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LinkedinIndustrial IoT (IIoT) is a subcategory of the Internet of things that particularly deals with the use of IoT in optimizing industrial setups through machine to machine (M2M) communication using connected devices and sensors. Additionally, techniques like machine learning and big data are used to enable organizations to extract insights rapidly which proves to be extremely useful at various levels from supply chain to maintenance and repair. A textbook definition for IIoT would generally highlight a connection between the cyber and the physical – essentially the information technology (IT) and the operational technology (OT).
However, there are certain challenges and pitfalls that are always associated with any IIoT setup. Some of these are:
1) Difficulties in integrating legacy devices to modern IoT frameworks.
2) In areas with limited internet connectivity, it is difficult to use cloud-based services for storing data, which is essential when an enormous amount of data is generated. Using the cloud also raises data security issues.
OUR OBJECTIVE
We aim at creating a system that uses concepts of edge computing to eliminate the requirement of a cloud-based system in response to the rise of IIoT applications. Traditionally, IoT devices collect and transmit information that gets sent back to the cloud, which in many cases can overwhelm cloud-based systems with respect to speed and reliability when the data bandwidth generated is large. Edge computing allows organizations to process data from sensors and equipment locally and in real-time. Edge computing is emerging as a solution to the latency issues common in cloud-based solutions, allowing organizations to avoid delays and lag time that lead to safety issues, production hiccups, and open the door to security threats.
Another important feature that we plan on implementing is the visualization of the data using an Augmented Reality environment, which basically creates a digital representation of all of the data which is generated by the sensor around the devices they are connected to in the form of graphs and tables with as little latency as possible. The aim here is to ensure safer and more accurate repair jobs as the maintenance personnel and front-line workers can monitor the parameters simultaneously while working on the machine in an intuitive manner.
Technologies used:
For creating the AR environment we will be using the Vuforia Engine in Unity Editor, which is a popular and useful authoring platform. Vuforia Engine is one of the most widely used software development kit (SDK) for creating AR apps by adding advanced computer vision functionality to any application and creating 3-D models which can be thrown into the workspace. It also allows the user to interact with the spaces un the real world. Vuforia supports Android, iOS, Lumin, and UWP devices.
For establishing the communication framework, we will be using an MQTT protocol based publish-subscribe model developed by IBM for the exchange of data. MQTT is an open OASIS ISO standard running over TCP/IP, but can use any ordered, lossless, bi-directional connection that supports MQTT. We have used Eclipse Mosquitto, an open-source MQTT message broker. The MQTT protocol provides a lightweight means of information exchange and is significantly less power hungry making it ideal for embedded systems and IoT applications. The mosquitto project also provides a C library for creating clients which would come in handy.
However, there are certain challenges and pitfalls that are always associated with any IIoT setup. Some of these are:
1) Difficulties in integrating legacy devices to modern IoT frameworks.
2) In areas with limited internet connectivity, it is difficult to use cloud-based services for storing data, which is essential when an enormous amount of data is generated. Using the cloud also raises data security issues.
OUR OBJECTIVE
We aim at creating a system that uses concepts of edge computing to eliminate the requirement of a cloud-based system in response to the rise of IIoT applications. Traditionally, IoT devices collect and transmit information that gets sent back to the cloud, which in many cases can overwhelm cloud-based systems with respect to speed and reliability when the data bandwidth generated is large. Edge computing allows organizations to process data from sensors and equipment locally and in real-time. Edge computing is emerging as a solution to the latency issues common in cloud-based solutions, allowing organizations to avoid delays and lag time that lead to safety issues, production hiccups, and open the door to security threats.
Another important feature that we plan on implementing is the visualization of the data using an Augmented Reality environment, which basically creates a digital representation of all of the data which is generated by the sensor around the devices they are connected to in the form of graphs and tables with as little latency as possible. The aim here is to ensure safer and more accurate repair jobs as the maintenance personnel and front-line workers can monitor the parameters simultaneously while working on the machine in an intuitive manner.
Technologies used:
For creating the AR environment we will be using the Vuforia Engine in Unity Editor, which is a popular and useful authoring platform. Vuforia Engine is one of the most widely used software development kit (SDK) for creating AR apps by adding advanced computer vision functionality to any application and creating 3-D models which can be thrown into the workspace. It also allows the user to interact with the spaces un the real world. Vuforia supports Android, iOS, Lumin, and UWP devices.
For establishing the communication framework, we will be using an MQTT protocol based publish-subscribe model developed by IBM for the exchange of data. MQTT is an open OASIS ISO standard running over TCP/IP, but can use any ordered, lossless, bi-directional connection that supports MQTT. We have used Eclipse Mosquitto, an open-source MQTT message broker. The MQTT protocol provides a lightweight means of information exchange and is significantly less power hungry making it ideal for embedded systems and IoT applications. The mosquitto project also provides a C library for creating clients which would come in handy.
