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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. Wireless Computing/Sensing Systems. >3.3B cell phones vs. 600M Internet-connected PC’s in 2007
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Is Your Car Talking with My Smart Phone?orDistributed Sensing and Computing in Mobile Networks CristianBorcea Department of Computer Science, NJIT
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
Ubiquitous Computing Vision • Computing, communication, and sensing anytime, anywhere • Wireless systems cooperate to achieve global tasks • How close are we from this vision?
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
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
Our Research • What programming models, system architectures, and protocols do we need when everything connects?
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
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
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#
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
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
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
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
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
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
Tranzact: Place-based Ad Hoc Social Collaboration Hungry What’s on the menu? Chicken teriyaki Cafeteria
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
Thank you! http://www.cs.njit.edu/~borcea/