The number of industrial devices connected to the Internet continues to increase every year and is projected to reach 41.6 billion endpoints by 2025.
Machine learning (ML) at the Edge is revolutionising the IIoT by bringing intelligent decision-making and data processing to the source. This paradigm shift allows for faster predictions and reduced reliance on transmitting large amounts of raw data across a network.
In this blog post, we explore how AI for edge computing empowers productivity in IIoT, accelerates AI initiatives, and ensures data privacy.
How Edge AI Works
Edge AI technology refers to the combination of artificial intelligence (AI) capabilities with the flexibility of Edge computing. Edge AI deploys AI algorithms and models directly on edge devices, such as smartphones, IoT devices, drones, and other hardware endpoints.
AI models are locally trained using federated learning, which supports machine learning at the edge. This method ensures scalability across distributed data sources and reduces the risk of system overload or downtime.
The integration of federated learning and Edge AI systems is particularly vital in today’s digital age, given the increasing amount of data and the inevitability of data privacy concerns.
Machine learning at the edge includes the following steps:
- Data Collection: Edge AI system devices with sensors gather raw data from their surroundings or connected systems.
- Data Pre-Processing: The collected data undergoes pre-processing, which may involve cleaning, normalising, and transforming it into a suitable format for machine learning models.
- Model Inference: ML models deployed at the edge analyse the preprocessed data locally on the device or gateway. These models can make predictions, classifications, or other analyses based on incoming data.
- Decision Making: Based on the insights generated by the ML model, real-time decisions can be made at the edge without the need to send data back to a central server for processing.
- Feedback Loop: Edge ML models can continuously learn and improve by incorporating new data inputs and feedback, adapting to changing conditions, and enhancing their performance over time.
Join OctaiPipe and explore how our federated Edge AI infrastructure can optimise decision making, enhance data processing, and revolutionise your operations.
Understanding Intelligent Edge Devices
Traditional edge devices, such as edge routers, WAN devices, routing switches, firewalls, and multiplexers, handle data traffic, link multiple networks, and combine data from multiple sources. Intelligent edge devices, on the other hand, are equipped with advanced processors and memory.
Unlike traditional edge devices, which are limited in their computational power and primarily perform basic tasks, like data sensing and transmission, intelligent edge devices can handle more complex computations and perform real-time analytics locally. Through PLCs, edge servers, and industrial gateways, they play a crucial role in industrial automation and are changing the way organisations operate and make decisions.
Intelligent edge devices include:
- Sensors measure a condition or event, trigger a response, and send data to the next destination. There are countless types of sensors, including GPS, motion, optical, temperature, humidity, and vibration.
- Actuators serve as the physical connectivity link between electronic devices like sensors. Actuators receive sensor signals and instructions from advanced edge devices and initiate actions using electric, air, or hydraulic power.
- IoT Gateways connect numerous sensors and other devices to cloud computing platforms for analytics, computation, processing, and storage.
- M2M Devices connect equipment or machines to transfer data and enable automation. Examples include wearable devices, automated SCM, telemetry services, and smart home metering devices.
Benefits of Edge ML
Machine learning at the edge augments the overall reliability and effectiveness of various applications, from autonomous vehicles to industrial automation.
Lower Latency
By relocating machine learning tasks closer to the source of data, at the edge, the data processing time is significantly minimised. This instantaneous response is critical for applications like self-driving cars, drones, and medical systems that need immediate decision making.
Enhanced Security
Edge machine learning enables superior control over data security. Confidential information can be processed locally, minimising exposure to cloud services, which enhances privacy and reduces risk.
Optimised Bandwidth Use
Rather than sending all data to a central cloud, edge devices conduct initial processing. This method improves bandwidth usage, particularly in situations where connectivity is limited.
Cost Efficiency
The integration of machine learning into edge data centres reduces expenses related to data transfer and cloud infrastructure. Decision-making is more efficient, leading to improved real-world results.
Increased Accuracy
Merging cloud-based machine learning with inference at the edge ensures precise results. It eliminates actions based on false positives or negatives, enhancing the overall reliability of the system.
Implementing Edge ML in IIOT
As technology evolves, several industries are adopting Edge ML for rapid data processing and analytics, particularly in the context of smart factories and interconnected machinery.
Here are some industries Edge ML is transforming:
- Energy: Edge ML helps optimise power generation and distribution in the energy sector. It analyses real-time data from various sensors and can predict potential equipment failures and schedule maintenance, improving overall operational efficiency.
- Smart Utilities: Smart utilities leverage Edge ML to improve their resource management and service delivery. Intelligent analysis of consumption data helps in forecasting demand, enabling better load balancing, and preventing service disruptions.
- Connected Shipping: Edge ML helps in tracking and managing fleet operations, predicting maintenance needs, and improving route optimisation, leading to significant cost savings.
- Smart Cities & Buildings: Edge ML in smart cities and buildings enhances utility management and security by processing data at the source for faster response times, boosting safety and efficiency.
- Manufacturing: In manufacturing, Edge ML boosts predictive maintenance, quality control, and process optimization to cut costs, enhance productivity, and thrive in a competitive market.
Join the Federated Edge AI Revolution with OctaiPipe
Edge AI applications can pose challenges due to expensive data transfers and inconsistent network connections. OctaiPipe emerges as a promising solution to these challenges, enabling private and resilient Edge AI models in IIoT.
OctaiPipe is a revolutionary federated Edge AI infrastructure designed specifically for the creation, implementation, and management of AI solutions in industrial IoT settings without the need to move or access data.
It stands out as the only solution that integrates federated machine learning, Edge ML, and FL-Ops capabilities. This unique combination facilitates on-device learning, making the delivery and training of more private, cost-effective, fast, and autonomous Edge AI models in IoT possible.
Main Features of OctaiPipe:
- Federated Learning: Allowing edge devices or systems to independently train and implement machine learning models, negating the need to transfer vast amounts of data to central models.
- Edge ML: It comes with in-built machine learning algorithms and ready-to-use models that operate directly on edge devices. This ensures high levels of privacy and security for Edge AI applications.
- FL-Ops: It automates the administration and scaling of distributed ML applications throughout their lifecycle on the edge or cloud. This offers a uniform method for training, scaling, and updating models.
