Abstract
In real-time crowdsensing, the key factor is the freshness of information. However, due to the randomness and uncertainty of mobile users, some tasks will not get status updates, and leaving a lot of outdated information. To solve this problem, we use efficient task assignment strategy to ensure the freshness of the data at the receiver’s side. We take space and time into consideration. First, we develop a matrix-based location matching mechanism for the service provider to achieve accurate location-based task allocation without disclosing the location of mobile users and the sensing area of tasks. Then we use “age of information” (AoI) as a metric to represent freshness of information and we consider a simple linear AoI-aware allocation strategy algorithm to reduce the average AoI across the system and analyze its AoI performances. Extensive simulations are performed to validate our analytical results. Both analysis and simulation results verify the effectiveness of the proposed scheduling strategy.
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Zhao, M., Yang, S. (2020). Improve Data Freshness in Mobile Crowdsensing by Task Assignment. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_35
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DOI: https://doi.org/10.1007/978-3-030-57884-8_35
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