RFID as Ambient Data

Overview

There are tens of billions of standard radio-identifiable things shipping annually in the form of RAIN RFID (passive) tags and Bluetooth Low Energy (active) transceivers. In any given physical space today, it is not uncommon to discover such radio-identifiable things, often in the tens and even hundreds. The opportunistic discovery, identification, location, and interpretation of sensor data from these RFID devices represents ambient data, which affords computers the ability to make sense of the physical spaces they occupy. In this tutorial, we'll examine the collection and interpretation of ambient data from RFID, including the lookup of digital twins and the combination of data collected independently by co-located systems, leading to the concept of collective hyperlocal context.

Statistics on RFID Tags

By the end of 2021, there were over 112 Billion RAIN RFID tags deployed, with 95% of which supporting BLE.

Active vs Passive RFID

Active RFID

  • Spontaneously transmits its identifier, using its own source of power.
  • Human Scale: Identifiable within a physical space.

Passive RFID

  • Backscatters its identifier, powered by an external signal.

Ambient: Defined

Ambient refers to existing in the surrounding area.

Ambient Data

Ambient data refers to data existing in the surrounding area.

Steps to Ambient Data

1. Discovery

2. Identification

3. Location

4. Interpretation of sensor data

Discovery and Identification

  • Every Bluetooth Low Energy advertising packet includes a 48-bit advertiser address as an identifier, and may contain additional identifiers in the optional payload.
  • Every RAIN RFID tag includes a unique 160-bit tag identifier (TID) and may include an electronic product code (EPC) in its memory banks.

Sense-making from Data

Questions to consider:

  • How many?
  • What?
  • Who?

Opportunistic Location

Devices can offer meaningful location information based on their proximity and can be represented in a graph format.

Sensor Data Interpretation

The data can include various environmental metrics such as temperature, motion, occupancy, etc.

The Challenges Ahead

Empowering computers with their own means of gathering information will further enable them to observe, identify, and understand their environments.

Important Links

Conclusion

RFID and sensor technologies provide abundant opportunities to collect ambient data, which can be transformed into insightful hyperlocal context, furthering the capabilities of machine-readable representations in physical spaces.