The document discusses 5 trends in IoT and edge computing to track in 2019: 1) The growth of multi-locational hybrid data architectures to store data locally at the edge as well as in the cloud; 2) Increased convergence of facial analysis and machine learning, such as using cameras to detect driver fatigue; 3) The emergence of data marketplaces as enterprises purchase IoT data streams; 4) The convergence of IoT and blockchain to provide data provenance and immutability; 5) The rise of autonomous edge devices with local storage, ML models, and the ability to take actions without cloud latency.

1. FIVE TRENDS IN
IoT and Edge
Computing
TO TRACK IN 2019

2. Based on the industry use case, the storage
may be on-premises or on the cloud or even
have a hybrid model. However, in the last
couple of years, the edge is no longer just a
data-generation medium.
The challenges around network latency and
the need for immediate real-time
insights have pushed enterprises to evolve the
edge to be smarter. To enable this, in some
use cases in specific verticals, there is a new
need to store data locally at the edge as well.
This leads to the evolution of a new model of
multi-locational hybrid data architectures.
GROWTH OF
MULTI-LOCATIONAL
DATA STORAGE

3. Over the last couple of years, we have seen
limited success with video cameras capturing
and understanding human sentiment using
facial analysis for use cases across retail for
customer engagement.
For example: The camera inside a truck
watching the driver’s actions and facial
movements. It can quickly detect fatigue on
the driver’s face and alert them immediately.
Another requirement was to understand how
many times the driver is taking their eyes off
the road and is distracted with the radio.
The idea is to increase driver safety and
prevent loss of life or property in their fleet.
INCREASED
CONVERGENCE OF
FACIAL ANALYSIS
AND MACHINE
LEARNING

4. With the hyper-growth in data production
potentially reaching up to 163 zettabytes in
just a few years, and with IoT and streaming
data being a major contributor to that, every
enterprise is sitting on goldmines of data. As
vertical data ecosystems emerge as has been
evident in how insurance companies tap into
connected cars for driving history of its
subscribers.
The need for refining and building more
sophisticated and more reliable ML models
will warrant the need for more data. So,
naturally, enterprises will start purchasing
data – even subscribe to IoT data streams
directly.
EMERGENCE
OF DATA
MARKETPLACES

5. One of the key elements of streaming data
that we tend to overlook is the importance of
data provenance and lineage tracking. We
build our businesses amidst so many
compliance laws and regulations and it is only
fair that we keep track of the data – its origin,
the personas that handle it, the varying values
through the data chain etc.
With trust and data immutability being critical
requirements in this model, blockchain will be
an inherent part of this architecture.
CONVERGENCE OF
IOT AND
BLOCKCHAIN IS
INEVITABLE

6. As the realization dawns on us that device
autonomy is already a reality in a few
verticals today, we should start preparing for
accepting the autonomous edge for many
more use cases across automotive,
healthcare, retail, manufacturing etc.
When the use case cannot afford the cost of
latency for the sake of detailed analysis, the
edge has to become smarter, self-reliant, has
access to local storage, has a set of rules
(and a light-weight rules engine too maybe),
has ML models to score the data and even has
the capability to fire off a set of actions –
potentially involving other robotic devices.
RISE OF THE
AUTONOMOUS EDGE

7. T H A N K Y O U !
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