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  • 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


    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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