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The Internet of Things (IoT) is an emerging next-generation technology in thefourth industrial revolution. In industrial IoT networks, sensing devices arelargely deployed to monitor various types of physical processes. They arerequired to transmit the collected data in a timely manner to support real-timemonitoring, control and automation. The timeliness of information is veryimportant in such systems. Recently, an information-theoretic metric named the"value of information" (VoI) has been proposed to measure the usefulness ofinformation. In this work, we consider an industrial IoT network with a set ofheterogeneous sensing devices and an intelligent mobile entity. The concept ofthe value of information is applied to study a joint path planning and userscheduling optimisation problem. We aim to maximise the network-level VoI undermobility and communication constraints. We formulate this problem as a Markovdecision process (MDP), and an efficient algorithm based on reinforcementlearning is proposed to solve this problem. Through numerical results, we showthat the proposed method is able to capture the usefulness of data from bothtime and space dimensions. By exploiting the correlation property of the datasource, the proposed method is suitable for applications in resource-limitednetworks.

Optimal Mobility and Communication Strategy to Maximize the Value of Information in IoT Networks