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Is Your Car Talking with My Smart Phone? or Distributed Sensing and Computing in Mobile Networks Cristian Borcea Department of Computer Science, NJIT.

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Presentation on theme: "Is Your Car Talking with My Smart Phone? or Distributed Sensing and Computing in Mobile Networks Cristian Borcea Department of Computer Science, NJIT."— Presentation transcript:

1 Is Your Car Talking with My Smart Phone? or Distributed Sensing and Computing in Mobile Networks Cristian Borcea Department of Computer Science, NJIT

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

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

4 4 So Far … Not Very Close Nomadic computing Nomadic computing –Devices: laptops –Internet: intermittent connectivity –Work: typical desktop applications Mobile communication Mobile communication –Devices: PDAs, mobile phones, Blackberries –Internet: continuous connectivity –Work: and web Experimental sensor networks 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 Hard to program distributed applications over collections of wireless systems –Systems Distributed across physical space Distributed across physical space Mobile Mobile Heterogeneous: both hardware and software Heterogeneous: both hardware and software Resource-constrained: battery, bandwidth, memory Resource-constrained: battery, bandwidth, memory –Networks Large scale Large scale Volatile: ad hoc topologies, dynamic resources Volatile: ad hoc topologies, dynamic resources Less secure than wired networks Less secure than wired networks 5

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

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

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

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

10 10 Mobile Social Computing Applications (MSCA) People-centric 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 Place-centric –How crowded is the cafeteria now? –Which are the places where CS students hang out? How to program MSCA? How to program MSCA? Challenges: capturing the dynamic relations between people and places, location systems, privacy, battery power Challenges: capturing the dynamic relations between people and places, location systems, privacy, battery power

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

12 12 MobiSoC Architecture

13 Learning Emergent Geo-Social Patterns Example: GPI Algorithm GPI identifies previously unknown social groups and their associated places 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 Clusters user mobility traces across time and space Its results can 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 13

14 14 Location System Hardware-based location systems not feasible Hardware-based location systems not feasible –GPS doesnt 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! The user has no control over her location data! Software-based location systems that run on mobile devices preferable 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 15 Mobile Distributed System Architecture MSCA split between thin clients running on mobiles and services running on servers 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 MSCA clients communicate synchronously with the services and receive asynchronous events from MobiSoC Advantages Advantages Faster execution Faster execution Energy efficiency Energy efficiency Improved trust Improved trust

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

17 17 Tranzact: Place-based Ad Hoc Social Collaboration Whats on the menu? Cafeteria Chicken teriyaki Hungry

18 18 MobiSoC Implementation Runs on trusted servers Runs on trusted servers –Beta release: https://sourceforge.net/projects/mobisoc/ Service oriented architecture over Apache Tomcat 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 KSOAP is J2ME compatible and can be used to communicate with clients Client applications developed using J2ME on WiFi-enabled Windows-based smart phones Client applications developed using J2ME on WiFi-enabled Windows-based smart phones –Clarissa: Location engine: modified version of Intels Placelab Location engine: modified version of Intels Placelab –Accuracy meters

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

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

21 21 Ad Hoc Networks as People-Centric Mobile Sensor Networks Typical devices: smart phones and vehicular systems Typical devices: smart phones and vehicular systems Run distributed services 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 Entitytracking Parking spot finder Traffic jam predictor

22 22 Problems with Traditional Client-Server Model in Ad Hoc Networks When service stops satisfying context requirements, client must discover new service 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 23 Virtual service end-point Migratory Services Model Client n1n1n1n1 C n2 n2n2 n2 n3 n3 n3 n3 Context Change! (e.g., n 2 moves out of the region of interest) MS cannot accomplish its task on n 2 any longer ServiceMigration MS State Migratory Service Service MS State Migratory Service Service

24 24 One-to-One Mapping between Clients and Migratory Services n1n1n1n1 C1 Meta-service n3n3n3n3 MCreate Migratory Service MS1 State n2n2n2n2 n5 n5 n5 n5 MS2 State C2 MS2 State n4 n4 n4 n4 MS1 State

25 25 Migratory Services Framework

26 26 TJam: Migratory Service Example TJam: Migratory Service Example Predicts traffic jams in real-time 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: 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 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) Development using HP iPAQs (running Linux), Nokia phones (running Symbian) SM platforms 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 28Outline Motivation Motivation MobiSoC: A middleware for mobile social computing MobiSoC: A middleware for mobile social computing Migratory Services: A context-aware service model for mobile ad hoc networks Migratory Services: A context-aware service model for mobile ad hoc networks RBVT: Road-based routing using real-time traffic information in vehicular networks RBVT: Road-based routing using real-time traffic information in vehicular networks Conclusions Conclusions New projects New projects –Mobius: A socially-aware peer-to-peer network infrastructure –Traffic safety using vehicular networks and sensor networks

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

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

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

32 32 Routing still a Big Problem for VANET Topological routing (e.g., AODV, DSR) suffers from frequent broken paths Topological routing (e.g., AODV, DSR) suffers from frequent broken paths S S N1N1 D N1N1 D a) At time t b) At time t+Δt N2N2 S D Dead end road N1N1 N2N2 Geographical routing (e.g., GPSR) frequently routes packets to dead-ends Geographical routing (e.g., GPSR) frequently routes packets to dead-ends

33 33 RBVT Routing Make decisions based on Make decisions based on –Road topology –Real-time data about vehicular connectivity on the roads More stable paths More stable paths –Consist of wirelessly-connected road intersections –Geographical forwarding used within road segments

34 Reactive and Proactive RBVT RBVT-R (reactive) 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) 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 34

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

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

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

38 38Outline Motivation Motivation MobiSoC: A middleware for mobile social computing MobiSoC: A middleware for mobile social computing Migratory Services: A context-aware service model for mobile ad hoc networks Migratory Services: A context-aware service model for mobile ad hoc networks RBVT: Road-based routing using real-time traffic information in vehicular networks RBVT: Road-based routing using real-time traffic information in vehicular networks Conclusions Conclusions New projects 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 Decentralized two-tier infrastructure for mobile social computing P2P tier 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 Mobile tier –Runs mobile applications and collects geo-social information using ad hoc communication 39 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 Add road-side sensors that communicate among themselves as well as with vehicles passing by Improvement over VANET-only solutions Improvement over VANET-only solutions –Better detection of dangerous events –Better network connectivity –Persistent location-based storage Research Research –Communication protocols between vehicles and sensors –Programming API over this heterogeneous environment 40

41 41 Acknowledgments Work sponsored by NSF grants: Work sponsored by NSF grants: –CNS , CNS , IIS , IIS (mobile social computing) –CNS , CNS (vehicular networks) Students: Students: –Daniel Boston, Ankur Gupta, Achir Kalra, Josiane Nzouonta, Neeraj Rajgure Collaborators: Collaborators: –Grace Wang (CS), Quentin Jones (IS), Adriana Iamnitchi (Univ. of South Florida), Liviu Iftode (Rutgers), Oriana Riva (ETH Zurich)

42 42 Thank you!


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