The rapid expansion and enhanced computing capabilities of IoT devices have led to an enormous surge in data generation. This trend continues to accelerate as the rollout of 5G networks drives a higher number of connected mobile devices.
Historically, cloud computing and AI promised to accelerate innovation by generating actionable insights from data. However, the sheer scale and complexity of data produced by connected devices have now surpassed the capacity of traditional network and infrastructure systems. Conventional architectures, despite their reliability, often face challenges related to latency, bandwidth limitations, and security risks when processing data in real time. These delays can hinder performance, especially in critical applications such as autonomous vehicles, healthcare systems, and industrial automation.
Edge computing is a decentralized computing model designed to address this challenge. Enterprise applications are brought closer to data sources, such as IoT devices or local edge servers. This proximity enables significant business advantages, including quicker insights, faster response times, and more efficient bandwidth usage.
Transmitting all device-generated data to a centralized cloud or data center often leads to bandwidth constraints and latency issues. Edge computing provides a more effective solution by processing and analyzing data near its point of origin. Since the data doesn’t need to travel over a network for centralized processing, latency is minimized. Additionally, edge computing, especially when combined with 5G networks, facilitates faster and more extensive data analysis, unlocking the potential for richer insights, quicker responses, and enhanced customer experiences.
Devices at the Edge: Harnessing the Potential
From connected vehicles to intelligent robots on factory floors, the volume of data generated by devices is at an all-time high, yet much of this IoT data remains untapped. For instance, a study by McKinsey & Company revealed that an offshore oil rig produces data from 30,000 sensors, but less than one percent of this data is used to inform decision-making.
Edge computing leverages the growing computational power within devices to deliver real-time insights and predictive analysis. This enhanced analytical capability at the edge can drive innovation by improving quality and adding value. However, it also brings strategic considerations: How should you manage the deployment of workloads that utilize this increased computing capacity? How can you use embedded intelligence in devices to more effectively influence operational processes for employees, customers, and overall business performance? To fully capitalize on the value of these devices, a substantial amount of computation must be shifted to the edge.
Components of Edge Computing
Edge Devices: Many of the devices we use daily—such as smart speakers, watches, and phones—are already performing edge computing, as they collect and process data locally while interacting with the physical environment. Internet of Things (IoT) devices, point of sale (POS) systems, robots, vehicles, and sensors also qualify as edge devices when they perform local computing and communicate with the cloud.
Network Edge: Edge computing doesn’t necessarily require a dedicated “edge network” and can operate on individual edge devices or even routers. When a distinct network is involved, it represents just another point along the continuum between users and the cloud, where 5G technology becomes essential. 5G offers powerful wireless connectivity with low latency and high-speed cellular data, unlocking new possibilities like autonomous drones, remote telesurgery, and smart city initiatives. The network edge is especially advantageous in scenarios where placing compute resources on-site is impractical or expensive, yet high responsiveness is necessary (making the cloud too distant for real-time operations).
On-premises Infrastructure: This refers to hardware for managing local systems and connecting them to the network. Examples include servers, routers, containers, hubs, and bridges.
Edge vs. Cloud vs. Fog Computing
Edge computing is often discussed alongside cloud and fog computing, but while these concepts share similarities, they are distinct and should not be used interchangeably. To understand the differences between edge, cloud, and fog computing, it’s useful to recognize their commonality: all three pertain to distributed computing and involve the physical placement of computing and storage resources relative to where data is generated. The key distinction lies in the location of these resources.
Edge computing refers to deploying computing and storage resources directly at the point where data is produced. This approach places compute power and storage at the network’s edge, close to the data source. For instance, several servers and storage units could be installed atop a wind turbine to process sensor data generated by the turbine itself. Similarly, a railway station may deploy computing resources to handle sensor data from track and rail traffic locally. After processing, the results can be sent to a data center for further analysis, archiving, or integration with broader datasets.
Cloud computing involves the deployment of vast, scalable computing and storage resources in multiple globally distributed locations, known as regions. Cloud providers also offer a wide range of pre-configured services tailored for IoT operations, making the cloud an attractive centralized platform for IoT solutions. Despite the cloud’s ability to handle complex analytics with abundant resources, the nearest regional cloud facility can still be located hundreds of miles from the data collection point, relying on the same unreliable internet connections as traditional data centers. In essence, cloud computing can serve as an alternative or complement to traditional data centers, bringing centralized computing closer to the data source—but not directly to the network edge.
Fog computing provides a middle ground between the cloud and the edge. In some cases, a cloud data center might be too distant, while edge deployments might lack the necessary resources or be too distributed to be effective. Fog computing addresses this by placing computing and storage resources “within” the data ecosystem but not necessarily right at the data source.
Fog computing is particularly suited to environments generating large amounts of IoT or sensor data across wide geographic areas, where defining a clear network edge is impractical. Examples include smart buildings, smart cities, and smart utility grids. For instance, in a smart city, data is used to monitor and optimize public transit, municipal services, and utilities, guiding urban planning efforts. A single edge deployment may be insufficient to manage such complexity, so fog computing leverages multiple fog nodes within the environment to collect, process, and analyze data.
Note: While fog computing and edge computing share similar definitions and architectures, and the terms are often used interchangeably even by experts, they differ in where they position computing resources relative to the data.
Your Journey to Edge Computing: Things to Consider
Edge computing enables businesses to tap into the vast amounts of unused data generated by connected devices, unlocking new business opportunities, boosting operational efficiency, and delivering faster, more reliable, and consistent customer experiences. By processing data locally, advanced edge computing models can significantly enhance performance. A thoughtful approach to edge computing ensures workloads remain current based on predefined policies, helps maintain data privacy, and complies with data residency laws and regulations.
However, adopting edge computing comes with its own set of challenges. An effective model must address concerns around network security, management complexities, and the limitations posed by latency and bandwidth. A robust edge computing framework should be capable of:
- Managing workloads across multiple clouds and devices
- Seamlessly deploying applications to all edge locations
- Ensuring openness and flexibility to meet evolving requirements
- Operating securely and with confidence
When considering different types of edge computing—whether cloud edge, IoT edge, or mobile edge—it’s essential to choose a solution that supports these core objectives.
Manage software distribution at scale: Optimize software deployment by reducing the need for excessive administrators, cutting associated costs, and ensuring that software is deployed efficiently, wherever and whenever it’s required.
Utilize open-source technology: Adopt an edge computing solution that fosters innovation and accommodates the wide variety of equipment and devices available in today’s market.
Address security concerns: Ensure that workloads are assigned to the appropriate machines at the right time. Implement straightforward governance and policy enforcement mechanisms to align with your enterprise’s standards.
Partner with a trusted expert: Choose a vendor with a proven multi-cloud platform and an extensive service portfolio designed to enhance scalability, boost performance, and fortify security in your edge computing deployments. Inquire about additional services that optimize intelligence and efficiency at the edge.
Edge Computing, IoT and 5G possibilities
Edge computing is continuously advancing, incorporating new technologies and practices to improve its capabilities and performance. One of the most significant trends is the growing availability of edge services, which are projected to be accessible globally by 2028. Currently, edge computing is often tailored to specific situations, but it is expected to become more widespread, reshaping how the internet is used and creating new layers of abstraction and potential applications for edge technology.
This shift is evident in the increasing number of products focused on compute, storage, and network appliances specifically designed for edge computing. Multi-vendor partnerships are also expanding, enhancing product interoperability and flexibility at the edge. A notable example is the collaboration between AWS and Verizon to improve connectivity at the edge.
Emerging wireless communication technologies like 5G and Wi-Fi 6 will further influence edge deployments in the coming years, enabling new virtualization and automation possibilities, such as improved vehicle autonomy and the migration of workloads to the edge. These advancements will also make wireless networks more adaptable and cost-effective.
5G and Edge Computing
Edge computing came into the spotlight alongside the rise of IoT and the massive amounts of data generated by these devices. As IoT technologies are still relatively new, their ongoing evolution will also influence the future of edge computing. One promising development in this area is the emergence of micro modular data centers (MMDCs). Essentially, an MMDC is a compact data center contained within a portable system that can be deployed nearer to data sources—whether across a city or within a region—bringing computing capabilities closer to the data without positioning the edge directly at the source.
The Future of Edge Computing in Your Industry
Edge computing techniques are fundamentally employed to collect, filter, process, and analyze data “in-place” at or near the network edge. This approach effectively utilizes data that cannot be transferred to a centralized location—often due to the massive volume of data making such transfers costly, technologically unfeasible, or potentially in violation of compliance requirements like data sovereignty. This concept has led to numerous real-world applications and use cases:
Manufacturing: An industrial manufacturer implemented edge computing to monitor its manufacturing processes, facilitating real-time analytics and machine learning at the edge to identify production errors and enhance product quality. The deployment of environmental sensors throughout the manufacturing facility allowed for insights into the assembly and storage of each product component, as well as their stock duration. As a result, the manufacturer can now make quicker and more informed business decisions regarding factory operations and facilities management.
Farming: Imagine a business that cultivates crops indoors without the use of sunlight, soil, or pesticides. This innovative approach can reduce growth times by over 60%. By utilizing sensors, the business can monitor water usage, and nutrient levels, and determine the ideal time for harvesting. Data is continuously collected and analyzed to assess the impact of environmental factors, allowing for ongoing improvements to the crop-growing algorithms and ensuring that the crops are harvested at their peak quality.
Network Optimization: Edge computing can enhance network performance by monitoring user activity across the internet and using analytics to identify the most reliable, low-latency paths for each user’s traffic. Essentially, edge computing is employed to “direct” network traffic to optimize performance for time-sensitive applications.
Workplace Safety: Edge computing can integrate and analyze data from on-site cameras, employee safety devices, and various sensors to help organizations monitor workplace conditions and ensure compliance with safety protocols. This is particularly valuable in remote or hazardous environments like construction sites or oil rigs.
Improved Healthcare: The healthcare sector has significantly increased the volume of patient data collected from devices, sensors, and various medical equipment. This vast amount of data necessitates using edge computing to implement automation and machine learning techniques, allowing for quick access to data, filtering out “normal” information, and pinpointing problematic data. This enables clinicians to take prompt action to prevent health incidents in real-time.
Transportation: Autonomous vehicles generate and utilize between 5 TB and 20 TB of data daily, collecting information on location, speed, vehicle status, road conditions, traffic, and other vehicles. This data needs to be aggregated and analyzed in real-time while the vehicle is in motion, requiring substantial onboard computing—effectively turning each autonomous vehicle into an “edge” device. Additionally, this data can assist authorities and businesses in managing vehicle fleets based on real-time conditions.
Retail: Retail businesses generate vast amounts of data from surveillance, inventory management, sales figures, and other real-time operational details. This mixed data can be analyzed using edge computing, uncovering business opportunities such as forecasting sales, effective campaigns or promotions, and optimizing vendor orders. Given that retail environments can differ greatly from one location to another, edge computing serves as an effective solution for local data processing at each store.
Read more about cloud computing, AI, and data analytics on our blog page.
