Posts Tagged AWS IoT Core

AWS IoT Core for LoRaWAN, AWS IoT Analytics, and Amazon QuickSight

In the following post, we will learn how to monitor indoor air quality (IAQ) using a private LoRaWAN sensor device network. The devices transmit their sensor telemetry to AWS through a LoRaWAN gateway using the newly released AWS IoT Core for LoRaWAN service. We will then analyze and visualize the sensor data using AWS IoT Analytics and Amazon QuickSight.

Amazon QuickSight Dashboard showing IAQ sensor data

Introduction

On December 15, 2020, AWS announced support for Semtech’s low-power, long-range wide area network (LoRaWAN) connectivity. LoRaWAN devices and gateways can now connect to AWS IoT Core using AWS IoT Core for LoRaWAN. AWS IoT Core for LoRaWAN is a fully managed feature that enables customers to connect wireless devices that use the LoRaWAN protocol with the AWS cloud. Using AWS IoT Core, customers can now set up a private LoRaWAN network by securely connecting their LoRaWAN devices and gateways to the AWS cloud — without developing or operating a LoRaWAN Network Server (LNS).

AWS IoT Core for LoRaWAN website

The LoRa Alliance describes the LoRaWAN specification as a Low Power, Wide Area (LPWA) networking protocol designed to wirelessly connect battery operated ‘things’ to the internet in regional, national, or global networks, and targets key Internet of Things (IoT) requirements such as bi-directional communication, end-to-end security, mobility and localization services. The LoRaWAN specification defines both the device-to-infrastructure (LoRa) physical layer parameters and the (LoRaWAN) protocol, providing seamless interoperability between manufacturers.

According to Wikipedia, LoRa (Long Range) is a proprietary low-power wide-area network modulation technique. It is based on spread spectrum modulation techniques derived from chirp spread spectrum (CSS) technology. It was developed by Cycleo of Grenoble, France, and acquired by Semtech, the founding member of the LoRa Alliance, and it is patented.

AWS-qualified Hardware

Along with the AWS IoT Core for LoRaWAN announcement, AWS released a list of qualified gateways found in the AWS Partner Device Catalog. The catalog helps customers discover qualified hardware that works with AWS services to help build and deliver successful IoT solutions. AWS IoT Core for LoRaWAN supports open-source gateway-LNS protocol software called LoRa Basics Station, an implementation of a LoRa packet forwarder. The AWS IoT Core for LoRaWAN gateway qualification program enables customers to source pre-tested LoRaWAN gateways and developer kits that meet the required LoRa Basics Station specifications.

AWS Partner Device Catalog

LoRaWAN Gateway

In this post, we will use the AWS-qualified LoRaWAN-compliant MiniHub Pro gateway by Browan Communications. At $109, MiniHub Pro is a low-cost gateway that uses LoRa Basics Station to forward RF packets received from LoRaWAN devices (uplinks) to the LoRaWAN Network Server (LNS), part of the AWS IoT Core for LoRaWAN service. The MiniHub Pro is based on FreeRTOS, the real-time operating system for microcontrollers.

AWS-qualified LoRaWAN-compliant MiniHub Pro gateway

LoRaWAN Devices

In addition to the gateway, we will use a set of two Model TBHV110 915 Mhz Healthy Home Sensor IAQ (aka Tabs Healthy Home), also by Browan Communications Inc. According to Browan, the Healthy Home Sensor utilizes LoRaWAN connectivity (LoRaWAN 1.0.3) to communicate the temperature, relative humidity, volatile organic compound (VOC) levels, and indoor air quality (IAQ) of the surrounding environment. The Healthy Home Sensor costs $60 per sensor. The gateway and two devices used in this post were purchased from Cal-Chip Connected Devices at a total retail cost of $229 plus tax and shipping.

Healthy Home Sensor IAQ available from Cal-Chip Connected Devices

IAQ Index

The Healthy Home Sensor determines an IAQ Index, a reading between 0–500, indicating general air quality. According to the CDC, monitoring and maintaining good indoor air quality and proper ventilation have become essential to reduce the potential airborne spread of COVID-19.

Chart courtesy of the Browan Healthy Home Sensor (IAQ) Reference Guide

Source Code

The source code for this post is available on GitHub. Use the following command to git clone a local copy of the project.

git clone --branch main --single-branch --depth 1 \
https://github.com/garystafford/lorawan-iot-core-demo.git

The Lambda function and its associated AWS resources are deployed using the AWS Serverless Application Model (SAM). The primary AWS resources used in this demonstration are shown in the architectural diagram below.

The architecture of this post’s demonstration

Adding Gateways and Devices

In combination with the manufacturer’s product manual, the AWS documentation walks you through adding your LoRaWAN gateways and devices with AWS IoT Core for LoRaWAN. Adding gateways and devices is easy and straightforward on AWS as long as you have the appropriate GatewayEUI, DevEUI, AppEUI (aka JoinEUI), and AppKey, supplied by the manufacturer or retailer.

Adding a LoRaWAN device to AWS IoT Core for LoRaWAN

Over-the-Air Activation (OTAA)

In this post, the gateway and sensor devices are connected to AWS IoT Core for LoRaWAN using Over-the-Air Activation (OTAA). According to both The Things Network and AWS, OTAA is preferred over Activation by Personalization (ABP) as it is more secure. When using OTAA, temporary session keys are derived from root keys on each activation. ABP uses static keys, is less secure, and should not be used if not explicitly required by the use case.

Adding a LoRaWAN gateway to AWS IoT Core for LoRaWAN

The LoRaWAN gateway is added to AWS IoT Core for LoRaWAN. The gateway is then configured locally using the information received from IoT Core. With the MiniHub Pro, you can enable FreeRTOS Over-the-Air Updates and Configuration and Update Server (CUPS).

Configuring the MiniHub Pro gateway for Over-the-Air (OTA) updates
Configuring the MiniHub Pro gateway for Configuration and Update Server (CUPS)

Below, we see that the MiniHub Pro gateway has been successfully added to IoT Core and has exchanged uplink frames (RF packets) through that connection with the AWS IoT Core for LoRaWAN, which is acting as the LoRaWAN Network Server (LNS).

MiniHub Pro gateway added to AWS IoT Core for LoRaWAN

We then add the two Healthy Home Sensor devices in a similar fashion.

Adding a LoRaWAN device to AWS IoT Core for LoRaWAN

Below, we see that one of the two Healthy Home Sensor devices has successfully connected to AWS IoT Core for LoRaWAN and has also transmitted uplink frames via the MiniHub Pro gateway.

Healthy Home Sensor added to AWS IoT Core

IoT Destinations

Messages from the devices are received from the gateway and routed to an IoT rule using a Destination. According to AWS, a destination describes the AWS IoT rule that processes the data from a wireless device so that AWS IoT services can use the data. Many devices can share a single destination.

AWS IoT Core wireless connectivity destination connecting the device to an IoT Rule

IoT Rules

IoT rules, according to AWS, tell AWS IoT what to do when it receives messages from your devices. Rules extract data from messages, evaluate expressions using message data, and invoke one or more actions when the rule’s conditions are met.

IoT Rule that transforms base64 encoded binary device messages and passes them to IoT Analytics

Using AWS IoT Core for LoRaWAN, the device’s messages are base64 encoded binary messages. The IoT rule’s query statement is a SQL statement that determines which messages are forwarded to Actions. In the case of LoRaWAN, the query statement contains an inline call to an AWS Lambda function. The function accepts a number of input parameters. It decodes the base64 encoded message, decodes and translates the binary message, and builds a Python dictionary with the results. Finally, the dictionary is serialized to JSON and returned to the IoT rule.

SELECT WirelessDeviceId, WirelessMetadata, "tbhv110_915" as PayloadDecoderName,
aws_lambda("arn:aws:lambda:us-east-1:111222333444:function:lorawan-iot-core-TransformLoRaWANBinaryPayloadFunc-RQOC3C6CKGWZ",
{"PayloadDecoderName": "tbhv110_915",
"PayloadData": PayloadData,
"WirelessDeviceId": WirelessDeviceId,
"WirelessMetadata": WirelessMetadata}) as PayloadData
view raw lorawn_iot_rule.sql hosted with ❤ by GitHub
IoT rule query statement

Healthy Home Sensor Messages

LoRa uses a license-free sub-gigahertz radio frequency of 915 MHz in North America. The Healthy Home Sensor device transmits binary messages over this radio frequency using the LoRaWAN 1.0.3 specification. Binary messages sent by the Healthy Home Sensor device to the MiniHub Pro gateway have a payload length of 11 bytes, as follows:

Chart courtesy of the Browan Healthy Home Sensor (IAQ) Reference Guide

The device encrypts its binary message, containing sensor data, using AES128 CTR mode before transmitting it. AWS IoT Core for LoRaWAN decrypts the binary message, then encodes the decrypted binary message payload as a base64 string.

The final JSON-format message delivered by AWS IoT Core for LoRaWAN the IoT rule contains the device’s base64 encoded binary message (PayloadData), along with additional information about the source LoRaWAN device and the LoRaWAN gateway that transmitted the message.

{
"WirelessDeviceId": "3a3c05d6-a3a3-434f-c5f9-47aa04d41117",
"PayloadData": "AAs0LNsCAQB/ADM=",
"WirelessMetadata": {
"LoRaWAN": {
"DataRate": "3",
"DevEui": "40bb42d1c49fa3c1",
"FCnt": 1426,
"FPort": 103,
"Frequency": "904500000",
"Gateways": [
{
"GatewayEui": "d788ab85896d60b8",
"Rssi": -69,
"Snr": 9.5
}
],
"Timestamp": "2021-04-07T16:02:10Z"
}
},
"PayloadDecoderName": "tbhv110_915"
}

The IoT rule’s query statement calls a Lambda function to decode and transform the base64 encoded binary message. The Lambda calls the correct decoder, which contains logic to decode each byte of the binary message. AWS has developed the IoT Rule decoder Lambda along with several binary message payload decoders, freely available on GitHub, including:

  • Browan Tabs Object Locator
  • Dragino LHT65, LGT92, LSE01, LBT1, LDS01
  • Axioma W1
  • Elsys
  • Globalsat LT-100
  • NAS Pulse Reader UM3080

Since ASW did not include the Browan Healthy Home Sensor device in the list of provided decoders, I have created a new Python-based decoder using the message payload information detailed in the device’s product manual.

def dict_from_payload(base64_input: str, fport: int = None):
""" Healthy Home Sensor IAQ (TBHV110) binary payload decoder """
decoded = base64.b64decode(base64_input)
# Byte 0, bit 0
status = decoded[0] & 0b00000001 # (1 << 1) – 1
# Byte 1, bits 3:0
battery = decoded[1] & 0b00001111 # (1 << 4) – 1
battery = (25 + battery) / 10
# Byte 2, bits 6:0
board_temp = decoded[2] & 0b01111111 # (1 << 7) – 1
board_temp = board_temp 32
# Byte 3, bits 6:0
rh = decoded[3] & 0b01111111 # (1 << 7) – 1
# Byte 5-4, bits 15:0
eco2 = decoded[5] << 8 | decoded[4]
# Byte 7-6, bits 15:0
voc = decoded[7] << 8 | decoded[6]
# Byte 9-8, bits 15:0
iaq = decoded[9] << 8 | decoded[8]
# Byte 10, bits 6:0
env_temp = decoded[10] & 0b1111111 # (1 << 7) – 1
env_temp = env_temp 32
result = {
'Status': status,
'Battery': battery,
'BoardTemp': board_temp,
'RH': rh,
'ECO2': eco2,
'VOC': voc,
'IAQ': iaq,
'EnvTemp': env_temp,
}
return result
view raw tbhv110_decoder.py hosted with ❤ by GitHub

The decoder first decodes the base64 encoded binary message payload.

# base64 encoded binary message
AAs0LNsCAQB/ADM=

# base64 decoded 11-byte binary message
00000000 00001011 00110100 00101100 11011011 00000010 00000001 00000000 01111111 00000000 00110011
# as hexadecimal
00 0b 34 2c db 02 01 00 7f 00 33
# as decimal
0 11 52 44 219 2 1 0 127 0 51

The decoder then processes each byte of the binary message payload, bit-by-bit, and applies any additional logic to derive the final sensor values. Decoding the base64 encoded binary message payload and placing it back into the original message, we are left with the following message structure:

{
"WirelessDeviceId": "3a3c05d6-a3a3-434f-c5f9-47aa04d41117",
"PayloadData": {
"Status": 0,
"Battery": 3.6,
"BoardTemp": 20,
"RH": 44,
"ECO2": 731,
"VOC": 1,
"IAQ": 127,
"EnvTemp": 19
},
"WirelessMetadata": {
"LoRaWAN": {
"DataRate": "3",
"DevEui": "40bb42d1c49fa3c1",
"FCnt": 1426,
"FPort": 103,
"Frequency": "904500000",
"Gateways": [
{
"GatewayEui": "d788ab85896d60b8",
"Rssi": -69,
"Snr": 9.5
}
],
"Timestamp": "2021-04-07T16:02:10Z"
}
}
}

IoT Rule Actions

Messages returned by the rule’s query and processed by the Lambda function are passed to an AWS IoT rule action. AWS IoT rule actions specify what to do when a rule is triggered. This rule’s action sends a message to an AWS IoT Analytics channel.

AWS IoT Rule query statement and IoT Analytics action

AWS IoT Analytics

IoT Analytics, according to AWS, is a fully-managed service that makes it easy to run and operationalize sophisticated analytics on massive volumes of IoT data. IoT Analytics consists of five primary components: channels, pipelines, data stores, datasets, and notebooks.

AWS IoT Analytics Management Console

The IoT rule sends messages to the IoT Analytics channel. The channel publishes the data to a pipeline. The pipeline consumes messages from the channel and enables you to process and filter the messages before storing them in the data store. The data store receives and stores the messages. You retrieve data from a data store by creating a SQL dataset or a container dataset. The SQL dataset contains a SQL query, which is executed against the data store. According to the documentation, both Amazon Athena and Amazon IoT Analytics’ SQL expressions are based on PrestoDB.

AWS IoT Analytics Dataset

The SQL query I have written is specific to the data collected by the Healthy Home Sensor device. In Amazon QuickSight, we will use this dataset to construct an IAQ monitoring dashboard.

ts_utc ts_local wirelessdeviceid battery board_temp rh env_temp voc iaq eco2 rssi snr sensor_location
2021-04-12T18:38:52.000Z 2021-04-12T14:38:52.000-04:00 3a3c05d6-a3a3-434f-c5f9-47aa04d41117 3.6 69.8 53 68.0 1 82 730 -73 9.25 Second floor office
2021-04-12T18:39:21.000Z 2021-04-12T14:39:21.000-04:00 1ec1fe6d-9db0-2334-dee3-48a58d821704 3.6 68.0 57 66.2 1 93 1088 -78 10.0 First floor living room
2021-04-12T18:43:52.000Z 2021-04-12T14:43:52.000-04:00 3a3c05d6-a3a3-434f-c5f9-47aa04d41117 3.6 69.8 52 68.0 1 81 715 -73 10.0 Second floor office
2021-04-12T18:44:21.000Z 2021-04-12T14:44:21.000-04:00 1ec1fe6d-9db0-2334-dee3-48a58d821704 3.6 68.0 56 66.2 1 95 1104 -76 7.0 First floor living room
2021-04-12T18:48:52.000Z 2021-04-12T14:48:52.000-04:00 3a3c05d6-a3a3-434f-c5f9-47aa04d41117 3.6 69.8 52 68.0 1 93 814 -75 9.0 Second floor office
2021-04-12T18:49:21.000Z 2021-04-12T14:49:21.000-04:00 1ec1fe6d-9db0-2334-dee3-48a58d821704 3.6 68.0 57 66.2 1 92 1064 -77 10.25 First floor living room
2021-04-12T18:53:52.000Z 2021-04-12T14:53:52.000-04:00 3a3c05d6-a3a3-434f-c5f9-47aa04d41117 3.6 69.8 52 68.0 1 101 874 -76 8.75 Second floor office
2021-04-12T18:54:21.000Z 2021-04-12T14:54:21.000-04:00 1ec1fe6d-9db0-2334-dee3-48a58d821704 3.6 68.0 58 66.2 1 89 1026 -78 10.25 First floor living room
2021-04-12T18:58:52.000Z 2021-04-12T14:58:52.000-04:00 3a3c05d6-a3a3-434f-c5f9-47aa04d41117 3.6 69.8 53 68.0 1 119 1020 -75 9.5 Second floor office
2021-04-12T18:59:21.000Z 2021-04-12T14:59:21.000-04:00 1ec1fe6d-9db0-2334-dee3-48a58d821704 3.6 68.0 59 66.2 1 88 1012 -77 9.5 First floor living room
2021-04-12T19:03:52.000Z 2021-04-12T15:03:52.000-04:00 3a3c05d6-a3a3-434f-c5f9-47aa04d41117 3.6 69.8 53 68.0 1 122 1048 -72 7.0 Second floor office
2021-04-12T19:04:21.000Z 2021-04-12T15:04:21.000-04:00 1ec1fe6d-9db0-2334-dee3-48a58d821704 3.6 68.0 59 66.2 1 80 916 -76 8.25 First floor living room
view raw healthy_home.csv hosted with ❤ by GitHub
Final data used for analysis

Amazon QuickSight

Amazon QuickSight, according to AWS, is a scalable, serverless, embeddable, machine learning-powered Business Intelligence (BI) service built for the cloud. QuickSight lets you easily create and publish interactive BI dashboards that include Machine Learning-powered insights. AWS IoT Analytics provides direct integration with Amazon QuickSight.

Using the ‘AWS IoT Analytics’ data source, we can build a QuickSight data set by importing the IoT Analytics dataset built in the previous step. In QuickSight, we are able to preview the data, change field types, apply filters, add calculated fields (e.g., sensor_location), and exclude fields if desired.

The final data is then saved (cached) in SPICE, QuickSight’s Super-fast, Parallel, In-memory Calculation Engine.https://programmaticponderings.wordpress.com/media/509f9c79155177fd1480eeee2bd23593Final data used for analysis

Using this dataset, we can build a QuickSight Analysis. The analysis will use a variety of visual types to display sensor data, such as temperature, relative humidity, volatile organic compound (VOC) levels, indoor air quality (IAQ), the sensor device’s battery levels, and the gateway’s SNR (Signal-to-Noise Ratio) and RSSI (Received Signal Strength Indication).

Constructing an Amazon QuickSight Analysis using the IoT Analytics IAQ dataset

We can then securely share the Analysis we have built with data consumers as a QuickSight Dashboard, accessible through a web browser or using the Amazon QuickSight mobile app.

Amazon QuickSight Dashboard showing IAQ sensor data

Below, we see how QuickSight is able to visualize indoor air quality data for multiple sensor locations over the course of a single day. In this visual, sensor data is captured and displayed in five-minute increments. The visual contains reference lines indicating both good and bad IAQ thresholds. Analyzing the results, we can quickly see how external and internal environmental conditions, HVAC systems, air filtration devices, external ventilation through windows and doors, and human activity all impact IAQ throughout the day in different areas of the dwelling.

Conclusion

In this post, we learned how easy it is to activate, configure, and start collecting sensor telemetry from LoRaWAN devices and gateways using the newly released AWS IoT Core for LoRaWAN service. We observed the seamless integration of AWS IoT Core, IoT Analytics, and Amazon QuickSight to transform, store, and visualize sensor data. Extending this example to add notifications and auto-remediation based on sensor data is equally as easy with services such as AWS IoT Events.

Reference

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