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  • Open Access

    ARTICLE

    Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

    Lifeng Wang1, Jing Yu2,*, Wenlu Ji1

    Energy Engineering, Vol.121, No.2, pp. 523-540, 2024, DOI:10.32604/ee.2023.041881

    Abstract Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users, the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies. For this purpose, a power grid-flexible load bilevel model is constructed based on dynamic pricing, where the leader is the dispatching center and the lower-level flexible load acts as the follower. Initially, an upper-level day-ahead dispatching model for the power grid is established,… More >

  • Open Access

    ARTICLE

    Improving Federated Learning through Abnormal Client Detection and Incentive

    Hongle Guo1,2, Yingchi Mao1,2,*, Xiaoming He1,2, Benteng Zhang1,2, Tianfu Pang1,2, Ping Ping1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 383-403, 2024, DOI:10.32604/cmes.2023.031466

    Abstract Data sharing and privacy protection are made possible by federated learning, which allows for continuous model parameter sharing between several clients and a central server. Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate, but because the clients are independent, the central server cannot fully control their behavior. The central server has no way of knowing the correctness of the model parameters provided by each client in this round, so clients may purposefully or unwittingly submit anomalous data, leading to abnormal behavior, such as becoming malicious attackers or defective clients.… More >

  • Open Access

    ARTICLE

    An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities

    Jiaxing Wang, Lanlan Rui, Yang Yang*, Zhipeng Gao, Xuesong Qiu

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2355-2384, 2023, DOI:10.32604/cmc.2023.034993

    Abstract Crowdsensing, as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information, has received extensive attention in data collection. Since crowdsensing relies on user equipment to consume resources to obtain information, and the quality and distribution of user equipment are uneven, crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data, which affects the widespread use of crowdsensing. This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain, such as immutability… More >

  • Open Access

    ARTICLE

    A Techno-Economical Characterization of Solar PV Power Generation in Rwanda: The Role of Subsidies and Incentives

    Morris Kayitare1,2,*, Gace Athanase Dalson2,3, Al-Mas Sendegeyad4

    Energy Engineering, Vol.120, No.9, pp. 2155-2175, 2023, DOI:10.32604/ee.2023.028559

    Abstract Standalone Solar PV systems have been vital in the improvement of access to energy in many countries. However, given the large cost of solar PV plants’ components, in developing countries, there is a dear need for such components to be subsidised and incentivised for the consumers to afford the produced energy. Moreover, there is a need for optimal sizing of the solar PV plants taking into account the solar information, energy requirement for various activities, and economic conditions in the off-grid regions in Rwanda. This study aims to develop optimally sized solar PV plants suited to rural communities in Rwanda.… More > Graphic Abstract

    A Techno-Economical Characterization of Solar PV Power Generation in Rwanda: The Role of Subsidies and Incentives

  • Open Access

    ARTICLE

    Do Research Incentives Promote Researchers’ Mental Health?

    Liujian Gu1, Tao Wang1, Chuanyi Wang1,*, M. James C. Crabbe2, Xiao-Guang Yue3

    International Journal of Mental Health Promotion, Vol.25, No.8, pp. 903-914, 2023, DOI:10.32604/ijmhp.2023.028157

    Abstract Background: Researchers have a higher risk of anxiety and depression than the general population, so it is important to promote researchers’ mental health. Method: Based on the data from 3210 global researchers surveyed by the journal Nature in 2021, confirmatory factor analysis, OLS regression and other regressions were used to explore the research incentive dimensions and their effects on researchers’ mental health. Results: (1) Material incentive factors, work-family life balance factors, good organizational environment and spiritual motivation had significant positive effects on researchers’ mental health. (2) The spiritual motivation could better promote researchers’ mental health than the other factors. (3)… More >

  • Open Access

    ARTICLE

    Lightweight Storage Framework for Blockchain-Enabled Internet of Things Under Cloud Computing

    Xinyi Qing1,3, Baopeng Ye2, Yuanquan Shi1,3, Tao Li4,*, Yuling Chen4, Lei Liu1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3607-3624, 2023, DOI:10.32604/cmc.2023.037532

    Abstract Due to its decentralized, tamper-proof, and trust-free characteristics, blockchain is used in the Internet of Things (IoT) to guarantee the reliability of data. However, some technical flaws in blockchain itself prevent the development of these applications, such as the issue with linearly growing storage capacity of blockchain systems. On the other hand, there is a lack of storage resources for sensor devices in IoT, and numerous sensor devices will generate massive data at ultra-high speed, which makes the storage problem of the IoT enabled by blockchain more prominent. There are various solutions to reduce the storage burden by modifying the… More >

  • Open Access

    Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

    Zhe Sun1, Jiyuan Feng1, Lihua Yin1,*, Zixu Zhang2, Ran Li1, Yu Hu1, Chongning Na3

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1867-1886, 2022, DOI:10.32604/cmc.2022.022290

    Abstract Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the security aggregation mechanism can be… More >

  • Open Access

    ARTICLE

    Incentive-Driven Approach for Misbehavior Avoidance in Vehicular Networks

    Shahid Sultan1, Qaisar Javaid1, Eid Rehman2,*, Ahmad Aziz Alahmadi3, Nasim Ullah3, Wakeel Khan4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6089-6106, 2022, DOI:10.32604/cmc.2022.021374

    Abstract For efficient and robust information exchange in the vehicular ad-hoc network, a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel. In addition, we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards. Unfortunately, there may be some misbehaving nodes and due to their selfish and greedy approach, these nodes may not help others on the network. To deal with this challenge, trust-based misbehavior avoidance schemes are generally… More >

  • Open Access

    ARTICLE

    External Incentive Mechanism Research on Knowledge Cooperation-Sharing in the Chinese Creative Industry Cluster

    Changchun Gao1, Shiyu Liu1,*, Chenhui Yu1, Peng Guo2

    Computer Systems Science and Engineering, Vol.38, No.3, pp. 365-379, 2021, DOI:10.32604/csse.2021.016506

    Abstract The creative industry is a knowledge-based industry, but it is difficult and complex to create knowledge for enterprises. The principle of cooperation-sharing posits that companies’ limited resources prohibit them from gaining a competitive advantage in all business areas. Therefore, cooperation-sharing can help businesses overcome this hurdle. Cooperation-sharing expedites economic development, breaks the barrier of independent knowledge creation, and enhances resource utilization. However, the effectiveness and stability of knowledge cooperation-sharing are key problems facing governments and other regulators. This study can help regulators promote honesty in enterprise cooperation-sharing. Based on the hypothesis of bounded rationality, the evolutionary game theory was used… More >

  • Open Access

    ARTICLE

    Research on Crowdsourcing Price Game Model in Crowd Sensing

    Weijin Jiang1,2, Xiaoliang Liu1,2,*, Dejia Shi1, Junpeng Chen1,2, Yongxia Sun1,2, Liang Guo3

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1769-1784, 2021, DOI:10.32604/cmc.2021.016609

    Abstract Crowd-Sensing is an innovative data acquisition method that combines the perception of mobile devices with the idea of crowdsourcing. It is a new application mode under the development of the Internet of Things. The perceptual data that mobile users can provide is limited. Multiple crowdsourcing parties will share this limited data, but the cost that the crowdsourcing party can pay is limited, and enough mobile users are needed to complete the perceptual task, making the group wisdom is really played. In this process, there is bound to be a game between the crowds and the mobile users. Most of the… More >

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