Artificial Intelligence (AI) is transforming in the way we live, work and our ability to derive important conclusions from the nearly endless amount of information around us. The insights derived from AI enable businesses to make more informed decisions quickly. However, this overflow of data can lead to problems due to massive amounts of computational power required to collect and process data. A new approach addressing many of these issues is Edge Computing, which moves processing closer to the source of the data.
Edge Computing offloads AI computing operations from centralized systems to AI-enabled Edge devices, such as smart cameras, smart phones or regular cameras outfitted with smart AI boxes. An Edge device does not need to transmit large volumes of raw video streams to a centralized system. It processes the video stream locally and outputs simple, light metadata with the required business metrics and KPIs. Distributing Artificial Intelligence computing to the Edge greatly reduces latency and bandwidth requirements for the AI systems to function effectively.
Flexibility and Simplicity
AI-enabled Edge devices can be installed in even the most remote locations. Their low-power consumption and limited network and bandwidth availability requirements allow users to deploy AI computing in the most inhospitable, remote locations where internet connectivity is unreliable. Edge AI is perfectly suited for applications in:
Edge Computing allows AI applications to be scaled quickly and easily by adding more Edge devices as needed without significantly changing the infrastructure. Users can be flexible in designing local networks because the workload is greatly reduced.
Increased Security and Privacy
Edge Computing increases data privacy and security, enabling organizations to comply with regulatory requirements and protect sensitive information by transmitting data locally. Edge Computing avoids sharing sensitive information that can be intercepted over the network. Only processed and selected metadata is shared. An Edge system does not need to transmit private video streams with facial information over a network. It only needs to produce a reliable alarm when the targeted element is detected, with metadata describing where and when.
Processing video streams locally reduces the risk of system failures and mitigates their effect when they do happen. For instance, in a scenario where the network becomes unavailable, an Edge Computing system can continue to process video streams and store the resulting metadata to be sent later on when the network becomes available again. This is not possible on a Cloud-based system, because storing raw video streams takes up more storage resources than select metadata, like time and location and Cloud-based systems require constant and reliable network connection.
Edge Computing relies on low-power devices which greatly reduces carbon footprints when compared to data centers and centralized servers. The reduced amount of data that needs to be transmitted across systems also diminishes some environmental impact, due to the limited physical infrastructure these systems require. This reduction in infrastructure and required power can help your company achieve sustainability goals while lowering costs over time.
Edge Computing is changing the way industries build their information systems. Providing a more flexible, secure and sustainable approach, Edge Computing is enabling everyday AI use across more and more industries. We at Noema believe that edge-based AI applications can deliver specialized, real-world solutions to optimize, automate, and improve processes across many industries in a secure and non-invasive manner. Learn more about Edge Computing and AI applications at www.noema.tech.