The SNIA Cloud Storage Technologies Initiative (CSTI) webcast on IoT explored how the explosion of data generated from IoT devices creates unique challenges in the way we store, transmit and curate data. If you missed the webcast, you can watch it on-demand. This topic generated several interesting questions.  As promised during the live event, here are answers to them all:

Q. Do IoT devices consume as much data as they produce?

A. It really depends on the device. There are some like sensors that will only produce data and transmit it on, on the other hand the more intelligence built into these devices the more need there might be to consume data to drive that intelligence. In the future, it’s possible there will be much more device to device (or peer to peer) traffic between IoT devices, cutting out the leg back to the data center altogether for data that doesn’t need to be there.

Q. How can we educate the Manufacturers to start adding
security features needed to the IoT devices?

A. This is being managed through legislation in places such
as Europe. But it probably isn’t the manufacturers that need educating. They
already know the need for security and the risk of having poor practices. The
people that need to be educated are the users and consumers of the technology.
This will mean the market will move to reward those that care about security
and punish those that ignore it. Mostly, the lapses in security have been more
about mistakes and unintended consequences of pushing a certain feature,
however an educated market place can make those judgements much better. From a
manufacturing perspective, there needs to be a better, standardized way of
reporting incidents and security flaws in such a way that organizations have time
to respond with patches before the information can be exploited. We already
have a pretty good model for this in software engineering with principles such
as lead time from discovery to public disclosure to enable time for fix. This
leads to bug bounties and other measures that encourage secure design. These
same principles could map easily into the IoT world as well and with that
educated marketplace, there will be many more guardians.

Q. To have efficient IoT devices, WiFi plays a critical
role to have proper synchronization with uninterrupted information. So, is WiFi
5 efficient enough to get it connected with many devices or would WiFi 6 be
required to have many connected IoT devices?

A. WiFi 6 (812.11ax standard) provides faster speeds and
better performance in congested areas. So yes, this can potentially bring
benefits to an IoT implementation. Of course, there is a dependency on device
availability and not all vendors have adopted the standard fully yet. This is
largely due to the certificate only being issued in September 2019. We also
have the emergence of 5G radio technology that will one day be the standard for
wireless networks servicing mobile phones. This also provides higher speeds and
better congestion management as well as power efficiency required when
deploying many devices. In summary, the WiFi standards continually advance and
IoT traffic will absolutely be able to take advantage of that. We must also
ensure that our data acquisition, persistence and management keep pace and that
if we are plugging this into real-time networks, are inference engines are
deployed to cope with both the scale and volume of data the new technologies
can deliver.

Q. In your camera example, is there an opportunity to do
that inference at the edge directly on the camera or in close proximity to the
camera?  Network connectivity then
becomes less of a latency concern.

A. Absolutely. Cameras are becoming intelligent in that
inference engines can form part of the IP camera. This removes the latency
issue for immediate inference, but there is a limited capacity meaning that
models will need to be pruned and optimized potentially sacrificing accuracy.
If the inference is happening near the camera, which is very often the case
even if it’s not on camera, then latency from the camera and video management
system can impact the solution. However, the ability to improve accuracy and
model complexity as well as the ability to aggregate multiple data sources
together, might mean this is a requirement. An example might be leveraging
video analytics for social distancing. In order to resolve an object’s position
in 3-dimensional space with reasonable accuracy it becomes necessary to track
from at least two cameras so that we can apply trigonometry to calculate angles
and therefor position relative to known markers. An on-camera solution won’t
help here, but a near camera, edge-based solution, would.

Q. Is there any collaboration between the IoT efforts at
SNIA and another SNIA initiative around Computational Storage?  Many IoT devices include some form of storage
already and the idea of localized processing where the data is created and
stored, may help solve some of the latency and security challenges mentioned.

A. Yes, there is synergistic work taking place, and the SNIA Computational Storage Special Interest Group is developing an extensive set of use cases for a variety of on-drive computational services that can help with the latency challenges. There is also work underway to define a set of security models based on threat challenges that CS shares with other systems and devices, and some that apply uniquely to them. There will be a number of overview and technical documents this year that address these issues, and as is usual for SNIA, they will be publicly available on the SNIA website.