# Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
## 3 Digital Twins
### Edge Computing: Enabling the Digital Twin  
Smart manufacturing is more than just collecting massive data sets through a myriad of disparate connected devices. At its core, smart manufacturing is the potential to be able to use the collected data efficiently to decide, predict, and take actions in real-time to optimise the manufacturing output. As the adoption of digital technologies in manufacturing grows, it will increase the requirements for more localised decision-making, storage capabilities, and analysis at the edge, known as edge computing. Although the idea of edge computing is not new, it has recently emerged as the essential component of smart manufacturing, which aims to quicken the process of digital transformation [17]. Edge computing enables manufacturers to transform enormous amounts of machine-generated data into meaningful and useful information [8]. It accomplishes this by using Internet of Things (IoT) devices that are networked, such as environmental sensors, alarms, or motor driven units. This makes it possible for big data analytics to materialise right at the source of the data. A robotic arm or conveyor system are examples of an operational technology (OT) computing framework that is nearest to the IoT sensed data collection sources. As they frequently exist the farthest from the core of the information technology (IT) computing framework, these are considered as being at the \"edge\". The IoT gateway anchors an open source software platform, e.g., IOTech's Edge Xpert [18] or KepWare [19], which is often the preferred method to provide a way to link the sensed devices, enable localised decision-making, and relay the relevant device information for further analysis at the cloud. These edge platforms must be able to support industrial protocol standards such as OPC UA, CAN bus, etc., and may also need to support different wireless protocols such as cellular, WiFi, Bluetooth or Zigbee, LoRa, etc. An edge platform imparts the following key functions [20]:  
* Interoperability--provides the required protocol conversion for communications to be acknowledged between devices unable to communicate with each other.
* Localised processing--facilitates the unloading of computing jobs from smart devices by caching information and functioning as a private cloud suitable for remote access.
* Quality of service--increases the efficacy of available network bandwidth while decreasing endpoint bottlenecks.
* Security--provides advanced security solutions compared to those implemented on each endpoint, hence building a better defensive strategy for the entire network within the factory.
* Local storage--saves transmission costs by just transmitting the necessary data to the cloud. In certain cases, it is advantageous to have the edge device act as the computing node to record data and make localised analytical decisions.  
As the manufacturing sector transitions from single-domain digitalised sites to cross-domain digitalised sites, the focus will shift to a system of systems approach where pivotal points of interoperability can be used to inform and interact at a large scale. The \"system of systems\" endeavour that utilises digital twins interoperating with one another needs a consistent IoT platform strategy, based on open specifications, strong resiliency principles, security, and standardisation, as when multiple networking technologies are involved, standards are needed to regularise the interactions between devices and sites.