  Driven by the rapid growth of Internet of Things applications, tremendous
data need to be collected by sensors and uploaded to the servers for further
process. As a promising solution, mobile crowd sensing enables controllable
sensing and transmission processes for multiple types of data in a single
device. In this paper, a typical user is considered that is required to sense
and transmit data to a server, while it is assumed to remain busy and incapable
of sensing data during an interval. An optimization problem is formulated to
minimize the energy consumption of data sensing and transmission by controlling
the sensing and transmission rates over time, subject to the constraints on the
sensing data sizes, transmission data sizes, data casualty, and sensing busy
time. This problem is highly challenging, due to the coupling between the rates
as well as the existence of the busy time. To deal with this problem, we first
show that it can be equivalently decomposed into two subproblems, corresponding
to a search for the amount of data size that needs to be sensed before the busy
time (referred to as the height), as well as the sensing and transmission rate
control given the height. Next, we show that the latter problem can be
efficiently solved by using the classical string-pulling method, while an
efficient algorithm is proposed to progressively find the optimal height
without the exhaustive search. Moreover, the solution approach is extended to a
more complex scenario where there is a finite-size buffer at the server for
receiving data. Last, simulations are conducted to evaluate the performance of
the proposed design.
