With the increasing growth of the Industrial Internet of Things (IIoT), businesses are finding new ways to collect, process, and analyse data. Instead of relying only on cloud-based systems, they now increasingly use decentralised and edge data processing methods.
According to Gartner, by 2025, the majority of enterprise-generated data, approximately 75%, will be generated and processed outside of the cloud. In this blog, we will discover the rise of Edge AI, which brings on-device intelligence and data processing closer to the source of data generation.
What Are Edge AI and Edge Devices?
Edge AI, also referred to as Edge Artificial Intelligence, is a cutting-edge technology that merges AI capabilities with the adaptability and scalability of edge computing. An edge device refers to a network endpoint that serves as the interface between the data centre and the physical world. There are two types of edge devices: traditional and intelligent edge.
Traditional edge devices transfer data over a secure network with limited processing capability. Examples include edge routers, WAN devices, Routing Switches, Firewalls and Multiplexers.
On the other hand, intelligent edge devices are smart devices that can perform edge computing tasks near the data source for industrial automation. These devices are used in industrial IoT to connect the entire system’s onboard processing or analytics capabilities. Intelligent edge devices enable smart factories and IIoT platforms to facilitate advanced automation and analytics.
Some intelligent edge devices include:
- Sensors: Measure conditions or events, trigger actions and route data. Examples include GPS, motion, optical, temperature, humidity, and vibration sensors.
- Actuators: Serve as the physical connectivity bridge between electronic devices and machine movement. They receive signals and instructions from intelligent edge devices and trigger actions using electric, air, or hydraulic power.
- IoT Gateways: Connect multiple sensors and devices to cloud computing platforms for analytics, computing, processing, and storage.
- M2M Devices: Connect equipment or machines to transfer data and facilitate automation.
It is worth noting that traditional and intelligent edge devices can be used in conjunction, allowing assets and machinery to be connected within a platform.
How Does Edge Computing Work?
Traditionally, data calculations occurred in the cloud or data centres. However, edge computing captures and processes information close to the data source or desired event by using sensors, computing devices, and machinery.
Edge devices translate the protocols used by local devices, such as Bluetooth, Wi-Fi, Zigbee, and NFC, into the protocols used by the cloud, such as AMQP, MQTT, CoAP, and HTTP. These devices are crucial in securely transferring and organising IoT data between the cloud and local devices.
Beyond security and privacy benefits, organisations are also deploying edge servers, gateway devices, and other equipment to reduce computing time and build their connected infrastructure. Edge computing is used more often than ever before. This computing paradigm is essential for complex systems like IIoT where a good amount of data is processed continuously.
How Is Edge Computing Used for IIoT?
Edge computing has emerged with the growth of industrial IoT applications, and nowadays, almost every industry is utilising edge computing as they operate on complex systems. Instead of relying solely on the cloud to manage data, IIoT devices use edge computing to process data locally. This process reduces the distance between the server and the client, addressing the challenge of overwhelming data transmission between devices.
Edge devices can communicate with each other wirelessly, providing advantages, such as true automation in Industry 4.0 applications, remote infrastructure management, and edge intelligence.
To understand how it is applied, let’s evaluate some use cases of edge computing and IIoT:
- Condition-Based Maintenance: In various manufacturing industries, including aircraft, defence equipment, tractors, forklifts, and assembly lines, sensors detect patterns, vibrations, and other irregularities, allowing identification of maintenance issues as they arise.
- Real-Time Safety Monitoring: Edge computing and IIoT devices enable organisations to monitor worker safety, even in remote areas with unreliable connectivity. Edge technology is used in applications, such as oil rigs, mining operations, and factories, where employees operate hazardous equipment.
- Autonomous Vehicles: Drones, self-driving cars, and connected shipping vessels possess sensors that enable them to perceive their surroundings and adapt to unpredictable and constantly changing circumstances.
Benefits of Edge Computing for IIoT
It is no surprise that edge computing brings several advantages to the Industrial Internet of Things (IIoT). That’s why its market size is multiplying. In fact, the edge computing market size was valued at $1.7 billion in 2017 and is projected to reach $16.6 billion by 2025.
Here is a closer look at the benefits that accelerate the use of edge computing devices for IIoT systems.
Faster Processing
Edge computing reduces data exchange time by moving computing and decision-making closer to the network edge. For instance, real-time analysis is essential in monitoring equipment performance or detecting failures. By minimising network latency, edge computing significantly improves response time for IIoT applications.
Enhanced Security and Reliability
Traditional cloud computing poses security risks as data flows through a centralised architecture. Edge computing distributes and mitigates the security risk between edge devices versus the cloud.
Federated learning for İoT enables edge devices to train machine learning models using local data while maintaining data privacy. This method is precious in industries where data privacy and security are paramount, such as critical infrastructure and defence.
Cost Savings
Edge computing offers cost savings by distributing computation and data storage on-premise. It reduces expenses associated with network data transmission, data upload/download frequency, and time-series data manipulation.
Scalability
As digital factory operations expand, the computing needs of an organisation will grow. Edge and cloud computing enable cost-effective scalability. They can be easily scaled up or down without requiring expensive shutdowns of critical resources, such as factory operations.
Navigating the Edge: OctaiPipe’s Approach
Now that we better understand the fundamental aspects of edge computing and edge AI, let’s delve into how OctaiPipe assists organisations in overcoming the hurdles and challenges of edge models.
OctaiPipe is an on-device machine learning platform that combines federated learning, Edge ML, and ML-Ops capabilities to deliver cost-efficient, secure, and scalable Edge AI models in industrial IoT environments.
Key Features of OctaiPipe:
- Federated Learning: Enables decentralised Edge devices or systems to train and deploy machine learning models, eliminating the need to transfer large amounts of data to central models.
- Edge ML: Includes built-in machine learning algorithms and pre-packaged models that run directly on Edge devices, allowing high privacy and security for Edge AI use cases.
- FL-Ops: Automates the management and scaling of ML distributed applications throughout their lifecycle on the Edge or Cloud. This provides a standardised approach to training, scaling, and updating models.
OctaiPipe is the ultimate Edge AI platform that enhances productivity, accelerates AI initiatives, and ensures data privacy. It allows data scientists to deploy Edge AI models without requiring additional skills, making it ideal for large-scale IoT environments.
