indoor data management: status and challenges

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010

Talk at Skolkovo Institute of Science and Technology, Moscow, Russia, October 2, 2014.

Indoor Data Management: Status and Challenges

Demetris Zeinalipour

Data Management Systems Laboratory

Department of Computer Science

University of Cyprus

http://dmsl.cs.ucy.ac.cy/


Demetris Zeinalipour, Skoltech, Moscow, Russia, 2/10/142

Motivation• People spend 80-90% of their time indoors –

USA Environmental Protection Agency 2011.• >85% of data and 70% of voice traffic

originates from within buildings – Nokia 2012.



Computing Shift• October 2011: The Economist. "Beyond the PC"

• February 2012: Canalys validated Economist's forecast, initiating the Post-PC era.

• April 2013: IDC reports another important development– Smartphone sales exceed the sale of Feature phones for the

first time in history due to increased sales in developing regions.

– 51.6% (216M) Smartphones vs.

48.4% (186M) Feature Phones

Sa

les (M

illion

s)

Year



Computing Shift

Source: http://goo.gl/vYJZCJ

• Power-efficient Von-Neumann Architecture Artifacts

• Latest Smartphone SOC (Qualcomm Snapdragon 810) features 4 x A57 (faster) cores + 4 A53 (eco) cores with 64 bit support and 20nm device fabrication

• Indicative benchmark:– Intel Xeon X5650 (6-cores,

2.67GHz): 13,703

– Snapdragon 801 (4-cores, 2.45GHz): 2,924



Networking Shift Wireless Data Transfer Rates

Plot Courtesy of H. Kim, N. Agrawal, and C. Ungureanu, "Revisiting Storage for Smartphones", Best Paper Award at the 10th USENIX Conference on File and Storage Technologies (FAST'12), San Jose, CA, February 2012.

4G ITU peak rates:•100 Mbps (high mobility, such as trains and cars) •1Gbps (low mobility, such as pedestrians and stationary users)

Storage Interfaces on Servers:iSCSI(1Gbps or 10Gbps), SAS (6Gbps), FC(8Gbps)



• A smartphone crowd is constantly moving and sensing providing large amounts of opportunistic data enabling new applications

Human Shift

“Crowdsourcing with Smartphones”, Georgios Chatzimiloudis, Andreas Konstantinidis, Christos Laoudias, Demetrios Zeinalipour-Yazti, IEEE Internet Computing, Special Issue: Sep/Oct 2012 - Crowdsourcing, May 2012. IEEE Press, Volume 16, pp. 36-44, 2012.



The Indoor Frontier• Indoor Applications using Smartphones:

– In-building Navigation: Museums, Airports, Malls– Asset Tracking and Inventory Management– Augmented Reality– Smart Houses and Elderly support



Indoor Data Management• Indoor Data Management, deals with all aspects of

handling data as a valuable resource: acquisition, modeling, processing, query processing, privacy, energy, etc.

• In this overview talk, I will attempt to cover the current state but also identify future challenges.

• The presentation is carried out through the lens of an experimental Indoor Information System we developed at the University of Cyprus, coined Anyplace.



Viewer, Widget

Navigator

Modeling

Anyplace Indoor Information Service

Location

Processing / Indexing

Privacy, Search



Presentation Outline• Indoor Data Management

– Introduction– Location– Privacy– Modeling– Testbeds– Latest: Big-data, Device Diversity,

Prefetching Radiomaps



Location• Location (position): identifies a point or an area on

the Earth's surface.• Global Navigation Satellite Systems (GNSS) have played

an important role in Outdoor (Spatial) Data Management:• Current: Global Positioning System (US), GLONASS (Russian)• Upcoming: Galileo (European), Indian Regional Navigation Satellite

System (IRNASS), BeiDou-2 (Chinese) – Many civilian uses with advent of GIS technologies since ‘70 (ESRI)

Location-based ServicesPrecision AgricultureGeographic MappingNavigation



Location (Outdoor)• GNSS Drawbacks for Indoor Location:

– Low availability indoors due to the blockage or attenuation of the satellite signals.

– High start-up time.– Power Demanding (continuously receive signals).

Zeinalipour et. Al IEEE Internet Computing 2012



Location (Outdoor)• Cell ID:

– Cell ID is the Unique Identifier of Cellular Towers.

• Cell ID Databases– Skyhook Wireless (2003), MA, USA (Apple,

Samsung): 30 million+ cell towers, 1 Billion Wi-Fi APs, 1 billion+ geolocated IPs, 7 billion+ monthly location requests and 2.5 million geofencable POIs.

– Google Geolocation “Big” Database (similar)

Disadvantages:• Low accuracy: 30-50m

(indoor) to 1-30km (outdoor).• Serving cell is not always the

nearest.



Location (Indoor)• Inertial Measurement Units (IMU)

– 3D acceleration, 3D gyroscope, digital compass using dead reckoning (calculate next position based on prior).

• Disadvantages– Suffers from drift (difference between where the

system thinks it is located, and the actual location)

• Advantages– Sensors are available on smartphones.– Newer smartphones (iphone 5s) have motion co-

processors always-on reading sensors and even providing activitity classifiers (driving, walking, running, or sleeping, etc.)

wikipedia



Location (Technologies)

Rainer Mautz, ETH Zurich, 2011

: Spatial extension where system performance must be guaranteed

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Hybrid



• References– [Airplace] "The Airplace Indoor Positioning

Platform for Android Smartphones", C. Laoudias et. al., Best Demo Award at IEEE MDM'12. (Open Source!)

– [HybridCywee] "Indoor Geolocation on Multi-Sensor Smartphones", C.-L. Li, C. Laoudias, G. Larkou, Y.-K. Tsai, D. Zeinalipour-Yazti and C. G. Panayiotou, in ACM Mobisys'13. Video at: http://youtu.be/DyvQLSuI00I

– [UcyCywee] IPSN’14 Indoor Localization Competition (Microsoft Research), Berlin, Germany, April 13-14, 2014. 2nd Position with 1.96m! http://youtu.be/gQBSRw6qGn4

– [Anyplace] Crowdsourced Indoor Localization and Navigation with Anyplace, In ACM/IEEE IPSN’14

– 1st Position at EVARILOS Open Challenge, European Union (TU Berlin, Germany).

Cywee / Airplace

WiFi Fingerprinting in Anyplace



WiFi Fingerprinting• Received Signal Strength Indicator (RSSI)

– Power measurement present in a received radio signal measured in [ dBm, Decibel-milliwatts, x = 10.log10(P / 1mW) ]

• 80 dBm = 100 kW Transmission power of FM radio (50 km)• 0 dBm = 1 mW• -80dBm = 10 pW | Max RSSI (-30dBm) to Min RSSI: (−90 dBm)• -110dBm = 0.01 pW | WiFi AP is visible but out of data range.

• Advantages– Readily provided by smartphone APIs .

– Low power 125mW (RSSI) vs. 400 mW (transmit)

• Disadvantages– Complex propagation conditions (multipath, shadowing)

due to wall, ceilings.

– RSS fluctuates over time at a given location (especially in open spaces).

– Unpredictable factors (people moving, doors, humidity)



WiFi Fingerprinting• Mapping Area with WiFi Fingerprints

– n APs deployed in the area

– Fingerprints ri = [ ri1, ri2, …, rin]

– Averaging )(1

1 mrrM

m iMi



WiFi Fingerprinting• Mapping Area with WiFi Fingerprints

– Repeat process for rest points in building. (IEEE MDM’12)– Use 4 direction mapping (NSWE) to overcome body

blocking or reflecting the wireless signals. – Collect measurements while walking in straight lines

(IPIN’14)



WiFi Positioning• Positioning with WiFi Fingerprint

– Collect Fingerprint s = [ s1, s2, …, sn]

– Compute distance || ri - s || and position user at:

• Nearest Neighbor (NN)

• K Nearest Neighbors (wi = 1 / K)

• Weighted K Nearest Neighbors (wi = 1 / || ri - s || )

s = [ -70, -51]

RadioMap

r1 = [ -71, -82, (x1,y1)]r2 = [ -65, -80, (x2,y2)]…rN = [ -73, -44, (xN,yN)]

NN, KNN, WKNN



WiFi Positioning Demo

"The Airplace Indoor Positioning Platform for Android Smartphones", C. Laoudias, G. Constantinou, M. Constantinides, S. Nicolaou, D. Zeinalipour-Yazti, C. G. Panayiotou, Best Demo Award at IEEE MDM'12. (Open Source!)

Video



Hybrid IMU/WiFi Positioning• We engaged in an Industrial NRE contract with Cywee Taiwan Ltd, a

hardware/software motion processing company (Mobisys’13)• The result was a Hybrid IMU/WiFi Positioning system with the following additional

features:– Location Fusion: WiFi / IMU (3-axis accelerometer, gyroscope, and digital compass) using a

particle filter.– MapMatching: to handle inaccurate IMU location estimates (e.g., void passing through walls).– Magnetic Mapping: detect and handle magnetic abnormalities due to electrical appliances

and refining the orientation.



Hybrid WiFi/IMU Positioning Demo

“Indoor Geolocation on Multi-Sensor Smartphones", C.-L. Li, C. Laoudias, G. Larkou, Y.-K. Tsai, D. Zeinalipour-Yazti and C. G. Panayiotou, in ACM Mobisys'13. Video at: http://youtu.be/DyvQLSuI00I

Video



Location Privacy• An Indoor Positioning Service can continuously

“know” (surveil, track or monitor) the location of a user while serving them.

• Location tracking is unethical and can even be illegal if it is carried out without the explicit user consent.

• Imminent privacy threat, with greater impact that other location tracking concerns, as it can occur at a very fine granularity. It reveals:

– The stores / products of interest in a mall.– The book shelves of interest in a library– Artifacts observed in a museum, etc.



Location Privacy• Users don’t know where IPS operate their data and

whether these conform or not to latest legislative efforts and reforms:

– EU Data Protection Directive.

– US White House Counsumer Privacy Bill of Rights

– US-EU Safe Harbor guidelines

– US Do-Not-Trck Online Act

• IPS might become attractive targets for hackers, aiming to steal location data and carry out illegal acts (e.g., break into houses).

• IPS should be considered as fundamentally untrusted entities, so we aim to devise techniques that are exploit IPS utility with controllable privacy to the user.



Assumptions• No Low-level attacks:

– No spoofing or buffer overflow attacks by IPS– Attacks can be thwarted by network operators, Secure

communication channels, firewalls, etc.

• No modified responses– External entity could certify that IPS returns consistent responses.

• No Access to User Identifiers– Mobile Equipment Identifier (MEID), Network

Identifiers (MAC, IP), Cookies and Tracking Codes.– Can be prevented, changed or obfuscated (e.g., IP

anonymization networks I2P)

• Our aim is to protect only against an untrusted IPS– NSA is reveiled to track cellphone locations worldwide (5B records /

day) for Co-traveler and other projects.



Location Privacy

RadioMap Service

...

I can see these Reference Points,

where am I?

(x,y)!

User u- Privacy-Preserving Indoor Localization on Smartphones, Andreas Konstantinidis, Paschalis Mpeis, Demetrios Zeinalipour-Yazti and Yannis Theodoridis, in IEEE TKDE’14 (second round).- Towards planet-scale localization on smartphones with a partial radiomap", A. Konstantinidis, G. Chatzimilioudis, C. Laoudias, S. Nicolaou and D. Zeinalipour-Yazti. In ACM HotPlanet'12, in conjunction with ACM MobiSys '12, ACM, Pages: 9--14, 2012.



Temporal Vector Map (TVM)

RadioMap (server-side)

WiFi

WiFi

WiFi

...

Bloom Filter (u's APs)

K=3 Positions

User u

Set Membership Queries• Contains false positives • Doesn’t contain false negatives



TVM – Bloom FiltersBloom filters – basic idea:

- allocate a vector of b bits, initially all set to 0- use h independent hash functions to hash every

Access Point seen by a user to the vector.

The filters any bloom(row) that overlaps with the query bloom filter (i.e., bitwise &)

0 1 0 0 1 0 0 1 0 0

AP2AP13AP2 AP13

b



TVM – Bloom Filters• The most significant feature of Bloom filters is that there

is a clear tradeoff between b and the probability of a false positive.– Small b: Too many false positives– Large b: “No” false positives

• Given h optimal hash functions, b bits for the Bloom filter we can estimate the amount of false positives produced by the Bloom filter:

– False Positive Ratio:

– Size of vector:

h-h/b )e-(1fpr

)fpr-ln(1

h b

h



TVM ContinuousCamouflage trajectories





Prefetching Radiomaps.



Modeling• Indoor spaces exhibit complex topologies. They are

composed of entities that are unique to indoor settings: – e.g., rooms and hallways that are connected by doors.– Conventional Euclidean distances are inapplicable in indoor space,

e.g., NN of p1 is p2 not p3.

Jensen et. al. 2010

Symbolic Model used in Anyplace



Modeling• Geometric Model: uses points in N-dimensional

space, allowing the calculation of Lp-norm distances.• Symbolic Model: uses reference points (e.g., rooms)

to establish a structure for distance computation.• We use a graph-based model G(V,E), V={rooms}

E={doors,corridors,stairs,elevators} - Becker 2005.• This allows direct usage of graph algorithms

(shortest path, connectivity, traversals, etc.)• To provide spatial range queries, we additionally need

to a complementary geometric extend to V.



Anyplace Viewer: http://anyplace.cs.ucy.ac.cy/

Modeling

Video



Smartphone Testbeds• Experimenting with real smartphones

encapsulates logistical challenges.– Measure power consumption with profiler or

localization accuracy at various locations in a building without moving around.

– Manage experimental data for trace-driven experimentation (repeatability or mockup experiments).

– Manage a smartphone cluster on 50 buses moving in a city and collecting network state (MAC, Cell-ID, etc.)

– Study Linear Correlation of RSSI across different 802.11 networking stacks in a controlled environment.

"Managing smartphone testbeds with smartLab”, G. Larkou, C. Costa, P. Andreou, A. Konstantinides, D. Zeinalipour-Yazti, 27th USENIX Large Installation System Administration Conference (LISA'13), Washington D.C., USA, Nov. 3–8, 2013.



Smartphone Testbeds• We developed a comprehensive architecture for

managing smartphone clusters through the web.– 40+ Android Devices, Real Sensors, Real Computing Stack– Different Connection Modalities: 3G, Wifi, Wired, Remote.

Static Androids Mobile Androids

SmartLab: http://smartlab.cs.ucy.ac.cy/



Smartphone Testbeds

Rent

See/Click

Shell

File Sys.

Automation

Debug

Data

Manage

SmartLab http://smartlab.cs.ucy.ac.cy/



Mockup Experiments

“Sensor Mockup Experiments with SmartLab", Demo at 13th ACM Intl. Conference of Information Processing in Sensor Networks (IPSN'14), Berlin, Germany, 2014.

"Managing big data experiments on smartphones", Distributed and Parallel Databases (DAPD '14), Springer US, 2014 (accepted).

• A mockup enables testing of a design.– In our context, it refers to the

process of feeding a smartphone with recorded values.

– GPS, RSSI, Accelerometer, Compass, Orientation, Temperature, Light, Proximity, Pressure, Gravity, Altitude

– Enables us to test a system without a particular functionality (e.g., Altitude).

Video



Mockup Experiments

Crowdsource RSS of AP in

buildings

Benchmark Localization Algorithms



Mockup Experiments• Example Mockup Experiments

– A) Testing 3 localization algorithms on different phones.– B) Map simulation on 8 devices for faster simulation.



Big Data Processing• Logging “big” quantities of RSSI fingerprints in the cloud,

calls for scalable processing architectures.– Historic RSSI for buildings (Offline Data)– Online RSSI that arrive from Crowdsourcers (Online Data)

• Apache Hadoop is nowadays widely endorsed by the industry and academia for offline processing of data using the Map/Reduce programming paradigm.

• Newer trends provide:• performance

abstractions.• In-Memory

processing concepts



• Massively process RSS log traces to generate a valuable Radiomap

• Processing current logs in Anyplace for a single building takes several minutes!

• Challenges in MapReduce:– Collect Statistics (count,

RSSI mean and standard deviation)

– Remove Outlier Values.– Handle Diversity Issues

Big Data Processing



• Quality: Unreliable Crowdsourcers, Multi-device Issues, Hardware Outliers, Temporal Decay, etc.– Remark: There is a

Linear Relation between RSS values of devices.

– Challenge: Can we exploit this to align reported RSS values?

"Crowdsourced Indoor Localization for Diverse Devices through Radiomap Fusion", C. Laoudias, D. Zeinalipour-Yazti and C. G. Panayiotou, "Proceedings of the 4th Intl. Conference on Indoor Positioning and Indoor Navigation" (IPIN '13), Montbeliard-Belfort France, 2013.

Device Diversity




• Problem: When a users moves inside an indoor space connectivity might be lost – intermittent connectivity.– WiFi AP out of range.

• As such, continuous localization with input from the IPS is a challenging task.

• Preloading the complete building or area map (like in GPS) is difficult due to scale and due to frequently updated data (by crowdsourcers)



Prefetching Radiomaps• Preprocessing:

A. Cluster Fingerprints

B. Use historic movement data

or building-rank to build

probability transition graph.

• Task:– Given a user at a position

with network connectivity exploit transition graph to compute the next cluster of fingerprints to download?


Talk at Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia, October 2, 2014.

Indoor Data Management: Status and Challenges

Thanks – Questions?Demetris Zeinalipour

Data Management Systems Laboratory

Department of Computer Science

University of Cyprus

http://dmsl.cs.ucy.ac.cy/



Crowd Micro-blogging

• Social Media (Facebook,Linked-in,Twitter) utilize a Social Graph (friendship, follower, followee) to map the relationships between users.

• Challenges:– Applications many times require location-based

rather than social-based interactions, e.g.,• Send out Help message to closest neighbors.• Car-to-Car communication.

– Location-based services suffer from bootstrapping• e.g., Check in to Foursquare and find nobody else there

– Interacting with the Crowd, calls for stronger Privacy!



Crowd Micro-blogging• We developed an experimental social network after

receiving an Industrial Award by the Appcampus Program (Microsoft, Nokia & Aalto, Finland).– Ranked among the 5 best apps of the given program among

3500 submissions.– A few thousand downloads and active users on our big-data

backend.

http://rayzit.com/



• Rayzit User Map

Crowd Micro-blogging

Research: Paper currently under submission at a major Data Engineering conference in respect to “Distributed All K Nearest Neighbor Queries”



Location (Applications)

Rainer Mautz, ETH Zurich, 2011

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