Written by Rodney Guzman, CDO/Co-Founder/Owner
The Enterprise Guide to Edge Computing in IoT
The internet of things (IoT) is one of the most prominent tech trends of the last decade. The explosive growth of IoT device computing power has resulted in the collection and storage of huge amounts of data. As enterprises build or expand their IoT footprint and deploy new devices, data volumes will continue to skyrocket. This will make it increasingly difficult to extract and report on actionable data that will help drive business forward.
The IoT market is expected to grow to 41.6 billion connected devices or “things” generating 79.4 zettabytes of data in 2025. Many industries are showing more interest in IoT technology as innovation advances and new capabilities offer deeper intelligence. Traditionally, IoT devices or sensors send data collected directly to the cloud which presents many limitations including data latency and heavy reliance on cloud services.
Unlocking Value With Edge Computing in IoT
With edge computing, the data processing work is done right at the edge of the network where IoT devices connect the cloud to the physical world. This advancing technology helps solve key challenges as it transforms the way organizations are collecting, processing, and transmitting data from millions of devices globally. With IoT edge computing, companies can collect real-time data, analyze trends, generate insights, track resources, and respond quickly and strategically to business problems.
AI and machine learning algorithms on the edge can help IoT devices identify, generate, and send only relevant data from devices to the cloud for faster analysis and better reporting and management. This technology enables devices and sensors to detect specific patterns or instances to then gather and process before sending it to the cloud. For example, manufacturers can automate the monitoring of systems across a factory floor such as machinery showing signs of deterioration or stress. With quick response times from edge computing, plant operators can be alerted about critical problems so they can take action immediately.
However, edge computing is more than data processing on IoT devices themselves. Part of what makes the technology so powerful is the fact that it leverages cloud services to run analytics on the data to gain better insight on what’s happening in the physical world.
Reduced cost of ownership: Network costs can increase significantly as the number of IoT devices an organization deploys rises. Enterprises across industries can expect a 10 to 30% reduction in costs from using edge computing and an average operational cost savings of 10 to 20%, according to Analysys Mason. As edge computing on IoT devices helps send only pertinent information to the cloud, the need for data storage on the network is significantly minimized.
Enterprises across industries can expect a 10 to 30% reduction in costs from using edge computing and an average operational cost savings of 10 to 20%
The ability to scale: Without edge computing, managing hundreds and thousands of IoT devices is a huge undertaking and can often present challenges when it comes to changing settings on specific devices, deploying software, and so on.
Intelligent edge computing can help enterprises more easily test and make updates to modules — or units of execution — such as changing the logic to gather different data points. The ability to drill down and update groups or individual devices from a central location can help organizations capture smarter data insights more easily.
Reduced latency: Technology is advancing at an incredible pace as consumers and enterprises now rely on real-time data processing. However, sending all IoT device-generated data to centralized cloud storage can cause substantial latency issues. Edge computing processes and analyzes data closer to the point of collection which means latency is significantly reduced, unlike with data that would be analyzed after it is sent to the cloud.
Minimal network reliance: For many IoT deployments, network connection can constrain devices operability. Edge computing allows for autonomous operations as the devices themselves include storage to host data if a network connection issue occurs.
Scenarios for Edge and IoT Modernization
Organizations looking to modernize with IoT edge computing can leverage a modular approach in which it’s easier to develop, deploy, monitor, and scale applications. Independent modular services can be changed or scaled without affecting overall performance. As well, enterprises can isolate potential problem areas, test, and redeploy individual modules to quickly troubleshoot issues that may occur, rather than restructuring the entire IoT architecture.
Here are two scenarios in which enterprises can implement microservices whether they are scaling their existing IoT ecosystem or building one for the first time.
Modernizing Existing IoT Legacy Systems
Collecting and storing data generated by older edge computing devices can be expensive and inefficient to maintain. Legacy architectures often rely on response setups in which a remote server requests a value from the “dumb” or older edge computing resource at particular times or intervals. This can lead to transmitting outdated or irrelevant information to the cloud. Data that is slow to process and aggregate can hold organizations back as they’re unable to quickly report and execute on it. Modern edge computing capabilities can pre-process data at the edge of the network and send key data points to the cloud for greater accuracy and easier manageability.
Modernizing older IoT infrastructures with modular edge computing will help improve data intelligence and minimize latency to keep up with the quickly advancing world. Technology leaders will save time on addressing issues related to outdated computing so they can build logic to gain better business insight.
Building a New IoT Edge Architecture
Organizations that are looking to deploy IoT devices from scratch will need to consider scalability for years to come. As IoT infrastructures change or expand over time, companies will need to anticipate how they will support this type of growth.
Building out a scalable IoT architecture will help companies make extensive changes to accommodate new systems down the road without disrupting or slowing down performance or output speed. Modular edge computing also allows for easier testing of logic and redeployment of a particular component that has been changed. For example, updating the logic on a group of IoT devices instead of deploying it across the board.
Choosing IoT Microservices That Align With Your Business Needs
Enterprises should be thinking ahead to how devices will be used and the types of technology that will be needed to support demand in the future. Leaders in this space partner with providers that offer services that integrate well with others so you can develop highly customizable and flexible IoT applications to meet organizational goals today and in the years to come. Microsoft’s Azure IoT Edge, for example, is a service built on Azure IoT Hub that allows you to deploy AI, Azure and third-party services, or your own business logic to IoT edge devices.
The Path Forward
The amount of data generated by IoT devices is higher than ever before and shows no signs of slowing. Gartner estimates that by 2025, 75% of data will be processed outside the traditional data center or cloud. If your enterprise is either modernizing legacy systems or building up from scratch, an IoT architecture that is able to rapidly innovate and adapt is a strategic necessity today. Laying the groundwork for rapid innovation and growth will help your enterprise thrive in a quickly changing world, whether that’s reducing inefficiencies, monitoring operations, identifying new cost savings opportunities, or delivering elevated customer experiences.