A Location-based Management System for Enterprise Wireless LANS Ranveer Chandra, Jitendra Padhye, Alec Wolman, Brian Zill Microsoft Research, NSDI 2007

Motivation Location information in WLAN management tool Helpful to address: – Is AP placement adequate? – Any areas that clients consistently experience poor performance? – How does distance affect a client’s performance? – Any areas that have no coverage?

System Architecture

Air Monitors: packet logging and filtering Database: storage of summaries of data Inference engine: does complex data analysis and answers management questions

System Design Determining AirMonitor location – Use login history to determine the primary user – Get the location of AirMon based on user-office # mapping – Security and privacy? Locating a Transmitter: – StrongestAM – Centroid – Spring-and-ball AP tracking, each AirMon is assigned to track the 3 nearest Aps Address matching: Determine who sent what data

StrongestAM Given the start and end time and sender MAC address, find out the avg RSSI seen by each AirMon for that transmitter Report the location of the AirMon as the location of the client Disadvantages: – Fluctuations make it impossible to pick strongest AirMom without ambiguity

Centroid Choose all AirMons within 15% of the strongest AirMon and the center of all these AirMons No AirMons within 15%, then choose the location of the strongest AirMon Not affected by fluctuation in the RSSI Disadvantage: the placement of AirMons is all on one side, a significant error

Spring-and-Ball Each AirMon broadcasts probes for the others to compute avg RSSI; a profile is then generated for each AirMon that captures the RSSI-distance relationship Use Centroid to get the approximate location of the transmitter Compute the difference bet. the estimated RSSI and the actual RSSI observed by each AirMon

Spring-and-Ball (cont.) Each difference is a force, sign signifies whether the “spring” is compressed or stretched Compute the cumulative force of all these force vectors Move the estimated location by a small distance Recalculate the cumulative force until: – 5000 iterations have elapsed – The magnitude of the error falls below 0.1 – The force at the new location exceeds by 10% the min force seen so far Take the location that generates the min force

Spring-and-Ball (cont.) Overcome the key limitation of Centroid algorithm, i.e., all AirMons are on one side Issues: – Frequency of profiling – Do time-of-day effects create significant errors? – What type of curve to fit the data?