  Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a
flexible and scalable sensing coverage with a low deployment cost, by employing
mobile users/devices to perform sensing tasks. In this work, we propose a novel
MCS framework with data reuse, where multiple tasks with common data
requirement can share (reuse) the common data with each other through an MCS
platform. We study the optimal assignment of mobile users and tasks (with data
reuse) systematically, under both information symmetry and asymmetry, depending
on whether the user cost and the task valuation are public information. In the
former case, we formulate the assignment problem as a generalized Knapsack
problem and solve the problem by using classic algorithms. In the latter case,
we propose a truthful and optimal double auction mechanism, built upon the
above Knapsack assignment problem, to elicit the private information of both
users and tasks and meanwhile achieve the same optimal assignment as under
information symmetry. Simulation results show by allowing data reuse among
tasks, the social welfare can be increased up to 100~380%, comparing with those
without data reuse. We further show that the proposed double auction is not
budget balance for the auctioneer, mainly due to the data reuse among tasks. To
this end, we further introduce a reserve price into the double auction (for
each data item) to achieve a desired tradeoff between the budget balance and
the social efficiency.
