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Protocols for the Internet of Things & Serverless Architecture |
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Markus Tacker
Senior R&D Engineer
Markus.Tacker@NordicSemi.no
Twitter: @coderbyheart
coderbyheart.com
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- 1980: Born in Germany (Xennial)
- 1998: first business building websites
- 2003: Mediengestalter für Digital- und Printmedien, Fachrichtung Medienoperating nonprint
- 2012: B.Sc. Computer Science (Univ. Wiesbaden)
- 2017+: in Trondheim, at Nordic Semiconductor
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First full time cloud engineer working on nrfcloud.com building Software-as-a-service (Saas) offering.
Application Group to work on on open-source end-to-end examples of real world IoT products.
- Application data
- Data protocols
- Transport protocols
- Wireless radio protocols
- Summary
- Scenario overview
- Amazon Web Services (AWS) implementation
- Microsoft Azure
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Typical IoT Data Protocol Configuration
:::notes
What you see here is a typical configuration for cellular IoT devices.
:::
- Device State
- Device Configuration
- Past Data
- Firmware Updates
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- sensor readings (like position, temperature)
- information about its health (like battery level)
Because the latest state should be immediately visible: buffer data in a Digital Twin.
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:::notes
A device needs to send its sensor readings (like position, temperature) and information about its health to the backend, first an foremost is the battery level a critical health indicator. This data is considered the device state.
Because we want to always be able to quickly see the latest state of the device, a digital twin can be used to store this state on the backend side: whenever the device sends an update, the digital twin is updated. This allows an application to access the most recent device state immediately without needing to wait for the device to connect and publish its state.
:::
- implement situational awareness on the device
- only send relevant data
- did a sensor reach a critical threshold?
- has enough time passed since last update?
- is the connection good enough?
:::notes
It is an important criterion for the robustness of any IoT product to gracefully handle situations in which the device is not connected to the internet. It might even not be favorable to be connected all the time—wireless communication is relatively expensive consumes a lot of energy and therefore increases the power consumption.
To optimize for ultra-low power consumption, we want to turn off the modem as quickly as possible and keep it off as long as possible.
This can be achieved by making the device smart and allowing it to decide based on the situation whether it should try to send data.
For example could an asset tracker use the motion sensor to decide whether to publish its state frequently or if it detects no movement for a while go into a passive mode, where it turns of the modem and waits until it detects movement again. It could also use the internal clock to wake up every hour to sent a heartbeat, after all we might want to know that the device is healthy, even it is not in motion.
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- change behaviour of device in real time (e.g. sensor sensititiy, timeouts)
- configure physical state (e.g. locked state of a door lock)
- cloud side owns device state (because the device may lose power and state)
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:::notes
Depending on the product we might also want to change the device configuration. This could on the one hand be use during development to tweak the aforementioned behavior using variables instead of pushing a new firmware over the air to the device. We observe firmware sizes of around 250 KB which will, even when compressed, be expensive because it will take a device some time to download and apply the updated, not to mention the costs for transferring the firmware update over the cellular network. Especially in NB-IoT-only deployments is the data rate low. Updating a fleet of devices with a new firmware involves orchestrating the roll-out and observing for faults. All these challenges lead to the need to be able to configure the device, which allows to tweak the behavior of the device until the inflection point is reached: battery life vs. data granularity. Interesting configuration options are for example the sensitivity of the motion sensor: depending on the tracked subject what is considered "movement" can vary greatly. Various timeout settings have an important influence on power- and data-consumption: the time the device waits to acquire a GPS fix, or the time it waits between sending updates when in motion.
On the other hand is device configuration needed if the device controls something: imaging a smart lock which needs to manipulate the state of a physical lock. The backend needs a way to tell the device which state that lock should be in, and this setting needs to be persisted on the backend, since the device could lose power, crash or otherwise lose the information if the lock should be open or closed.
Here again is the digital twin used on the cloud side to store the latest desired configuration of the device immediately, so the application does not have to wait for the device to be connected to record the configuration change. The implementation of the digital twin then will take care of sending only the latest required changes to the device (all changes since the device did last request its configuration are combined into one change) thus also minimizing the amount of data which needs to be transferred to the device.
:::
Cellular IoT devices need to send data about past events: they will be offline most of the time.
:::notes
Imagine a reindeer tracker which tracks the position of a herd. If position updates are only collected when a cellular connection can be established there will be an interesting observation: the reindeers are only walking along ridges, but never in valleys. The reason is not because they don't like the valley, but because the cellular signal does not reach deep down into remote valleys. The GPS signal however will be received there from the tracker because satellites are high on the horizon and can send their signal down into the valley.
There are many scenarios where cellular connection might not be available or unreliable but reading sensors work. Robust ultra-mobile IoT products therefore must make this a normal mode of operation: the absence of a cellular connection must be treated as a temporary condition which will eventually resolve and until then business as usual ensues. This means devices should keep measuring and storing these measures in a ring-buffer or employ other strategies to decide which data to discard once the memory limit is reached.
Once the device is successfully able to establish a connection it will then (after publishing its most recent measurements) publish past data in batch.
On a side note: the same is true for devices that control a system. They should have built-in decision rules and must not depend on an answer from a cloud backend to provide the action to execute based on the current condition.
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- Store data on device until it can connect again
- Send data batched to the cloud
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- 2-3 magnitudes larger than a control message (~250 KB)
- notification via control channel (MQTT)
- download via data channel (HTTP): less overhead, supports resume
:::notes
Arguably a firmware update over the air can be seen as configuration, however the size of a typical firmware image (250 KB) is 2-3 magnitudes larger than a control message. Therefore it can be beneficial to treat it differently. Typically an update is initiated by a configuration change, once acknowledged by the device will initiate the firmware download. The download itself is done out of band using not MQTT but HTTP(s) to reduce overhead.
Additionally firmware updates are so large compared to other messages that the device may suspend all other operation until the firmware update has been applied to conserve resources.
:::
- great potential for optimization
- initiating and maintaining network connection is magnitudes more expensive compared to other device operations (for example reading a sensor value)
- invest a substantial amount into optimizing these when developing an ultra-low power product
:::notes
It's these messages that are exchanged between your devices and your backend which are the most important aspect to optimize for when developing an ultra-low power product because initiating and maintaining network connection is relatively expensive compared to other device operations (for example reading a sensor value).
It is therefore recommended to invest a substantial amount of time to revisit the principles explained here and customize them to your specific needs. The more the modem-uptime can be reduced and the smaller the total transferred amount of data becomes, the longer your battery will last.
:::
- JSON
- Alternatives to JSON
- Protocol buffers
- Flatbuffers
- CBOR
:::notes
Let's look at the "default" protocol for encoding Application data and what alternatives exist to reduce the amount of data needed to transmit a typical device message: a GPS location.
:::
{
"v": {
"lng": 10.414394,
"lat": 63.430588,
"acc": 17.127758,
"alt": 221.639832,
"spd": 0.320966,
"hdg": 0
},
"ts": 1566042672382
}"default" data protocol for IoT (AWS, Azure, Google Cloud)
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👍 human readable
👍 schema-less (self-describing)
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👎 overhead
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:::notes
JSON offers very good support in tooling and is human readable. Especially during development its verbosity is valuable.
:::
GPS location message
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{
"v": {
"lng": 10.414394,
"lat": 63.430588,
"acc": 17.127758,
"alt": 221.639832,
"spd": 0.320966,
"hdg": 0
},
"ts": 1566042672382
}:::
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00 00 00 00 00 00 00 00
09 00 e0 cf ac f6 c9 76
42
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JSON
114 bytes
without newlines
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Protocol Buffers
65 bytes (-42%)
source
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:::notes
Consider this GPS message. It contains a lot of data which is intended for humans, but not needed for machines sending or receiving the data.
The pure binary message would be transmitting only the 6 floats and 1 integer of the message. However a strucured message format is always preferred because we also want to ensure its integrity.
In JSON notation this document (without newlines) has 114 bytes. If the message were to be transferred using for example Protocol Buffers the data can be encoded with only 65 bytes (a 42% improvement).
See also: RION Performance Benchmarks
:::
developers.google.com/protocol-buffers
- send structured data in efficient binary format without overhead
- message format can be changed (ammended) without breaking existing clients
- small implementation for embedded devices exists: nanopb
:::notes
In the comparison on the previous slide we showed how using Protocol Buffers can dramatically reduce the transferred data size, while keeping a typed message.
The implementation of Protocol Buffers is however quite big (for a resource constrained device like the nRF9160), and no official encoder/decoder implementation exists for C, inofficial does.
Flatbuffers is the best candidate with similar data savings.
Especially the ability to access members of a message directly in place makes it ideal for memory-constrained devices: no need to create a second copy of the received values.
It also offers flexibility during development is also supported because FlatBuffers offers a schema-less (self-describing) version.
Unfortunately there is no official support in the nRF Connect SDK or Zephyr as of now.
:::
- evolution of Protocol Buffers
- access a buffer without parsing
- smaller library, C implementation exists
- wire format size a little bigger compared to Protocol Buffers
- schema-less (self-describing) messages are supported
:::notes
In the comparison on the previous slide we showed how using Protocol Buffers can dramatically reduce the transferred data size, while keeping a typed message.
The implementation of Protocol Buffers is however quite big (for a resource constrained device like the nRF9160), and no official encoder/decoder implementation exists for C, inofficial does.
Flatbuffers is the best candidate with similar data savings.
Especially the ability to access members of a message directly in place makes it ideal for memory-constrained devices: no need to create a second copy of the received values.
It also offers flexibility during development is also supported because FlatBuffers offers a schema-less (self-describing) version.
Unfortunately there is no official support in the nRF Connect SDK or Zephyr as of now.
:::
- maps JSON to binary structures
- zero configuration needed between exchanging parties
- support for embedded devices: tinycbor
:::notes
Therefore the best alternative to JSON right now is CBOR.
CBOR is standard for encoding JSON data in a set of binary structures. It reduces volume by using more compact one byte values to replace two or more punctuation marks.
:::
GPS location message
:::::::::::::: {.columns}
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{
"v": {
"lng": 10.414394,
"lat": 63.430588,
"acc": 17.127758,
"alt": 221.639832,
"spd": 0.320966,
"hdg": 0
},
"ts": 1566042672382
}:::
::: {.column}
A2 61 76 A6 63 6C 6E 67
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37 63 6C 61 74 FB 40 4F
B7 1D 81 F1 06 68 63 61
63 63 FB 40 31 20 B4 BF
8F CD 68 63 61 6C 74 FB
40 6B B4 79 80 F5 5D E6
63 73 70 64 FB 3F D4 8A
B4 FA 48 30 1A 63 68 64
67 00 62 74 73 1B 00 00
01 6C 9F 6A CC FE
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JSON
114 bytes
without newlines
:::
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CBOR
86 bytes (-24%)
source
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:::notes
This shows the possible savings when encoding the GPS location message using CBOR.
:::
Look into denser data protocols!
JSON is for Humans.
- devices always™ send the same structure:
no need to transmit it - less data to send
- less money spent on data (grows linear with № of devices)
- less energy consumed = longer device lifetime
- lower chance of failed transmit
- MQTT+TLS
- MQTT-SN+(D)TLS
- CoAP/LWM2M+(D)TLS
common protocol for "ecommerce" cloud vendors
(AWS,
Azure,
Google Cloud)
- great fit for asynchronous, event oriented communication: MQTT is bidirectional pub/sub model
- overhead:
- topic name in every MQTT package
№ of topics per device: ~3 - TLS handshake with AWS IoT broker: ~10 KB
- topic name in every MQTT package
- Supported out of the box in nRF Connect SDK
:::notes
MQTT with TLS is the default protocol when using IoT offerings from "ecommerce" cloud vendors like Amazon, Microsoft or Google. It's a great fit for the event-driven communication in IoT and allows both sides to initiate communication.
However the protocol overhead for both MQTT and TLS are substantial: the initial handshake is large, and then every MQTT package contains repeated information. The MQTT topic name is quite long (typical size is around 60 Byte), which could actually be omitted.
:::
- optimized version designed specifically IoT
- supports UDP
- use numeric IDs instead of strings for topic names
- better offline support
- not supported out of the box in nRF Connect SDK
- not supported by cloud vendors: needs a (stateful) Gateway
:::notes
MQTT-SN was specifically designed for IoT devices and tries to address the issues mentioned earlier.
The main differences involve:
- Reducing the size of the message payload
- Removing the need for a permanent connection by using UDP as the transport protocol.
:::
- common protocol in Telco clouds (Verizon’s Thingspace, AT&T’s IoT Platform)
- typically used for device management (carrier library)
- support in nRF Connect SDK (CoAP client sample, LwM2M client sample)
- not supported by cloud vendors: needs a (stateful) Gateway.
Proof-of-concept AWS IoT-LwM2M Gateway: github.com/coderbyheart/leshan-aws
:::notes
This protocol is mostly used for device management. Especially LwM2M comes with a large set of predefined operations (e.g. firmware update) and uses very lightweight messaging. It also supports UDP out of the box which makes it an ideal protoco for resource constraint devices.
However there is no out-of-the box support by ecommerce cloud vendors, so here again one needs to operate a Gateway.
:::
:::notes
The nRF9160 supports two cellular networking protocols: LTE-M and NB-IoT. Fundamentally they both provide IP connectivity to your device, however they are significant differences, which are important to consider when developing your IoT product.
See this comparison
:::
- 375 kbps downlink, 300 kbps uplink
- ~100 kbps application throughput running IP
- supports roaming (same as LTE)
- typically uses frequency bands above 2 Ghz
- ms-latency
:::notes
LTE-M (also known as Cat-M1) is designed for low power applications requiring medium throughput. It has a narrower bandwidth of 1.4 MHz compared to 20 MHz for regular LTE, giving longer range, but less throughput. The throughput is 375 kbps downlink and 300 kbps uplink, providing approximately 100 kbps application throughput running IP. It is suitable for TCP/TLS end-to-end secure connections. Mobility is fully supported, using the same cell handover features as in regular LTE. It is currently possible to roam with LTE-M, meaning it is suitable for applications that will operate across multiple regions. The latency is in the millisecond range offering real time communication for time-critical applications.
:::
- 60 kbps downlink, 30 kbps uplink
- typically uses frequency bands below 2 Ghz
- no roaming support (some Telcos do offer custom solution)
- good indoor/underground penetration characteristics
- long range
:::notes
NB-IoT (also known as Cat-NB1) is a narrowband technology standard that does not use a traditional LTE physical layer, but is designed to operate in or around LTE bands and coexist with other LTE devices. It has a bandwidth of 200 kHz, giving it longer range and lower throughput compared to LTE-M and regular LTE. The throughput is 60 kbps downlink and 30 kbps uplink. It is suitable for static, low power applications requiring low throughput.
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for medium throughput applications requiring low power, low latency and/or mobility
- asset tracking
- wearables
- medical
- POS
- home security
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for static, low throughput applications requiring low power and long range
- smart metering
- smart agriculture
- smart city
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:::notes
LTE-M is perfect for medium throughput applications requiring low power, low latency and/or mobility, like asset tracking, wearables, medical, POS and home security applications.
NB-IoT is perfect for static, low throughput applications requiring low power and long range, like smart metering, smart agriculture and smart city applications. It also provides better penetration in, for example, cellars and parking garages compared to LTE-M.
:::
- no silver bullet - multiple conflicting dimensions need to be considered
- highly depends on use case scenario
- ultra-low power relevant in all scenarios
Serverless computing allows me to focus on implementing the solution, and not spend time on maintaining the infrastructure needed to execute it.
More detailed analysis: martinfowler.com/articles/serverless.html
- build globally scalable solutions with a small team
- suprises are rare, allows me to go on vacation
:::notes
Going serverless allows me and a rather small team of engineers to build a globally scalable software solution with a very high confidence in it's scalability and an ease of mind during operations.
In the last years since I have been working fully serverless, I have not once experienced a surprising event that impacted our production system.
Yes, we experience issues which gradually become more serious over time, but in general it happens in a way that does not interfere with my vacation plans.
Serverless not only enables horizontal scalability, but allows to update each component of the solution individually: the code for a lambda function can be replaced without affecting other functions. This means no more waiting minutes for a monolithic container to come up again after a fix has been deployed.
:::
- Serverless implementations depend on stateless, event-driven implementations.
- This allows to process as many requests as needed, in parallel.
- However, you have to deal with eventual consistency.
Instead of restarting the instance,
I can update an individual function.
:::notes
Serverless not only enables horizontal scalability, but allows to update each component of the solution individually: the code for a lambda function can be replaced without affecting other functions. This means no more waiting minutes for a monolithic container to come up again after a fix has been deployed.
:::
- Testing cloud-native solutions really is a challenge.
- More ceremony involved, defining infrastructure as code is now your job.
- Impossible to run it locally.
- Huge mindset shift from the classical application server model (LAMP, Tomcat, Rails), to a serverless, stateless, eventual consistent application development model.
:::notes
You can see my talk about testing cloud-native solutions here: https://coderbyheart.com/it-does-not-run-on-my-machine/
:::
- Many, many devices, that don't share global state
- Needs to scale to billions of devices, connections, message per day
- Many different operations with different business needs (processing) on small datasets, suits serverless microservice model very well
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Please share your feedback!
Markus.Tacker@NordicSemi.no
Twitter: @coderbyheart
Latest version:
bit.ly/tek03iot