In the evolving landscape of technology, edge computing has emerged as a transformative paradigm, opening up new opportunities. With the roll-out of 5G services, the use of edge computing in enterprises is dramatically expanding as companies and consumers connect more devices to the internet. Estimated at $15.59 billion currently, the edge computing market is expected to reach $32.19 billion by 2029, growing at a compound annual growth rate (CAGR) of 15.6 per cent during the period 2024-29.
Edge computing is also revolutionising several industries and businesses by enabling real-time data analysis and minimising reliance on centralised data storage, thereby effectively tackling latency concerns.
A look at the key applications of edge computing across enterprise verticals…
Healthcare
With the constant influx of patient information, medical records and diagnostic data, healthcare is becoming increasingly data-driven. Traditional cloud computing approaches have limitations, particularly in real-time data processing and analysis. Edge computing represents a new frontier in healthcare systems, powered by mobile and point-of-care technologies. It offers new, cost-effective solutions for healthcare informatics. For instance, edge computing devices can be used for automating care delivery, and leveraging artificial intelligence (AI) to enhance diagnosis speed and accuracy.
By processing healthcare data at the edge, latency is reduced, improving application performance and accuracy.
Edge computing also enables remote patient monitoring at home, thereby minimising hospital visits. Further, medical devices and equipment often require rapid decision-making capabilities to serve patients effectively. Edge computing supports real-time health- or fitness-related decision-making based on data collected from fitness equipment and critical care devices.
Furthermore, security and privacy are of utmost importance given the sensitive nature of healthcare data. By minimising the amount of data that must travel across networks, edge computing reduces the risk of data breaches. In addition, edge computing and AI are revolutionising the operating room with AI-assisted surgery, marking a significant transformation in medical practices.
Banking and financial services
Finance is among the most tightly regulated industries globally, and regulatory requirements are expected to further intensify in the coming years. While cloud computing offers the ability to analyse large amounts of data, it also requires sensitive data to travel across borders, which increases the likelihood of breaches. Edge computing, however, can bridge this gap by allowing sensitive financial data to be processed locally within national borders, thereby significantly decreasing the amount of data transmitted to the cloud.
For banks, particularly traditional retail banking firms, edge computing offers a chance to expedite their digital transformation. By utilising edge computing, retail banks can deliver highly personalised customer experiences. The technology also enables real-time monitoring of banks’ financial health. Thus, the use of edge computing provides the benefit of keeping the data localised, in turn, reducing the risk of data breaches.
Retail
The retail segment is at the forefront of adopting edge computing use cases, driven by the need for continuous innovation and enhancement of the shopper’s experience. Edge computing can significantly elevate both in-store and online shopping experiences for consumers. In physical stores, it enables the use of applications requiring ultra-low latency, such as mixed reality mirrors in fitting rooms, smart shelves at counters, and automated checkout systems. Moreover, edge computing supports retailers in quickly evolving and adapting their applications due to its greater scalability compared to traditional on-premise or cloud solutions.
In the retail industry, edge computing has become essential for real-time data processing and analysis near the data source. By processing information locally and enabling real-time decision-making based on data collected in each warehouse, edge computing optimises and accelerates the process of inventory management in warehouses. This results in improved customer experiences, streamlined store operations, and the introduction of new technologies such as self-checkout and augmented reality systems.
Autonomous vehicles
Autonomous vehicles are a prime example of edge computing use cases, as they require real-time analysis of all the data necessary to drive safely and reliably. Real-time analysis on the cloud, however, can be problematic due to the volume of data generated by autonomous vehicles. The possibility of latency issues or a lack of necessary connectivity when sending data to the cloud could result in unsafe delays. The amount of data generated by these vehicles is staggering, with industry estimates placing data generation in terabytes.
Edge infrastructure has made it possible for companies to utilise the power of machine learning (ML)/AI and create predictive maintenance schedules for their vehicles. The role of edge computing will increase significantly as more connected vehicles hit public roads.
Moreover, EaaS (edge-as-a-service) will allow automakers to take advantage of the skills and resources offered by end-to-end edge infrastructure providers. This will provide a complete edge solution for automakers, enabling them to bring new connected and autonomous features to the mass market.
Manufacturing
As we advance towards more digitalised factories, the benefits of edge computing have become increasingly clear. The rise of edge computing has led to the concept of Industry 4.0 for smart industries, with manufacturing being an early adopter and showcasing numerous potential use cases.
Edge computing processes data near the end-device, reducing data transportation costs and ensuring reliable data access. Additionally, the availability of high-performance computing resources at the edge allows organisations to leverage AI and ML applications more effectively.
In the realm of manufacturing, edge computing plays a crucial role in enhancing operational efficiency by enabling real-time data analysis. It supports swift and informed decision-making within the manufacturing environment. By strategically placing IoT sensors and edge computing devices within manufacturing plants, data is processed near its source, greatly minimising delays and promoting proactive maintenance. This evolution not only enhances production uptime but also propels the manufacturing sector forward.
Agriculture
Edge computing is also transforming agriculture by enhancing the management of resources such as fertilisers and pesticides while enabling real-time decisions based on sensor data. This technology aids in reducing waste and making agriculture more sustainable. Besides monitoring machinery, edge computing provides forecasts for maintenance needs, predicting potential equipment failures by analysing performance data. This proactive approach allows for preemptive maintenance, reducing equipment downtime, and prolonging the lifespan of agricultural machinery.
One of the most significant benefits of edge computing in agriculture is the ability to remotely monitor various aspects of farm operations.
Additionally, edge computing empowers the development of guided robotics for smart farms, automating tasks such as vegetable harvesting and crop spraying, among other functions. Autonomous machines, such as tractors, can communicate with nearby sensors to gather essential data about the environment, optimising their performance. By enabling data collection closer to its source, edge computing allows for more precise and immediate responses to agricultural challenges. Edge devices can perform certain data processing tasks on-site, improving efficiency and streamlining decision-making.
Going forward, the use of edge computing in agriculture will offer farmers the ability to maintain better control over their operations, enhance resource management, and improve overall productivity. Thus, edge computing promises to play a key role in the evolution of modern, smart farming practices.
Smart cities
The purpose of a smart city is to optimise its services for citizens and enhance levels of safety, sustainability, cost savings, and everyday functions. Edge computing encompasses a wide range of applications within this realm. It assists civic organisations, such as traffic agencies, public transportation departments and private transportation companies, in managing their vehicle fleets and optimising overall traffic flow by making rapid adjustments based on real-time, on-the-ground data.
Moreover, edge devices allow city workers and regional planners to process the data coming from sensors on power grids, public infrastructure, public facilities, private buildings, and other areas. This immediate access to information enables them to quickly assess needs and speed up responses, thereby improving the quality of life in their communities.
Into the future
Edge computing promises to be a pivotal force in the evolution of industrial and IT landscapes. As there is an anticipated surge in IoT-connected devices, edge technology will play a key role in managing vast amounts of data, marking a significant step forward in the next industrial revolution.
Nonetheless, addressing resource constraints, security risks, and interoperability challenges is crucial to realise the full potential of edge computing. Challenges can be overcome by adopting scalable architectures, integrating AI and ML, implementing robust security measures and fostering collaborative ecosystems.
Going forward, as the technology matures, emerging businesses will benefit from innovative solutions that will streamline operations and push Industry 4.0 into the next era, facilitating smart solutions and advancements in processes.
