Abstract
The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.
Highlights
The Internet of Things (IoT) is envisioned to make our lives easier
Developers have options when wishing to reduce the data and associated costs and latency. They can use the limited processing of Edge Computing (EC) devices to reduce the data being sent to the cloud using various sampling techniques or perform Machine Learning (ML) for Predictive Maintenance (PM) on the EC device or even use a hybrid approach in which ML/PM is carried out using EC and the cloud
We propose an Edge-Cloud based architecture that utilises data reduction at the edge informed by network-level information and PM analytics constraints from the cloud to reduce the data required for analysis tasks
Summary
The Internet of Things (IoT) is envisioned to make our lives easier. Since its inception, almost every sector has somehow exploited it. In IIoT, a traditional approach to collect data is to stream it from sensing devices to the cloud where it is processed and modelled. Developers have options when wishing to reduce the data and associated costs and latency They can use the limited processing of EC devices (e.g. sensors) to reduce the data being sent to the cloud using various sampling techniques or perform ML for PM on the EC device or even use a hybrid approach in which ML/PM is carried out using EC and the cloud. Reviewing traditional approaches which help in reducing data in IoT These techniques help in reducing the data where generated and result in consuming fewer communication resources (e.g. network bandwidth) and require less cloud resources for storage and computation.
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