The explosive growth of IoT isn’t hypothetical; it’s happening today in every sector. Digital transformation is here, and while it is changing industries in new ways, that change doesn’t always come easy. Challenges abound, and when it comes to edge computing, companies are finding some common concerns, including:
· Latency: Cloud computing is becoming more powerful by the day, but some use cases require data to be processed within milliseconds. Sending data to a backend system, whether it’s located in a public cloud, private cloud or data center, introduces too much latency for these use cases.
· Perishable data: Not all data is useful in the data center. Sometimes data needs to be acted upon right away with low latency. Perishable data, like the data generated by autonomous vehicles, can’t wait until tomorrow. It must be analyzed at the edge for immediate action.
· Limited bandwidth: Bandwidth at the edge is often limited, so deciding what data to send can be difficult. Sending everything back to the data center is expensive, time consuming and inefficient. To make the best use of resources and to save time and money, it’s essential to prioritize data and send only what you need.
· Uninteresting data: Assets and sensors generate huge volumes of data, but not all of it matters to you. You might want to know when an asset is operating outside of normal parameters, but perhaps you don’t need to capture, store, transmit, and analyze the data that’s generated when it’s operating normally. You might also want to reduce the amount of data collected to relieve the burden and overhead of data management.
· Security: Bringing assets onto the network puts them at risk of being infected with malware. With the growth in smart assets and the fact that cyber attacks are escalating in number and sophistication, securing assets and protecting the integrity of the data and network is vital.
What are some of the other IoT Edge challenges you see in this space?