  The Internet of Things (IoT) is an emerging next-generation technology in the
fourth industrial revolution. In industrial IoT networks, sensing devices are
largely deployed to monitor various types of physical processes. They are
required to transmit the collected data in a timely manner to support real-time
monitoring, control and automation. The timeliness of information is very
important in such systems. Recently, an information-theoretic metric named the
"value of information" (VoI) has been proposed to measure the usefulness of
information. In this work, we consider an industrial IoT network with a set of
heterogeneous sensing devices and an intelligent mobile entity. The concept of
the value of information is applied to study a joint path planning and user
scheduling optimisation problem. We aim to maximise the network-level VoI under
mobility and communication constraints. We formulate this problem as a Markov
decision process (MDP), and an efficient algorithm based on reinforcement
learning is proposed to solve this problem. Through numerical results, we show
that the proposed method is able to capture the usefulness of data from both
time and space dimensions. By exploiting the correlation property of the data
source, the proposed method is suitable for applications in resource-limited
networks.
