IoT and Edge Computing: Extracting Insights in Real-Time

Data is the Future

People around the world continue to generate data in massive amounts.

In 2010, people around the world generated the same amount of data - 5 exabytes - in two days as they generated throughout all human history up to the year 2003

Data has become all the more critical for fast-growing economies such as India. Healthcare, education, transportation and mobility, cleantech and renewable energy have all become prospective sectors in the 21st century. Although diverse in their activities, these sectors are massively engaged in exploring the immense possibilities of data to drive both efficiency and productivity in their business.

IoT is Driving Real-Time Analysis at the Network’s Edge

For many IoT applications, it has become critical for data to be screened and analyzed where it is generated - from sensors in a car, surveillance cameras, drones, personal devices, robots, gateways, etc. – and even transformed there. The ability to deliver real-time analytics at the network’s edge can improve operational efficiencies, provide safer driving, create more secure environments, foresee upcoming maintenance, identify customer buying behaviors, and enable a world of opportunities.

Network latency is a challenge. It takes too long to store and forward data, whose value exists now. Edge storage manages data captures and provides the compute capabilities that aggregate and analyze that data in real time, to deliver immediate and actionable insights at the device level.

Edge Analysis Delivers Real-Time Value

Artificial intelligence (AI), machine learning (ML), image, voice and gesture recognition, and other technologies deployed onto edge devices interact with captured data in real time to deliver valuable insights. The full digestion of analytics can be transferred to the cloud where it can be used to further train AI models for machine learning or archived for future use. The ability to access this information in real time, ultimately, creates a more efficient and effective business, operation, or environment, enabling greater opportunities for monetization of the IoT application.

What makes real-time analytics at the edge become possible or even preferred, not just now but in years to come, is the possibility of conducting artificial intelligence and machine learning (ML). AI and ML are becoming increasingly more complex, versatile and sophisticated to enable real-time analysis at the edge, on the hub and, ultimately, in the cloud to get more value from all of the IoT data collected. Data can be sent to the cloud, where machine learning and AI can be trained to watch for patterns and gain insights from large data sets over time.

IOT EXPERTS AGREE

In ABI Research’s view, one of the most significant trends in the Internet of Things (or the connected world—the Internet of Everything—as a whole) is the shifting balance between edge computing and cloud computing. The early days of the IoT and its conceptual precursor, M2M, have been characterized by the critical role of cloud platforms as application enablers. Intelligent systems have largely relied on the cloud level for their intelligence, and the actual devices of which they consist have been relatively unsophisticated. This old premise is currently being shaken up, as the computing capabilities on the edge level advance faster than those of the cloud level.