Example ● 83 points ● 8.23 m of distance RadioMap between them ● Few-bodies time (9 AM) ● 115 points ● 42 inside shops Test ● 73 in corridors ● 5.56 m of minimum distance
Example 9 A.M. 4 P.M.
Example ● Results with Airplace ● w-kNN algorithm
Summary ● WiFi is an electromagnetic wave: microwaves. ● It is affected by: ○ Reflection ○ Refraction ○ Absorption ○ Diffraction Changes in the environment can affect values of WiFi measured ● Life tissues absorb microwaves. ● Different tissues have different absorptions Changes in the number of people affect values of WiFi measured ● Different frequencies are affected in different ways.
Part 3: The training step
https://www.flickr.com/photos/shonk/57302289 https://www.flickr.com/photos/shonk/57302289
Reference Data
Reference Data
Reference Data
Reference Data RSSI1, RSSI2, RSSI3, RSSI4, RSSI5
Reference Data Strong, Medium, Medium, Medium, Weak Medium, Strong, Strong, Medium, Medium Weak, Medium, Medium, Strong, Medium Weak, Weak, Medium, Medium, Strong
Reference Data Medium, Strong, Strong, Medium, Medium Medium, Strong, Strong, Medium, N/A Medium, Strong, Medium, Medium, Medium Strong, Strong, Strong, Medium, Medium
Reference Data Medium, Strong, Strong, Medium, N/A https://pixabay.com/es/persona-icono-salida-de-emergencia-1332793/
Reference Data Very extreme case N/A , Weak, Strong, N/A , N/A
Reference Data Medium, Strong, Strong, Medium, Medium Medium, Strong, Strong, Medium, Medium Medium, Strong, Strong, Medium, Medium Medium, Strong, Strong, Medium, Medium
Reference Data Strong ~ -40dBm Medium, Strong, Strong, Medium, Medium Strong ~ -50dBm Medium, Medium, Strong, Medium, Medium Medium, Strong, Strong, Medium, Medium-Weak Strong ~ -30dBm Strong ~ -35dBm Medium-Weak, Strong, Strong, Medium, Medium https://commons.wikimedia.org/wiki/File:Mobile_phone_font_awesome.svg https://www.goodfreephotos.com/vector-images/mobile-cellphone-vector-clipart.png.php https://pixabay.com/es/smartphone-tel%C3%A9fono-m%C3%B3vil-tel%C3%A9fono-1132675/ https://commons.wikimedia.org/wiki/File:Mobile_phone.svg
https://i.ytimg.com/vi/OyRW9uFSmh0/maxresdefault.jpg
Reference Data Strong, Medium, Medium, Medium, Weak Medium, Strong, Strong, Medium, Medium Weak, Medium, Medium, Strong, Medium Weak, Weak, Medium, Medium, Strong
Reference Data Cover all the environment Consider spatial density Consider temporal density Consider device heterogeneity Consider dynamics of the environment https://pixabay.com/p-303768/?no_redirect
Reference Data Now I have the training data... I have a perfect Indoor Location System https://c2.staticflickr.com/4/3228/2373073659_d231a0cc65.jpg
Reference Data You need independent data to fine tune and validate your system http://www.relatably.com/m/img/valid-memes/78c0b38fecebd3c736c8123b34fc69059aedce91ada224ee82677ba7707e14f9.jpg
Validation data Avoid using Training Data! It may provide slanted information. N consecutive fps: (N-1) Training and 1 for validation ? No!
Validation data
Validation data
Validation data
Validation Data Necessary to: 1. Provide an estimation of the IPSs error: geometric error, hit detection rates,... 2. Calibrate your System: kNN algorithms and variants 3. Filter APs 4. Among many other useful operations :-)
Validation Data What happens if there is no Validation Data ? 1. k-fold Cross-Validation of training data 2. Consider groups of samples: ref point, user, device, day, among others to increase diversity and independence of the sets ~ 60-80% training, 40-20% validation
Validation Data Now I have the validation data... I have a perfect Indoor Location System https://c2.staticflickr.com/4/3228/2373073659_d231a0cc65.jpg
Validation Data Your system may work fine All the contexts have not been considered
Operational Data vs. Testing Data Depending on the main objective of the IPS, you may have 1. Operational Data Fingerprints from working system + Feedback from users 2. Testing Data Fingerprints explicitly collected for testing Research: Training + Validation + Test Better if Test Data is Blind UJIIndoorLoc has T + V + Blind TS !!!
Mapping Strategies Slow procedure! High precision in reference points High precision on the fingerprint measures Consecutive / Cumulative values Dense radio map Simple
Mapping Strategies Slow procedure! High precision in reference points High precision on the fingerprint measures Consecutive / Cumulative values Dense radio map Simple
Mapping Strategies Slow procedure! High precision in reference points High precision on the fingerprint measures Consecutive / Cumulative values Dense radio map Simple
Mapping Strategies Fast procedure! You have a few reference or calibration points Depends on user’s velocity Fingerprint readings may be close or far to ref. points Fingerprint attached to a segment of the path The reference point may be displaced to the real path Light radio map Complex
https://c1.staticflickr.com/9/8429/7755469546_61f6d51490_b.jpg
https://pixabay.com/p-1546436/
Storing Data - Raw data - Database: mySQL, HADOOP, mongoDB,... Record as maximum information as possible! mac, rssi, channel, bssid, .... position (XYZ), room, area, floor, building, ...
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