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The rapid progression of Cloud Computing (CC) technology has ushered in innovative ecosystem concepts such as Mobile Cloud Computing (MCC). In this context, the incorporation of Unmanned Aerial Vehicles (UAVs) into these cloud ecosystems has unlocked new avenues for use cases such as delivery services, disaster response, and surveillance. However, this integration presents challenges in resource management and service selection due to the unique constraints of drones and variations in service quality. This paper proposes a Game Theory-based UAV-cloud of Service Selection Architecture (GT-SSA) to address resource management and service selection challenges. By leveraging game theory in our proposal, GT-SSA optimizes decision-making for Client Drones and Provider Drones, enhancing service selection efficiency. GT-SSA proved its resilience to scalability concerns, as evidenced in Discovery Delay, Consumption Delay, End-to-End Delay, and Energy consumption. Moreover, when GT-SSA is compared with the Game Theory approach for Cloud Services in MEC- and UAV-enabled networks (GTCS), GT-SSA outperforms GTCS in terms of Successful Execution Rate, Average Execution Time, and Energy consumption. Our research also reveals that game theory surpasses fuzzy logic in terms of service selection efficiency.

In this work, a novel power auctioneers network is proposed to allow the sharing of resources from Edge Users in exchange for performance gains using either the Cooperative Non-Orthogonal Multiple Access (C-NOMA) or Half-Duplex Successive Relaying based C-NOMA protocol. The latter is presented as a novel method of using two relays in C-NOMA through successive relaying to overcome the multiplexing loss in traditional C-NOMA of the far user. This system exploits two matching-based algorithms based on game theory, namely Conventional Distributed Algorithm (CDA) and Pragmatic Distributed Algorithm (PDA), to handle the challenge of user pairing and power allocation in multi-carrier networks. These approaches use distributed methods to perform tasks on user devices to remove the strain on the base station's resources. Monte Carlo simulations demonstrate that the addition of these games to a wireless network provides a significant performance gain compared to traditional orthogonal multiple access methods, unoptimised C-NOMA, and the Distributed Matching Algorithm. Specifically, the cooperative game of PDA is shown to perform better in several scenarios, including when the subcarrier assignment was fixed and when EUs could occupy multiple subcarriers to enhance their throughput.

The increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and defenders in CPS. This survey provides a comprehensive review of the state-of-the-art research on the application of game theory in the security of CPS, with a specific focus on attack models and security analysis. We employ machine learning algorithms to classify existing research papers based on the attack target and the types of attacks they address. Our classification reveals significant trends and gaps in the current literature, offering insights into the effectiveness of different game-theoretic approaches and the prevalence of various attack models. By synthesizing the findings from over 800 research papers, we highlight the strengths and limitations of existing methodologies and propose directions for future research. This survey aims to serve as a valuable resource for researchers and practitioners seeking to enhance the security of CPS through game-theoretic frameworks and machine learning techniques.

Recent studies have highlighted the importance of implementing clustering schemes in Wireless Body Area Networks (WBANs) to address challenges such as scalability, network topology changes, spectrum scarcity, and management. However, many existing approaches focus only on conventional performance metrics and overlook the integration of spectrum trading and efficient spectrum utilization. This paper proposes a novel clustering control scheme based on fuzzy logic and Nash equilibrium to enhance scalability, network stability, and resource management in WBANs. Our approach employs multi-criteria decision-making to optimize cluster head (CH) selection and routing strategies using reinforcement learning to achieve quality of service (QoS). Additionally, a secure lightweight Diffie-Hellman key exchange is used to protect data transmission. The proposed protocol outperforms existing protocols, including TAFLR, EQRSRL, and SEBA, in terms of throughput (3.2 kbps), packet delivery ratio (93%), delay (0.31 s), cluster efficiency (95%), and energy consumption (0.43 J).

The financial landscape has undergone a profound shift due to the decentralisation and digitisation brought about by cryptocurrencies. As people begin to reevaluate traditional notions of monetary ownership, cryptocurrencies will be poised to play an even more critical role in the future. However, the widespread adoption of cryptocurrencies has been hampered by several challenges, including volatility and scalability. The concepts of Stablecoin and Payment Channel Network (PCN) are promising proposals for these problems, providing effective individual solutions. While PCN addresses the scalability issue of cryptocurrencies, its transaction reliability is not always up to par for widespread use. Similarly, the increasing centralisation of Stablecoins contradicts the fundamental principle of introducing cryptocurrencies. This paper introduces a decentralised model that integrates both approaches to establish a monetary system while mitigating their inherent drawbacks. It encompasses a subsidised node and stablecoin designs for PCN with a dedicated and incentivised layer of entities to facilitate reliable PCN transactions and contribute to stabilising the underlying currency of PCN. It introduces an integrated network-backed stablecoin for PCN, achieving both stability and scalability concurrently. Also, approximately half of the initial funding required to establish payment channels is derived from external sources, ensuring a guaranteed return on investment within an under-collateralised and decentralised environment. Our system has been substantiated through game theory and simulations, demonstrating its capacity to stabilise the coin’s value and effectively fulfil the expected functionalities of a currency. Empirical tests have confirmed its retention of PCN’s scalability, even with additional steps integrated to implement the proposed protocol. We also highlight its resilience to potential attacks. Our solution will improve transaction reliability, collateralisation, and underlying coin stability, making cryptocurrencies more suitable for widespread adoption.

Energy market rules should incentivize market participants to behave in a market and grid conform way. However, they can also provide incentives for undesired and unexpected strategies if the market design is flawed. MARL is a promising new approach to predicting the expected profit-maximizing behavior of energy market participants in simulation. However, reinforcement learning requires many interactions with the system to converge, and the power system environment often consists of extensive computations, e.g., optimal power flow (OPF) calculation for market clearing. To tackle this complexity, we provide a model of the energy market to a basic multi-agent reinforcement learning (MARL) algorithm in the form of a learned OPF approximation and explicit market rules. The learned OPF surrogate model makes an explicit solving of the OPF completely unnecessary. Our experiments demonstrate that the model additionally reduces training time by about one order of magnitude but at the cost of a slightly worse performance. Potential applications of our method are market design, more realistic modeling of market participants, and analysis of manipulative behavior. Source code available at https://github.com/Digitalized-Energy-Systems/marl_clearing_and_bidding.

On-demand charging scheduling is an important kind of online charging scheduling method in Wireless Rechargeable Sensor Networks (WRSNs). Its dynamism and energy adaptability enables Mobile Chargers (MCs) to adjust charging paths in real-time based on the energy state of the network, which has attracted widespread attention since its proposal. While most existing research about it has focused on optimizing the charging utility in the single MC scenario, we aim to optimize on-demand charging scheduling in the scenario of collaborative charging among multiple MCs in this work. Firstly, we have designed a dynamic charging request-sending control mechanism. This mechanism enables sensors to send charging requests according to their remaining lifetime, improving their charging success rate. Secondly, We have modeled the process of MCs’ on-demand collaborative charging as a complete information game between MCs. We have designed detailed game rules and proper payoff functions to ensure the survival rate of sensors and improve the Energy Utilization Efficiency(EUE) of the charging process. We have verified the performance of our charging scheduling mechanism through simulations. The simulation results show that our work has good dynamic adaptability, which can effectively improve the EUE of the charging process and reduce the number of sensor failures.

Breast cancer is a critical health issue affecting the well-being of women. Breast cancer is one of the most common causes of the increase in the mortality rate of women around the world. Early detection of breast cancer can, to some extent, decrease the number of deaths caused and also improve treatment outcomes and patient survival rates. Traditional Machine learning (ML) and Deep learning (DL) approaches have proven to be more successful in predicting breast cancer. In terms of privacy and early detection, Federated Learning (FL), a decentralized ML approach, offers a promising solution for training predictive models on distributed healthcare data while ensuring privacy and security. This paper proposes a novel framework that combines the benefits of integrating Shapley values and game theory concepts with FL for breast cancer prediction. The framework uses Shapley values for feature selection from 30 features of the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine machine learning repository (UCI ML). The framework also addresses the issue of poor-performing clients by introducing a payoff mechanism based on individual client accuracy. Clients with higher accuracy are given greater influence in the model aggregation process, encouraging client competition and improving the overall model performance. Our framework proves to be promising by achieving a prediction accuracy of 94.73% in the FL environment. The proposed approach provides a privacy-preserving solution for breast cancer prediction in an FL environment, by combining Shapley values and game theory. The results of this study can help in the development of more accurate and robust breast cancer prediction models, contributing to improved patient outcomes and healthcare decision-making.

Wireless Sensor Networks (WSNs) play an instrumental role in monitoring and data collection across a plethora of domains. As these networks expand and get more intricate, the need for energy-efficient and smart sensing models becomes paramount. This paper introduces the energy-efficient collaborative sensing model using game theory for WSNs. This model is unique in its approach, amalgamating game theory and reinforced learning to ensure optimal energy consumption without compromising the quality of service. A cornerstone of this model is the introduction of the Selection Propensity Index (SPI), which helps in the decision-making process of choosing the right sensors for specific tasks. Moreover, the model employs a Distributed Anticipatory Time-slot selection Algorithm (DATA), an RL-based algorithm that facilitates collaborative communication. This aspect ensures that the sensors while communicating, select the time slots that would result in minimal energy expenditure and optimal data transmission. Simulations are performed over the proposed model and the results obtained showcase that the proposed model outperforms the existing similar methods in terms of energy efficiency, packet drop ratio, and throughout. The proposed model exhibits an enhancement in terms of network lifetime by 202%, throughput by 30%, and is faster by more than 60% compared to the existing methods running on full load, thereby providing an innovative approach to energy efficiency in sensor networks.

This paper explores the scenario that a jammer attacks an intelligent transmitter which can sense and adapt to the jamming environment. Given the non-cooperative relationship between the transmitter and the jammer, two main challenges are addressed in this paper: how to model their interactions and how to devise a jamming strategy without prior knowledge of the transmitter. A non-zero-sum game is used to model and analyze such non-cooperative interactions. An approximate mixed-strategy Nash equilibrium (NE) under complete information is derived to serve as a benchmark for comparison. According to the non-zero-sum game model, a Deep Q-Network (DQN) approach is proposed to determine jamming strategies by exploiting the detection results of the legitimate signals, such as Acknowledgements (ACKs) feedback and the modulation recognition results obtained by the jammer. Simulation results demonstrate that, without requiring complete information about the transmitter, the proposed DQN approach can achieve a similar utility as the benchmark strategy using complete information. Compared to other learning-based jamming schemes and random jamming strategy, the proposed DQN approach achieves a higher packet error rate for the communication transceiver with reduced jamming power consumption.

The concept of differential privacy (DP) can quantitatively measure privacy loss by observing the changes in the distribution caused by the inclusion of individuals in the target dataset. The DP, which is generally used as a constraint, has been prominent in safeguarding datasets in machine learning in industry giants like Apple and Google. A common methodology for guaranteeing DP is incorporating appropriate noise into query outputs, thereby establishing statistical defense systems against privacy attacks such as membership inference and linkage attacks. However, especially for small datasets, existing DP mechanisms occasionally add excessive amount of noise to query output, thereby discarding data utility. This is because the traditional DP computes privacy loss based on the worst-case scenario, i.e., statistical outliers. In this work, to tackle this challenge, we utilize per-instance DP (pDP) as a constraint, measuring privacy loss for each data instance and optimizing noise tailored to individual instances. In a nutshell, we propose a per-instance noise variance optimization (NVO) game, framed as a common interest sequential game, and show that the Nash equilibrium (NE) points of it inherently guarantee pDP for all data instances. Through extensive experiments, our proposed pDP algorithm demonstrated an average performance improvement of up to 99.53 % compared to the conventional DP algorithm in terms of KL divergence.

Edge Computing provides an effective solution for relieving IoT devices from the burden of handling Machine Learning (ML) tasks. Further, given the limited storage capacity of these devices, they can only accommodate a restricted amount of data for training, resulting in higher error rates for ML predictions. To address this limitation, IoT devices can leverage Edge Computing and collaborate in the learning process through a designated peer acting as an Edge device. However, the transmission of offloaded tasks over a wireless access network poses challenges in terms of time and energy consumption. Consequently, although collaborative learning can diminish the variance of the learned model, it introduces a communication cost, dependent on the chosen Edge device. In light of these considerations, this paper introduces a coalition formation game that proposes a distributed Federated Learning approach, where devices autonomously and efficiently select the most suitable Edge device, aiming to minimize both their learning error and communication cost.

Electric vehicles (EVs) have emerged as the pivotal strategy for mitigating carbon emissions. Nevertheless, the lack and imbalance of charging facilities have greatly reduced consumers’ willingness to buy EVs, thereby leading to stagnation in the EV market. Although many studies focus on the imbalance in the EV market, few of them have explored the pricing strategies of charging service providers from the perspective of consumers. Therefore, to assist charging service providers in optimizing pricing decisions, we propose a consumer-perspective utility model. Specially, this utility model analyzes both time and travel costs. Furthermore, considering three charging technologies including fixed charging, mobile charging vehicles, and mobile charging robots, we then apply game theory to characterize charging services providers competition scenarios between charging services providers. After theoretical and numerical analyses, we have the following five conclusions: 1) with the high time cost for consumers, the profits of fixed charging exceed those of mobile charging vehicles; 2) mobile charging robots intensify competition in the charging market and lower the price of conventional charging services; 3) mobile charging robots also reduce the demands for fixed charging services and expedite technological substitution; 4) cost of consumers time has a positive influence on mobile charging vehicles but a negative effect on other charging services; and 5) profits of the three charging providers increase with higher consumer time costs. Finally, we propose an innovative market analyzing tool based on research findings to assist charging service providers in their decision-making process.

Voltage sag is one of the reasons that hinders the high-quality and efficient development of high-tech manufacturing users. Custom power device (CPD) is one of the effective ways to solve voltage sag. The reason why most high-tech manufacturing users are hesitant to invest CPD is that the economic benefits of CPD are vague. To solve this problem, a new premium power service (PPS) for CPD based on trial-purchase model is proposed. By trialing CPD, users can choose to purchase or cancel the devices after trialing, so as to improve the enthusiasm of users to invest premium power and ensure the smooth progress of PPS. According to the operation process of the proposed service, an optimization model of service scheme is established with the goal of maximizing the interests of power utility and user, to optimize the return ratio of users’ mitigation benefits and the length of trial service. The revenue distribution model based on Game theory (Nash negotiation) is established to obtain the revenue distribution strategies of power utility and devices company respectively. The feasibility of proposed service is verified by a semiconductor user in China. The trial-purchase premium power service could be replicable and applicable, which provides a new choice both for power utility and users.

Gathering useful and trustworthy wireless data using crowdsourcing to train/validate machine learning (ML) algorithms can be difficult due to two factors: 1) correctness and reliability of the gathered data from various independent wireless access points (APs) can be unknown, and 2) designing a metric that can capture the value of the collected data for the considered model can be a challenge. To address the challenge of reliable data collection, we propose a game-theoretic crowdsourcing-based wireless data collection mechanism that can be used to reliably collect the data. We also propose a metric that can capture the value of the collected data for the considered model. Our proposed method provides protection against selfish deviations and unlike other game-theoretic works does not make an assumption that the actions of the crowdsourcing agent APs are known to the crowdsourcing entity. We consider a technique that can be used to infer the actions of agent APs and propose that the participants utilize the n-round Win-stay lose shift (WSLS) strategy. In our work, we compare the performance of the proposed strategy against various other game-theoretic strategies, such as always high, always low, WSLS with probability p, and tit for tat. In our performance evaluation, we utilize both real and synthetic wireless channel utilization data. Our results show that the n-round WSLS strategy outperforms the other game-theoretic strategies.

Connecting smart industrial components to computer networks revolutionizes business operations. However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. Researchers presented cloud computing and fine-grained access control to overcome these challenges. However, traditional centralized computing systems involve single points of failure. To mitigate these challenges, we have proposed a secure and incentive-based data-sharing framework for IIoT systems using blockchain technology. We leverage blockchain due to its ability to provide secure and tamper-resistant data storage and sharing as participants store their data on a distributed ledger (DL), preventing unauthorized access. A security protocol is designed that utilizes the properties of elliptic curve cryptography (ECC). Moreover, Shapley value is employed to calculate revenue and distribute it fairly. To perform the formal security evaluation, we have conducted extensive simulations using the Automated Validation of Internet Security Protocols and Applications (AVISPA) and Scyther protocol simulation tools, which demonstrated that our protocol is robust against various adversarial attacks. The experimental results show that the proposed incentive distribution framework demonstrated fairness in the distribution of revenue among participants.

For the problem of power allocation between a distributed multistatic radar network and a smart jammer, the application of non-cooperative game theory is employed to address the issue in this paper. Consequently, three scenarios of power allocation games are examined. The first two game scenarios, characterized by information asymmetry, are categorized under the Stackelberg game framework, while the final scenario, with information symmetry, is classified as a non-cooperative game. Through the power allocation analyses of the three game scenarios, it is observed that both the radar system and the jammer possess a first-mover advantage. Additionally, the existence and uniqueness of the Nash equilibrium in the games are demonstrated. Based on the best response strategies within the games, three corresponding power allocation game algorithms are proposed. Ultimately, the convergence and performance comparison of the three power allocation game algorithms are validated through simulation experiments.

Accurately identifying vulnerable transmission lines in power system is paramount for averting cascading failures and guaranteeing the safety and stability of power grid operations. This paper introduces a vulnerability assessment method for transmission lines in power system, which incorporates network topology and system operational states. The enhanced degree index is used to assess the lines’ structural vulnerability. In order to evaluate the variations in power flow during regular system operation or following a failure, the improved power transfer distribution factor (PTDF) and line outage distribution factor (LODF) are used to build the line operational state vulnerability indices. To mitigate the limitations of a single assignment method, subjective indicator weights are determined using the hierarchical analysis method, while objective weights are calculated using the entropy value method. These weights are subsequently distributed using game theory. The effectiveness of the proposed method is verified through simulations on the IEEE 39-bus and IEEE 118-bus systems. The experimental results show that the proposed method has higher precision compared with other methods. And the new index can identify the vulnerable lines of the system, either due to their position in the network or by the power they transmit along the network.

The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.

Electric power grids are evolving into complex cyber-physical power systems (CPPSs) that integrate advanced information and communication technologies (ICTs) but face increasing cyberspace threats and attacks. This study considers CPPS cyberspace security under distributed denial of service (DDoS) attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resources. Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks. Different resource allocation strategies are adopted by attackers and defenders under the three cases of attack-free, failed attack, and successful attack, which lead to a corresponding consumption of resources. A multidimensional node value analysis is designed to introduce physical and cybersecurity indices. Simulation experiments and numerical results demonstrate the effectiveness of the proposed model for the appropriate allocation of defense resources in CPPSs under limited resource availability.

In the ever-changing world of decision-making, when game theory and reinforcement learning(RL) come together, they create a fascinating combination that shows a new way to solve complex problems in many fields. The combination of game theory and RL is a powerful convergence that opens up a hopeful new frontier for dealing with complex decision-making problems in many different fields. Research on the convergence of game theory and RL has shown to be beneficial, providing essential insights into challenging decision-making issues in various disciplines. This study investigates the recent developments of game theory and RL approaches through a systematic review and highlights the significance of game theory in boosting reinforcement algorithms and increasing the interaction of autonomous vehicles, safeguarding edge caching, and more. It offers a thorough account of the developments at the confluence of game theory and RL. The reviewed papers mainly focus on broad themes and address three important research questions: the impact of game theory on multi-agent reinforcement learning (MARL), the significant contributions of game theory to RL, and the significant impact areas. Following the methodology, search outcomes, and study areas is a discussion on game theory-related terminology, followed by study findings. The review’s conclusions offer ideas for further study and open research questions. The importance of game theory in advancing MARL, the potential of game theory in promoting RL strategies, and the opportunities for combining game theory and RL in cutting-edge fields like mobile edge caching and cyber-physical systems(CPS) are all emphasized in the conclusion. This review article advances our knowledge of the theoretical underpinnings and real-world applications of game theory and RL, laying the groundwork for future improvements in decision-making techniques and algorithms.

This research is imperative due to the pressing need for improved patient recruitment in clinical trials, addressing challenges such as delays and high costs. By introducing a classification model and a game theoretic approach for clinical trial setting, we aim to boost trial efficiency, advance healthcare research, and enhance patient outcomes. This research is critical for revolutionizing recruitment strategies and accelerating medical progress. In this paper, we present a classification model that has been specifically designed to address this issue effectively. The proposed model employs an Autoencoder, augmented by a super classification model that merges Logistic Regression, Support Vector Machines, Random Forest Trees, and Decision Trees using a stacking classifier. The output of the super classifier is further processed by a meta classifier to obtain the final result. Notably, the model achieves a training accuracy of 99.576% and a validation accuracy of 83.45%, illustrating its robust classification performance and its potential to streamline patient recruitment, reducing delays and resource consumption. In addition to the classification model, this study formulates a three-layer game theoretic model involving Patients, Doctors or Clinical Investigators, and Research Firms. Within this static repeated game setting, players sequentially strategize to optimize their recruitment strategies, while research firms aim to optimize their overall interaction. The paper proposes a novel optimal solution that strikingly balances the payoffs of all three players. Moreover, the work presents a necessary condition and closed form for the existence of an equilibrium in the game, offering a strategic approach to recruitment optimization, and striking a balance between stakeholders. This equilibrium-seeking solution has the potential to revolutionize recruitment dynamics and foster collaboration. Additionally, the study’s theoretical contributions lay the groundwork for future research in this critical healthcare domain.

For an extended period, a technological architecture known as cloud IoT links IoT devices to servers located in cloud data centers. Real-time data analytic are made possible by this, enabling better, data-driven decision making, optimization, and risk reduction. Since cloud systems are often located at a considerable distance from IoT devices, the rise of time-sensitive IoT applications has driven the requirement to extend cloud architecture for timely delivery of critical services. Balancing the allocation of IoT services to appropriate edge nodes while guaranteeing low latency and efficient resource utilization remains a challenging task. Since edge nodes have lower resource capabilities than the cloud. The primary drawback of current methods in this situation is that they only tackle the scheduling issue from one side. Task scheduling plays a pivotal role in various domains, including cloud computing, operating systems, and parallel processing, enabling effective management of computational resources. In this research, we provide a multiple-factor autonomous IoT-Edge scheduling method based on game theory to solve this issue. Our strategy involves two distinct scenarios. In the first scenario, we introduced an algorithm containing choices for the IoT and edge nodes, allowing them to evaluate each other using factors such as delay and resource usage. The second scenario involves both a centralized and a distributed scheduling approach, leveraging the matching concept and considering each other. In addition, we also introduced a preference-based stable mechanism (PBSM) algorithm for resource allocation. In terms of the execution time for IoT services and the effectiveness of resource consolidation for edge nodes, the technique we use achieves better results compared with the two commonly used Min-Min and Max-Min scheduling algorithms.

Hierarchical Federated Learning (HFL) has emerged to overcome the shortcomings of conventional Federated Learning (FL) due to communication obstacles between the end users and the cloud server and the congestion at the backhaul of wireless network implementations. In this paper, we consider a wireless user-edge-cloud HFL network where the transmissions of the users’ local model parameters to the edge are multiplexed via the Non-Orthogonal Multiple Access (NOMA) technique. The joint problem of association and uplink transmission power allocation of the users to the edge is formulated and solved as a non-cooperative game in satisfaction form. Diverging from the prevailing research that proposes centralized solution concepts, each user makes autonomous decisions regarding its association and power level so as to attain a minimum acceptable tradeoff of three vital network factors. The latter includes the global model’s training accuracy and the users’ consumed energy and time during transmission. Different types of equilibria are explored, i.e., the Satisfaction Equilibrium (SE) and Minimum Efficient Satisfaction Equilibrium (MESE) which not only fulfills users’ minimum tradeoff but also minimizes the overall network’s cost. Algorithms based on Reinforcement Learning (RL) and Best Response Dynamics (BRD) are, then, devised to conclude the SE and MESE points. The proposed framework is evaluated via modeling and simulation, verifying its efficiency in achieving an equitable balance in the network.

Peer-to-peer (P2P) energy trading has gained significant importance in recent years due to the growing energy needs worldwide. To ensure the effective and efficient implementation of P2P energy trading, it is necessary to analyze the concept from multiple dimensions. This study aims to investigate the challenges that may hinder the smooth flow of P2P energy trading and identify strategies to overcome them. Technical, cybersecurity, renewable energy integration, economic, pricing mechanisms, and regulatory challenges are among the key obstacles that may curtail the full potential of P2P energy trading. In addition, the full achievement of the P2P energy trading potential requires a global response from stakeholders to ensure widespread acceptance and adoption. Game theory and agent-based modeling can effectively address these challenges and facilitate the successful implementation of P2P energy trading.

The fifth generation (5G) mobile communication adopted the usage of Millimeter Wave (mmWave) bands to ignite prospects of gigabit data rates in mobile networks. However, mmWave propagation is highly susceptible to competing factors of user and topographic dynamics: they formulate irregular cell patterns. The irregularities in mmWave cell patterns cause unreliable connectivity and can instigate unnecessary Handoffs (HOs). This behavior ultimately increases the risk of 5G link failures. To improve mmWave link connectivity hence guarantee continuous connectivity in 5G mobile communication, this paper proposes a HO scheme that predicts target link deterioration patterns to select the most reliable mmWave link for a mobile user. The scheme is based on Game Theory (GT) and Jump Markov Linear Systems (JMLS). JMLSs are known to account for abrupt/erratic changes in system dynamic predictions. We amalgamate GT with JMLS capability to predict target mmWave link pattern/behavior after the HO execution. Specifically, given channel gain and received power variation over distance, the GT-JMLS HO scheme predicts the sustainability of the signal-interference-noise ratio (SINR) pattern of a target link above threshold. This is paramount to reducing the selection of mmWave links that prematurely fail or require multiple HOs to sustain connectivity over a short distance or period. Our simulation results show that our proposed HO scheme offers target links with higher: throughput, energy efficiency, reliability, and longer dwell time between HOs than classical HO schemes.

Non-cooperative games serve as a powerful framework for capturing the interactions among self-interested players and have broad applicability in modeling a wide range of practical scenarios, ranging from power management to path planning of self-driving vehicles. Although most existing solution algorithms assume the availability of first-order information or full knowledge of the objectives and others' action profiles, there are situations where the only accessible information at players' disposal is the realized objective function values. In this article, we devise a bandit online learning algorithm that integrates the optimistic mirror descent scheme and multi-point pseudo-gradient estimates. We further prove that the generated actual sequence of play converges a.s. to a critical point if the game under study is globally merely coherent, without resorting to extra Tikhonov regularization terms or additional norm conditions. We also discuss the convergence properties of the proposed bandit learning algorithm in locally merely coherent games. Finally, we illustrate the validity of the proposed algorithm via two two-player minimax problems and a cognitive radio bandwidth allocation game.

The issue of cybersecurity has gained significant prominence in the context of safeguarding the privacy and integrity of information, especially with the increasing prevalence of interconnected devices such as Mobile Ad-hoc networks (MANETs). Due to their nature, particularly in light of the unavoidable security flaws that arise from the complexity of current and future systems and services, it is crucial to explore how to prevent cyberattacks efficiently. On the other hand, game theory is one of the most significant methods used in this field. Yet, survey articles that consider game theory for security in MANETs are less common. Therefore, in this paper, we survey the articles related to game theory for security in MANETs, presenting an overview of the basic MANETs concepts and their main vulnerabilities. Also, we will propose a taxonomy for recent works, highlighting the limits of the existing works, and outlining some potential future directions for cybersecurity in Ad-hoc networks.

Transportation networks are essential to the functioning of modern societies. They provide access to goods, services, and opportunities, connecting people and places. However, the efficient and effective management of these networks is a complex challenge that requires careful consideration of numerous factors, including capacity, demand, congestion, and user behavior. Game theory (GT) has emerged as a powerful tool for analyzing transportation systems and developing strategies to optimize their performance. This manuscript provides a comprehensive review of the application of GT in transportation networks from both microscopic and macroscopic perspectives. Specifically, it examines how GT has been used to analyze the behavior of individual travelers and transportation providers and how it has been used to develop strategies for managing congestion, improving efficiency, and reducing emissions. Lastly, the manuscript provides a roadmap for future research and highlights the challenges in the application of GT in transportation networks.

This study examines pricing decisions by considering two manufacturers’ warranty and bundling strategies using spatial competition in a duopolistic market. With the development of advanced technology and evolving societal needs, manufacturers now sell a product with its complement—a warranty—using either a bundled base warranty or an unbundled extended warranty strategy. To investigate the impact of the bundling strategy on warranty and pricing models, we develop two scenarios for the pure bundling and mixed bundling scenarios. By constructing a Nash equilibrium model and assessing customer preferences, we determine the optimal warranty length and level of customer demand corresponding to a manufacturer’s pricing and strategic decisions. The analysis demonstrates that one manufacturer’s strategic decisions are interdependent with those of its competitor. Moreover, a longer warranty period in the unbundled extended warranty strategy not only attracts customers and increases demand but also imposes additional costs on manufacturers. As a result, while providing more personalized features and flexibility, the unbundled extended warranty strategy is not always more profitable. Thus, a manufacturer must carefully evaluate its warranty and bundling strategies when making pricing decisions to gain a competitive advantage. Finally, numerical and sensitivity analyzes confirm the results of the theoretical analysis and provide managerial insights.

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party. We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

Based on the paradigm shift in modern warfare, ground forces conduct pilot operations of wireless unmanned maneuvering systems, such as tactical drones, in the form of manned and unmanned cooperative tactics after deploying the relevant systems in the battlefield. However, security considerations for relevant systems are limited to the scope of using only the existing end-to-end encryption and public key authentication modules, and no defense strategy to actively respond to specialized cyber-electronic warfare threats has been officially established. To drastically reduce both the potential attack surface and security vulnerabilities of drones employed in network-centric-warfare, a proactive defense technology that expires the effectiveness of attacks by avoiding invasive action at the target is expected to be essential. Accordingly, this paper proposes the concept of active moving-target-defense (MTD), an element of cyber deception that minimizes the rate of success of cyber-attacks while conversely maximizing both defense predominance and attack complexity asymmetrically, exclusive according to partially observable Markov decision process (POMDP)-based threat modeling that considers both the internal and external operation sequences of target drones. To optimally design the proposed drone-type MTD based on the Pareto frontier, we additionally advanced and simulated a drone-based defensive deception game framework (D3GF), which represents a general-sum combat framework reflecting decision logics such as the perfect Bayesian Nash equilibrium, stochastic Stackelberg, and partial signal game. This study was conducted to compare and calculate the efficiencies of the drone-type MTD’s deceptive defense, which had not been considered in prior studies, by unique environmental features inside and outside the drone. Furthermore, we conducted a detailed performance evaluation considering game metrics based on sensitivity analysis. Hereafter, the drone-type MTD will be extended into an actual active drone protection technology combining cyber flare-type avoidance strategies and cyber camouflage-type disarrangement strategies by expanding its optimization domain as a hypergame, while integrating it with drone decoy elements.

With the emergence of the Internet-of-Things (IoT), artificial intelligence, and communication technologies, cyber-physical systems (CPS) have revolutionized the engineering paradigm with profound applications in many aspects of society including homes, energy, agriculture, health-care, transportation, business, and manufacturing. A CPS uses suitable computational techniques such as game theory to enable different entities to interact with one another for taking necessary actions to obtain selected objectives. Recent literature on CPS has extensively used game theory to approach a variety of technical challenges. In order to make these contributions more accessible to a broader audience, there is a need for studies that can provide readers with a comprehensive understanding of different types of CPS and their attributes, then clearly outline why game theory is relevant for modeling different aspects of CPS, and also discuss how game theory has been used in relevant literature to date. This paper bridges this gap by 1) providing a general discussion of different types of CPS and their characteristics; 2) giving an overview of different types of game-theoretic approaches; 3) explaining why game theory is appropriate for modeling different types of CPS; and 4) finally, studying how game theory has been used in different CPS types to address their challenges. Further, we also identify some key research challenges for future investigation where game theory could be applied as a potential solution.

Recently, the lack of spectrum resources has become a key technical bottleneck to develop the Industrial Internet of Things (IIoT). Based on cognitive radio technology, the cognitive IIoT (CIIoT) paradigm can improve spectrum utilization via opportunistically accessing the idle spectrum bands. In this study, a novel cooperative spectrum sharing scheme is presented for the CIIoT system platform. The main challenge of our scheme is to effectively share the limited spectrum resource via cooperative sensing and dynamic accessing techniques. To achieve a mutually desirable solution for different CIIoT devices, we design a learning game model using the ideas of multi-agent reinforcement learning (MARL) and the negotiated aspirations bargaining solution (NABS). In the learning mechanism, individual CIIoT devices adaptively select their cooperative sensing policy according to the MARL model. In the bargaining mechanism, the available spectrum resource is dynamically shared through the NABS, which is obtained based on the devices’ selected sensing policy. By investigating the contribution of MARL to game theory, the proposed scheme can effectively guide intelligent CIIoT devices toward a socially optimal outcome. Numerical simulation results demonstrate that the normalized device payoff, CIIoT system throughput and device fairness of our approach are better than those of existing benchmark protocols. Finally, we present the key challenges and future direction of our research in the CIIoT system operations.

Federated learning (FL) can guarantee privacy by allowing local users only upload their training models to central server (CS). However, the existence of Byzantine or inactive users may cause model corruption or inactively participation in FL. In this paper, a game theory based detection and incentive method is designed for Byzantine and inactive users. Specifically, a differential aggregate gradient descent (DAGD) algorithm is adopted to improve the stability and fasten the convergence. Then the loss function is modified by considering Byzantine and inactive users. For Byzantine users, a random Euclidean distance (RED) voting method is designed to identify Byzantine users, and after identification, Byzantine users are motivated by the game theory. For inactive users, when inactive users are detected by contribution, Nash equilibrium with a mixed strategy is used to solve the problem of inactive users’ participation in FL. Extensive experimental results show that Byzantine users and inactive users can be detected and motivated by the algorithm. Meanwhile, compared with other methods, the accuracy of the optimized model is improved with reduced training time.

The increasing integration of utility-scale wind/photovoltaic farms in transmission networks is posing new technological challenges that must be faced to ensure grid stability. Voltage control is one of the issues that must be tackled. Making use of the available voltage controllers in the grid, it is possible to design sophisticated and robust coordination algorithms for a more efficient performance. The intention of this work is to show the feasibility of applying non-cooperative game-theory to distributed voltage compensator controls in AC transmission networks, in which no evident efforts can be found in the related literature. In contrast to previous works, we define sets of STATCOMs that participate in the game without any centralized entity. The identification of sets is based on sensitivity coefficients, which model the impact that the variation of the setpoint on an element has on other nodes. With the use of sets, the approach is more robust and scalable. Another relevant advantage of the proposal is the use of minimum and non-sensitive data exchange, with which the local controllers reach the optimal solution with an iterative procedure in a fast way. The effectiveness of the proposal is evaluated for the IEEE 14-bus test system with three STATCOM units. Each STATCOM may be associated to a PV or a wind large-scale farm. The simulation output reveals that the proposed algorithm is close to the solution provided by a centralised control. When compared with consensus-based algorithms, our proposal converges faster and thus it proves the feasibility.

This study utilizes hot dry rock (HDR) geothermal energy, which is not affected by climate, to address the capacity allocation of photovoltaic (PV) -storage hybrid power systems (HPSs) in frigid plateau regions. The study replaces the conventional electrochemical energy storage system with a stable HDR plant assisted by a flexible thermal storage (TS) plant. An HPS consisting of an HDR plant, a TS plant, and a PV plant is proposed. Game approaches are introduced to establish the game pattern model of the proposed HPS as the players. The annualized income of each player is used as the payoff function. Furthermore, non-cooperative game and cooperative game approaches for capacity allocation are proposed according to the interests of each player in the proposed HPS. Finally, the proposed model and approaches are validated by performing calculations for an HPS in the Gonghe Basin, Qinghai, China as a case study. The results show that in the proposed non-cooperative game approach, the players focus only on the individual payoff and neglect the overall system optimality. The proposed cooperative game approach for capacity allocation improves the flexibility of the HPS as well as the payoff of each game player. Thereby, the HPS can better satisfy the power fluctuation rate requirements of the grid and increase the equivalent firm capacity (EFC) of PV plants, which in turn indirectly guarantees the reliability of grid operation.

The Radio Access Network (RAN) slice planning is a key phase within the RAN slice management and orchestration process. Based on the performance requirements of requested RAN slices and key performance indicators of the RAN and existing RAN slices, the RAN slice planning mainly consists of deciding (a) the feasibility of deploying new RAN slices; (b) re-configuring the existing RAN accordingly; and (c) the need to renegotiate the Service Level Agreements (SLAs) and/or expand the RAN (i.e., radio resources, carriers, cells etc) if one or more RAN slices cannot be accommodated in a first attempt. Under this context, we propose a framework for planning RAN slices which require their data sessions get a Guaranteed Bit Rate (GBR) and the probability of blocking such sessions is below a threshold. To meet such requirements, our framework plans the amount of prioritized radio resources for new and already deployed RAN slices. We formulate the RAN slice planning as multiple ordinal potential games and demonstrate the existence of a Nash Equilibrium solution which minimizes the average probability of blocking data sessions for all the RAN slices. We perform detailed simulations to demonstrate the effectiveness of the proposed solution in terms of performance, and renegotiation capability.

In enterprise management systems (EMS), augmented Intelligence of Things (AIoT) devices generate delay-sensitive and energy-intensive tasks for learning analytics, articulate clarifications, and immersive experiences. To guarantee effective task processing, in this work, we present a cloud-assisted fog computing framework with task offloading and service caching. In the framework, tasks make offloading decisions to determine local processing, fog processing, and cloud processing with the goal of minimal task delay and energy consumption, conditioned on dynamic service caching. To this end, we first propose a distributed task offloading algorithm based on noncooperative game theory. Then, we adopt the 0–1 knapsack method to realize dynamic service caching. At last, we adjust the offloading decisions for the tasks offloaded to the fog server but without caching service support. In addition, we conduct extensive experiments and the results validate the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Time-varying electricity prices in demand response (DR) programs motivate users to change their consumption behavior with the aim of electricity bill cost minimization. Coordinated DR schemes aim to flatten the aggregate load profile and mitigate rebound peaks. However, a comprehensive model which integrates supply and demand sides has to be developed to consider system constraints and renewable energy resources. In this regard, this article proposes a bi-level framework for supply and demand side energy management in an islanded microgrid. In the first level, consumers schedule their appliances’ operation to minimize their expenses. In the second level, a dynamic economic dispatch problem is solved which finds the power generation schedule for the generators resulting in the minimum electricity production cost. A game-theoretic turn-based decentralized algorithm is suggested which manages the interaction between both levels. Simulation results show that the proposed algorithm minimizes users’ electricity bill costs while satisfying power system constraints. [ABSTRACT FROM AUTHOR]

With the increasing popularity of online retailing, in this article, we investigate the alternative online operation strategies regarding commission price when selling through the online platforms. We exploit a triadic network setting that consists of one online platform firm and two competing vendors and examine the strategic choice of the involved parties to effectively compete in the marketplace. We develop the game models for alternative commission pricing strategies and obtain the equilibrium solutions. Through a comparison of equilibrium solutions, our analysis reveals that alternative commission pricing strategies lead to the different economic results for the online platform and the two vendors individually and collectively. While vendors can take necessary actions (e.g., an additional fee for an exclusive pricing right) to maximize their own benefit, competing vendors can also respond to their rival's strategic choice to minimize the negative impact of the deal reached by the other two parties. Our findings also suggest that the exclusive pricing right that the leader vendor tries to secure results in a higher commission fee paid to the online platform. Through modeling strategic behavior and consequential economic results in the setting of a triad network, our research also responds to the proposition that the structural and behavioral approaches represent incommensurable ideas, and we show that a combined theoretical strategy appears well adapted to the complex realities of interfirm networks. [ABSTRACT FROM AUTHOR]

Motivated by the rising environmental protection awareness and the importance of green product design, in this article we develop two models to investigate the quality and greenness decisions and product line choices of a green manufacturer as well as the corresponding impact of product line length on environmental performance. In our models, both the manufacturer and consumers show environmental responsibility behaviors. We find that the manufacturer extends the product line when the unit fit cost, the green cost, the quality cost, the market scale of low-segment consumers, and the environmental responsibility level (or unit environmental cost) are low. In the presence of two products with different market positioning, our analysis reveals that: first, owing to the consumer's self-selection behavior, product quality and greenness may be distorted from the efficient quality and greenness; second, product line extension may improve both the environmental performance and the manufacturer's utility, i.e., achieving a win–win outcome; and third, a higher unit environmental cost (environmental responsibility level or a longer product line) will improve the environmental performance if the unit environmental cost is high. In the continuous green valuation setting, interestingly, we find different results that the manufacturer with a high environmental responsibility level may extend its product line, and the environmental performance increases with the unit environmental cost in the single-product case. [ABSTRACT FROM AUTHOR]

Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an “amplitude-lifetime” game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers. [ABSTRACT FROM AUTHOR]

This article addresses a cooperative game-oriented optimal constraint-following problem for fuzzy mechanical systems. The state of the concerned system is affected by possibly (fast) time-varying uncertainty. The fuzzy set theory is adopted to describe such uncertainty. The task is to drive the system to obey a set of prescribed constraints optimally. Since the control objective may be changing along with the system uncertainty, a time-varying $\beta $ -measure is defined to gauge the constraint-following error; based on which, an adaptive robust control scheme with two tunable parameters is then proposed to render it to be uniform boundedness and uniform ultimate boundedness. For the seeking of the optimal design parameters, two cost functions, each of which is dominated by one tunable parameter, are developed with the fuzzy information, and thereout a two-player cooperative game is formulated. Finally, the optimal design problem is successfully solved: with the existence, uniqueness, and analytical expression of the Pareto optimality. [ABSTRACT FROM AUTHOR]

Currently, electroencephalogram (EEG) is mostly analyzed in a supervised way, which requires EEG labels (e.g., EEG classification). With the ever-increasing amount of unlabeled/mislabeled EEG in neuropsychiatric disorder diagnosis, BCI, and rehabilitation, manually labeling of EEG data is a labor intensive and time-consuming process, and few labs have developed algorithms to analyze EEG in an unsupervised manner (i.e., EEG clustering). In this paper, we propose a cooperative game inspired approach to cluster multi-trial EEG data. The idea is to map multi-trial EEG clustering to the coalition formation in a cooperative game, and then identify cluster center (the EEG trial with highest Shapley value) and assign EEG trials into proper clusters based on their cross correlation-transformed Shapley values. We demonstrate the mapped EEG cooperative game is convex, and it leads to an algorithm for multi-trial EEG clustering named CoGEEGc. The CoGEEGc yields high-quality multi-trial EEG clustering with respect to intra-cluster compactness and inter-cluster scatter. We show that CoGEEGc outperforms 15 state-of-the-art EEG or time series clustering approaches through detailed experimentation on real-world multi-trial EEG datasets. Comparison against 15 methods with four theoretical properties of clustering further illustrates the superiority of CoGEEGc, as it satisfies two properties while other approaches only satisfy one. [ABSTRACT FROM AUTHOR]

Although emerging relay-based communication networks boast of increased transmission coverage and capacity, they currently lack schemes that optimize the simultaneous utilization of the best-quality channels, especially when such channels belong to heterogeneous frequency bands. In this article, we present a centralized oracle based on a bipartite graph model for multiband multichannel allocation, in this case, to relay nodes (RNs). We also indicate the reasons for which this technique is not viable for deployment. We then go on to develop a customized greedy heuristic, the traditional choice for distributed multiband multichannel assignment to RNs, which results in suboptimal performance. Since the greedy approach does not offer a performance-bound guarantee, we also explore a better online, distributed multiband multichannel allocation strategy by proposing a sequential algorithm based on game theory. Computer-based simulation results demonstrate that our proposed game-theoretic approach significantly outperforms the traditional distributed and centralized methods with a Nash equilibrium convergence guarantee. [ABSTRACT FROM AUTHOR]

In current blockchain systems, the transaction fee is often not enough to cover the storage cost, jeopardizing blockchain sustainability in the long run. Such a storage sustainability issue is partially due to miners’ heterogeneous storage costs and users’ low-intensity fee competition. Motivated by these two observations, we propose a Fee and Transaction Expiration Time (FTET) mechanism to alleviate this issue. Specifically, we model the blockchain operation as a three-stage game. In Stage I, the system designer proposes the storage sustainability mechanism. In Stage II, each user decides whether to propose transactions and the corresponding transaction fees. In Stage III, each miner decides which transactions to include in the block. Although the analysis of the heterogeneous miner interaction is technically challenging, we fully solve it in closed-form motivated by how miners select transactions in practice. The equilibrium analysis reveals that high-storage-cost miners admit transactions with fees above a time-increasing threshold. Under the optimal FTET mechanism, the blockchain system can achieve the storage sustainability without any social welfare loss, comparing with the maximum achievable social welfare without the storage sustainability constraint. Moreover, the optimal FTET mechanism achieves a higher social welfare than the fee mechanism in current practice by selectively rejecting some transactions suffering high delays. Finally, we implement a blockchain prototype to compare the performance of the optimal FTET mechanism with the mining round time adjustment (MRTA) mechanism. The optimal FTET mechanism achieves higher social welfare (94.5% on average) and better storage sustainability. We find that more pending transactions may lead to lower transaction fees. [ABSTRACT FROM AUTHOR]

Advances in communications and networking technologies are driving the computing paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data and resources. Opposite to centralized intelligence, Hierarchical Federated Learning (HFL) relieves overwhelmed communication overhead and enjoys the advantages of high bandwidth as well as abundant computing resources while retaining privacy-preserving benefits of Federated Learning (FL). It is difficult to balance system overhead and model performance in the HFL framework, while it could be solved by introducing an incentive mechanism. Although the incentive mechanism can alleviate the above anxiety by compensating relevant participants, some limitations (multi-dimensional properties, incomplete information and unreliable participants) will significantly degrade the performance and efficiency of the designed mechanism. To address the challenges caused by the above limitations, we propose InFEDge, a blockchain-based incentive mechanism in the HFL. The InFEDge considers 1) multi-dimensional individual properties to model system participants and proves the uniqueness of Nash equilibrium with the closed-form solution. Meanwhile, 2) we transform the problem under incomplete information into a contract game where we obtain the optimal solution. Moreover, 3) we also leverage the blockchain to provide economic incentives, prevent unreliable participants’ disturbance and further ensure data privacy by implementing the mechanism in the smart contract to offer a credible, faster, and transparent resource trading system. Experimental evaluations on a proof-of-concept testbed along with real traces demonstrate the superiority of our mechanism. Further, our method solves a real-world user allocation problem for future communications and networking. [ABSTRACT FROM AUTHOR]

Directed Acyclic Graph (DAG) is a popular approach to achieve scalability of blockchain networks. Due to its high efficiency in data communication and great scalability, DAG has been widely adopted in many applications such as Internet of Things (IoT) and Decentralized Finance (DeFi). DAG-based blockchain, nevertheless, faces the key challenge of transaction inclusion collision due to the high concurrency and the network delay. Particularly, the transaction inclusion collision in DAG-based blockchain leads to the revenue and throughput dilemmas, which would greatly degrade the system performance. In this paper, we propose “TIPS”, the Transaction Inclusion Protocol with Signaling, which broadcasts a signal indicating the transactions in the block. We show that with the prompt broadcast of a signal, TIPS substantially reduces the transaction collision and thus resolves these dilemmas. Moreover, we show that TIPS can defend against both the denial-of-service and the delay-of-service attacks. We also conduct intensive experiments to demonstrate the superior performance of the proposed protocol. [ABSTRACT FROM AUTHOR]