Episode Transcript
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Speaker 1 (00:00):
Imagine stepping into a world where well almost everything around
you is talking, not just your phone, but like the
smart meter outside, maybe a sensor on a bridge miles away.
They're not just sitting there, they're active, sharing data, making
things smarter, our cities, our homes. There's this whole Internet
of Things or IoT explosion, and the growth is just
an incredible billions of devices already more coming online constantly.
(00:24):
Today we're doing a deep dive into a technology that's,
you know, quietly making a lot of this happen. NBIOT.
That's narrowband Internet of Things. We've pulled together insights mainly
from a great source LTE cellular narrow band Internet of
Things and BIOT practical projects for the cloud and data
visualization by doctor Hassum Fata. So our mission today it's
(00:45):
really to unpack what nbiot actually is and maybe more importantly,
why it's such a big deal. We'll look at where
it came from, the actual hardware involved, how these devices
talk to the cloud, where the data lands, and crucially,
how we make sense of it all visually. The goal
is for you to get a really clear picture, no
confusing jargon, just why this tech is so vital for well,
our increasingly connected world. Okay, let's get into it. When
(01:05):
we think cellular, yeah, we usually jump to smartphones, but
the tech behind it all has come such a long way, right, Oh, absolutely,
it's night and day from those early like two G,
three G systems GSM gprs, which were mostly just for
calls maybe basic data, to today's four GLTE and now
five G, which are these data powerhouses. They handle streaming, video, AI,
(01:26):
cloud stuff. It's a huge shift, it really is.
Speaker 2 (01:30):
And what's fascinating I think is that NBIOT isn't just
another step in that phone focused evolution. It's a specific
branch purpose built, you know, for the IoT. It wasn't
an afterthought. Its roots are in three GPP release thirteen
that was sort of pre five G tech, and then
it got extended and released fifteen, which is more aligned
with five glt E specs.
Speaker 1 (01:48):
Ye.
Speaker 2 (01:48):
Now, release fifteen did bring in slightly higher speeds cat
NB two they call it, but the core idea of
the fundamental operation stayed the same. It's really designed for
what we call machine type communication MTC and these low
power wide area LPWA scenarios. The whole point is connecting
tons and tons of devices very efficiently, often need them
to last years on a single battery charge.
Speaker 1 (02:08):
And when you say tons of devices, the scales almost
hard to grasp, isn't it. It is the source mentioned
projections of over five billion devices connected via nbiot by
twenty twenty five. That's yea yeah. They call it massive IoT,
massive IoT exactly, So what does this actually mean for
you listening? Why should you care? Will? Nbiot isn't just
about faster downloads on your phone. It's enabling completely new things.
(02:32):
Think smart homes, maybe controlling lights or heating super efficiently.
Speaker 2 (02:36):
Or security systems that don't need constant battery swaps.
Speaker 1 (02:38):
Right, and smart cities managing traffic better, monitoring air quality
with sensor scattered everywhere.
Speaker 2 (02:44):
At grids, smart water meters, utilities.
Speaker 1 (02:46):
Yeah, wearables too, for health tracking, remote sensors out in fields,
for farming.
Speaker 2 (02:52):
Object tracking, for logistics, even critical control systems.
Speaker 1 (02:56):
The key insight really is that nbiot makes it cheaper
and more power to connect these devices, so we can
put them in places and use them in ways we
just couldn't before. It expands where we get data from Exactly.
Speaker 2 (03:07):
It's getting into those challenging environments, places where power or
cost was a barrier. That's the game changer. We're getting
granular data we never had before.
Speaker 1 (03:15):
Okay, so we see the potential, huge potential, But how
do we actually build this? It starts with the hardware, right,
the actual boards it does.
Speaker 2 (03:23):
The practical starting point is often an NBIOT hardware board.
Speaker 1 (03:27):
And what's cool the Source highlights is that these boards
are often compatible with things like our adrenal software and tools.
That makes it way more.
Speaker 2 (03:33):
Accessible, yeah, hugely accessible. It brings it within reach for hobbyists, students,
not just you know, big engineering teams. It lowers that
barrier to entry.
Speaker 1 (03:42):
So what's actually on these boards? What makes them tick?
Speaker 2 (03:45):
Okay, diving in, You've got a couple of key players.
First is the micro controller unit, the MCU. The source
mentions the microchip sam D twenty one G eighteen chipset.
And the key thing here isn't just any chip. It's
a low power but still high performance ARM Cortex M
zero plus based microcontroller. It's got decent specs forty eight
(04:05):
never herds, two hundred fifty six KB flash, thirty two KBSRAM.
But the real emphasis is low power, engineered for efficiency
for longevity.
Speaker 1 (04:13):
Right, Because these devices might sit out in a field
for what ten years exactly.
Speaker 2 (04:17):
Battery changes are often just not feasible at that scale.
Then you have the cellular modem itself. The Quicktail BG
ninety six is a really common one. Mentioned its strengths well.
It supports global frequency bands, which is crucial if you
want to deploy worldwide ultralow power consumption. Again, vital data
rates are up to three hundred and seventy five kilobits
per second, both down and up. Now, it does operate
(04:40):
in half duplex on LTE networks.
Speaker 1 (04:42):
Half duplex like a walkie talkie you talk where you listen,
but not both at once.
Speaker 2 (04:47):
Precisely, it sounds like a limitation, but it's actually a
deliberate choice to save even more power. Perfect for devices
that mostly just send small bits of data occasionally, and
a really important feature for many uses. Built in GNSS
Global Navigation Satellite System, so you high precision location data
right from the device.
Speaker 1 (05:03):
Turns a sensor into a tracker basically exactly. Okay, so
besides the brain and the communicator, what other physical bits
are on the board, the nuts and bolts.
Speaker 2 (05:11):
You'll typically find a nano USIM card slot, just like
your phone You need a SIM card, usually two USB ports,
one for programming the MCU, one for interacting directly with
the modem, which is handy for testing and configuration. And importantly,
separate antenna connectors, one for the LTE cellular signal and
one for the GNSS signal to make sure you get
(05:32):
the best possible reception for both.
Speaker 1 (05:33):
Got it. So you've got this specialized efficient hardware. How
do you actually command it? How do you tell the
modem connect to this network or send this data.
Speaker 2 (05:41):
That's where AT commands come in. They're essentially the language
you use to talk to the modem. Think of them
like text based instructions. You send a command, the modem
responds or performs an action. For instance, you might use
at plus q ECFG and news can't say ten three
zero two zero one to tell it hey prioritize searching
for the LTE cast NB one network first. It gives
you fine grain control. You can do security things like
(06:04):
at plus c K to manage locks on the simcard
or query information. At plus GSN gets you the device's
unique IMEI number, at plus QCCID gets the simcards ID,
and a really critical one is activating the data connection
the PDP context like AT plus C G D C
E C O, N T one I, P M two,
M N B sixteen, dot Com, dot T That tells
(06:26):
it how to connect to the Internet via in this
case AT and T's network. It shows you the level
of control you have to optimize things, which is super
important for large deployments.
Speaker 1 (06:33):
So it's not just theoretical. Then you actually need a
physical simcard and a plan from a mobile operator.
Speaker 2 (06:38):
Absolutely just like your phone operators like AT and T,
T Mobile, Verizon, and even specialized IoT virtual operators like Hologram.
They provide the actual nbiot network coverage Without that network,
the hardware, however, smart is just well disconnected.
Speaker 1 (06:55):
Right, Okay, this is where it gets really interesting for me.
The devices collect the data, then what where does it
all go?
Speaker 2 (07:02):
Straight to the cloud that's the central hub.
Speaker 1 (07:04):
The digital brain.
Speaker 2 (07:05):
Yeah.
Speaker 1 (07:05):
The source material leans heavily on Amazon Web Services IoT
aws IoT right.
Speaker 2 (07:11):
And setting that up involves a few key steps. Make
sure everything talks securely and efficiently. First, you have device management.
You actually register your device in AWSIOT as a thing.
You give it a unique name, maybe like nbiot temp
Sensor one twenty three, at attributes like its location. Then
security certificates. This is non negotiable. You generate a unique
(07:31):
certificate for the device or private key keep secret, and
you need a root certificate to verify aws's identity.
Speaker 1 (07:37):
That's the digital handshake, basically, to make sure everyone is
who they say they are exactly.
Speaker 2 (07:41):
It prevents eavedropping, ensures data integrity. Without it, it's not secure.
Speaker 1 (07:45):
And you need rules. Right, you can't just let any
device do anything it wants connected precisely.
Speaker 2 (07:50):
That's where policies come in. You define exactly what that
specific device is allowed to do. Can it publish data
to which specific topics? Can it subscribe to receive data about?
Granular control for security. And finally you set up rules
for data processing. These are like automated actions. An incoming
message comes in, say matching a pattern like select from
(08:11):
sensors building a floor three, and the rule triggers in action.
That action could be storing the data, sending an alert,
maybe triggering another cloud function. It's how raw data starts
becoming useful.
Speaker 1 (08:22):
Okay, storing the data where does it typically land? In
this AWS setup?
Speaker 2 (08:26):
The source points to Dynamo dB. It's a no SQL
key value.
Speaker 1 (08:29):
Database, no SQL, so not like traditional databases with rigid tables.
Speaker 2 (08:33):
Exactly. It's big advantage heirs being schemeless. You don't have
to define the exact structure of your data upfront. If
one sensor sends temperature and humidity and another sends GPS
and battery level, Dynamo dB handles it.
Speaker 1 (08:45):
Easily ah flexible. That makes sense for IoT with all
its different device types, huge advantage.
Speaker 2 (08:51):
Typically you'd use the device's IMEI as the main identifier,
the partition key, then maybe a timestamp is the sort keys.
You can easily query data by time time all the
actual sensor readings get bundled into a payload attribute.
Speaker 1 (09:04):
Gotcha. Okay, so we have the path device like secure
connection IRAQ flateral's database. What are the actual languages the
protocols making that connection happen efficiently?
Speaker 2 (09:15):
The big one the workhorse for the application layer in
nbiots MQTT message q telemetry transport. Its superpower is being
incredibly lightweight, perfect for these constrained devices, small memory, low
processing power and crucially saving battery.
Speaker 1 (09:30):
Lightweight is key.
Speaker 2 (09:31):
Absolutely. It uses that published subscribe model we touched.
Speaker 1 (09:34):
On the Digital Noticeboard idea.
Speaker 2 (09:35):
Exactly, devices published messages to a topic could be city
traffic sensor, main street and anything interested maybe a dashboard
or another system subscribes to that topic to get the
messages super efficient. That also has useful features like retained messages.
The last message on a topic can be saved for
new subscribers, and something called a WILL message.
Speaker 1 (09:55):
A will message right, a last will and testament kind of.
Speaker 2 (09:58):
If a device disconnects on and expectedly maybe it loses
power or crashes, the MQTT broker can automatically send out
a pre defined WILL message on its behalf maybe sensor
one twenty three offline. Really useful for alerts clever.
Speaker 1 (10:12):
Okay, so m QTT handles the messaging. What about keeping
it secure?
Speaker 2 (10:15):
That's where SSLTLS comes in secure socket layer or transport
layer security. It encrypts the communication between the modem and
the cloud. Absolutely essential. You can figure this using those
AT commands. Again, things like at plus qstl CFG and
at plus qs will open to set up the secure tunnel.
And while QTT is common the modems built in tcpip
stack means it can also do standard Internet stuff like
(10:36):
HTTP requests if needed, using commands like at plus coo,
at plus coys end at plus QHTTPG E t OH
and one more crucial thing firmware updates over the air
or DFOTA. These modems can receive updates remotely, usually small
delta updates, just the changes to fix bugs or add
features without having to physically touch thousands of devices DFTA.
Speaker 1 (10:57):
Yeah, that's huge for maintenance, especially for devices way out
in the middle of nowhere.
Speaker 2 (11:01):
Absolutely essential for managing large fleets long term.
Speaker 1 (11:04):
You know, collecting all this data, sending it securely, storing it,
that's amazing engineering. But raw data is just well raw
data isn't it numbers in a database. It only really
comes alive when we can see it, visualize it. That
old saying a picture is worth a thousand words. It's
especially true here trying to spot trends or problems in
all that IoT data.
Speaker 2 (11:24):
Could agree more. Visualization turns that flood of information into
something understandable, actionable.
Speaker 1 (11:29):
So how does the data get formatted for this? What's
the common language for the data itself?
Speaker 2 (11:34):
The dominant format discussed in the source and really industry
wide for this kind of thing is Jason JavaScript Object notation.
If big advantages are that it's human readable, which is
great for debugging, it's mature and basically every tool and
cloud platform under the sun understands it. So a temperature
sensor might send something simple like device IIDE sensor forty
five timestamp one six seven eight eight eight eight six
(11:56):
four hundred do temp twenty two point five unit c
easy to read, easy for software.
Speaker 1 (12:01):
To parse, makes sense. Are there alternatives?
Speaker 2 (12:04):
There is CBR Concise Binary object representation. It's a binary format,
so it's generally more compact than Jason. Uses less data
on the.
Speaker 1 (12:13):
Wire, smaller data size. That sounds good for nbiot.
Speaker 2 (12:16):
It can be, yeah, especially if bandwidth is really tighter.
Every byte of battery matters, But the trade off is
it's not human readable and you need specific libraries to
encode and decode it. So while CBR exists and has
its uses, Jason's ease of use and wider support often
make it the more practical choice.
Speaker 1 (12:34):
Right now, okay, Jason? It is mostly so, how do
we turn that Jason data into say, dots on a
map or charts showing temperature changes for location data?
Speaker 2 (12:43):
The Google Maps JavaScript APIs are a very common choice.
You can easily plot the GPS coordinate's coming from your
MBIOT devices right onto a familiar Google map embedded in
a web page or application.
Speaker 1 (12:54):
Right so for tracking trucks or finding lost equipment.
Speaker 2 (12:57):
Exactly, asset tracking, fleet management, navigation, tons of applications. The
process is straightforward. Get a Google Maps apikey, use some
JavaScript to initialize a map, read the latest latitude and
longitude from your database for a device, and plank a
marker on the map. You see your stuff moving in
near real time.
Speaker 1 (13:15):
And for other sensor data like that temperature reading.
Speaker 2 (13:19):
For that, libraries like chart dot js are really popular.
It's an open source JavaScript library that makes it easy
to create nice looking interactive charts, bar charts, line charts, etc.
Write in a web browser using HTML five canvas. So
you could easily pull temperature readings from Dynamo dB and
plot them on a line chart against time instantly see
the daily temperature cycle, spot unusual spikes or dips. It
(13:42):
makes patterns jump out.
Speaker 1 (13:44):
Yeah, much better than staring at a spreadsheet full of numbers.
Speaker 2 (13:46):
Definitely. It brings the data to life.
Speaker 1 (13:48):
This is where it all comes together, isn't it. This
isn't just theory or textbecs anymore. Nbiot is actually out
there making real changes. That's the exciting part.
Speaker 2 (13:57):
It really is seeing the applications make the underlying tech meaningful.
Speaker 1 (14:01):
Like in the smart home we mentioned lighting, heating, security,
but also things like elder care sensors detecting falls maybe
or tracking devices for kids or pets that can last
ages on one.
Speaker 2 (14:12):
Charge bis peace of mind.
Speaker 1 (14:14):
Totally, and smart transportation traffic control that adapts, smart parking
telling you where spots are, smooth toll collection, logistics tracking
for delivery companies.
Speaker 2 (14:24):
Even vehicle safety systems talking to each other.
Speaker 1 (14:26):
Are the infrastructure and smart farming measuring soil conditions, humidity, rainfall,
detecting pests, automating irrigation, helping farmers grow more with less waste.
It's pretty amazing. The breath of it.
Speaker 2 (14:38):
It truly is diverse, and it raises the question how
does this look when a whole city embraces it? How
does it scale up? The city of Coral Gables, Florida
is a really interesting case study here. They're often cited
as a leader in building a comprehensive smart city ecosystem.
Speaker 1 (14:53):
Oh yeah, what are they doing? Specifically?
Speaker 2 (14:56):
They've deployed a really wide array of sensors and actuators.
Environment sensors monitoring everything from temperature and humidity to water levels,
air quality, even noise pollution. They have smart parking, smart
street lighting. They use drones, GPS for managing city vehicles,
RF sensors to understand traffic flow.
Speaker 1 (15:15):
Wow, okay, so data on everything pretty much.
Speaker 2 (15:18):
Public safety uses CCTV and smart policing tools connected to
the network. They have digital kiosks. They monitor the structural
health of bridges and buildings, and they're integrating connected vehicles.
It's a whole interconnected web that sounds like.
Speaker 1 (15:30):
An ocean of data. How do they possibly manage that
and make sense of it?
Speaker 2 (15:34):
They have what they call a smart city hub. The
source calls it a digital supermarket, which I quite like.
It's a central platform that gathers all this diverse data,
standardizes it and analyzes it. And this provides actionable insights.
It's not just data for data's sake. Traffic engineers use
it to design safer roads or tweak signal timings. Urgan
(15:55):
planners can see the actual impact of new developments. Businesses
can even look at anonymaled foot traffic data. Public safety
gets better situational awareness for emergencies.
Speaker 1 (16:04):
So it actually helps them run the city better.
Speaker 2 (16:06):
That's the goal. They use a horizontal integration model, basically
a central cloud platform where everything connects and shares data
feeding into city dashboards. And what's really cool is that
a lot of this is public. You can actually go
to www. Dot Coral Gables dot com, forward slash smart
City and see some of the data and platforms. It
shows how this tech can create more responsive, transparent communities.
Speaker 1 (16:27):
That's fantastic, a real world example of it all working together. Okay,
So that brings us towards the end of our deep dive. Today.
We've gone from the basics of nbiot why it's different,
looked at the hardware, the journey to the cloud, the protocol, storage,
the visualization right, making sense of it all visually, and
finally seeing how it's making a real impact in everything
from our homes to entire cities like Coral Gables. It
(16:49):
leaves you thinking, though, as nbiot keeps connecting billions more devices,
bringing in this constant stream of real time data from
well everywhere, what new questions does That raise questions about
how much we rely on this data, about the ethics
of managing all this information, maybe even what it means
to be informed when literally everything around US is generating data,
(17:11):
something to ponder,