In recent years, Edge AI has emerged as a game-changer for the industry, unlocking operational efficiency and operational resiliency, by bringing intelligence and data processing capabilities closer to the source of data generation.
Instead of centralised cloud computing, Edge AI places computational power closer to the data source, such as sensors, devices, or connected infrastructure. The Linux Foundation’s State of the Edge report predicts that by 2028, industries like energy, manufacturing, and smart cities will heavily increase their use of edge computing.
In this blog, we will explore the impacts of Edge AI and the deployment of artificial intelligence across connected industries.
Why Is Edge AI for IoT Essential?
Edge AI for IoT devices is essential to reduce the communication and processing latency of sending data to the cloud and back and ensures IoT devices can quickly analyse and respond to data in real time. Whether a smart home automation system adjusts the temperature based on user preferences or a connected vehicle optimises its functionalities, AI on Edge devices can deliver rapid and accurate responses.
Without having to send data to the cloud or a central server, Edge AI also minimises the need for data transmission over the network, resulting in reduced network and cloud costs, and increased network bandwidth. This decoupling also reduces the reliance of edge devices on the network to improve the reliability of operational systems.
Whilst minimising the data movement off the device, across the network and reducing the dependency on the cloud, Edge AI reduces the exposure to cyber attacks and mitigates the impact of a data breach.
A Promising Combination: Edge AI and Federated Learning
Federated learning (FL) for IoT is critical for next-generation Edge AI applications. Federated Learning is a privacy-preserving machine learning technique that enables data teams to train distributed models and collaborate using a global, shared ML model without sharing raw data submitted by IoT devices.
Let’s explore how various industries benefit from the powerful duo of Edge AI and federated learning.
Manufacturing
Edge AI and federated learning offer numerous benefits to the manufacturing industry. Firstly, Federated Edge AI enables collaboration amongst multiple devices, in multiple factories, in multiple regions and across multiple organisations. This unique capability acts as a catalyst to scale Industry 4.0 as it allows the industry to share learnings, create more accurate models, leverage expertise, and develop innovative solutions.
Manufacturers are already beginning to explore how to predict critical events and machine failures, diagnose malfunctions, and optimise control through the adoption of machine learning and predictive analytics. And if they can get their solutions into production and simultaneously scale, they are able to increase production and quality, reduce costs and waste, and manage production remotely.
Energy
In the landscape of critical energy infrastructure, edge computing and AI play a crucial role in managing energy systems. By balancing demand, generation, transport and consumption, Edge AI applications maintain assets through the energy supply chain to optimise and reduce energy usage and waste.
Smart grid systems, for example, use IoT applications for state estimation and monitoring, which help balance the residential distribution grid. Intelligent energy grids can then facilitate the seamless integration of renewable and domestic energy sources to balance supply and demand to ensure that excess energy is stored and distributed when needed. On-device federated intelligence ensures these assets, networks and energy grids are always able to leverage the best-in-class model and operate as efficiently as possible.
Smart Cities and Buildings
Edge AI has various use cases in smart cities and buildings, from monitoring and detecting access or accidents to optimising connectivity or building efficiency. By being utilised in surveillance cameras, Edge AI can detect suspicious behaviours, unlawful entry or accidents and falls to enhance staff security and safety.
In smart city infrastructures, like electricity grids and water networks, Edge AI helps identify issues in smart metres and water leakages in real time, optimising resource usage. Telecommunication providers also leverage on-demand machine learning services for private deployments as well as monitor base stations for anomalies and network faults.
Edge AI and federated learning also contribute to HVAC analytics and model training in smart buildings. Smart HVAC systems communicate with other smart devices and sensors, allowing them to automatically adjust based on custom settings and schedules. Intelligent HVAC systems enable building owners to control the climate of the building and increase comfort.
Transportation
The future of smart transportation systems depends on Edge AI. The automotive industry is on the verge of a technological revolution with collaborative machine learning in self-driving cars. These cars can process sensor data and make split-second decisions for safe navigation in dynamic environments.
Their localised processing reduces latency and improves responsiveness, ensuring passenger safety. With FL, there is continuous improvement in autonomous driving capabilities by aggregating insights from vehicles worldwide.
Connected Shipping
Edge computing is revolutionising the shipping industry. Connected shipping aims to improve industry efficiency through automation and big data utilisation. The data collected from ships is used to optimise vessel routes, predict maintenance needs, and ensure maximum efficiency for each vessel.
This technology optimises operations, increases efficiency, improves decision-making capabilities, and effectively manages fuel consumption.
Edge AI and FL can assist shipping companies in improving fleet performance, enhancing operational efficiency, and reducing their carbon footprint.
Revolutionise Edge AI Applications with OctaiPipe
Implementation of Edge AI can be challenging due to the cost of data transfer and unreliable network connectivity. As a next-generation technology platform, OctaiPipe integrates federated learning, Edge ML, and ML-Ops capabilities to enable private and resilient Edge AI models in IoT.
OctaiPipe is the only federated edge AI platform that enables data scientists to deploy Edge AI models in production within minutes, without needing to ever move or see the data. This makes OctaiPipe the ideal solution for implementing machine learning in large-scale IoT environments, with a focus on privacy, security, and cost-efficiency.
