Existing Incentive Mechanisms Research Articles

A multi‐dimensional incentive mechanism based on age of update in hierarchical federated learning

AbstractFederated learning represents a decentralized approach to machine learning, enabling numerous devices to collaboratively contribute to model training while ensuring the privacy of individual data. However, the existing incentive mechanism of hierarchical federated learning (HFL) only considers the data contribution of a single round, which needs to be revised. For non‐IID data sets, the continuous selection of any end devices will cause the weights to diverge in a specific direction. Therefore, a new metric is needed to avoid continuously selecting a certain end device to ensure the overall effectiveness. We introduce a metric to describe the importance of updates: age of update (AoU), which can help select end devices not selected in the previous round to promote a faster model convergence. We put forward an incentive mechanism based on AoU, reputation, and data quantity in HFL (ARDHFL). We have derived the optimal equilibrium solution for the three‐stage Stackelberg game. Based on this solution, we can ensure maximum edge‐cloud utility while incentivizing end devices to engage actively in HFL tasks and providing superior data to train the HFL model. Finally, we conducted extensive experiments to prove that ARDHFL can effectively improve the performance. Compared with the fixed scheme, random scheme, FMore and InFEDge, the testing accuracy of ARDHFL in the MNIST dataset has been improved by 29.7%, 9.3%, 6.8% and 6.1%, respectively. In the CIFAR‐10 dataset, it has been improved by 40.2%, 33.1%, 16.4% and 14.2%, respectively, and demands fewer communication iterations to achieve the same testing accuracy.

A dual incentive mechanism based on graph attention neural network and contract in mobile opportunistic networks

In mobile opportunistic networks, messages are transmitted through opportunistic contacts between nodes. Hence, the successful delivery of messages heavily relies on the mutual cooperation among nodes in the network. However, due to limited network resources such as node energy and cache space, nodes tend to be selfish, and they are unwilling to actively participate in message forwarding. In response to this challenge, lots of incentive mechanisms have been proposed. However, most of them rely on single incentives, there are issues such as inadequate handling of selfish nodes and vulnerability to malicious attacks, which ultimately lead to poor incentive effects. Therefore, in this paper, a Dual Incentive mechanism based on Graph attention neural network and Contract (DIGC) is introduced to encourage active participation of network nodes in data transmission. This incentive mechanism is divided into two steps. In the first step, the graph attention neural network is used to evaluate the reputation of nodes to achieve the goal of reputation-based incentive, and blockchain is employed to store and manage node reputation to ensure security and transparency. In the second step, an incentive based on contract theory is introduced, where personalized contracts were designed based on the different resources owned by nodes, thereby establishing a reward mechanism to encourage collaborative transmission. Extensive simulations based on two real-life mobility traces have been done to evaluate the performance of our DIGC compared with other existing incentive mechanisms. The results show that, our proposed mechanism can greatly improve throughput and reduce average delay while ensuring the overall delivery performance of the network.

A Budget Constraint Incentive Mechanism Based on Risk Preferences of Collaborators in Edge Computing

Mobile Edge Computing (MEC) is a new distributed computing method based on the mobile communication network. It can provide cloud services and an IT service environment for application developers and service providers at the edge of the network. Computation offloading is a crucial technology of edge computing. However, computation offloading will consume the resources of the edge devices, and therefore the edge devices will not offload computation unconditionally. In addition, the service quality of edge computing applications is related to the cooperation rate of edge devices. Therefore, it is essential to design an appropriate incentive mechanism to motivate edge devices to execute computation offloading. However, the current existing incentive mechanisms have two problems: Firstly, existing mechanisms do not account for probability distortions under uncertainty in collaborator utility valuation models. Secondly, the platform ignores the risk preferences of collaborators in multiple rounds of decision-making. To address these issues, we propose an incentive mechanism based on risk preference, IMRP. The IMRP considers the collaborator’s probability distortion, introduces an uncertain utility bonus scheme, and builds a probability distortion model to influence the collaborator’s willingness to offload tasks. The IMRP also considers the collaborator’s risk preference and builds the collaborator’s risk preference model to influence the collaborator’s bidding decision. Simulation results show that our mechanism effectively improves the cooperation rate of edge devices and the utility of the requester.

A Location-Based Crowdsensing Incentive Mechanism Based on Ensemble Learning and Prospect Theory

Crowdsensing uses the participants’ smart devices to form a new perception network. The coverage of crowdsensing’s tasks determines the quality of services. Under the constraint of budget and the number of participants, the platform needs to increase the participation duration of participants through incentive mechanisms to increase the coverage of tasks. There are two problems with the existing incentive mechanisms: (1) many incentives ignore the participants’ characteristics, and using a single incentive mechanism for different participants will make the incentive effect not reach the expectation; (2) many incentives will affect the effectiveness because of the decision problem caused by asymmetric information. Inspired by ensemble learning and prospect theory, this paper proposes the Incentive Mechanism based on Ensemble Learning and Prospect Theory (IMELPT). First, we propose the Deep-Stacking-Generation algorithm based on Dropout (DSGD), to predict the participants and distinguish whether they are long-term or short-term participants. If the participants are short-term, we incentivize them through the Short-term Participant Incentive Mechanism based on Prospect Theory (SPIMPT). We increase the participation duration by transforming the change in reward into asymmetric information that aligns the participant’s goal with the platform. If the participants are long-term participants, we motivate them through the Long-term Participant Incentive Mechanism (LPIM), to maintain the participation rate of participants by maximizing their utility. Theoretical analysis and experiments on real datasets demonstrated that IMELPT can reliably improve the coverage of crowdsensing tasks.

Connectivity-Aware Contract for Incentivizing IoT Devices in Complex Wireless Blockchain

Blockchain is considered the critical backbone technology for secure and trusted Internet of Things (IoT) in the future 6G network. However, deploying a blockchain system in a complex wireless IoT network is challenging due to the limited resources, complex wireless environment, and the property of self-interested IoT devices. The existing incentive mechanism of blockchain is not compatible with the wireless IoT network. In this paper, to incentivize IoT devices to join the construction of the wireless blockchain network, we propose a multi-dimensional contract to optimize the blockchain utility while addressing the issues of adverse selection and moral hazard. Specifically, the proposed contract considers the IoT device’s hash power and communication cost and especially explores the connectivity of devices from the perspective of complex network theory. We investigate the energy consumption and the block confirmation probability of the wireless blockchain network via simulations under varied network sizes and average link probability. Numerical results demonstrate that our proposed contract mechanism is feasible, achieves 35% more utility than existing approaches, and increases utility by 4 times compared with the original PoW-based incentive mechanism.

Hierarchical Two-Stage Robust Planning for Demand-Side Energy Storage with Dynamic Carbon Incentive Mechanism

Demand-side energy storage is an important foundation for enhancing load flexibility to accommodate renewable energy. With the widespread adoption of renewable energy, demand-side energy storage planning, and its incentive mechanism have also attracted the attention of a large number of scholars. However, there are still few studies on incentives from a carbon perspective. To fill the gap, a dynamic carbon incentive mechanism is proposed in this study. In addition, a hierarchical two-stage robust planning model for demand-side energy storage that incorporates the proposed carbon incentive mechanism is developed. At the first level, the economic dispatch is performed, and bus carbon intensities are calculated based on the carbon emission flow theory. The second level is a two-stage robust planning model to obtain the optimal capacities of demand-side energy storage, which is solved based on the nested column and constraint generation algorithm. The proposed model is implemented and evaluated on the MATLAB/YALMIP platform using IEEE 24-bus power systems. The results validate the efficacy of the model in promoting carbon-oriented demand-side energy storage planning, leading to a substantial reduction of carbon emissions by 8.44%. Notably, when compared to existing incentive mechanisms, the proposed carbon incentive mechanism exhibits distinct advantages in achieving carbon reduction with less both subsidy costs and fixed investments.

An efficient user demand response framework based on load sensing in smart grid

The current residential electricity demand is increasing. The demand side response of smart grid power users aims to enable users to reasonably plan their own power consumption through price incentives, so as to solve the problems of unreasonable power energy structure and low utilization rate. It is prominent to mine the rules of user response behaviors and design a reasonable incentive mechanism to maximize the enthusiasm of all participants. The traditional demand response is to ensure the stability of the power system from the macro-control load of the grid, which cannot meet the personalized requirements of power users. The existing incentive mechanism also does not comprehensively consider the profits of grid companies, low-voltage users, aggregators and other parties. In this paper, we propose a user demand response framework based on load awareness. Firstly, we devise a user demand response behaviour model based on short-term memory network. Secondly, we propose a demand response incentive scheme based on electric power scores. We also construct a deviation optimization integration adjustment model based on game theory to achieve the balance of profits among grid, aggregators and low-voltage users. The extensive experimental results show the effectiveness of our proposed framework.

Decision Analysis under Behavioral Economics—Incentive Mechanism for Improving Data Quality in Crowdsensing

Due to the profitability and selfishness of crowdfunding system users, under fixed budget conditions, there are problems, such as low task completion rate due to insufficient participants and low data quality. However, the existing incentive mechanisms are mainly based on traditional economics, which believes that whether users participate in tasks depends on whether the benefits of the task outweigh the costs. Behavioral economics shows that people judge the value of gains and losses according to a reference point. The weight given to losses is more important than the weight given to the same gains. Therefore, this article considers the impact of reference dependency and loss aversion on user decision-making and proposes a participant selection mechanism based on reference dependency (PSM-RD) and a quality assurance mechanism based on loss aversion (QAM-LA). PSM-RD uses reference points to influence user pricing and selects more participants based on relative value. QAM-LA pays additional rewards based on the data quality of participants and motivates them to improve data quality by reconstructing utility functions. The simulation results show that compared with the ABSee mechanism, data quality has improved by 17%, and the value of completed tasks has increased by at least 40%.

A Two-Stage Stackelberg Game Wind-Storage Planning Model Considering a Bus Carbon Intensity Incentive Mechanism

In the trend of power system decarbonization, wind power and energy storage planning studies are in full swing. Incentives for wind power and energy storage development are important research topics. However, most studies have proposed incentives from an energy perspective, and few focus directly on incentives from a carbon perspective. To this end, this paper proposes a novel unified bus carbon intensity incentive mechanism from the carbon perspective. A two-stage Stackelberg game wind storage planning model that considers the proposed carbon incentive mechanism is constructed. In the first stage, the carbon flow of the entire system is obtained following economic dispatch based on the carbon emission flow theory. In the second stage, a Stackelberg game wind-storage planning model is constructed to model the game between the power generation company and the power grid company. Through simulation analyses of these cases, the proposed incentive mechanism can effectively guide the construction of wind turbines and energy storage at the best buses. Additionally, by comparing the proposed incentive with the existing incentive mechanisms, this paper finds that the proposed incentive mechanism can achieve a higher carbon reduction effect with a similar total investment in wind turbines and energy storage.

Incentive Mechanism for Improving Task Completion Quality in Mobile Crowdsensing

Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure the stability of the sensing data quality. Most of the existing incentive mechanisms for data quality in crowdsensing are based on traditional economics, which believe that the decision of participants to complete a task depends on whether the benefit of the task is greater than the cost of completing the task. However, behavioral economics shows that people will be affected by the cost of investment in the past, resulting in decision-making bias. Therefore, different from the existing incentive mechanism researches, this paper considers the impact of sunk cost on user decision-making. An incentive mechanism based on sunk cost called IMBSC is proposed to motivate participants to improve data quality. The IMBSC mechanism stimulates the sunk cost effect of participants by designing effort sensing reference factor and withhold factor to improve their own data quality. The effectiveness of the IMBSC mechanism is verified from three aspects of platform utility, participant utility and the number of tasks completed through simulation experiments. The simulation results show that compared with the system without IMBSC mechanism, the platform utility is increased by more than 100%, the average utility of participants is increased by about 6%, and the task completion is increased by more than 50%.

Incremental incentive mechanism design for diversified consumers in demand response

Demand response has been proven to be an effective way to improve energy utilization efficiency. It is notable that the diversified characteristics of residential consumers, which many greatly affect its performance in demand response, have not been fully considered in existing incentive mechanisms. In this paper, an incremental incentive mechanism for incentive-based demand response (IBDR) is proposed, in which consumers obtain different incentives according to the increment of response, so that the incentive can follow the change of consumers' marginal cost. We theoretically illustrate that the proposed incremental incentive mechanism can effectively improve the profit of load service entity (LSE), as well as the benefit of highly flexible consumers, compared with other existing incentive mechanism. In practice, LSE's bidding strategy in the day ahead market is affected by the intraday IBDR strategy that cannot be known in advance. In order to solve the bidding problem with incomplete information in the day ahead market, we propose an asynchronous double-interaction deep reinforcement learning (DRL) algorithm to maximize LSE’s cumulative profit of multiple time slots throughout the day. Numerical simulation results show that the proposed mechanism can improve the consumers' response depth while reducing the unit incentive cost, and the proposed DRL algorithm has relatively stable and satisfactory performance even in highly uncertain environment.

Research and Optimization on the Incentive Mechanism for High School Teachers under the Background of the New College Entrance Examination—Taking Huizhou L High School as an Example

After the reform of the new college entrance examination, new changes such as the class-taking system have posed new challenges to teachers’ work. Based on the two-factor theory of American management scientist Herzberg, taking L High School in Huizhou City as an example, this paper uses the questionnaire method and interview method to investigate the front-line high school teachers, and investigates the front-line teachers’ opinions on the existing incentive mechanism and evaluate. Combined with questionnaire data and interview content, the deficiencies of the existing incentive mechanism are analyzed. Through the survey, L secondary schools scored poorly in motivation, and there were unsatisfactory items in hygiene factors. The main reasons are that the performance appraisal mechanism is not scientific enough, the school decision-making not unfair and open enough, and the training and learning do not meet the needs. After sorting out the existing deficiencies and reasons, this paper puts forward improvement measures for hygiene factors and motivation factors, including improving the performance appraisal system, reforming professional title evaluation and other measures to ensure the effect of teachers’ job satisfaction and mobilizing teachers’ work enthusiasm and to provide references for other schools to promote the sound development of my country’s education.

An Incentive Mechanism for Mobile Crowd Sensing in Vehicular Ad Hoc Networks

In the mobile crowdsensing of vehicular ad hoc networks (VANETs), in order to improve the amount of data collection, an effective method to attract a large number of vehicles is needed. Therefore, the incentive mechanism plays a dominant role in the mobile crowdsensing of vehicular ad hoc networks. In addition, the behavior of providing malicious data by vehicles as data collectors will have a huge negative impact on the whole collection process. Therefore, participants need to be encouraged to provide data honestly to obtain more available data. In order to increase data collection and improve the availability of collected data, this paper proposes an incentive mechanism for mobile crowdsensing in vehicular ad hoc networks named V-IMCS. Specifically, the Stackelberg game model, Lloyd’s clustering algorithm and reputation management mechanism are used to balance the competitive relationship between participants and process the data according to the priority order, so as to improve the amount of data collection and encourage participants to honestly provide data to obtain more available data. In addition, the effectiveness of the proposed mechanism is verified by a series of simulations. The simulation results show that the amount of available data is significantly higher than the existing incentive mechanism while improving the amount of data collection.

Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Mobile Crowdsensing Systems

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers’ sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers’ different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets.

Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.

Pain-FL: Personalized Privacy-Preserving Incentive for Federated Learning

Federated learning (FL) is a privacy-preserving distributed machine learning framework, which involves training statistical models over a number of mobile users (i.e., workers) while keeping data localized. However, recent works have demonstrated that workers engaged in FL are still susceptible to advanced inference attacks when sharing model updates or gradients, which would discourage them from participating. Most of the existing incentive mechanisms for FL mainly account for workers’ resource cost, while the cost incurred by potential privacy leakage resulting from inference attacks has rarely been incorporated. To address these issues, in this paper, we propose a contract-based personalized privacy-preserving incentive for FL, named Pain-FL, to provide customized payments for workers with different privacy preferences as compensation for privacy leakage cost while ensuring satisfactory convergence performance of FL models. The core idea of Pain-FL is that each worker agrees on a customized contract, which specifies a kind of privacy-preserving level (PPL) and the corresponding payment, with the server in each round of FL. Then, the worker perturbs her calculated stochastic gradients to be uploaded with that PPL in exchange for that payment. In particular, we respectively derive a set of optimal contracts analytically under both complete and incomplete information models, which could optimize the convergence performance of the finally learned global model, while bearing some desired economic properties, i.e., budget feasibility, individual rationality, and incentive compatibility. An exhaustive experimental evaluation of Pain-FL is conducted, and the results corroborate its practicability and effectiveness.

A Green Stackelberg-game Incentive Mechanism for Multi-service Exchange in Mobile Crowdsensing

Although mobile crowdsensing (MCS) has become a green paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, existing incentive mechanisms are not effective to stimulate users’s active participation and service contribution in multi-service exchange in MCS due to its specific features: a large number of heterogeneous users have asymmetric service requirements, workers have the freedom to choose sensing tasks as well as participation levels, and multiple sensing tasks have heterogeneous values which may be untruthful declared by the corresponding requesters. To address this issue, this article develops a green Stackelberg-game incentive mechanism to achieve selective fairness, truthfulness, and bounded efficiency while reducing the burden on the platform. First, we model the multi-service exchange problem as a Stackelberg multi-service exchange game consisting of multi-leader and multi-follower, in which each requester as a leader first chooses the reward declaration strategy and thus the payment for each sensing task, each worker as a follower then chooses the sensing plan strategy to maximize her own utility. We next introduce the concept of virtual currency to maintain the selective fairness to balance service request and service provision between users, in which a user earns/consumes virtual currency for providing/receiving services, and thus no one can always get services without providing services. Then, we present two novel algorithms to compute the unique Nash equilibrium for the sensing plan determination game and the reward declaration determination game, respectively, which together forms a unique Stackelberg equilibrium for the proposed game. Afterwards, we theoretically prove that the proposed green Stackelberg-game incentive mechanism achieves the desirable properties of selective fairness, truthfulness, bounded efficiency. Finally, extensive evaluation results are provided to support the validity and effectiveness of our mechanism compared with both baseline and theoretical optimal approaches.

A cooperative communication scheme based on blockchain

Traditional cooperative communication is based on the complete trust between source and relay, and relay voluntarily helps source to forward information. However, in practice, relay may not be willing to forward information for source, and source may not trust relay due to the tampering or dropping of information. Existing incentive mechanisms that encourage relay to cooperative cannot establish trust foundation between source and relay, and cannot guarantee the security of data transmission. Blockchain naturally has properties of tamper-proof, transparent, and full traceability, it can effectively solve the trust problem and improve the security of data transmission. So we introduce blockchain into cooperative communication, propose a cooperative communication scheme based on blockchain, and design a set of detailed working procedure for this scheme. In the working procedure, we propose an optimal relay selection scheme and a consensus mechanism based on mobile terminal credit value (CM-MTCV) to encourage terminals to cooperation and achieve fast consensus. The simulation results show that our scheme can save 6.74% energy cost compared with the maximum-minimum scheme when the number of relay nodes is 8, the delay of CM-MTCV is 1/2000 of that of PoW, the transaction throughput of CM-MTCV is 2000 times of that of PoW.

Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

Incentive mechanisms are crucial for motivating adequate users to provide reliable data in mobile crowdsensing (MCS) systems. However, the privacy leakage of most existing incentive mechanisms leads to users unwilling to participate in sensing tasks. In this paper, we propose a privacy-preserving incentive mechanism based on truth discovery. Specifically, we use the secure truth discovery scheme to calculate ground truth and the weight of users’ data while protecting their privacy. Besides, to ensure the accuracy of the MCS results, a data eligibility assessment protocol is proposed to remove the sensing data of unreliable users before performing the truth discovery scheme. Finally, we distribute rewards to users based on their data quality. The analysis shows that our model can protect users’ privacy and prevent the malicious behavior of users and task publishers. In addition, the experimental results demonstrate that our model has high performance, reasonable reward distribution, and robustness to users dropping out.

PRICE: Privacy and Reliability-Aware Real-Time Incentive System for Crowdsensing

Crowdsensing is regarded as a critical component of the Internet of Things (IoT) and has been widely applied in smart city services. Incentive mechanism design, data reliability evaluation, and privacy preservation are the research focuses of crowdsensing. However, most existing incentive mechanisms fail to protect data privacy and evaluate data credibility, simultaneously. Moreover, traditional privacy and reliability-aware incentive schemes are usually challenging to realize real-time reward distribution. To this end, we first point out a single-time slice of failure problem in real-time incentive mechanisms and propose a two-layer truth discovery model (TLTD) to resolve this problem. Then, a reliability-aware real-time incentive mechanism (RRIM) is designed based on the proposed TLTD. In order to evaluate data reliability in a privacy-preserving manner, we build a privacy-preserving truth discovery solution ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PriTD</monospace> ) based on secure computation protocols. Finally, our proposed system [privacy and reliability-aware real-time incentive system for crowdsensing (PRICE)] integrating the aforementioned protocols realizes real-time reward distribution, data reliability evaluation, and privacy protection, simultaneously. Theoretical analysis and experimental evaluations on a synthetic and real-world data set demonstrate the feasibility and efficiency of the proposed PRICE.