Power delivery issues are increasingly evident in cyber-physical smart grid systems as energy transactions frequently overlook the physical constraints of distribution, leading to transmission congestion and compromising network security and reliability. This article presents a novel and cost-effective solution to power delivery challenges by utilizing electric vehicles (EVs) with dynamic transportation capabilities as free carriers. Unlike traditional approaches, a deep reinforcement learning (DRL)-based optimization framework is designed to effectively manage incomplete information in real-time. Our method first introduces an investment-free model that leverages existing EV routes to transport energy during congestion, operating in a “free-riding” transmission mode. This not only enhances network reliability but also curtails costs. Then, we develop a Markov decision process (MDP) for sequential decision-making of 24-h optimal control, aimed at minimizing operational losses including load shedding and battery degradation. To deal with the stochastic nature of energy requests and EV routes in the control problem, we employ a model-free DRL algorithm to tackle the challenge of incomplete information. An Actor-Critic network, combining value-based and policy-based approaches, helps discover approximately optimal strategies in a continuous action space. Finally, the simulation results numerically demonstrate the performance of the proposed method.

To realize carbon emission reduction, restricting regional carbon emissions while meeting electricity usage is a critical but not trivial problem. In this paper, we propose a game-based pricing scheme for joint carbon emission rights (CER) and electricity trading between the electricity prosumers within a microgrid. For modeling and theoretical analysis, we first introduce the utility functions of electricity producers and consumers, which are determined by CER and electricity prices in a coupled way. Then, the multi-leader multi-follower (MLMF) Stackelberg game and non-cooperative game are employed to formulate the electricity and CER pricing and trading, respectively. The game equilibriums convince that optimal prices for both electricity and CER exist to satisfy electricity usage while meeting the carbon emission restriction. For implementation, the blockchain with smart contracts is developed to undertake the CER and electricity trading in a transparent and credible way. A prototype system based on Fabric blockchain verifies the feasibility of the proposed scheme, which demonstrated a five-fold increase in the economics and electricity generation utility of the microgrid and achieved a 2% reduction in carbon emissions compared to the baseline model.

Blockchain has gained significant adoption in energy trading, offering benefits for both economy and environment. Consensus, in particular, is a decisive factor for blockchain-based energy trading systems to operate efficiently and securely. However, the consensuses currently applied have been criticized for being too energy intensive or not sufficiently decentralized, which counteracts the positive effect of energy trading. Besides, consensus and energy trading are treated separately in many energy trading blockchain-based studies. In this article, we propose a green and efficient consortium blockchain-enabled transaction system for energy trading, meeting the requirement of low energy consumption under security. We then design a two-stage consensus mechanism called proof-of-energy that is coupled to trading through “energy” and naturally uses the monetary rewards to stimulate prosumer participation. Specifically, it retains a strong degree of decentralization, which selects a dynamic delegation with high historical energy generation and motivates delegates to compete for new blocks by solving a meaningful puzzle. Furthermore, a variable block reward is investigated as the incentive to regulate trading and consensus behavior within a reasonable range of energy consumption. Finally, we design a two-layer iterative algorithm to obtain the optimal consensus strategy and block rewards, taking the noncooperative game approach with the consideration of the strategy effect on the pricing model. Our simulation results show that the proposed blockchain-enabled system has a high energy efficiency ratio that improves the social welfare and reduces the consensus overhead.

This paper investigates the uplink performance of a general cell-free massive multiple-input multiple-output (CF-mMIMO) system, in which all access points (APs) and user equipments (UEs) suffer from hardware impairments (HWIs). Besides, there are several reconfigurable intelligent surfaces (RISs) that aim to improve the coverage quality, spectral efficiency (SE), and energy efficiency (EE). Relying on the knowledge of only imperfect channel state information, a tight closed-form expression for the lower-bound achievable SE is derived. Based on this expression, we quantitatively investigate the impacts of different system parameters on uplink SE and EE, and conduct a tradeoff analysis between using more APs versus using more RISs with respect to the above performance metrics. In addition, we also design a max-min SE algorithm that takes into account both large-scale fading decoding weights and power control coefficients to guarantee UE fairness. Specifically, the proposed algorithm admits a closed-form solution and is therefore memory-efficient and time-saving. Both the theoretical analysis and the effectiveness of the proposed max-min SE algorithm are verified via extensive simulations.

Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.

Although crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Existing incentive mechanisms are not effective to avoid socially undesirable equilibrium due to the following features of competitive crowdsourcing: in the presence of imperfect monitoring, heterogeneous workers with competing interest tend to beat their opponents for larger self-profit, and the fact that they can freely and frequently change their opponents makes the situation much more complicated. Taking these features into consideration, this article proposes a mechanism design problem to enforce cooperation and extort selfish works simultaneously, with the objective of maximizing the requester's utility. To solve the problem, we integrate binary ratings with differential pricing to develop a novel rating protocol. By establishing a mathematical model for the problem and quantifying necessary and sufficient conditions for a sustainable social norm, we provide design guidelines for optimal rating protocols and design a low-complexity algorithm to select optimal design parameters. Finally, extensive evaluation results demonstrate the performance of our proposed rating protocol and reveal how intrinsic parameters impact on design parameters.

The abuse of personal identity information is one of the most serious problems worldwide. Most social services or businesses use the identity authorization to confirm their validity and legality and the copies of users' identity certification are usually recorded by the service providers. It is easy to leak the users' identity information due to the untrustworthy service provider or single-point security failure, and various social problems are then caused. To deal with such problems, this paper proposes a Blockchain-based Identity Authorization mechanism (BIA). First, an Identity Authorization Module (IAM) is devised, which reads the identity certificate and transform the identity plaintext to ciphertext under the authorization by the user's identity certificate entity and password. IAM guarantees the security of identity information by keeping its plaintext offline. Second, a Business Contract Module (BCM) is designed, which provides a general smart contract framework for identity authorization that can be adopted by most of social services or businesses. Third, a double-chain blockchain infrastructure is developed, whereby the encrypted identity information and service smart contracts are respectively recorded in the tamper-resistant, non-repudiable, and publicly verifiable way. Finally, a prototype system has been developed to verify the security, feasibility and effectiveness of the proposed BIA.

In proof-of-work (PoW)-based blockchain networks, the miners contribute their distributed computation in solving a crypto-puzzle competition to win the reward. To secure stable profits, some miners organize mining pools and share the rewards from the pool in proportion to each miner's contribution. However, some miners may exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this paper, we propose a new game-theoretic framework to incentivize miners mining honestly and help to bring about a higher total welfare of blockchain networks. We first formulate the mining process as a noncooperative iterated game. We then propose a mechanism in terms of zero-determinant strategies (ZD strategies) to encourage the cooperative mining and improve the efficiency of mining in PoW-based blockchain networks. In addition, we theoretically analyze the maximum system welfare of the target pool through the method of optimization. Numerical illustrations are also presented to support our theoretical results.

Macrotasking crowdsourcing systems (MCSs), such as Google Helpouts and Elance have emerged as an effective paradigm for improving human intelligence and activity to solve a wide variety of tasks. Requesters often post tasks to the MCS and competitive workers solve the tasks to earn the reward. However, rational and selfish workers in the MCS aim to strategically maximize their own benefit by exhibiting malicious behaviors, thereby decreasing the efficiency of systems. Herein, we present a novel game-theoretic mechanism to incentivize the competitive and selfish workers to provide high-quality solutions in the MCS. We first formulate the crowdsourcing problem as a multiplayer iterated game with incomplete information, where each worker has certain private information (such as solution quality), but does not know what other workers do. Subsequently, we propose an incentive mechanism in terms of zero-determinant (ZD) strategies aiming to improve the social welfare of the MCS, which serves to incentivize the competitive selfish workers toward high-quality solutions. Moreover, we find the conditions for reaching the maximum social welfare of the MCS. Numerical illustrations demonstrate a high and stable social welfare of the MCS with the proposed ZD strategies mechanism.

Weighted vertex cover (WVC), a generalized type of vertex cover, is one of the most important combinatorial optimization problems. In this paper, we provide a novel solution to the WVC problem from the view of network engineering. We model the WVC problem as an asymmetric game on weighted networks, where each vertex is treated as an intelligent rational agent rather than an inanimate one. Under the framework of asymmetric game, we find that strict Nash equilibriums of the asymmetric game are the intermediate states between the WVC states and the minimum WVC (MWVC) states. Besides, we propose best response algorithms with memory and feedback to solve the WVC problem, and find that a better approximate solution to the MWVC can be obtained under the feedback-based best response algorithm. Numerical illustrations verify the performance of the proposed game solution on weighted networks. Our findings pave a new way to solve the WVC problem from the perspective of asymmetric game, which opens a bottom-up avenue to address the combinatorial optimization problems.

Despite the increasing popularity and the perceived promise of crowdsourcing, its openness presents individuals with an opportunity to exhibit antisocial behavior, such as free-ride and attack to decrease the social welfare, which is considered as a crowdsourcing contest dilemma. Hence, incentive mechanisms are needed to compel rational and selfish individuals to contribute well behavior in tasks. In this paper, we integrate the pricing and reputation schemes to design a novel socially optimal rating protocol based on game theory, in which each player is tagged with a rating to represent its social status, and players are encouraged to contribute good behaviors to increase their ratings, thus receive higher rewards. In particular, we analyze how the players' behaviors are influenced by the incurred costs and the designed payment, as well as their long-term utilities. By quantifying the sufficient and necessary conditions under which all players comply with the social norm in their self-interests, we formulate the rating protocol design problem, and analyze the impacts of the design parameters in order to characterize the optimal design, that maximizes the social welfare to achieve the social optimum. Finally, illustrative results show the validity and effectiveness of our proposed protocol design for crowdsourcing contest dilemma.

In self-organized mobile ad hoc networks (MANETs), network functions rely on cooperation of self-interested nodes, where a challenge is to enforce their mutual cooperation. In this paper, we study cooperative packet forwarding in a one-hop unreliable channel which results from loss of packets and noisy observation of transmissions. We propose an indirect reciprocity framework based on evolutionary game theory, and enforce cooperation of packet forwarding strategies in both structured and unstructured MANETs. Furthermore, we analyze the evolutionary dynamics of cooperative strategies and derive the threshold of benefit-to-cost ratio to guarantee the convergence of cooperation. The numerical simulations verify that the proposed evolutionary game theoretic solution enforces cooperation when the benefit-to-cost ratio of the altruistic exceeds the critical condition. In addition, the network throughput performance of our proposed strategy in structured MANETs is measured, which is in close agreement with that of the full cooperative strategy.