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

• Cristian Borcea
• Department of Computer Science, NJIT

2
Wireless Computing/Sensing Systems

• gt3.3B cell phones vs. 600M Internet-connected
  PCs in 2007
• gt600M 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

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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

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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

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Social Computing in the Internet
Myspace
Facebook
LinkedIn

• Social networking applications improve social
  connectivity on-line
• Stay in touch with friends
• Make new friends
• Find out information about events and places

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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

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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

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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

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MobiSoC Architecture
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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

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Location System

• 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!
• 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

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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

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Clarissa Location-enhanced Mobile Social Matching
MatchTypeHangout Time 1-3PM Co-Location
required
Match Alert
Match Alert
MatchTypeHangout Time 2-4PM Co-Location
required
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Tranzact Place-based Ad Hoc Social Collaboration
Cafeteria
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MobiSoC Implementation

• Runs on trusted servers
• Beta release https//sourceforge.net/projects/mob
  isoc/
• 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 Intels
  Placelab
• Accuracy 10-15 meters

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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

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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
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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
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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

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Migratory Services Model
Context Change! (e.g., n2 moves out of the region
of interest) MS cannot accomplish its task on n2
any longer
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One-to-One Mapping between Clients and Migratory
Services
M
Meta-service
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Migratory Services Framework
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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
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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

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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

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Vehicular Ad Hoc Networks (VANET)
Vehicle-to-vehicle short-range wireless
communication

• 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

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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

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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

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Routing still a Big Problem for VANET

• Topological routing (e.g., AODV, DSR) suffers
  from frequent broken paths

• Geographical routing (e.g., GPSR) frequently
  routes packets to dead-ends

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RBVT Routing

• 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

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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

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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

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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

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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

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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

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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
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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

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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, Achir Kalra, Josiane
  Nzouonta, Neeraj Rajgure
• Collaborators
• Grace Wang (CS), Quentin Jones (IS), Adriana
  Iamnitchi (Univ. of South Florida), Liviu Iftode
  (Rutgers), Oriana Riva (ETH Zurich)

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Thank you! http//www.cs.njit.edu/borcea/