Ruckus Wireless SPoT - Location Based Best Practices

Part 1 of this blog will discuss how Ruckus has overcome the issues of the path loss model, and touch on design goals that should be used to achieve a successful deployment.

The above image goes a long way to explaining the theory of the 'path loss model'. If channel attenuation can be applied to the received transmissions from a device within the range of 3 access points, trilateration techniques can be used to position the device. However, what this model doesn't take into consideration is obstacles within the coverage of the access points.

To overcome this problem as much as possible, Ruckus Wireless SPoT performs an RF Fingerprint of the whole environment.

It does this by dividing the environment into a grid-like area, with 'anchor points' being used to mark out this area. The figure below works to 3m being the distance between each anchor point. A mobile device is then used to calibrate the deployment, by being positioned at each anchor point and transmitting a signal to the access points in its vicinity. The surrounding access points will measure the received signal strength (RSS) and tag this vector of RSS to the anchor point on the map. The AP's measurements are saved, being used to build a database. After capturing data at all anchor points, the completed database will form the radio map of the environment. Thus the radio map gives us a one-to-one mapping of received power from access points (identified by their MAC addresses) and the actual position within the environment.

Similar to designing for connectivity, creating a bespoke network for location based services (LBS) is a skilled and by no means perfected art. There are however some guidelines to follow that will go a long way to securing a successfully deployed LBS network in most environments.

Boiling it down to basics, design goals for location technologies like Ruckus' SPoT system (i.e. RF fingerprinting with pattern matching algorithms) should focus on a few key accuracy factors:

  1. The calibrated data in the radio map should be as close as possible to actual RSS vectors from that same physical location
  2. The calibrated data for other calibration points is as different as possible from the data for the true position
  3. RSS should be as high as possible

Part 2 of this blog will go into more detail of how to actually achieve the above key accuracy factors, so watch this space!