LEMON: An RSS-Based Indoor Localization Technique Israat T. Haque, Ioanis Nikolaidis, and Pawel Gburzynski Computing Science, University of Alberta, Canada

2 Outline of the Talk Introduction LEMON (Location Estimation by Mining Oversampled Neighborhoods) Studied Factors Characterizing Robustness Dynamic Profiling Conclusions

3 Indoor Localization The problem is indoor localization. GPS is ineffective in indoors. Several proposals rely on existing WiFi infrastructure constrained by number and placement of APs. What is the potential of special purpose RF-based localization infrastructure? Can it be done inexpensively?

4 Localization Methods RF-based localization can be based on a number of factors RSS (Received Signal Strength) TOA (Time Of Arrival) AOA (Angle Of Arrival) We narrow our attention to RSS-based approaches Range-free: consider connectivity among neighbors. (DV- distance) Range-based: estimate distance between transmitter and receiver. (RADAR) Fingerprinting or profiling: RSS-based ”map” is generated to be used to estimate location. (RADAR)

5 LEMON Pegs capture signal strength samples Tags send signals ● RSS-based approach that uses profiling. ● RSS samples are collected using a set of nodes called Pegs. ● The sending devices are called a Tag. ● Localization is a two-step process: profiling and estimation.

6 Profiling Means collecting RSS samples from known locations to generate a radio signal strength ”map” of the studied area. The tag transmits and the received RSS from each Peg is collected at a master node and stored in the database. This record is termed as association list. [ (x, y),,,...,, /E/ ] position Peg ID + signal strength Parameters

7 Estimation A node/tag sends a query signal from an unknown location and the RSS of the transmission is recorded by the Pegs. Candidate samples are all the samples in profiled point database that inlcude the Peg P that reported the highest RSS. (This is as a means to pre-filter relevant samples.) Discrepancy between the query and candidate samples is measured using Euclidean distance in the signal space. K matches with the smallest distance from the query sample are determined. The location is their weighted average. [ (?, ?),,,...,, /E/ ]

8 The Hardware EMSPC11 from Olsonet ( ● We use the same device for Pegs and Tags. ● Contains microcontroller and radio transceiver. ● Operates at ~916MHz (in 256 channels) and 8 definable xmit power levels

9 Experiment Data collection phase Experimental set up

10 Results ● We tested LEMON in different indoor areas on our campus. ● Different grid size (3X5m to 10X4m) ● single room and multiple rooms ● Error is measured as the Euclidean distance between the true and estimated locations. ● Average error distance is less than 1m in every cases.

11 Factors Studied The number of nearest neighbor K Number and placement of profiling samples Number and placement of Pegs Elevation and orientation of the device

12 Robustness of LEMON We observed that RSS variation impacts the estimation. To handle this issue, we are looking into three alternative definitions of reliability for the sake of robustness studies. Channel reliability: RSS samples are gathered through multiple channels. Cross-correlation (RSS and distance) of the Pegs for different channels is derived. The most reliable channel is the one with the highest # of Pegs with cross-correlation factor greater than some threshold.

13 Robustness of LEMON (cont'd) Peg Reliability We measure the cross-correlation of the Pegs. Based on which weight is assigned to each Peg while taking RSS discrepancy. RSS reliability RSS is divided into 3 levels; low, medium, and high. For each level reliable RSS is the one with variance smaller than a threshold. This reliability factor (0 or 1) is then considered as a weight factor when taking RSS discrepancy.

14 Dynamic Profiling Use dummy Tags in the monitor area to identify changes in the monitored environment. If environment changes are identified, then replace stored samples in the DB with new ones from the changed environment. We may use iRobot to make the data collection phase automatic.

15 Conclusions An RSS-based profiling indoor localization technique called LEMON is presented. A summary of the factors that impact the performance of LEMON is given. We are looking into characterizing the reliability of the measurements and to subsequently use the reliability characterization to enhance LEMON's performance. Flexible methods of profiling are also proposed.