As data volumes continue to grow, having a centralised data centre or cloud infrastructure leads to bandwidth and latency issues. In this regard, edge computing solutions have emerged as a practical and efficient alternative, allowing data to be processed and analysed closer to its point of origin. Enterprises across verticals have started turning to edge computing to enable near-real-time data handling and processing.
A look at the uptake of edge computing across enterprise verticals…
Manufacturing and industrial processes
The domain of manufacturing and industrial processes is extremely competitive, with companies striving to gain a competitive edge. As we move towards more digitalised factories, the “edge” that edge computing provides has become increasingly apparent. The emergence of edge computing has given rise to the term ‘Industry 4.0’ for smart industries. Enabled by edge computing, more smart factories have become operational. These smart factories curate data via sensors installed on the machines and throughout the factory floor, thereafter analysing the date and gaining insights. This delivers the speed required for manufacturing and industrial operations, enabling automated assembly lines to function rapidly and necessitating real-time interventions to address problems.
Artificial intelligence (AI), internet of things (IoT) and edge computing are some of the key technologies driving the smart city revolution. The purpose of a smart city is to optimise its services for citizens and generate enhanced levels of safety, sustainability, cost savings and everyday functions. Edge computing helps smart cities in realising this goal by minimising transmission delay between devices. Furthermore, it fosters greater network resilience and facilitates data centralisation to support a large number of systems. A smart city relies heavily on connectivity and latency, which cannot be solely guaranteed by cloud infrastructure. Linking up remote areas to a few central systems dispersed around the city reduces problems such as latency or downtime.
The integration of retail and edge computing has become increasingly important in today’s digital landscape. Retail encompasses the sale of goods and services, both online and through physical stores, while edge computing handles the processing and analysis of data close to its source of collection. Edge AI (a combination of edge computing and AI) holds immense potential in various industries, offering high returns on investments through the automation of repetitive tasks, increased efficiency and revenue growth. It is used to analyse customer data and predict their purchasing patterns, leading to optimised product offerings and improvement in sales. Edge AI can track points of sale and online shopping transactions, monitor inventory levels in real time, identify discrepancies and prevent stockouts, resulting in increased revenue and efficient cost savings. It is also used to detect and prevent theft in retail environments. This technology can be applied to smart shelves and robotics for inventory management and stocking shelves, resulting in increased efficiency and reduced labour costs. Prime examples of edge computing in the retail space include Dell Solutions for retail AI edge computing, Nvidia Fleet Command (manages configuration and scales AI at the edge) and Deep North Video Analytics.
Edge computing offers new, cost-effective solutions for healthcare informatics. Instead of sending data to the cloud, the processing is completed at the place where data is generated, whether it’s in the devices or networks at the clinic, hospital or even directly on patients’ devices outside of clinical settings. As a result, doctors can diagnose and begin treating conditions faster, thereby improving patient outcomes. A recent development in this field has been the collaboration among Cisco Systems, Inc., Bharti Airtel and Apollo Hospitals for developing 5G-connected ambulances. The objective of this tie-up is to enhance the adoption of private 5G and edge computing solutions across the healthcare sector. Additionally, by implementing edge computing, telehealth providers can provide their patients with a more streamlined and integrated experience, leading to higher customer engagement and satisfaction. Patients can now have remote consultations with healthcare professionals, access their medical records and monitor their health and wellness using wearable devices anywhere, any time.
The agricultural sector is vital to the stability and development of society. The widespread adoption of agricultural IoT has led to the explosive growth of sensors and an increase in data. The large amounts of data often lead to an increase in the load on cloud servers, which reduces response time. This issue is effectively addressed by edge computing models, which utilise computer resources available in the local network, thereby reducing latency.
Meanwhile, edge computing also enables agricultural robots to use computer vision and preloaded field data to gather insights. Additionally, it helps in making the right decisions about possible environmental hazards or natural disasters. Finally, edge computing helps in farm automation. With capabilities similar to agricultural robots, IoT edge computing allows for putting a greenhouse or even an entire farm on autopilot mode. This indicates that the entire ecosystem can autonomously perform tasks without relying on remote servers to process the accumulated data and make decisions regarding day-to-day processes such as watering the plants, controlling the temperature, feeding the cattle, and managing humidity and light. This enables farms and greenhouses to operate independently without the need for a connection to the main server, and make decisions based on data from local sensors.
Security and worker safety
Edge computing solutions help in combining and analysing data from on-site cameras, employee safety devices and various other sensors. This allows businesses to monitor workplace conditions and ensure that employees adhere to established safety protocols, especially in remote and hazardous environments such as construction sites or offshore oil rigs. Meanwhile, surveillance systems can benefit from the low latency and reliability of edge computing, as it is often necessary to respond to security threats within seconds. Edge computing devices can also be used in conjunction with video monitoring and biometric scanning to ensure that only authorised individuals gain entry to restricted areas.
Virtual reality (VR) and augmented reality (AR) both require real-time processing of large data sets, as any lag in analysis could result in delayed subsequent actions. Combining and synchronising the real world and user motions with a digital world requires vast amounts of graphic rendering processes. Due to the intense rendering required for graphics, on-device processes are augmented by splitting workloads between AR/VR devices and edge cloud. Graphics rendering on the edge cloud enhances low latency, controller tracking, hand tracking and motion tracking and photon processing. This process is also known as split rendering. The only flipside to this is that rendering the on-edge cloud requires fast and reliable 5G connections to deliver optimal results. Additionally, businesses can leverage edge computing technology to enable unique and customised AR/VR experiences, such as personalised shopping displays.
Autonomous vehicles and traffic management
Autonomous vehicles are a prime edge computing use case, as they can only operate safely and reliably when equipped to analyse all the data required to drive in real time. The reduced latency in data transmission and processing significantly helps in autonomous driving, where real-time decision-making is crucial. Edge computing improves data security by processing and analysing data locally, reducing the risk of data breaches and unauthorised access to sensitive information. Moreover, it is cost-effective as it reduces the cost of data transmission and processing by minimising the need to transmit data to a central cloud server for processing. Edge computing can contribute towards effective city traffic management. Basic tasks, ranging from optimising taxi frequency to notifying about the opening and closing of extra lanes, can be automated with edge computing. This automation will help users in making effective decisions, in turn, saving time and reducing traffic congestion.
The road ahead
Governments worldwide are increasingly implementing edge computing solutions, leading to a widespread adoption and increasing awareness across various sectors. According to IDC’s latest study, spending on edge solutions is projected to reach $208 billion in 2023 and is anticipated to grow to $317 billion in 2026. Edge computing solutions can assist in discovering new business opportunities, increasing operational efficiency, and providing faster and more reliable experiences for customers, making it a safe investment for all sectors.