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URL:
https://www.kinetica.com/blog/iot-in-motion/
Submission: On March 23 via api from DE — Scanned from DE
Submission: On March 23 via api from DE — Scanned from DE
Form analysis 5 forms found in the DOM
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THE INTERNET OF THINGS IN MOTION (IOTIM)
The Convergence of IoT and Geospatial Analytics
Chad Meley | February 21, 2022
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Sensors have evolved from taking readings over time to taking readings over
space and time. Understanding this trend and the resulting impacts are essential
for innovators seeking to create value in the next wave of IoT products and
services.
FROM TRANSACTIONS TO INTERACTIONS TO OBSERVATIONS
The earliest form of data used for analytics described transactions. Examples of
transactions include when an order is placed, inventory is replenished, or
revenue is collected. The era of big data emerged when organizations began to
harness interactions data. Interactions make a record of decisions made by
humans. Examples of interactions include liking something on social media,
watching a show through a streaming service, browsing the web, or playing a
video game. What drives data volume growth today is proliferation of devices
that capture observations. Curt Monash defines it as data that is more about
observing humans and machines than recording the choices of humans. For example,
sensors are able to monitor our pulse, the flow of traffic at an intersection,
drone traffic in the air, the temperature of mechanical part, the location of
goods as they move through the supply chain, the amount of energy consumption at
a building, and so on. Sensors that observe humans and machines are synonymous
with the Internet of Things (IoT), as the readings are available online for
processing at the edge or cloud.
FROM READINGS OVER TIME TO READINGS OVER TIME & SPACE
The first generation of IoT data were readings over time. A sensor that is
capturing a reading like the temperature of a device would do so in meted time
intervals, allowing monitoring for anomalies. The next generation of IoT are
readings over time and space. Sensors are increasingly taking a reading with
stamps on the time and location of the item. Most items are in motion:
inventory, vehicles, planes, people, etc. Even things that are fixed in location
like crops where sensors take readings of soil moisture require a longitude and
latitude in order to send irrigation equipment to the right location when
needed.
Cost of sensors and devices that generate geospatial data is falling rapidly
with corresponding proliferation. The cost of location-enabled chips for
cellular connectivity is expected to decline by 70% from 2017 to 2023. Costs of
launching a satellite have fallen sharply over the past decade on a per-kilogram
basis, meaning more data-collecting satellite launches over the next few years.
The expansion of 5G networks is aiding in the collection of greater volumes of
geospatial data. Bluetooth tags with integrated power-harvesting are expected to
drop in price by two-thirds.
CRITICAL CAPABILITIES FOR ANALYZING THINGS ACROSS TIME & SPACE
With even greater data volumes and the growth of geospatial tags, new
capabilities are needed to extract the full value from IoT data. Prior
generation databases were never designed to handle non-explicit joins and
real-time advanced analytics. Trying to use prior gen database for modern IoT
analytics results in excessive costs and needless latency.
NON-EXPLICIT DATA FUSION
Traditional databases are optimized for joining primary and foreign keys, such
as customer_numbers, session_ids, and order_numbers. That breaks down when
trying to join time and space data with other data to provide context. Geo-joins
are used to bring the power of location to a query. For instance, how many
drones came within 500 meters of an airplane? Examples of geo-joins include
Intersect, Within a Distance, Completely Within, Closest, and many others.
Temporal joins answer questions like, “What was the last stock price at the time
of order?” Examples include Start, Finish, Meet, Right Overlap, As-of, Left
Overlap, Contain, Intersection, and many others.
DATA FRESHNESS
Traditional databases are optimized for transaction properties (ACID) at the
sake of data freshness. The majority of IoT use cases gladly trade off the
potential of a missing reading (among billions) for real-time insights.
Traditional databases lock down the database tables while being loaded. They
also require indexes and summary tables to be rebuilt to reflect the new data,
further adding to the latency. Next generation IoT databases enable simultaneous
query and ingest. They also take advantage of data level parallelism (aka
vectorization) to remove the need for time consuming hacks and tricks to
overcome performance limitations.
SPATIO-TEMPORAL ANALYTICS
Value comes from not just managing IoT data but analyzing it. Because modern IoT
data is both time series and geospatial, in-database analytics that are purpose
built for spatio-temporal insights are required. This requires functions such as
ST_Geometry, Entity Tracks, Heatmaps & Contours, Window Functions, As-Of Joins,
Shortest Path, Centrality, Map Matching, and more.
See how leading IoT companies like Verizon, The US Air Force, T-Mobile, Ford,
Kaiser Permanente, US Post Office, and more are moving into the next generation
of IoT analytics at www.kinetica.com.
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