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Mobile Edge Computing (MEC) has been proposed as an efficient solution for Mobile crowdsensing (MCS). It allows the parallel collection and processing of data in real time in response to a requested task. A sensing task can be one-time or continuous, with multiple readings collected over time. Integrating MEC and continuous sensing in MCS is challenging due to many factors, including workers&#x2019; mobility, edge node placement, task location, Reputation, and data quality. In addition, guarantying cooperative communication in the presence of Anomalous data while maintaining a high quality of service (QoS) is a fundamental issue in continuous sensing. A stability-based edge node selection and anomaly detection-based decision-making framework for worker recruitment in continuous sensing is proposed to address these challenges. It can a) Select the most stable edge nodes in the area of interest (AoI), b) Dynamically cluster the workers according to their movement in the AoI, c) Locally detect and eliminate anomalies within the sensing data, and d) Adopt a feedback mechanism that ensures the cooperation between the edge nodes to eliminate untrustworthy workers in the whole sensing period and future tasks. A real-life dataset is used to evaluate the efficiency of the proposed framework. Results show that the framework outperforms the baselines by achieving higher QoS while introducing lower delay, energy consumption, and less resource consumption.

Trust-3DM: Trustworthiness-based Data-Driven Decision-Making Framework using smart Edge Computing for Continuous Sensing