Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve aflexible and scalable sensing coverage with a low deployment cost, by employingmobile users/devices to perform sensing tasks. In this work, we propose a novelMCS framework with data reuse, where multiple tasks with common datarequirement can share (reuse) the common data with each other through an MCSplatform. We study the optimal assignment of mobile users and tasks (with datareuse) systematically, under both information symmetry and asymmetry, dependingon whether the user cost and the task valuation are public information. In theformer case, we formulate the assignment problem as a generalized Knapsackproblem and solve the problem by using classic algorithms. In the latter case,we propose a truthful and optimal double auction mechanism, built upon theabove Knapsack assignment problem, to elicit the private information of bothusers and tasks and meanwhile achieve the same optimal assignment as underinformation symmetry. Simulation results show by allowing data reuse amongtasks, the social welfare can be increased up to 100~380%, comparing with thosewithout data reuse. We further show that the proposed double auction is notbudget balance for the auctioneer, mainly due to the data reuse among tasks. Tothis end, we further introduce a reserve price into the double auction (foreach data item) to achieve a desired tradeoff between the budget balance andthe social efficiency.
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