Towards Factory Schedule based Adaptation for Reliable Networking in Industrial IoT

In this paper, a novel paradigm of adapting wireless communications based on the factory schedules in an indoor factory floor is explored. Since the fourth industrial revolution (4IR) strengthens the industries with a wide range of advanced applications such as digital twin and predictive maintenance, a large number of devices and systems are deployed densely near machines in a factory floor. Such new devices and systems, along with the existing systems, are networked by the Industrial Internet of Things (IIoT) wirelessly for their functioning. Devices closer to machines have a greater probability of interference from the operation of the machine. Since the operation of machines in a factory are often scheduled, their impact on wireless communication can be learnt and predicted. The first step towards it is, to estimate the effect of various processes in a factory schedule on wireless communication. This research empirically investigates the extent and nature of the impact of factory schedules and presents its findings. The obtained results indicate that the machines influence the packet reception rate (16% on an average), positively for certain nodes while negatively for others. Furthermore, based on these findings, this paper categorizes the factory schedules into two broad categories (`Macro' and `Micro' processes) and discusses the challenges in developing a mechanism to detect and classify them. The interference detection and identification can have applications such as scheduling delay-tolerant traffic and adjusting power levels while transmitting a packet apart from increasing reliability of IIoT.