  In this paper, based on the spatio-temporal correlation of sensor nodes in
the Internet of Things (IoT), a Spatio-temporal Scope information model (SSIM)
is proposed to quantify the scope valuable information of sensor data, which
decays with space and time, to guide the system for efficient decision making
in the sensed region. A simple sensor monitoring system containing three sensor
nodes is considered, and two optimal scheduling decision mechanisms,
single-step optimal and long-term optimal decision mechanisms, are proposed for
the optimization problem. For the single-step mechanism, the scheduling results
are analyzed theoretically, and approximate numerical bounds on the node layout
between some of the scheduling results are obtained, consistent with the
simulation results. For the long-term mechanism, the scheduling results with
different node layouts are obtained using the Q-learning algorithm. The
performance of the two mechanisms is verified by conducting experiments using
the relative humidity dataset, and the differences in performance of the two
mechanisms are discussed; in addition, the limitations of the model are
summarized.
