BEARS SinBerBEST
APEC: Auto Planner for Efficient Configuration of Indoor Positioning Systems
Ming Jin, Ruoxi Jia, Costas Spanos
# routers # fingerprints
WiFi, GSM, iBeacon…
• Fingerprin(ng: RADAR, Horus • Model based: RF prop. model
Indoor posi(oning system Setup Cost Accuracy
User preferences U(lity
LocaCon Priority
VisiCng Frequency
Maximize!
Budget
APEC: design the fingerprints-‐based IPS that accounts for user preferences and budget constraints
IntroducCon and ContribuCon
Hierarchical Bayesian Signal Model (HBSM)
U(lity Op(miza(on
Implementa(on of APEC
Objec(ve func(on
Algorithm Design
• Learning-‐to-‐learn to improve RSSI es(ma(on
• Objec(ve as a theore(cal solu(on close to actual loss
• Computa(onal tractable as a guidance for field usage
v Local priority map v Local frequency map MEDIUM Priority
LOW Freq.
HIGH Frequency
MED Frequency HIGH Priority
LOW Priority
Objec(ve: customer behavior analysis in stores
User Preferences
Priority'weighted'misclassifica3on'loss'
1
2Expecta3on'over'visi3ng'freq.'
3HBSM'randomness'
4 Number/loca3on'of'routers/fingerprints'
OpCmizaCon Framework
Minimiza'on)of)the)expected)loss:)
Weighted)cost)of)loca'on)confusion)
Frequency) Priority)
Misclassifica'on)rate)
APEC Algorithm
v θrt (router locations): Given M possible locations, choose K to place the routers. v θfp (fingerprints): Distribute the number of fingerprints to be collected.
Tasks:
v Exhaustive: search all possible router/fingerprints combinations (intractable!) v Greedy: stochastically optimize through coordinate descent (heuristic!)
Strategies:
Hierarchical Bayesian Signal Model (HBSM)
How can we make efficient use of fingerprints?
Pass-‐Loss Model Fingerprints Parameter Es(ma(on
Fingerprints Es(ma(on
Gaussian Process
Map
*Neighborhood covariance func(on
Top layer: Hyper-‐priors
Mean of RSSI follows Loss-‐Path Model and Gaussian Process
BoWom layer: ObservaCons Inference
Es(ma(on
RSSI observa(on at loca(on i for K routers that work independently
HBSM: SpaCal variance
Measurement error
v Radio map reconstrucCon: empirical Bayes and Gaussian process regression.
Length'(m)'
Width'(m
)'
Toy Case Study
Heuris(c 1: increment the number of fps at many random batches of locaCons and choose the best
Router'set'index'
Expe
cted
'loss'
Op#mal'(Exhaus#ve)''
Decrease'of'batch'size'
Random'selec3on'
Sorted'from'the'highest'loss'to'the'lowest'for'each'router'configura5on'
BEST!'
Router'set'index'
Expe
cted
'loss'
Top$10$router$setups$
Random$selec3on$Monotone'increasing'
Sorted'from'the'lowest'loss'to'the'highest'for'each'router'configura5on'
4
Heuris(c 2: router locaCons can be chosen assuming uniform fingerprints allocaCon
Priority and Freq. Map • HIGH Priority for
cubicles: automatic climate control
• MED Priority for shared spaces: energy apportionment
• LOW Priority for corridors
Field Deployment
Hypothesis 1: the expected cost is a good indicator of the actual cost of the system
• Strong correlation between expected & actual cost
• Predict IPS performance based on the router-fingerprints configuration
Expected(cost(op+mized(by(APEC(
Actual(cost(
Expected(cost:(
Actual(cost:(Theore&cal*
Actual*misclass.*
Hypothesis 2: APEC Greedy performs well for the actual cost of the system (solu(on superiority)
Alignment)of)expected)cots)to)actual)cost)
Exp$A:$5"routers" Exp$B:$7"routers"
Size%represents%density%of%fingerprints%
High%priority%areas%1
3
2Lower%priority%areas%
Closer%to%routers%
Visualiza(on
Conclusion and Future v APEC: systemaCcally opCmizes the locaCons of APs and fingerprints v Implement and visualize APEC configuraCon on mobile pla\orms
Publica(on: APEC: Auto Planner for Efficient ConfiguraCon of Indoor PosiConing Systems, 9th InternaConal Conference on Mobile Ubiquitous CompuCng, Systems, Services and Technologies, 2015
