# Towards Optimised Data Transport and Analytics for Edge Computing

## TL;DR

This paper proposes a distributed data transport and processing model for edge computing that enhances IIoT analytics capabilities in SMEs without requiring significant hardware investments.

## Contribution

It introduces a scalable, secure distributed model that improves data analytics for industrial IoT, addressing resource limitations in SMEs.

## Key findings

- Enables more complex analysis on limited hardware
- Improves security in data transport
- Facilitates scalable edge analytics

## Abstract

Industrial organisations, particularly Small and Medium-sized Enterprises (SME), face a number of challenges with regard to the adoption of Industrial Internet of Things (IIoT) technologies and methods. The scope of analytics processing that can be performed on data from IIoT-enabled industrial processes is typically limited by the compute and storage resources that are available, and any investment in additional hardware that is sufficiently flexible and scalable is difficult to justify in terms of Return On Investment (ROI). We describe a distributed model of data transport and processing that eases the take-up of IIoT, whilst also enabling a capability to securely deliver more complex analysis and future insight discovery, than would be possible with traditional network architectures.