to the Stackelberg Equilibrium are proven. Detailed numerical results are presented highlighting the key operational features and the performance benefits of the proposed framework.

Recent technological advances in the use of Unmanned Aerial Vehicles (UAVs) and Wireless Powered Communications (WPC) have enabled the energy efficient operation of the Public Safety Networks (PSN) during disaster scenarios. In this paper, an energy efficient information flow and energy harvesting framework capturing users' risk-aware characteristics is introduced based on the principles of Contract Theory. To better support the operational effectiveness of the proposed framework, users are clustered in rescue groups following a socio-physical-aware group formation mechanism, while rescue leaders for each group are selected. A reinforcement learning approach is applied to enable the optimal matching between the UAVs and the rescue leaders in a distributed and efficient manner. The proposed contract-theoretic framework models the UAVs-victims relation based on a labor market setting via offering rewards to the users (incentives) in order to compensate them for their invested labor (reporting information). Detailed numerical results demonstrate the benefits and superiority of the proposed framework under different settings.

The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs' data to be offloaded to the selected MEC servers, while the existence of at least one Nash Equilibrium (NE) is proven by exploiting the power of submodular games. A best response dynamics framework and two alternative reinforcement learning algorithms are introduced that converge to an NE, and their tradeoffs are discussed. The overall framework performance evaluation is achieved via modeling and simulation, in terms of its efficiency and effectiveness, under different operation approaches and scenarios.

Given the substantial penetration of social networks in citizens' everyday life activities, the success of a public safety system depends on the citizens' incentivization by the Emergency Control Center (ECC), and their effective effort contribution in the overall disaster management operation. In this paper, we introduce a formal method based on the principles of Contract Theory, to identify the optimal rewards to the citizens from the ECC's perspective, and the optimal invested effort from the citizens' side, referred to as contract pairs. The identification of these contract pairs (i.e., rewards and respective efforts) between the ECC and each citizen, depend on each citizen's social and communication characteristics that are used to define their specific type and profile, while they are properly reflected in the corresponding designed utility functions to be optimized. The problem under consideration is treated for both cases of complete (ideal) and incomplete (realistic) information availability, with respect to the level of knowledge of the ECC about the exact type of each citizen. The overall framework was evaluated via modeling and simulation, in terms of its efficiency and effectiveness, by studying multiple operation approaches and scenarios.

In this article, a sociotechnical consideration of the congestion management problem in museums is presented, by treating museums as dynamic social systems, where the momentum of the experience is controlled by visitors themselves. Visitors are considered as prospect-theoretic utility maximizers, whose behavioral risk attitudes affect not only their personal decisions and experiences but also those of others, thus creating an interdependent social system. To address the congestion problem within such a probabilistic and uncertain environment, pricing is introduced as an effective mechanism to drive visitor actions in efficient operation points, preserving museum operation stability. The corresponding problem of determining the time invested by each visitor at museum exhibits toward optimizing their obtained experience, as expressed via properly designed prospect-theoretic utility functions with pricing, is formulated and treated as a noncooperative game. The theory of S-modular games is adopted to prove existence and convergence to Nash equilibrium. Based on this framework, we study and analyze the validity and effectiveness of pricing as a tool to manage congestion in museums.

Multi-access Edge Computing (MEC) has emerged as a flexible and cost-effective paradigm, enabling resource constrained mobile devices to offload, either partially or completely, computationally intensive tasks to a set of servers at the edge of the network. Given that the shared nature of the servers' resources introduces high computation and communication uncertainty, in this paper we consider users' risk-seeking or loss-aversion behavior in their final decisions regarding the portion of their computing tasks to be offloaded at each server in a multi-MEC server environment, while executing the rest locally. This is achieved by capitalizing on the power and principles of Prospect Theory and Tragedy of the Commons, treating each MEC server as a Common Pool of Resources available to all the users, while being rivarlous and subtractable, thus may potentially fail if over-exploited by the users. The goal of each user becomes to maximize its perceived satisfaction, as expressed through a properly formulated prospect-theoretic utility function, by offloading portion of its computing tasks to the different MEC servers. To address this problem and conclude to the optimal allocation strategy, a non-cooperative game among the users is formulated and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal data offloading, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation, while useful insights about the users' data offloading decisions under realistic conditions and behaviors are presented.

In this paper, a flexible resource sharing paradigm is introduced, to enable the allocation of users' computing tasks in a social cloud computing system offering both Virtual Machines (VMs) and Serverless Computing (SC) functions. VMs are treated as a safe computing resource, while SC due to the uncertainty introduced by its shared nature, is treated as a common pool resource, being susceptible to potential over-exploitation. These computing options are differentiated based on the potential satisfaction perceived by the user, as well as their corresponding pricing, while taking into account the social interactions among the users. Considering the inherent uncertainty of the considered computing environment, Prospect Theory and the theory of the Tragedy of the Commons are adopted to properly reflect the users' behavioral characteristics, i.e., gain-seeking or loss-averse behavior, as well as to formulate appropriate prospect- theoretic utility functions, embodying the social- aware and risk-aware user's perceived satisfaction. A distributed maximization problem of each user's expected prospect-theoretic utility is formulated as a non-cooperative game among the users and the corresponding Pure Nash Equilibrium (PNE), i.e., optimal computing jobs offloading to the VMs and the SC, is determined, while a distributed low-complexity algorithm that converges to the PNE is introduced. The performance and key principles of the proposed framework are demonstrated through modeling and simulation.

This paper introduces a Multi-Agency DisAster Management (MADAM) framework for Unmanned Aerial Vehicle (UAV)-assisted public safety systems, based on the principles of game theory and reinforcement learning. Initially, the information quality and criticality (IQC) provided by each agency to an UAV-assisted public safety network is introduced and quantified, and the concept of Value of Information (VoI) that measures each agency's positive contribution to the overall disaster management process is defined. Based on these, a holistic cost function is adopted by each agency, reflecting its relative abstention from the information provisioning process. Each agency aims at minimizing its personal cost function in order to better contribute to the disaster management. This optimization problem is formulated as a non- cooperative game among the agencies and it is proven to be an exact potential game, thus guaranteeing the existence of at least one Pure Nash Equilibrium (PNE). We propose a binary log- linear reinforcement learning algorithm that converges to the optimal PNE. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority compared to other approaches is demonstrated.

In this paper a novel dynamic spectrum management scheme for 5G Non Orthogonal Multiple Access (NOMA) wireless networks is proposed, where users are offered the option to transmit via licensed and unlicensed bands. Users are enabled to determine the optimal allocation of their transmission power in each one of the bands, while the unlicensed band is treated as a Common Pool Resource (CPR) - being non-excludable and rivalrous - which may collapse due to over-exploitation. Towards providing a pragmatic modeling approach for decision making under a realistic setting of probabilistic uncertainty, while properly capturing user risk perceptions, we model the corresponding optimization problem under the principles of Prospect Theory, removing the common assumption that subjects are behaving as neutral utility maximizers, a concept that does not reflect user risk behavior peculiarities. The corresponding problem is formulated as a CPR game, while the existence and uniqueness of its Pure Nash Equilibrium point are proven, and a user centric distributed algorithm is devised that obtains the corresponding solution. Detailed evaluation results are presented, highlighting the operation and superiority of the proposed framework against conventional Expected Utility Theory based approaches, while providing useful insights about user optimal decisions under realistic behaviors.

Although communication networks have been constantly evolving and increasing their capacity with state-of-the-art solutions, it is still hard to provide reliable and high capacity communications in some cases referred to as challenged networks. In these networks, ensuring performance guarantees is hard either due to the lack of infrastructure or its limitations, such as in public safety networks, or due to the challenging communication medium, such as in mmWave networks. Challenged networks face now new constraints with the proliferation of services and applications, increasing number of connected devices, the high computing and control demands, unpredicted human behavior, and only partially-available information. CHANTS'19 aims at bringing researchers together to have a platform for discussing the challenges emerging with new use cases and real-life applications, such as edge computing or autonomous driving, and corresponding innovative approaches in tackling the limitations of the challenged networks. Moreover, this workshop aims at providing another perspective to the broader audience by listing a subset of problems that communication networks, despite the advances on many fronts, still have to tackle. The expected outcomes of CHANTS'19 have the potential to influence industrial thinking about the technologies of next-generation challenged networks.

The efficient management of a smart city and the improvement of the quality of humans' every-day life are becoming challenging problems due to smart cities' increased heterogeneity and complexity. In this paper, we present a novel socio-physical human orchestration framework to deal with the aforementioned issues, by capitalizing on recent advances in game theory and reinforcement learning. Initially, each human selects, in a distributed manner, a Point of Interest (PoI) that it wants to visit, by acting as stochastic learning automaton, exploiting the socio-physical conditions of the environment while learning from its previous experiences. As a result, those humans that have selected a specific PoI to visit, "compete" with each other in order to finally perform their visit. The humans' behavior is studied as a non-cooperative game among them, via adopting the theory of minority games, while the concluding Nash equilibrium point identifies the humans that will finally visit each PoI. A low complexity algorithm is introduced to realize the overall framework, while the performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority is demonstrated.

The evolution of communication systems in the direction of heterogeneity and mass connectivity through the deployment of 5G compatible technologies, is posing significant challenges to Wireless Internet Service Providers (WISPs)towards enhancing spectral efficiency and ensuring steady and uninterrupted operation. In this paper, we introduce a holistic framework that dynamically combines multiple access technologies while accounting for the users' Quality of Service (QoS)prerequisites and risk preferences. In emerging future wireless networks with flexible access technology interfaces, both bands operating over Orthogonal Frequency Division Multiple Access (OFDMA)and Non-Orthogonal Multiple Access (NOMA)become simultaneously available to the users as potential options of communications and usage. OFDMA technology, due to organizing the available spectrum into distinct resource blocks, provides free of interference but of relatively limited bandwidth service to the users, whereas NOMA has the potential to provide superior spectral capacity by accommodating all users in a single carrier. However, the latter comes at the expense of resource fragility and potential failure from over-exploitation, due to its fully shared nature and excessive competition among users. Considering users' diverse behavioral patterns when probabilistic uncertainty of the shared system's resources is assumed, we model the resource control problem under the principles of Prospect Theory, and solve it as a Fragile Common Pool of Resources (CPR)game converging to a unique Pure Nash Equilibrium (PNE)point. Decentralization of users' decisions under the proposed pragmatic approach enhances the stability and network's performance, which are confirmed by a series of comprehensive numerical results.

Quality of experience (QoE), quantified via appropriately defined utility functions, has been widely used as a means to express user satisfaction in social systems. In such systems, subjects are usually assumed to be neutral utility maximizers, a concept that does not properly reflect the user risk-seeking behavior peculiarities. In this paper, we address the issue of incorporating and assessing the impact of visitor behavioral factors within the cultural heritage space, by exploiting the power of prospect theory. Exhibits of the cultural heritage site are organized in two main categories, namely, safe exhibits and common pool of resources (CPR) exhibits, based on their popularity and attractiveness. The latter ones are considered as nonexcludable and rivalrous resources in nature. Consequently, the obtained visitor QoE expressed via the prospect-theoretic utility function, heavily depends on the cumulative time spent by all visitors in these exhibits, thus making their behaviors and decisions interrelated, acting more like a social competitive environment. To determine visitor optimal time investment in different types of exhibits, while taking into account the potential interdependence of visitors decisions, a noncooperative game among the visitors is formulated and solved in a distributed manner, such that each visitor maximizes his own prospect-theoretic utility function. Detailed evaluation numerical results are presented, highlighting the operation and superiority of the proposed framework while providing useful insights about visitor decisions under realistic conditions and behaviors.

Multipurpose devices have emerged as part of modern Internet of Things (IoT) ecosystems. Such nodes are able of interchanging their operation between different sensing modes providing a large mixture of information towards various IoT applications. In this paper, a novel framework is introduced to govern and properly define the dynamic operation of a multipurpose device network deployment. Initially, the problem of socio- physical energy-efficient device sensing mode selection is confronted. Each multipurpose device acts as a learning automaton and through a machine learning mechanism selects the most appropriate operation mode, in terms of maximizing the revenue/cost relation of the provider. In addition, towards improving the communication efficiency, a coalition formation mechanism among the nodes is proposed, which considers: (a) nodes' spatial proximity reflecting physical conditions such as channel quality, (b) energy availability, and (c) operation mode correlation between multipurpose devices expressing social metrics. Given the mode selection and coalition formation among nodes, a distributed utility-based power control mechanism is proposed to determine each device's optimal transmission power in a Non- Orthogonal Multiple Access (NOMA) wireless network environment in order to fulfill its Quality of Service (QoS) prerequisites. The performance of the proposed approach is evaluated through modeling and simulation under several scenarios, and its superiority is demonstrated.

In this paper an analytical framework for the joint allocation of multiple physical resources under a dynamic personalized pricing setting, in a NOMA wireless network, is designed. Under our proposed user-centric paradigm, price is treated as a resource itself rather than simply being a control parameter and represents the user willingness to pay towards obtaining certain QoS levels. Each user expresses her satisfaction with respect to her and other users choices through a specifically designed utility function based on her unique characteristics and preferences. The resource allocation problem under consideration, becomes a distributed utility maximization problem, where each user updates her controllable parameters in an autonomous manner targeting at her satisfaction maximization. The problem is modeled and solved as a multivariable non cooperative game, admitting a unique Nash Equilibrium (NE), whose convergence is reached via a distributed and low complexity algorithm. Detailed numerical results, clearly demonstrate that the proposed framework allows the users to better exploit the system's resources, and therefore improve their overall satisfaction, while achieving significant improvements in the system operation in terms of power savings and achievable data rate.

In this paper, the problem of joint users' uplink transmission power and rate allocation in NOMA wireless networks is studied, under the scenario that each user is simultaneously requesting multiple services. Each user is associated with a two-variable utility function that represents his satisfaction from his allocated resources (i.e., power and rate). In order to appropriately reflect the combined needs of the user for bundling different types of services under the same common umbrella, user's utility function consists of two parts while different percentages are adopted by each user for each part in order to express his need for real and non-real time services. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of S-modular multivariable games is adopted towards determining the Nash equilibrium point of the game. A distributed, iterative and low complexity algorithm for computing game's Nash equilibrium is introduced, while updating user's uplink transmission power and rate at the same step. Detailed numerical results exhibit the ability of the proposed framework to simultaneously satisfy diverse multiple services requested by the same user.

This paper tackles the problem of joint users' uplink transmission power and data rate allocation in multi-service two-tier femtocell networks. Each user-either macrocell (MUE) or femtocell user equipment (FUE)-is associated with a two-variable utility function that represents his perceived satisfaction with respect to his allocated resources (i.e., power and rate). User's utility function is differentiated based both on the tier that the user belongs to and the service he requests. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of supermodular games is utilized towards treating the two-variable optimization problem and the inherent multidimensional competition that arises among the users. The existence of proposed game's Nash Equilibrium (NE) point is analytically shown, while game's convergence to its NE point is proven. A distributed and iterative algorithm for computing the desired NE is introduced, where the optimal values of each user's uplink transmission power and data rate are simultaneously updated at the same step. The performance of the proposed approach is evaluated via modeling and simulation and its superiority compared to other state of the art approaches is illustrated.

Joint power and rate control is a key element to the efficient use of wireless system resources especially when considering heterogeneous services with various transmission rates and requirements. Although several game-theoretic approaches have appeared to solve this problem their stable outcome is extracted independently or semi-jointly, and the corresponding equilibrium solutions introduce several inefficiencies. To eliminate some of them and achieve a more socially desirable energy efficient operational point, in this paper we combine pricing mechanisms with a joint utility-based uplink transmission power and rate allocation non-cooperative game formulation. The existence of a unique Nash equilibrium is shown, and the respective game's convergence is proven. A distributed, iterative and low-complexity algorithm for computing the desired equilibrium point is presented while the performance effectiveness of the proposed approach is evaluated via modeling and simulation considering both linear and nonlinear pricing.

In this paper, the problem of efficient distributed power control via convex pricing of users' transmission power in the uplink of CDMA wireless networks supporting multiple services is addressed. Each user is associated with a nested utility function, which appropriately represents his degree of satisfaction in relation to the expected trade-off between his QoS-aware actual uplink throughput performance and the corresponding power consumption. Initially, a Multiservice Uplink Power Control game (MSUPC) is formulated, where each user aims selfishly at maximizing his utility-based performance under the imposed physical limitations and its unique Nash equilibrium point is determined. Then the inefficiency of MSUPC game's Nash equilibrium is proven and a usage-based convex pricing policy of the transmission power is introduced, which offers a more effective approach compared to the linear pricing schemes that have been adopted in the literature. Consequently, a Multiservice Uplink Power Control game with Convex Pricing (MSUPC-CP) is formulated and its unique Pareto optimal Nash equilibrium is determined. A distributed iterative algorithm for computing MSUPC-CP game's equilibrium is proposed, while the overall approach's efficiency is illustrated via modeling and simulation. © 2006 IEEE.

In this paper we address the problem of efficient power allocation in the uplink of CDMA wireless networks supporting simultaneously both real-time and non-real-time services with various and often diverse Quality of Service (QoS) prerequisites. Each mobile is associated with a nested QoS-aware utility function that characterizes its degree of satisfaction for the received service in terms of achieved goodput and corresponding power consumption. The problem is formulated as a non-convex non-cooperative Multi-Service Uplink Power Control (MSUPC) game where users aim selfishly at maximizing their utility-based performance under the imposed physical limitations. We first prove the existence and uniqueness of a Nash equilibrium point of MSUPC game, and then a distributed iterative algorithm is proposed in order to obtain MSUPC game's equilibrium point. Finally, the efficacy of the proposed approach with respect to the fulfillment of both types of services' QoS requirements is extensively illustrated via modeling and simulation.

In this paper we present a novel autonomic architecture for CDMA wireless networks, aiming at maximizing overall network's resources utilization while fulfilling various services QoS prerequisites. Founded on a common utility-based framework that provides enhanced flexibility in supporting various and often diverse QoS prerequisites, proper non-convex resource allocation optimization problems concerning both the downlink and uplink of the CDMA system, are set and solved. Then, decentralized algorithms for obtaining system's optimal resource allocation are proposed and used to enable user's QoS-aware selfoptimization functionalities via devising proper control loops that reside at mobile nodes and the base station. Finally, the efficacy of the proposed architecture is illustrated via modeling and simulation.