Edge computing and its criticality in IoT - Mobiloitte

Edge Computing and its Criticality in IoT

Edge computing is basically to compute and develop analytics on the device itself. While many of today’s always-connected tech devices take advantage of cloud computing, Internet of Things (IoT) manufacturers and application developers are starting to discover the benefits of edge computing.

Why edge computing?

This on-device approach helps

  • Reduce latency for critical applications
  • Lower dependence on the cloud
  • Better manage the massive deluge of data being generated by the IoT.
  • It can help reduce connectivity costs by sending only the information that matters instead of raw streams of sensor data. Very useful for devices that connect via LTE/cellular such as smart meters or asset trackers.

Edge computing provides new possibilities in IoT applications, particularly for those relying on machine learning for tasks such as object detection, face recognition, language processing, and obstacle avoidance.

Following this trend some OEMs are designing IP cameras which uses on-device vision processing to watch for motion, distinguish family members, and send alerts only if someone is not recognized or doesn’t fit pre-defined parameters. By performing computer vision tasks within the camera, the company reduces the amount of bandwidth, cloud processing, and cloud storage used versus the alternative of sending raw streams of video over the network. In addition, on-device processing improves the speed of alerts.

Security and privacy can also be improved with edge computing by keeping sensitive data within the device. For example, new retail advertising systems and digital signage are designed to deliver targeted ads and information based on key parameters set on field devices, such as demographic information.

Processing at the edge also reduces latency and makes connected applications more responsive and robust. Avoiding device-to-cloud data round trips is critical for applications using computer vision or machine learning — for instance, an enterprise identity verification system.

Some other use cases could be: Drone, autonomous vehicle, healthcare, remote monitoring oil & gas, AR.

Edge devices are being created with increasingly sophisticated compute capabilities. Couple that with not-so-far-off advanced connectivity technologies such as 5G, which will deliver faster, more robust, and massive connectivity, and it becomes obvious that we are about to witness the emergence of a new breed of smart devices and applications.

Reference: Networkworld