Jacob Harel of Zeidman Technologies recently noted that IoT developers are currently spending the majority of their resources finding ways to collect and analyze data.
“The twist, and pitfall, is that the amount of data moving through the system can swamp the system’s servers and hubs and overwhelm the databases,” he wrote in an Embedded Computing Design article.
“With the large number of new and sophisticated sensors available today that can easily connect to controllers and communicate through the network, eager engineers and product managers are happy to embrace the technology and take all the sensors they can get. The same goes for data—the bigger the better.”
As Harel notes, the attitude of “any [IoT] data is good data” may very well be one of the biggest issues product managers need to control.
“Finding ways to do more processing on the IoT edge device can reduce the amount of data sent,” he explained. “This can enable the use of smaller bandwidth, lower power communication, and overall lower power consumption for the system.”
It should be noted that McObject CEO Steve Graves recently expressed similar sentiments in a separate Embedded Computing Design article.
“A vast number of edge devices will need to store, retrieve and analyze some data right where they sit, before shipping anything ‘upstream’ to gateway or server-based data aggregation points that we usually think of in connection with Big Data,” Graves opined. “These data-crunching edge devices are more interesting ‘things’ that, in and of themselves, provide value to their users. These things ingest and produce a respectable amount of data that must be managed on the device, before it ever becomes part of Big Data (Cloud-based or otherwise).”
Meanwhile, Tom Kevan of Desktop Engineering says the IoT is already pushing measurement analytics to the edge of the network by essentially redefining the sensor’s place in the electromechanical ecosystem.
“No longer a discrete component working in isolation, the sensor interacts with computing and communications components to provide intelligence via two-way communications,” he explained. “[Companies] must adopt a systems engineering perspective, looking beyond individual components and viewing the sensor as part of a larger whole. Using this perspective, engineers must determine how the sensor fits and interacts with the other components in the node.”
In addition, emphasizes Kevan, design engineers must fit more and more functionality and components into smaller spaces. Perhaps not surprisingly, these design demands place a significant premium on miniaturization and packaging technologies.
“The power consumption design criterion for IoT sensor nodes has one rule: do more with less,” he continued. “This means that the node must be able to sense a physical property, perform analytics and transmit data to the Internet on a significantly reduced power budget, regardless of the power source technology.”
As we’ve previously discussed on Rambus Press, simple-function, sensor-laden endpoints can be expected to become ubiquitous as new layers of the IoT infrastructure go online. These environmentally aware, optimized ‘lite’ endpoints will capture, analyze and transfer data to various devices and the Cloud, but must do so with the right balance of power, performance, price and size.
One possible way of achieving this balance is to replace traditional lensed cameras with diffractive optics-based sensing, which is precisely why Rambus scientists pioneered Lensless Smart Sensor (LSS) technology for a new age of ubiquitous connectivity. To be sure, LSS is capable of reducing the cost and size of image-based sensing as compared to traditional cameras and other commonly-used sensing technologies.
More specifically, LSS technology offers an attractive mix of size, field of view, price and privacy – whether deployed as an occupant detection and counting sensor for smart home and commercial applications, or as an eye tracker in smart glass/augmented reality (AR)/virtual reality (VR) systems.