Dagstuhl Seminar 10042 Demetris Zeinalipour University of Cyprus

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Indoor Data Management: Status and Challenges Demetris Zeinalipour Assistant Professor Data Management Systems Laboratory Department of Computer Science University of Cyprus http: //www. cs. ucy. ac. cy/~dzeina/ Colloquium at Department of Computer Science, University of Cyprus February 17, 2015.

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Recorded Video� http: //youtu. be/m 1 n 6_koot. Jk Slides: http: //dmsl. cs. ucy. ac. cy/presentations. php 2 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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. 3 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Computing Shift • October 2011: The Economist. "Beyond the PC" Sales (Millions) Year • 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% (216 M) Smartphones vs. 48. 4% (186 M) Feature Phones Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 4

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Computing Shift • Power-efficient Von. Neumann Architecture Artifacts • Latest Smartphone SOC (Qualcomm Snapdragon 810) features 4 x A 57 (faster) cores + 4 A 53 (eco) cores with 64 bit support and 20 nm device fabrication • Indicative benchmark: – Intel Xeon X 5650 (6 -cores, 2. 67 GHz): 13, 703 – Snapdragon 801 (4 -cores, 2. 45 GHz): 2, 924 Source: http: //goo. gl/v. YJZCJ 5 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Networking Shift Wireless Data Transfer Rates 4 G ITU peak rates: • 100 Mbps (high mobility, such as trains and cars) • 1 Gbps (low mobility, such as pedestrians and stationary users) Storage Interfaces on Servers: i. SCSI(1 Gbps or 10 Gbps), SAS (6 Gbps), FC(8 Gbps) Plot Courtesy of H. Kim, N. Agrawal, and C. Ungureanu, "Revisiting Storage for Smartphones", Best Paper Award at the 10 th USENIX Conference on File and Storage Technologies (FAST'12), San Jose, CA, February 2012. 6 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Human Shift • A smartphone crowd is constantly moving and sensing providing large amounts of opportunistic data enabling new applications “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. 7 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 The Indoor Frontier • Indoor Applications using Smartphones: – – In-building Navigation: Museums, Airports, Malls Asset Tracking and Hospital Inventory Mngm. Augmented Reality Smart Houses and Elderly support 8 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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. 9 • Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Anyplace Indoor Information Service Modeling Processing / Indexing Privacy, Search Navigator Viewer, Widget Location 10 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Presentation Outline • Indoor Data Management – – – Introduction Location Privacy Modeling Testbeds Latest: Big-data, Device Diversity, Prefetching Radiomaps 11 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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), Bei. Dou-2 (Chinese) – Many civilian uses with advent of GIS technologies since ‘ 70 (ESRI) Navigation Geographic Mapping Precision Agriculture Location-based Services 12 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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). 13 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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 -50 m (indoor) to 1 -30 km (outdoor). Serving cell is not always the nearest. 14 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Location (Indoor) • Inertial Measurement Units (IMU) – • Disadvantages – • 3 D acceleration, 3 D gyroscope, digital compass using dead reckoning (calculate next position based on prior). 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 5 s) have motion co -processors always-on reading sensors and even providing activitity classifiers (driving, walking, running, or sleeping, etc. ) 15 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Location (Technologies) | : Spatial extension where system performance must be guaranteed | Indoor | | Outdoor Hybrid Rainer Mautz, ETH Zurich, 2011 16 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Wi. Fi Fingerprinting in Anyplace • References – – – [Airplace] "The Airplace Indoor Positioning Platform for Android Smartphones", C. Laoudias et. al. , Best Demo Award at IEEE MDM'12. (Open Source!) [Hybrid. Cywee] "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/Dyv. QLSu. I 00 I [Ucy. Cywee] IPSN’ 14 Indoor Localization Competition (Microsoft Research), Berlin, Germany, April 13 -14, 2014. 2 nd Position with 1. 96 m! http: //youtu. be/g. QBSRw 6 q. Gn 4 [Anyplace] Crowdsourced Indoor Localization and Navigation with Anyplace, In ACM/IEEE IPSN’ 14 1 st Position at EVARILOS Open Challenge, European Union (TU Berlin, Germany). Cywee / Airplace Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 17

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Wi. Fi Fingerprinting • Received Signal Strength Indicator (RSSI) – Power measurement present in a received radio signal measured in [ d. Bm, Decibel-milliwatts ] • • • Advantages – – • 80 d. Bm = 100 k. W Transmission power of FM radio (50 km) 0 d. Bm = 1 m. W -80 d. Bm = 10 p. W | Max RSSI (-30 d. Bm) to Min RSSI: (− 90 d. Bm) -110 d. Bm = 0. 01 p. W | Wi. Fi AP is visible but of data range. Readily provided by smartphone APIs. Low power 125 m. W (RSSI) vs. 400 m. W (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) Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 18

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Wi. Fi Fingerprinting • Mapping Area with Wi. Fi Fingerprints – n APs deployed in the area – Fingerprints ri = [ ri 1, ri 2, …, rin] – Averaging 19 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Wi. Fi Fingerprinting • Mapping Area with Wi. Fi 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) 20 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Wi. Fi Positioning • Positioning with Wi. Fi Fingerprint – – Collect Fingerprint s = [ s 1, s 2, …, 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 || ) Radio. Map s = [ -70, -51] NN, KNN, WKNN r 1 = [ -71, -82, (x 1, y 1)] r 2 = [ -65, -80, (x 2, y 2)] … r. N = [ -73, -44, (x. N, y. N)] 21 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Wi. Fi Positioning Demo Video "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!) Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 22

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Hybrid IMU/Wi. Fi Positioning • • We engaged in an Industrial NRE contract with Cywee Taiwan Ltd, a hardware/software motion processing company (ACM Mobisys’ 13) The result was a Hybrid IMU/Wi. Fi Positioning system with the following additional features: – – – Location Fusion: Wi. Fi / IMU (3 -axis accelerometer, gyroscope, and digital compass) using a particle filter. Map. Matching: 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. 23 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Hybrid Wi. Fi/IMU Positioning Demo Video “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/Dyv. QLSu. I 00 I Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 24

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Presentation Outline • Indoor Data Management – – – Introduction Location Privacy Modeling Testbeds Latest: Big-data, Device Diversity, Prefetching Radiomaps 25 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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. Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 26

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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 consumer Privacy Bill of Rights US-EU Safe Harbor guidelines US Do-Not-Track 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. 27 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Assumptions • Sound under passive attacker assumption – Learns from whatever is available on the system (log files, sockets, etc. ) w/out additional info about the user. • No Low-level attacks: – Transport Layer Security and NO Man-in-the-middle attacks Attacks can be thwarted by network operators. • No modified responses – External entity could certify 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 I 2 P) • Our aim is to protect only against an untrusted IPS – NSA is reveiled to track cellphone locations worldwide (5 B records / day) for Co-traveler and other projects. 28 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Location Privacy I can see these Reference Points, where am I? . . . (x, y)! Radio. Map Service 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 Hot. Planet'12, in conjunction with ACM Mobi. Sys '12, ACM, Pages: 9 --14, 2012. 29 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Temporal Vector Map (TVM) Bloom Filter (u's APs) Wi. Fi Set Membership Queries • Contains false positives • Doesn’t contain false negatives Wi. Fi. . . K=3 Positions Wi. Fi Radio. Map (server-side) User u 30 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 TVM – Bloom Filters Bloom 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. AP 13 AP 2 0 1 0 0 1 AP 13 0 0 1 0 0 b Then filter any bloom(row) that overlaps with the query bloom filter (i. e. , bitwise &) 31 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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: 32 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 TVM Continuous Camouflage trajectories 33 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Presentation Outline • Indoor Data Management – – – Introduction Location Privacy Modeling Testbeds Latest: Big-data, Device Diversity, Prefetching Radiomaps. 35 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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 p 1 is p 2 not p 3. Jensen et. al. 2010 Symbolic Model used in Anyplace Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 36

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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. 37 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Modeling Anyplace Viewer: http: //anyplace. cs. ucy. ac. cy/ Video Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 38

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Presentation Outline • Indoor Data Management – – – Introduction Location Privacy Modeling Testbeds Latest: Big-data, Device Diversity, Prefetching Radiomaps. 39 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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 smart. Lab”, G. Larkou, C. Costa, P. Andreou, A. Konstantinides, D. Zeinalipour-Yazti, 27 th USENIX Large Installation System Administration Conference (LISA'13), Washington D. C. , USA, Nov. 3– 8, 2013. Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 40

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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: 3 G, Wifi, Wired, Remote. Smart. Lab: http: //smartlab. cs. ucy. ac. cy/  Static Androids Mobile Androids 41 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Smartphone Testbeds Smart. Lab http: //smartlab. cs. ucy. ac. cy/ Rent Manage See/Click Shell File Sys. Automation Debug Data 42 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Mockup Experiments • A mockup enables testing of a design. – In our context, it refers to the process of feeding a smartphone with recorded values. Video – GPS, RSSI, Accelerometer, Compass, Orientation, Temperature, Light, Proximity, Pressure, Gravity, Altitude – Enables us to test a system without a particular functionality (e. g. , Altitude). “Sensor Mockup Experiments with Smart. Lab", Demo at 13 th 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). Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15 43

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Presentation Outline • Indoor Data Management – – – Introduction Location Privacy Modeling Testbeds Latest: Big-data, Device Diversity, Prefetching Radiomaps. 46 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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 47 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Big Data Processing • Massively process RSS log traces to generate a valuable Radiomap • Processing current logs in Anyplace for a single building takes several minutes! • Challenges in Map. Reduce: – Collect Statistics (count, RSSI mean and standard deviation) – Remove Outlier Values. – Handle Diversity Issues 48 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Device Diversity • Quality: Unreliable Crowdsourcers, Multidevice 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 4 th Intl. Conference on Indoor Positioning and Indoor Navigation" (IPIN '13), Montbeliard-Belfort France, 2013. 49 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Prefetching Radiomaps • Problem: When a users moves inside an indoor space connectivity might be lost – intermittent connectivity. – Wi. Fi AP out of range. 50 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Prefetching Radiomaps • Problem: When a users moves inside an indoor space connectivity might be lost – intermittent connectivity. – Wi. Fi AP out of range. * accepted at IEEE MDM’ 15, Pittsburgh, USA 51 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Prefetching Radiomaps • 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) 52 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Prefetching Radiomaps • Preprocessing: A. Cluster Fingerprints B. Use historic movement data 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? 53 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 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/ Colloquium at Department of Computer Science, University of Cyprus February 17, 2015.

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 | Indoor | | Outdoor | Location (Applications) Rainer Mautz, ETH Zurich, 2011 55 Demetris Zeinalipour, University of Cyprus, Nicosia, Cyprus 17/2/15