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  • Open Access


    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


    Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

    Rasha Sleem1, Nagham Mekky1, Shaker El-Sappagh2,3, Louai Alarabi4,*, Noha A. Hikal1, Mohammed Elmogy1

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5619-5638, 2022, DOI:10.32604/cmc.2022.023716

    Abstract The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques, such as the internet of things (IoT) and mobile crowdsensing (MCS). The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively, with each mobile user completing much simpler micro-tasks. This paper discusses the task assignment problem in mobile crowdsensing, which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals. The goal is to minimize aggregate sensing time for mobile users, which reduces energy consumption… More >

  • Open Access


    Preserving the Efficiency and Quality of Contributed Data in MCS via User and Task Profiling

    Dingwen Wang, Ming Zhao*

    Journal of Cyber Security, Vol.2, No.2, pp. 63-68, 2020, DOI:10.32604/jcs.2020.07229

    Abstract Mobile crowdsensing is a new paradigm with powerful performance for data collection through a large number of smart devices. It is essential to obtain high quality data in crowdsensing campaign. Most of the existing specs ignore users’ diversity, focus on solving complicated optimization problem, and consider devices as instances of intelligent software agents which can make reasonable choices on behalf of users. Thus, the efficiency and quality of contributed data cannot be preserved simultaneously. In this paper, we propose a new scheme for improving the quality of contributed data, which recommends tasks to users based on calculated score that jointly… More >

  • Open Access


    A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

    Shuyu Li1, Guozheng Zhang1, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 223-241, 2020, DOI:10.32604/cmc.2020.07499

    Abstract With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group… More >

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