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.

Shortest path core-selection incentive for federated learning platform with medical applications.

As the main technology to solve data islands and mine data value, federated learning has been widely researched and applied, and more and more federated learning platforms are emerging. The federated learning platform organizes users, devices and data to collaborate in a crowdsourcing manner and complete specific federated learning tasks. This paper designs the shortest path core-selection incentive mechanism by combining the VCG auction mechanism and the core concept of cooperative games. This mechanism solves the problems of overpayment, false-name attack, and deviation from the core of the VCG mechanism, and saves the expenditure of the federated learning task issuer. It adopts the VCG-nearest principle in the core selection, so that the federated learning task participants get rewards as close as possible to the outcome of VCG mechanism. This mechanism can guarantee four economic attributes: incentive compatibility, individual rationality, alliance rationality, and maximization of social efficiency. Medical experimental results illustrate the effectiveness of the mechanism.

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments.

The mechanism design theory can be applied not only in the economy but also in many fields, such as politics and military affairs, which has important practical and strategic significance for countries in the period of system innovation and transformation. As Nobel Laureate Paul said, the complexity of the real economy makes it difficult for "Unorganized Markets" to ensure supply-demand balance and the efficient allocation of resources. When traditional economic theory cannot explain and calculate the complex scenes of reality, we require a high-performance computing solution based on traditional theory to evaluate the mechanisms, meanwhile, get better social welfare. The mechanism design theory is undoubtedly the best option. Different from other existing works, which are based on the theoretical exploration of optimal solutions or single perspective analysis of scenarios, this paper focuses on the more real and complex markets. It explores to discover the common difficulties and feasible solutions for the applications. Firstly, we review the history of traditional mechanism design and algorithm mechanism design. Subsequently, we present the main challenges in designing the actual data-driven market mechanisms, including the inherent challenges in the mechanism design theory, the challenges brought by new markets and the common challenges faced by both. In addition, we also comb and discuss theoretical support and computer-aided methods in detail. This paper guides cross-disciplinary researchers who wish to explore the resource allocation problem in real markets for the first time and offers a different perspective for researchers struggling to solve complex social problems. Finally, we discuss and propose new ideas and look to the future.

Analysis of satisfaction and influencing factors of scientific research incentive mechanisms for young medical staff in a children′s hospital in Beijing / 中华医学科研管理杂志

Objective:This study aims to explore the satisfaction and influencing factors of scientific research incentive mechanisms for young medical staff in a children′s hospital in Beijing, and to provide references for the management department to develop relevant incentive strategies.Methods:A satisfaction questionnaire survey of scientific research incentive mechanisms was carried out during April and May, 2022. Collected data were analyzed by rank sum test and ordered logistic regression.Results:The level of overall satisfaction of 339 young medical staffs was 81.42%. The satisfaction rate of incentive factors was 80.50% and hygiene factors was 77.51%.According to the findings, there were significant differences in educational backgrounds, professional titles and job types, hygiene factors, and incentive factors ( P<0.05). The contributing factors of the job satisfaction were professional title, hygiene factors and incentive factors. Conclusions:The overall satisfaction with the incentive work of the young research staff surveyed was relatively at high-level. It is necessary to improve tailed hygiene factors to meet the characteristics young medical staff, such as setting up full-time scientific research working hours, exploring the establishment of diversified of scientific research sharing mechanisms to increase the utilization of the scientific research platform. It is important to enhance incentive factors to establish a long-term incentive mechanism for young personnel, such as setting up interdisciplinary research projects, exploring the establishment of a recommendation mechanism for outstanding young talents, in order to provide them with more opportunities for further intensive training.

Incentive mechanism for sharing and using EHR in medical consortiums based on performance evaluation.

Background: The construction of medical consortiums not only promotes active cooperation among hospitals, but also further intensifies active competition among them. The shared use of electronic health records (EHR) breaks the original pattern of benefit distribution among hospitals. Objective: The purpose of this paper is to establish an incentive mechanism for the shared use EHR, and to reveal the incentive effect and mechanism of key factors, and to put forward management suggestions for solving the real conflicts. Methods: We constructed a basic incentive model and an incentive model that introduces performance evaluation as a supervisory signal, based on analyzing the hospital cost function, the hospital benefit function, and the incentive contract function. Finally, the incentive effects of key factors before and after the introduction of performance evaluation were verified and compared using MATLAB simulation method. Results: The profit level and incentive coefficient of hospitals sharing EHR are independent of the amount of one-time government subsidies. Regardless of whether a performance evaluation supervisory signal is introduced or not, the incentive coefficients are increasing functions with respect to ρ, τ, but decreasing functions with respect to ß, δ, γ. After the inclusion of supervisory signal of performance evaluation in the model, the ability of hospitals to use EHR has a higher impact effectiveness on improving both incentive effects and benefit levels. The impact of the value-added coefficient on the level of earnings is consistently greater than it would have been without the inclusion of the performance evaluation supervisory signal. Conclusions: Enhancing the capacity of hospitals to use EHR and tapping and expanding the value-added space of EHR are 2 key paths to promote sustainable shared use of EHR. Substantive performance evaluation plays an important role in stabilizing incentive effects.

A Blockchain-Based Privacy Information Security Sharing Scheme in Industrial Internet of Things.

Due to the competitive relationship among different smart factories, equipment manufacturers cannot integrate the private information of all smart factories to train the intelligent manufacturing equipment fault prediction model and improve the accuracy of intelligent manufacturing equipment fault detection. The use of a low fault recognition rate model for smart factories will cause additional losses for them. In this work, we propose a blockchain-based privacy information security sharing scheme in Industrial Internet of Things (IIoT) to solve the sharing problem of private information in smart factories. Firstly, we abstract smart factories as edge nodes and build decentralized, distributed trusted blockchain networks based on Ethereum clients on simulated edge devices and propose an Intelligent Elliptic Curve Digital Signature Algorithm (IECDSA) to guarantee the ownership of shared information by edge nodes. Secondly, we propose the Reputation-based Delegated Proof of Stake (RDPoS) consensus algorithm to improve the security and reliability of the Delegated Proof of Stake (DPoS) consensus algorithm. Furthermore, we design and implement an incentive mechanism based on information attributes to increase the motivation of edge nodes to share information. Finally, the proposed solution is simulated. Through theoretical and simulation experiments, it is proved that the blockchain-based privacy information security sharing scheme in IIoT can improve the enthusiasm of edge nodes to share information on the premise of ensuring the security of information sharing.

Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm.

Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.

Incentive Mechanisms for Carbon Emission Abatement Considering Consumers' Low-Carbon Awareness under Cap-and-Trade Regulation.

In the era of sustainable development, reducing carbon emissions and achieving carbon neutrality are gradually becoming a consensus for our society. This study explores firms' incentive mechanisms for carbon emission abatement in a two-echelon supply chain under cap-and-trade regulation, where consumers exhibit low-carbon awareness. To boost the manufacturer's motivation for abatement, the retailer can provide four incentive strategies, i.e., price-only (PO), cost-sharing (CS), revenue-sharing (RS), and both (cost and revenue) sharing (BS). The equilibrium decisions under the four incentive strategies are obtained by establishing and solving game models. A two-part tariff contract is also proposed to coordinate the low-carbon supply chain. Finally, through comparisons and analyses, we find that: (1) Consumers' high low-carbon awareness can boost the manufacturer's incentive for carbon emission abatement (CEA), thus increasing supply chain members' profits. (2) It is more effective for the retailer to share its revenue to incentivize the manufacturer for abatement than to bear the investment cost of CEA. Thus, Strategy RS is better than Strategy CS and equivalent to Strategy BS. (3) The manufacturer and retailer have consistent incentive strategy preference under cap-and-trade regulation. Both firms prefer the incentive strategy with a higher cooperation level. (4) The incentive strategy with a higher cooperation level can also bring higher eco-social welfare under certain conditions.

A blockchain-based creditable and distributed incentive mechanism for participant mobile crowdsensing in edge computing.

With the popularization of portable smart devices, the advance in ubiquitous connectivity and the Internet of Things (IoT), mobile crowdsensing is becoming one of the promising applications to acquire information in the physical world of edge computing and is widely used in Smart Cities. However, most of the existing mobile crowdsensing models are based on centralized platforms, which have some problems in reality. Data storage is overly dependent on third-party platforms leading to single-point failures. Besides, trust issues seriously affect users' willingness to participate and the credibility of data. To solve these two problems, a creditable and distributed incentive mechanism based on Hyperledger Fabric (HF-CDIM) is proposed in this paper. Specifically, the HF-CDIM combines auction, reputation and data detection methods. First, we develop a multi-attribute auction algorithm with a reputation on blockchain by designing a smart contract, which achieves a distributed incentive mechanism for participants. Second, we propose a K-nearest neighbor outlier detection algorithm based on geographic location and similarity to quantify the credibility of the data. It is also used to update the user's reputation index. This guarantees the credibility of sensing data. Finally, the simulation results using real-world data set verify the effectiveness and feasibility of above mechanism.

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey.

Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning (BCFL) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL. Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL. Finally, we summarize the industrial application scenarios of BCFL.

Study of salary incentives mechanism for general practitioners from the perspective of integrated incentives: insights of the United Kingdom / 中华医院管理杂志

Optimizing the salary system of general practitioners and establishing a compensation incentives mechanism fitting its needs, would contribute effectively to the development of the general practitioner system in China. Fully leveraging the salary incentive system, the United Kingdom(UK) effectively improved the working enthusiasm of general practitioners and its quality of primary medical services, and limited its medical expenses as well. The authors expounded the incentive mechanism of general practitioners′ salary in the UK from such dimensions as salary model and salary structure, and introduced the implementation effect. Based on Robbins′ comprehensive incentive theory, the authors introduced the internal logic of the UK′s general practitioners′ salary incentive mechanism. Based on the UK experiences, along with existing problems in China, the authors recommended on setting up value-based salary levels, optimizing the salary structure, and establishing a dynamic adjustment mechanism. These ideas are expected to help optimize the salary incentive mechanism for general practitioners in China.

Ethical Enlightenment of Foreign Incentive Mechanism for Orphan Drugs Research and Development to China / 中国医学伦理学

There are a large number of patients with rare diseases in China, and most of them are faced with problems such as no medicine to cure, and have drugs outside of China but not inside. This paper combed the incentive policies of orphan drugs in the United States and the European Union, including orphan drugs legislation and setting up special management institutions, orphan drugs qualification certification, government funding, tax reduction and agreement assistance in the research and development process, providing accelerated listing channels in the examination and approval process, giving market monopoly period in the circulation stage, and giving priority to review. On this basis, it also explored the incentive measures of non-profit organizations in the research and development of orphan drugs, including providing financial support, clinical research and so on. From the perspective of guaranteeing the right to life and health of patients with rare diseases, the fairness and accessibility of medication, and the subjects’ right to know and privacy in clinical trials, this paper provides reference for perfecting the incentive mechanism of orphan drugs research and development in China.

Research on the Tournament Incentive Mechanism of the Safety Behavior for Construction Workers: Considering Multiple Heterogeneity.

The unsafe behavior of construction workers is one of the most important and direct causes of safety accidents. Managers usually develop effective incentives aimed at regulating worker safety behavior. Due to the large number of workers in construction projects, there are multiple differences in fairness preference, risk preference and ability level, which will lead to the complex effect of the traditional mechanism to regulate workers' safety behavior. In order to improve the effectiveness of incentive measures for worker safety behavior, this paper takes into account the multiple differences of individual workers' fairness preference, risk preference and ability level, based on the tournament mechanism to construct a competition incentive model. By designing a tournament reward and salary distribution for heterogeneous workers, the occurrence of unsafe behaviors can be reduced. The study found that in terms of the optimal level of safety investment, workers with risk aversion attitude generally invest higher than that of workers with risk preference, no matter whether they have a strong fairness preference or not; In terms of the distribution of tournament rewards, workers with a risk aversion attitude and a higher level of fairness preference need to be given higher incentives.

Contract Theory-Based Incentive Mechanism for Full Duplex Cooperative NOMA with SWIPT Communication Networks.

Cooperative Non-Orthogonal Multiple Access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) communication can not only effectively improve the spectrum efficiency and energy efficiency of wireless networks but also extend their coverage. An important design issue is to incentivize a full duplex (FD) relaying center user to participate in the cooperative process and achieve a win-win situation for both the base station (BS) and the center user. Some private information of the center users are hidden from the BS in the network. A contract theory-based incentive mechanism under this asymmetric information scenario is applied to incentivize the center user to join the cooperative communication to maximize the BS's benefit utility and to guarantee the center user's expected payoff. In this work, we propose a matching theory-based Gale-Shapley algorithm to obtain the optimal strategy with low computation complexity in the multi-user pairing scenario. Simulation results indicate that the network performance of the proposed FD cooperative NOMA and SWIPT communication is much better than the conventional NOMA communication, and the benefit utility of the BS with the stable match strategy is nearly close to the multi-user pairing scenario with complete channel state information (CSI), while the center users get the satisfied expected payoffs.

BlockPres: A Novel Blockchain-Based Incentive Mechanism to Mitigate Inequalities for Prescription Management System.

The study presents a blockchain-based incentive mechanism intended to encourage those in underserved communities to engage with healthcare services. The smart healthcare system, which is the result of the amalgamation of advanced technologies, has emerged recently and is increasingly seen as essential to meet the needs of modern society. An important part of the healthcare system is the prescription management system, but studies show that prescription affordability and accessibility play a part in creating unequal access for underserved communities. This is a form of unequal access that results in those living in underserved communities to become disengaged from accessing healthcare services. In New Zealand, the prescription management system plays a crucial role and this study seeks to address the issue by presenting the BlockPres framework, which uses a novel incentive mechanism to encourage patients to participate and engage with services in order to be rewarded. The blockchain attribute of immutability in BlockPres enhances equality and participation by providing sophisticated authorisation and authentication capabilities for healthcare providers and patients. BlockPres empowers the patient by assigning ownership or control of some patient information to the patient. A simulation is carried out using the Ethereum blockchain and the evaluation of successful transaction completion and superficial performance assessment demonstrates that the blockchain would be sufficient to cope with the needs of a prescription management system. Furthermore, for the simulation, a BlockPres Smart Contract is developed using solidity and implemented in Remix. The Ropsten network is used as the simulation environment and the initial results show that the proposed incentive mechanism mitigates unequal access.

Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain.

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant's vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.

Satisfaction and incentive mechanisms survey of scientific research in a tertiary specialized hospital in Shanghai / 中华医学科研管理杂志

Objective:To better understand the satisfaction with medical worker′s scientific research, raise the management and service levels of scientific research management department, and stimulate hospital workers devote to scientific research.Methods:This paper analyzes the main factors that affect the scientific researchers′ job satisfaction in a tertiary hospital in Shanghai based on questionnaire survey. SPSS 26.0 was used to test the reliability and validity of the scale. Two independent sample t-test, one-way ANOVA and LSD-T test are used to explore the factors that affect the scientific research enthusiasm of staffs in different positions.Results:The incentive measures of scientific research in hospital can be summarized into three different aspects: material/spiritual incentive mechanism, scientific research management system and personnel training system, the average scores were: X-≈3.97, X-≈ 4.09, X-≈ 3.84, according to which the scientific research management system has the highest satisfaction.Conclusions:The overall satisfaction of staff in the hospital needs to be improved. According to the characteristics of different staff, tailored research management measurements should be developed to mobilize the enthusiasm of hospital staff in scientific research, enhance their satisfaction, and further improve the scientific research capacity of the hospital.

Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa.

The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that ~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d'Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing system in the literature.

A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing.

Selection of the optimal users to maximize the quality of the collected sensing data within a certain budget range is a crucial issue that affects the effectiveness of mobile crowdsensing (MCS). The coverage of mobile users (MUs) in a target area is relevant to the accuracy of sensing data. Furthermore, the historical reputation of MUs can reflect their previous behavior. Therefore, this study proposes a coverage and reputation joint constraint incentive mechanism algorithm (CRJC-IMA) based on Stackelberg game theory for MCS. First, the location information and the historical reputation of mobile users are used to select the optimal users, and the information quality requirement will be satisfied consequently. Second, a two-stage Stackelberg game is applied to analyze the sensing level of the mobile users and obtain the optimal incentive mechanism of the server center (SC). The existence of the Nash equilibrium is analyzed and verified on the basis of the optimal response strategy of mobile users. In addition, mobile users will adjust the priority of the tasks in time series to enable the total utility of all their tasks to reach a maximum. Finally, the EM algorithm is used to evaluate the data quality of the task, and the historical reputation of each user will be updated accordingly. Simulation experiments show that the coverage of the CRJC-IMA is higher than that of the CTSIA. The utility of mobile users and SC is higher than that in STD algorithms. Furthermore, the utility of mobile users with the adjusted task priority is greater than that without a priority order.

Incentive Mechanism and Subsidy Design for Construction and Demolition Waste Recycling under Information Asymmetry with Reciprocal Behaviors.

To solve information asymmetry, we adopted the principal-agent framework to design the incentive mechanisms between the remanufacturer and the collector in the construction and demolition (C&D) waste-recycling industry. By using the model of reciprocity, we analyzed how the entities' behavioral motives affect their decisions in terms of the incentive mechanisms. The findings showed that the collector responds to their perception of the remanufacturer's intentions. If the perception is positive, they will make more effort in the collection work. If not, less effort will be put forth. Most importantly, we found that reciprocity helps to save the remanufacturer cost in the incentive mechanisms and makes the collector choose a higher effort level in the collection work. This finding showed that reciprocity serves to solve information asymmetry. By conducting a numerical simulation, we found that although a high subsidy policy can achieve rapid improvement of recycling-supply-chain performance, it is inefficient in maintaining friendly cooperation between the remanufacturer and the collector.