ConCaVa

Use case

LoRa weather station

Constraints

• Limited package size (51 bytes)
• 886Mhz spectrum, with limiting throughput

Attributes

• Wind speed
• Wind direction
• Gust speed
• Gust direction
• Rainfall
• Temperature
• Humidity
• Barometric pressure
• Ultraviolet (UV) light
• Solar radiation

Ways to get the data across

• API url parameters
• XML data
• Binary data

Smaller payload sizes are better.

API url parameters

The traditional way

http://weatherstation.wunderground.com/weatherstation/updateweathers
tation.php?id=KCASANFR5&password=XXXXXX&dateutc=2000-01-01+10%3A32%3
A35&windspeedmph=12&winddir=230&windgustmph=12&windgustdir=230&raini
n=0&tempf=70&humidity=90&baromin=29.1&uvl=20&solarrad=10
				

~185 bytes of data

XML data

	KCASANFR5
	XXXXXX
	2000-01-01+10%3A32%3A35
	12
	230
	12
	12
	0
	70
	90
	29.1
	20
	10

				

~393 bytes of data

Binary data

41 bytes of data

Binary payload

• Efficient
• Flexible
• Small payload size

Parsing

Which byte(s) contains what data?

• Metadata required to make sense of the data
• Dynamic metadata enables flexible binary payload

Solution

ConCaVa: Convert, calibrate, and validate

• Generic binary payload processor
• Replaceable components
• Token based authentication
• Plug-and-play/custom connectors
• Stores sensor data in Cloud Storage
  (the Orion Context Broker, for example)

Source on Github: https://github.com/kukua/

General dataflow

LoRaWAN dataflow

Three simple steps

1. Convert: use metadata to parse the binary payload
2. Calibrate: transform data to desired format
   (e.g. convert Fahrenheit to Celcius)
3. Validate: correct sensor misreadings
   (e.g. temperatures below −273.15°C)

ConCaVa supports every thinkable rule for converting, calibrating, and validating sensor data.

Example

For example: wind direction of 1 byte (integer value 0-255).

Convert:    binary 00011110 = integer value 30
Calibrate:  value = 360 * (value / 255)
Validate:   0 ≤ value ≤ 255
Result:     42.4°