1. CNR – ISTI and University of PisaPisa, Italy 1 Accuracy limits of in-room localisation using RSSI Francesco Potortì ◊, Alessandro Corucci ●, Paolo Nepa ●, Francesco Furfari ◊, Paolo Barsocchi ◊, Alice Buffi ● ◊ CNR – ISTI Istituto di Scienza e Tecnologie dell'Informazione “A. Faedo” ● Università degli Studi di Pisa

2. CNR – ISTI and University of PisaPisa, Italy 2 Finding accuracy limits ● Fixed motes transmitters – anchors ● On-body motes receivers – mobiles ● IEEE 802.15.4 radios – 2.4 GHz ● Only information is received power strenght – RSSI What is the maximum attainable location accuracy?

3. CNR – ISTI and University of PisaPisa, Italy 3 Assumptions and simplifications ● Complete, detailed knowledge of the environment ● Normal positioning error, σ = 10 cm ● Normal RSSI measurement error, σ = 2 dB ● Indoor, single-room scenario ● Dipole antennas ● One mobile receiver, many anchors ● Influence of body is not modelled

4. CNR – ISTI and University of PisaPisa, Italy 4 Modelling RSSI distribution - Room 7m x 5m, 3m high - One door, two cabinets - Ray-tracing algorithm - 3-d propagation model - 3 rd order reflections - 1 st order edge diffractions - All materials modelled - Horizontal 3 cm grid - 90 cm from the ground

5. CNR – ISTI and University of PisaPisa, Italy 5 A simple approximation: no wall reflections ● Used in most algorithmic methods ● Usually monotonic, does not account for antenna radiation pattern

6. CNR – ISTI and University of PisaPisa, Italy 6 Reality is very different...

7. CNR – ISTI and University of PisaPisa, Italy 7 … and very diverse

8. CNR – ISTI and University of PisaPisa, Italy 8 What is likelihood? ● A mobile is in the room, in an unknown location, and receives radio signals from a number of anchors ● It measures the signal strengths (RSSI values) and puts them into a vector ● For each point on the map, it estimates the probability of observing that RSSI vector when located on the given point, for Normal distribution of positioning and receiving errors ● The above probability is the likelihood at that point ● A common criterion for identifying the actual location is choosing the maximum likelihood point

9. CNR – ISTI and University of PisaPisa, Italy 9 ● 1-6-open ● 1-2-5 ● 002634.png Likelihood without reflections ● Likelihood of the mobile being in a given spot given RSSI received from three different anchors, with positioning and measurement errors. ● Maximum likelihood is red.

10. CNR – ISTI and University of PisaPisa, Italy 10 ● 1-6 ● 1-2-5 ● 000532.png Likelihood with reflections ● Same case as before (RSSI values of -51, -47, -48 dBm), but with reflections. ● Likelihood is maximum (light blue) in many places around the room.

11. CNR – ISTI and University of PisaPisa, Italy 11 Typical likelihood map with 4 anchors ● The circle represents the positioning error around the actual location ● The cross is the center of mass of top 10% likelihood

12. CNR – ISTI and University of PisaPisa, Italy 12 Typical likelihood map with 18 anchors ● Error is typically lower with more anchors ● Maximum likelihood gives better performance than center of mass

13. CNR – ISTI and University of PisaPisa, Italy 13 Performance ● No analytic way to compute performance: Montecarlo methods are needed ● At least 10000 randomly positioned points to get significant accuracy ● With 18 anchors, median localisation error is 21 cm ● Smaller anchor sets are chosen as the best ones

14. CNR – ISTI and University of PisaPisa, Italy 14 Meaning of performance ● Given the anchor positions, the maximum likelihood method gives the best possible performance, because it exploits all available information ● Good for obtaining a performance limit ● Does not tell how to attain that limit ● Direct use of maximum likelihood method is too complex: it requires – A detailed map of the environment – Computation of the RSSI map for each anchor – Computation of maximum likelihood for a given RSSI vector ● Performance is best when information is high

15. CNR – ISTI and University of PisaPisa, Italy 15 Possible uses ● Find the best placement for a given number of anchors, that is, the one that maximises information ● Remove the least important anchors from a given set of anchors ● Compare the performance of a given localisation method against the maximum likelihood limit ● Investigate how much information is added when considering different informations: – Using three-dimensional info – Using a number of mobile sensors – Tracking the mobile sensor with memory