Resource constraints undercut the return on investment (ROI) of IoT at the edge
While IoT at the edge of the network continues to make strides, resource constraints pose ample challenges to these devices.
Internet of Things (IoT) devices today benefit from lower network latency at the edge and greater data intelligence on device. This can enable a variety of tasks, from autonomous driving to real-time video streaming to preventative maintenance of equipment. Processing at the edge circumvents the time delays and data security challenges of centralized computing: Instead of sending data back and forth to a data center or a cloud, data is processed locally.
Companies are beginning to reap the benefits of edge processing in ways they barely imagined five years ago. Consider retailers, which now use edge processing for video surveillance at the register — not only to minimize product loss but also to target other customer services issues in checkout.
As a result, edge processing is coming into its own, complementing cloud architectures where tasks need real-time processing and lower latency. Analysts predict this will continue. While some 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud, by 2025, Gartner predicts this figure will reach 75%.
“Organizations that have embarked on a digital business journey have realized that a more decentralized approach is required to address digital business infrastructure requirements,” said Santhosh Rao, senior research director at Gartner.
“As the volume and velocity of data increases, so too does the inefficiency of streaming all this information to a cloud or data center for processing.”
Constrained Compute and Power Resources for IoT at the Edge
At the same time, edge-computing architecture suffers from various compute and power constraints. Edge devices often have smaller form factors than data center and cloud resources and may be located in hard-to-access locations. That results in power and compute constraints that limit their efficacy.
That’s problematic: Data-intensive processes at the edge, such as video streaming, data analytics and autonomous driving, have become increasingly prominent, but these tasks are also data hogs that require resources at the edge.
Experts discussed the newfound pros and cons of IoT devices at the edge in a session on the relationship between edge computing, connectivity and AI at Embedded IoT World in late April.
The compactness and lower latency of IoT devices at the edge can bring new challenges. Particularly, devices’ remoteness can pose challenges for resources.
“These sensors are at the very edge of the network,” said Colleen Josephson, Ph.D. candidate, electrical engineering, Stanford University and speaker at a session on IoT devices at the edge during Embedded IoT World. “Industrial moisture sensors … monitoring, pollution monitoring on streets, ecological and agricultural monitoring — all these places are at the outer reaches of the network, so they are going to be hard to contact.”
Constrained connectivity and remote access are paired with constrained power.
“If you have a sensor deep in the walls of a building and it’s designed to alert you if there is a leak, how do you power it? How do you bring connectivity to these far reaches of the network?” Josephson noted.
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