Proposed Incentive Mechanism Research Articles

Achieving Panoramic View Coverage in Visual Mobile Crowd-Sensing Networks for Emergency Monitoring Applications

Visual Mobile Crowd-Sensing(VMCS) collects photos by leveraging camera embedded in mobile users’ phones. There are two important issues in VMCS: determining whether photos collected by mobile users meet the requirements or not and designing an appropriate mechanism to attract mobile users to provide photos that meet the requirements. In this paper, we address those two issues when VMCS is applied to emergency monitoring applications. We first model an emergency scene as a disk region and define a coverage angle metric that quantifies the coverage ratio provided by each photo, and then formulate Maximize Coverage Angle under a Limited Budget(MCALB) problem. The goal of this paper is to recruit mobile users to provide panoramic view coverage for a disk while the total reward paid to participants does not exceed the budget. In our solution, we first propose a Coverage Angle Computation (CAC) algorithm to calculate the coverage angle of each uploaded photo. Then two incentive mechanisms: Guidance based Incentive Mechanism (GIM) and Coverage Prediction Incentive Mechanism (CPIM) are designed to encourage mobile users to upload photos with coverage angle that are not provided by other mobile users. Finally, we design a mobile app called I-share in android system to implement system. Meanwhile, we have recruited students to install I-share and simulated the information interaction between mobile users and server. We have conducted experiments by using I-share without/with embedded CAC algorithm to validate the efficiency of two incentive mechanisms. The experimental results demonstrate that our proposed incentive mechanisms effectively attract mobile users to provide panoramic view coverage of emergency scenes when the budget allows. Additionally, the CPIM outperforms the GIM, offering a higher coverage ratio with lower rewards.

Optimising load control of multi-type air conditioning in demand-side management

ABSTRACT With the continuous expansion of renewable energy capacity and the increasing demand for thermostatically controlled loads in China, new challenges have arisen for maintaining power supply stability. This paper establishes a control model for the aggregation of different types of air conditioning loads. To improve unsatisfactory demand response outcomes, a novel incentive mechanism based on tracking the target load curve is proposed. Additionally, a cost model for air conditioning load aggregators and the regional grid operator, along with an implementation scheme for trading mechanisms, is also developed. Case simulations validate the effectiveness and rationality of the proposed incentive mechanism.

Endogenous system-wide output prices in incentive regulation

We propose a simplification of frontier-based regulation currently implemented in several countries and demonstrate its potential application to the regulation of the Brazilian electricity transmission sector. A common regulation uses Data Envelopment Analysis (DEA) to estimate a cost function and mandates that firms adjust their costs to the DEA-estimated best-practice costs. Nevertheless, as DEA allows for individualized pricing of the services, such a DEA-based regulation may be challenging to understand and predict. Moreover, it may encourage strategic behavior in the selection of service profiles, thereby reducing competitive pressure on other firms with similar profiles. This could adversely affect the industry structure, leading to firms with suboptimal scales and scope. Therefore, instead of individualized output prices, we propose the use of a common set of endogenously determined prices that minimizes the total costs to end consumers, while ensuring individual rationality and incentive compatibility. We also present an extended approach that enables the regulator to strike a balance between rewarding well-performing agents and encouraging underperforming agents to save costs, while maintaining a minimal deviation from an expected aggregated payment. Achieving this balance is vital for incentivizing the agents to innovate and invest in more sustainable facilities in the electricity sector, promoting advancements in technology, energy efficiency, and environmental sustainability. Using Brazilian data, we demonstrate that the proposed incentive mechanism is operational and offers substantial advantages over the existing regulatory framework.

An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship.

In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship. First, the supremum and infimum incentives-based rule driven by trust relationship is defined. Based on the assumption that if incentive conditions are met, then experts will be willing to adjust their preferences, the incentive behavior-driven minimum adjustment consensus model is developed to generate optimal incentive-based recommendation preferences. Thus, the proposed incentive mechanism can effectively reduce the preference adjustment cost and promote group consensus reaching. Third, the updated trust relationships between experts are shown to be strengthen by the proposed incentive-driven preference revision. Consequently, the optimization model based on trust interaction relationship is constructed to obtain the final group preference matrix. Finally, a supplier selection case of high-end medical equipment is provided to illustrate the proposed method and show the rationality and advantages of the proposed methodology with both a sensitivity analysis and a comparison analysis.

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users’ future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility.

Integrating Blockchain and Deep Learning Into Extremely Resource-Constrained IoT: An Energy-Saving Zero-Knowledge PoL Approach

The convergence of blockchain and deep learning (DL) drives the intelligence of the Internet of Things (IoT) with security guarantees. However, the soaring resource consumption resulting from blockchain mining and DL model training has overwhelmed the extremely resource-constrained IoT. In this paper, we first build a blockchain and DL-empowered cloud-edge orchestrated framework for an extremely resource-constrained IoT environment. To solve the resource bottleneck of this framework, we then propose a Zero-Knowledge Proof of Learning (ZPoL) consensus approach to channel the meaningless Proof of Work (PoW) mining energy waste to valuable DL model training, while protecting the DL model privacy. Besides, to encourage resource-constrained IoT devices to perform meaningful DL model mining in our ZPoL consensus, we design a model quality-aware incentive mechanism based on a two-stage Stackelberg game. Moreover, we conduct extensive simulations and experiments to evaluate our proposed ZPoL-based framework. The numerical simulation illustrates our proposed incentive mechanism could motivate IoT devices to actively join in DL model mining. Compared with the existing blockchain and DL-enabled IoT system, experimental results demonstrate our proposed ZPoL-based framework could significantly reduce the communication, computation, and storage cost, which is more applicable to a resource-constrained IoT environment.

Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain.

Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme's efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.

Hierarchical Incentive Mechanism for Federated Learning: A Single Contract to Dual Contract Approach for Smart Industries

Federated learning (FL) has shown promise in smart industries as a means of training machine-learning models while preserving privacy. However, it contradicts FL’s low communication latency requirement to rely on the cloud to transmit information with data owners in model training tasks. Furthermore, data owners may not be willing to contribute their resources for free. To address this, we propose a single contract to dual contract approach to incentivize both model owners and workers to participate in FL-based machine learning tasks. The single-contract incentivizes model owners to contribute their model parameters, and the dual contract incentivizes workers to use their latest data to participate in the training task. The latest data draw out the trade-off between data quantity and data update frequency. Performance evaluation shows that our dual contract satisfies different preferences for data quantity and update frequency, and validates that the proposed incentive mechanism is incentive compatible and flexible.

Incentive Mechanism Design for Emergency Frequency Control in Multi-Infeed Hybrid AC-DC System

In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency security. Nevertheless, when regional power systems are operated by different decision-makers, the LCC-HVDC systems and their connected AC systems might be unwilling to participate in the EFC due to the costs and losses. In this paper, to incentivize the LCC-HVDC systems and their connected adjacent AC systems to participate in the droop-based EFC, a novel control-parameter-based incentive mechanism is proposed, which can flexibly deal with various possible power imbalances. Then, a non-cooperative-based incentive game model is formulated to quantitatively analyze the incentive mechanism in the MIDC system. An algorithm for seeking the Nash equilibrium is designed, and the uniqueness of Nash equilibrium is proven. Moreover, the individual rationality, incentive compatibility and social optimality of the proposed mechanism are analyzed and proven. The effectiveness of the proposed incentive mechanism is verified through a case study.

A privacy-preserving federated learning with a secure collaborative for malware detection models using Internet of Things resources

The rise in malware attacks presents a growing security threat in today’s digital landscape, emphasizing the need for advanced malware detection methodology. While numerous detection systems utilizing methodologies like machine learning (ML) have been developed, they often fail to detect new and unknown malware in real time. This is because their malware datasets are generated with a very limited source and are not dynamic for up-to-date malware. Thus, with of the growth of the Internet of Things, malware have also increasingly grown, which makes current malware detection systems outdated in a short time. Therefore, IoT device resources can be exploited for building malware datasets having various types of malware families. In addition, the approach of collecting data from IoT devices and training them in a centralized cloud (CC) server is no longer acceptable due to this approach incurs significant security and privacy issues. This paper proposes PPFL-SC, an efficient privacy-preserving federated learning with a secure collaborative supporting verification. A group-oblivious signcryption cryptography is designed to be used as a privacy-preserving technique for federated learning. The CC server aggregates data without compromising individual data privacy. Moreover, the Stackelberg incentive model is designed as an incentive mechanism for encouraging IoT devices to collaborate through their heterogeneous dataset in a malware detection model. Thus, the proposed incentive mechanism is capable of increasing the participation of IoT devices and decreasing their dropping out during model updating. A security analysis confirms that PPFL-SC fulfills all security requirements for privacy-preserving federated learning. Real-world dataset experiments validate the efficiency and practicality of the proposed PPFL-SC.

UAV-Assisted Wireless Charging Incentive Mechanism Design Based on Contract Theory

In wireless sensor networks, terminal devices with restricted cost and size have limited battery life. Meanwhile, these energy-constrained devices are not easy to access, especially when the terminal devices are located in severe environments. To recharge the energy-constrained devices and extend their network service time, unmanned aerial vehicles (UAVs) equipped with wireless power chargers are leased by the third-party control center. To incent the participation of UAVs with different charging capabilities and ensure the strategy-proofness of the incentive mechanism, a hidden information based contract theory model, specifically adverse selection, is introduced. By leveraging individual rationality and incentive compatibility, a contract theory based optimization problem is then formulated. After reducing redundant constraints, the optimal contract items are derived by Lagrangian multiplier. Finally, numerical simulation results are implemented to compare the prepared algorithm with three other baselines, which validates the effectiveness of our proposed incentive mechanism.

A game incentive mechanism for energy efficient federated learning in computing power networks

Computing Power Network (CPN) is emerging as one of the important research interests in beyond 5G (B5G) or 6G. This paper constructs a CPN based on Federated Learning (FL), where all Multi-access Edge Computing (MEC) servers are linked to a computing power center via wireless links. Through this FL procedure, each MEC server in CPN can independently train the learning models using localized data, thus preserving data privacy. However, it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers. To address these issues, we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers. Afterwards, we formulate a comprehensive algorithm to jointly optimize the communication resource (wireless bandwidth and transmission power) allocations and the computation resource (computation capacity of MEC servers) allocations while ensuring the local accuracy of the training of each MEC server. The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.

Economics of Semantic Communication System: An Auction Approach

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meanings of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called “semantic model trading”. Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called “semantic information trading”. To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them to achieve higher utilities than the baseline method.

Hiring a Team From Social Network: Incentive Mechanism Design for Two-Tiered Social Mobile Crowdsourcing

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem for the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. We present three system models for two-tiered social crowdsourcing system based on the arrival modes of registered users and social neighbors: offline model, semi-online model, and full-online model. We consider the tasks are associated with different end times. We present an incentive mechanism for each of three system models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness. We further show that our incentive mechanisms for semi-online model and full-online model can obtain averagely 51.1% and 39.7% value of approximate optimal untruthful offline algorithm, respectively.

Regulatory analysis of E-mobility for Brazil: A comparative review and outlook

The transport sector accounts for approximately 20% of the demand for primary energy resources. Also, the integration of energy generated by renewable sources in road transport has advanced mainly through the electrification of vehicles, supporting energy system flexibility. Sales of electric vehicles doubled in 2021 from the previous year to a new record of 6.6 million. Nearly 10% of global car sales were electric in 2021, four times the 2019 market share, bringing the total number of electric cars on the world's roads to about 16.5 million, triple the amount in 2018. Global sales of electric cars have kept rising strongly in 2022, with two million sold in the first quarter, up 75% from the same period in 2021. The behaviour of electric utilities is decisive for energy transition and sustainability success, considering the grid's adoption of energy storage system elements and electric vehicles. This study presents the obstacles of smart mobility, focusing on regulatory, governance, and acceptance challenges for electric mobility (e-mobility) services in Brazil. Regulatory systems for the expansion of e-mobility in different countries were considered from the perspective of electricity concessionaires. Ninety-three articles were selected through a systematic literature review and assessment. The research considers regulations from the reference countries, a preliminary evaluation of the feasibility of commercialising e-mobility in Brazil, and proposed incentive mechanisms for promoting e-mobility from an energy distribution perspective.

Noncooperative and Cooperative Urban Intelligent Systems: Joint Logistic and Charging Incentive Mechanisms

Autonomous vehicles (AVs) have become an emerging crucial component of the intelligent transportation system (ITS) in modern smart cities. In particular, coordinated operations of AVs can potentially enhance the quality of public services, e.g. logistic and AV charging services. However, the joint logistic and AV charging scenario involves the sophisticated interactions between a large number of complicated agents, dynamic logistic, and electricity prices in real-world systems. Since AVs are individuals owned by different parties, the design of attractive incentive to motivate them to provide multiple public services becomes a fundamental issue. In this paper, we develop an urban intelligent system (UIS) by exploiting the efficient incentive mechanisms, e.g. non-cooperative and cooperative game theoretic approaches, to motivate the AVs to provide logistic and charging services in UIS. For the non-cooperative game approach, we formulate the interaction between the selfish AVs and the aggregator as a Stackelberg game. Meanwhile, the aggregator, known as the leader in the game, aims to decide the logistic and electricity trading prices, and then the AVs, executed as the followers, determine their service schedules. Furthermore, considering that all the players are willing to cooperate, we develop a cooperative potential game for the selfless AVs to maximize the social welfare of the UIS. These case studies demonstrate the effectiveness and practicability of proposed incentive mechanisms that can motivate EVs to provide high quality logistic and charging services by maximizing their utilities. Also, both the proposed schemes provide significant system revenues than that of conventional system optimization-based approaches.

An incentive mechanism for horizontal federated learning based on principle of compound interest

Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. In order to enhance the robustness of federated learning, it is essential to provide corresponding returns to users to stimulate high quality users participating in the learning process as long as possible. Available incentive mechanisms for federated learning did not take unreliable and malicious users into account. As a result, these schemes will not only pay high quality participators, but also make malicious and unreliable participators get benefits. In this paper, we propose an incentive mechanism for horizontal federated learning system. In order to motivate high-quality users to participate in federated learning for a long time, we adopt the principle of compound interest to design the incentive strategy. Meanwhile, with our incentive mechanism, the malicious user will not obtain more in return than it pays, so that this kind of users can be inhibited from participating in learning. Experimental results demonstrate the effectiveness of the proposed scheme. Without exceeding the task publisher’s budget, the higher the accuracy of the local model a user provided, and the longer its participation time, the higher its yield. In addition, with the proposed incentive mechanism, the participation rate of malicious users decreased by 65%.

Asynchronous best-response dynamics of networked anti-coordination game with payoff incentives

How to understand and model the spatial collective decision-making behaviors in the evolutionary game context is an open problem, which attracts burgeoning interdisciplinary studies in the fields of graph theory and evolutionary game theory. This is a newly emerging research topic with wide applications under many real engineering, social and natural nonlinear systems. In this paper, we investigate the asynchronous best-response dynamics of anti-coordinating agents by developing two types of payoff incentive mechanisms, i.e., reward and punishment. We start from characterizing the equilibria of the best-response dynamics based on various types of graph structures. By offering relevant payoff incentives to targeted nodes, we further explore the global outcomes of strategy evolution. Current results suggest that both the reward and punishment can cause the strategy switches of targeted nodes, thus to induce a cascading effect of the evolutionary dynamics in the spatial gaming systems. Given the unlimited and limited budget cases for the payoff incentives, we calculate the optimal solutions for the added incentives, which facilitates the prediction on how the system dynamics be influenced by the proposed incentive mechanisms.

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

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.