The advent of the Internet of Things is already changing people’s daily lives, and its integration with artificial intelligence gives rise to a completely new technology landscape, Artificial Intelligence of Things (AIoT). The convergence of both technologies is set to redefine the future of industrial automation and lead the Industry 4.0 revolution. Conversely, the increasing use of connected devices generates a tremendous amount of data that requires processing to extract meaningful insights. Thus, the new level of processing power at the Edge offers near real-time insight from data collected, assisting in making data-driven decisions by sending that data back and forth to the servers and other smart computing processes.
The latest evolution of intelligent edge applications promises real-time insights by assessing data at the edge itself. Edge Intelligence allows data processing for decisions to be made locally, without sending back to the cloud.
AI on the network edge identifies and reports on occurrences detected by sensors and results in real-time analytics, fully deployed intelligence, integration of AI technologies, and completion of the lifecycle. Since AIoT devices are constantly producing data, analytics should be implemented in distinct ways to get the best possible outcomes.
The advancements in AI also provides a great improvement in handling decision making at the edge. With intelligence at the edge, decentralized nodes of a system are empowered to perform different kinds of data handling that may have traditionally been handled at a central point in a system. At the edge, the edge network components or nodes can process the data intelligently, possibly bundling, refining or encrypting it for sending into the data warehouse, which will enhance the agility of data-handling systems, as well as enable their safety.
Enabling Intelligence at the Edge
In some contexts, IoT requires a combination of edge computing and the power of the cloud. As the massive amount of data produced by IoT devices is increasing and that will continue to grow, businesses now realize the ever-increasing flow of data must be managed more efficiently that can derive actionable information, reduce costs, and boost business performance.
So for that, intelligent data management systems at the edge of the network are critical. By leveraging these systems, enterprises can eliminate the time consumption for data back-hauls and the parsing required to prepare data for business decisions.
Moreover, while enabling intelligence at the edge businesses need to consider challenges, including latency, cost of computing and data transfer, security, and design. The right fusion of computing, AI accelerators, storage, and networking can enable intelligence at the edge to thrive and even evolve. Also, bringing in the capacity of a core data center, to offload any passive edge applications, can assist in streamlining operations even further.