Is Your Car Talking with My Smart Phone? or Distributed Sensing and Computing in Mobile Networks

1. Is Your Car Talking with My Smart Phone?orDistributed Sensing and Computing in Mobile Networks CristianBorcea Department of Computer Science, NJIT

2. Wireless Computing/Sensing Systems • >3.3B cell phones vs. 600M Internet-connected PC’s in 2007 • >600M cell phones with Internet capability, rising rapidly • New cars come equipped with GPS, navigation systems, and lots of sensors • Sensor deployment just starting, but some estimates ~5-10B units by 2015

3. Ubiquitous Computing Vision • Computing, communication, and sensing anytime, anywhere • Wireless systems cooperate to achieve global tasks • How close are we from this vision?

4. So Far … Not Very Close • Nomadic computing • Devices: laptops • Internet: intermittent connectivity • Work: typical desktop applications • Mobile communication • Devices: PDAs, mobile phones, Blackberries • Internet: continuous connectivity • Work: email and web • Experimental sensor networks • Devices: Berkeley/Crossbow motes • Internet: possible through base station • Work: monitor environment, wildlife

5. Why? • Hard to program distributed applications over collections of wireless systems • Systems • Distributed across physical space • Mobile • Heterogeneous: both hardware and software • Resource-constrained: battery, bandwidth, memory • Networks • Large scale • Volatile: ad hoc topologies, dynamic resources • Less secure than wired networks

6. Our Research • What programming models, system architectures, and protocols do we need when everything connects?

7. Outline • Motivation • MobiSoC: A middleware for mobile social computing • Migratory Services: A context-aware service model for mobile ad hoc networks • RBVT: Road-based routing using real-time traffic information in vehicular networks • Conclusions • New projects • Mobius: A socially-aware peer-to-peer network infrastructure • Traffic safety using vehicular networks and sensor networks

8. Social Computing in the Internet • Social networking applications improve social connectivity on-line • Stay in touch with friends • Make new friends • Find out information about events and places Myspace Facebook LinkedIn

9. Mobile Social Computing • More than just social computing anytime, anywhere • New applications will benefit from real-time location and place information • Smart phones are the ideal devices • Always with us • Internet-enabled • Locatable (GPS or other systems) • 200-400 MHz processors • 64-128 MB RAM • GSM, WiFi, Bluetooth • Camera, keyboard • Symbian, Windows Mobile, Linux • Java, C++, C#

10. Mobile Social Computing Applications (MSCA) • People-centric • Are any of my friends in the cafeteria now? • Is there anybody nearby with a common background who would like to play tennis? • Place-centric • How crowded is the cafeteria now? • Which are the places where CS students hang out? • How to program MSCA? • Challenges: capturing the dynamic relations between people and places, location systems, privacy, battery power

11. MobiSoC Middleware • Common platform for capturing, managing, and sharing the social state of a physical community • Discovers emergent geo-social patterns and uses them to augment the social state

12. MobiSoC Architecture

13. Learning Emergent Geo-Social Patterns Example: GPI Algorithm • GPI identifies previously unknown social groups and their associated places • Fits into the people-place affinity learning module • Clusters user mobility traces across time and space • Its results can • Enhance user profiles and social networks using newly discovered group memberships • Enhance place semantics using group meeting times and profiles of group members

14. Location System • Hardware-based location systems not feasible • GPS doesn’t work indoors • Deploying RF-receivers to measure the signals of mobiles is expensive and not practical for large places • The user has no control over her location data! • Software-based location systems that run on mobile devices preferable • Use signal strength and known location of WiFi access points or cellular towers • Allow users to decide when to share their location

15. Mobile Distributed System Architecture • MSCA split between thin clients running on mobiles and services running on servers • MSCA clients communicate synchronously with the services and receive asynchronous events from MobiSoC • Advantages • Faster execution • Energy efficiency • Improved trust

16. Clarissa: Location-enhanced Mobile Social Matching MatchType=Hangout Time: 1-3PM Co-Location: required Match Alert Match Alert MatchType=Hangout Time: 2-4PM Co-Location: required

17. Tranzact: Place-based Ad Hoc Social Collaboration Hungry What’s on the menu? Chicken teriyaki Cafeteria

18. MobiSoC Implementation • Runs on trusted servers • Beta release: https://sourceforge.net/projects/mobisoc/ • Service oriented architecture over Apache Tomcat • Core services written in JAVA • API is exposed to MSCA services using KSOAP • KSOAP is J2ME compatible and can be used to communicate with clients • Client applications developed using J2ME on WiFi-enabled Windows-based smart phones • Clarissa: http://apps.facebook.com/matching/ • Location engine: modified version of Intel’s Placelab • Accuracy 10-15 meters

19. Outline • Motivation • MobiSoC: A middleware for mobile social computing • Migratory Services: A context-aware service model for mobile ad hoc networks • RBVT: Road-based routing using real-time traffic information in vehicular networks • Conclusions • New projects • Mobius: A socially-aware peer-to-peer network infrastructure • Traffic safety using vehicular networks and sensor networks

20. Ad Hoc Networks as Data Carriers • Traditionally, ad hoc networks used to • Connect mobile systems (e.g., laptop, PDA) to the Internet • Transfer files between mobile systems Internet Read email, browse the web File transfers

21. Ad Hoc Networks as People-Centric Mobile Sensor Networks • Typical devices: smart phones and vehicular systems • Run distributed services • Acquire, process, disseminate real-time information from proximity of regions, entities, or activities of interest • Have context-aware execution • Often interact for longer periods of time with clients Traffic jam predictor Entity tracking Parking spot finder

22. Problems with Traditional Client-Server Model in Ad Hoc Networks • When service stops satisfying context requirements, client must discover new service • Overhead due to service discovery • State of the old service is lost • Not always possible to find new service

23. Migratory Services Model Virtual service end-point Migratory Service MS Service Migration State C Client n3 MS Migratory Service State n2 n1 Context Change! (e.g., n2 moves out of the region of interest) MS cannot accomplish its task on n2 any longer

24. One-to-One Mapping between Clients and Migratory Services MS1 State n4 n5 n2 n1 n3 C1 Create Migratory Service M Meta-service C2 MS1 MS2 MS2 State State State

25. Migratory Services Framework

26. TJam: Migratory Service Example • Predicts traffic jams in real-time • The request specifies region of interest • Service migrates to ensure it stays in this region • Uses history (service execution state) to improve prediction • TJam utilizes information that every car has: • Number of one-hop neighboring cars • Speed of one-hop neighboring cars Inform me when there is high probability of traffic jam 10 miles ahead

27. Implementation • Implemented in Java • Java 2 Micro-Edition (J2ME) with CLDC 1.1 and MIDP 2.0 • J2ME with CDC • Development using HP iPAQs (running Linux), Nokia phones (running Symbian) • SM platforms • Original SM on modified KVM (HP iPAQs) – migration state captured in the VM • Portable SM on Java VM, J2ME CDC (Nokia 9500) – migration state captured using bytecode instrumentation

28. Outline • Motivation • MobiSoC: A middleware for mobile social computing • Migratory Services: A context-aware service model for mobile ad hoc networks • RBVT: Road-based routing using real-time traffic information in vehicular networks • Conclusions • New projects • Mobius: A socially-aware peer-to-peer network infrastructure • Traffic safety using vehicular networks and sensor networks

29. Safer driving Quick dissemination of traffic alerts More fluid traffic Real-time dissemination of traffic conditions, traffic queries, dynamic route planning In-vehicle computing & entertainment P2P file sharing, gaming, location-aware advertisements Vehicular Ad Hoc Networks (VANET) Vehicle-to-vehicle short-range wireless communication

30. EZCab: Automatic Cab Booking Application Need a cab • Use mobile ad hoc networks of cabs to book a free cab • Used HP iPaqs, GPS, WiFi

31. TrafficView: Traffic Monitoring Application • Provides dynamic, real-time view of the traffic ahead of you • Initial prototype • Laptop/PDA running Linux • WiFi & Omni-directional antennas • GPS & Tiger/Line-based digital maps • Road identification software • Second generation prototype (developed by Rutgers Univ) adds • Touch screen display • 3G cards • Possibility to connect to the OBD system

32. Routing still a Big Problem for VANET D N1 S a) At time t S N1 N1 S D N2 N2 D Dead end road b) At time t+Δt • Topological routing (e.g., AODV, DSR) suffers from frequent broken paths • Geographical routing (e.g., GPSR) frequently routes packets to dead-ends

33. RBVT Routing S Source I1 I3 I2 A B C I5 I4 Path in header: I8-I5-I4-I7-I6-I1 I6 I8 I7 E D car Destination Ij Intersection j • Make decisions based on • Road topology • Real-time data about vehicular connectivity on the roads • More stable paths • Consist of wirelessly-connected road intersections • Geographical forwarding used within road segments

34. Reactive and Proactive RBVT • RBVT-R (reactive) • Creates paths on-demand • Route discovery floods the network to find destination and records path • Route reply returns path to source • RBVT-P (proactive) • Connectivity packet unicasted periodically to discover the graph of wirelessly-connected road segments • When complete, connectivity packet flooded in the network to update the nodes with the new graph • Nodes compute shortest paths using this graph

35. Improved Geographical Forwarding • Remove overhead-prone periodic “hello” messages • Used to learn the neighbors • Replace them with distributed receiver-based next hop election • Self-election based on distance to destination, received power, and distance to sender • Messages piggybacked on 802.11 RTS/CTS

36. Evaluation • NS-2 simulator with 250 cars moving at 20-60mph • 15 concurrent CBR flows • Implemented a realistic vehicular traffic generator • Average delivery rate: RBVT-R is 71% better than AODV and 41% better than GSR • Average end-to-end delay: RBVT-P is one order of magnitude better than AODV and GSR

37. Conclusions and Lessons Learned • Smart phones and vehicular systems create large scale real-life mobile networks • Significant amount of system/networking research necessary to build applications over these networks • Testing in real-life conditions is a must • Ideally, at a decent scale as well • Power is the most important resource of a mobile system • Communication failures are the norm rather than the exception • Applications must be able to adapt to context and be robust to sensing errors

38. Outline • Motivation • MobiSoC: A middleware for mobile social computing • Migratory Services: A context-aware service model for mobile ad hoc networks • RBVT: Road-based routing using real-time traffic information in vehicular networks • Conclusions • New projects • Mobius: A socially-aware peer-to-peer network infrastructure • Traffic safety using vehicular networks and sensor networks

39. Mobius Network Infrastructure • Decentralized two-tier infrastructure for mobile social computing • P2P tier • Manages social state • Runs user-deployed services in support of mobile applications • Dynamically adapts to the geo-social context to enable energy-efficient, scalable, and reliable applications • Mobile tier • Runs mobile applications and collects geo-social information using ad hoc communication Application scenario: Community Multimedia Sharing System

40. Traffic Safety using VANET/Sensor Networks Symbiosis • Add road-side sensors that communicate among themselves as well as with vehicles passing by • Improvement over VANET-only solutions • Better detection of dangerous events • Better network connectivity • Persistent location-based storage • Research • Communication protocols between vehicles and sensors • Programming API over this heterogeneous environment

41. Acknowledgments • Work sponsored by NSF grants: • CNS-0831753, CNS-0454081, IIS-0534520, IIS- 0714158 (mobile social computing) • CNS-0520033, CNS-0834585 (vehicular networks) • Students: • Daniel Boston, Ankur Gupta, AchirKalra, JosianeNzouonta, NeerajRajgure • Collaborators: • Grace Wang (CS), Quentin Jones (IS), Adriana Iamnitchi (Univ. of South Florida), LiviuIftode (Rutgers), Oriana Riva (ETH Zurich)

42. Thank you! http://www.cs.njit.edu/~borcea/