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Edge AI in The 5G Era

With the development of 5G technology, Edge AI stands to face even greater speed and reduced latency for mobile data transmission.
  • Innotech
  • |
  • September 27, 2021

Edge AI is the integration of Edge Computing and Artificial Intelligence. Under this process, AI algorithms are applied either on the device itself or on a server in close proximity to the device. In other words, Edge AI takes the data and its processing to the closest point of interaction with the user, whether it be a computer, an IoT device or an Edge server. 

The device does not have to send the data to a cloud server for processing. Instead, it can process the data locally and make decisions in real time in milliseconds, which saves a considerable amount of communications cost and reduces latency. This also means that we can increase the level of security and data privacy by locally processing data without having to share it in a centralized cloud. Edge technology devices are also autonomous in nature; the devices do not require specialized maintenance by data scientists or AI developers and the graphic data flows are automatically delivered for monitoring.

Applications

Industrial IoTReduction of costs and improvement of safety through the monitoring of machinery for possible defects or errors in the production chain.
Autonomous vehiclesProcessing data and images in real time for the detection of traffic signs, pedestrians, other vehicles and roads, improving security in transportation.
HealthcareAnalysis of medical images in emergency care.
Security CamerasProviding intelligence to the detection process; the algorithmic processes will be carried out in real time in the system of surveillance itself.
Image and video analysisReal time recognition of scenes and spaces in smartphones.

With the development of 5G technology, Edge AI stands to face even greater speed and reduced latency for mobile data transmission, making it more potent. The synergizing of these two technologies will facilitate 5G based edge solutions that can enable companies to manage work loads across a massive volume of devices from different vendors with ease, providing the telecommunications industry with the agility required to deliver services with speed to its customers.

Challenges

  • Data scarcity at edge
  • Data consistency on edge devices
  • Poor adaptability of statically trained models

English (UK)