Mining Vehicular Context: The Full Spectrum Moustafa Youssef Wireless Research Center of E-JUST.

Slides:



Advertisements
Similar presentations
The Fully Networked Car Geneva, 4-5 March T. Russell Shields Chair, Ygomi LLC Vehicle Communications to Help the Environment.
Advertisements

Research Challenges in the CarTel Mobile Sensor System Samuel Madden Associate Professor, MIT.
Maurice Geraets Senior Director New Business NXP Automotive Eric-Mark Huitema IBM Smarter Transportation Leader.
Field Operational Tests in 7FP Fabrizio Minarini Head of Sector DG INFSO - ICT for transport.
VTrack: Energy-Aware Traffic Delay Estimation Using Mobile Phones Lenin Ravindranath, Arvind Thiagarajan, Katrina LaCurts, Sivan Toledo, Jacob Eriksson,
© Ricardo plc 2012 Eric Chan, Ricardo UK Ltd 21 st October 2012 SARTRE Demonstration System The research leading to these results.
Improving Transportation Systems Dan Work Civil and Environmental Engineering, UC Berkeley Center for Information Technology Research in the Interest of.
CS 495 Application Development for Smart Devices Mobile Crowdsensing Current State and Future Challenges Mobile Crowdsensing. Overview of Crowdsensing.
CROWDINSIDE: AUTOMATIC CONSTRUCTION OF INDOOR FLOOR PLANS
Alistair Murdoch Key Account Manager Finning UK Ltd.
Mining Motion Sensor Data from Smartphones for Estimating Vehicle Motion Tamer Nadeem, PhD Department of Computer Science NSF Workshop on Large-Scale Traffic.
Karl Aberer, Saket Sathe, Dipanjan Charkaborty, Alcherio Martinoli, Guillermo Barrenetxea, Boi Faltings, Lothar Thiele EPFL, IBM Research India, ETHZ.
Nericell: Rich Road and Traffic Monitoring using Mobile Smartphones
20 10 School of Electrical Engineering &Telecommunications UNSW UNSW 10 Author: James Carrapetta Supervisor: Dr Vijay Sivaraman Wireless.
Electrical and Computer Engineering Team Pishro-Nik and Ni Chris Comack - Simon Tang - Joe Tochka - Madison Wang Cars Against Automobile Accidents 10/9/08.
Spectrum as a Valuable Resource
Calling all cars: cell phone networks and the future of traffic Presentation by Scott Corey Article written by Haomiao Huang.
POLITECNICO DI TORINO TRIBUTE and DIMMER. DIMMER - The context One of the major challenges in today’s economy concerns the reduction in energy usage and.
Rutgers: Gayathri Chandrasekaran, Tam Vu, Marco Gruteser, Rich Martin,
Automated Driver Testing System Objective  Standardize and automate the procedure of evaluating the driving skills of a driver  Eliminate subjective.
Cooperative crash prevention using human behavior monitoring Susumu Ishihara*† and Mario Gerla† (*Shizuoka University / †UCLA) Danger ! ! !
Sensys 2009 Speaker:Lawrence.  Introduction  Overview & Challenges  Algorithm  Travel Time Estimation  Evaluation  Conclusion.
Institute for Transportation Research and Education – N.C. State University High Resolution In Vehicle Sensing Nagui M. Rouphail.
Truths and Myths about Traffic Data Truths and Myths about Traffic Data ITSA Presentation June 2007 AirSage Proprietary & Confidential.
Security Tracking and Advising for Taxi Customers Group Member Tanapol Euaungkanakul Chayanin Mukviboonchai Thanachit Viriyayanyongsuk.
A SEMINAR ON ACTIVE HIGHWAY SYSTEM PRESENTED BY :- ASHISH PANDEY, ECE, III YEAR.
Sponsored by the National Science Foundation 1 November 3, 2010 ParkNet: WiMax Marco Gruteser, WINLAB Rutgers Univ Ivan Seskar (WINLAB) Max Ott (NICTA)
Ultra Wideband Technology Group 6 Will Culberson Ben Henley.
Efficient Mapping and Management of Applications onto Cyber-Physical Systems Prof. Margaret Martonosi, Princeton University and Prof. Pei Zhang, Carnegie.
Innovative ITS services thanks to Future Internet technologies ITS World Congress Orlando, SS42, 18 October 2011.
By: Diana Ornelas.  A LAN that is inside & around the vehicle  Is a branch under VANET  4 types of communication:  Vehicle-to-vehicle  Intra-vehicular.
Best Practices on Monitoring Deployment Road Weather Monitoring Conclusions Yrjö Pilli-Sihvola Finnish Road Administration.
Nericell: Rich Monitoring of Road and Traffic Conditions using Mobile Smartphones Presented by Abdelrhman Magdy.
INTELLIGENT TRANSPORTATION SYSTEM BY – ANTARA DEY SIKDAR M.T.R.P, Ist SEMESTER B.E.S.U.
PRESENTATION SEMINAR ON GOOGLE DRIVERLESS CAR
Data Dissemination for Environment Monitoring Visalakshmi Suresh Prof. Paul Watson.
AUTOMATIC VEHICLE LOCATOR DO YOU WANT TO TRACK YOUR VEHICLE? PRESENTED BY, SAMBIDHAN MISHRA 8 TH SEM, MECH ENGG PKACE, BARGARH REGD NO
Online Construction of Analytical Prediction Models for Physical Environments: Application to Traffic Scene Modeling Anurag Umbarkar, Shreyas K Rajagopal.
1 Ubiquitous Computing Nov. 15, 2006 Ki-Joune Li.
Sentient Transportation Systems [Using sensor networks for building a full fledged transportation system for a township] Mobile Computing Class CEN 5531.
Project Summary I-95 Corridor Coalition Truck Parking Initiative.
2015 Traffic Records Forum The Impact of Advanced Vehicle Technology on Traffic Records October 27, 2015 Kenneth Leonard Director, Intelligent Transportation.
A UTOMATIC DRUNKEN DRIVER AND DROWSINESS DETECTION SYSTEM Student’s Name with USN No. Guide Name HOD Name College Name, Dept.
 Introduction  What is Driverless Car ?  History  Component  Action  Technology  Advantages  Disadvantages  Conclusion  Reference.
Simplifying Cloud Connectivity for Your Clients Presenter: Tom SharkeyTom Sharkey December 8,
Dejavu:An accurate Energy-Efficient Outdoor Localization System SIGSPATIAL '13.
INTERNATIONAL ENERGY AGENCY © OECD/IEA Consideration on Environmentally Friendly Vehicles Kazunori Kojima International Energy Agency 5 th Informal.
Modeling of Optimized Traffic Patterns Using GPS and Wireless Communications Between Traffic Lights and Vehicles Bryan Ward 11/3/06.
Leveraging SDN for The 5G Networks: Trends, Prospects and Challenges ADVISOR: 林甫俊教授 Presenter: Jimmy DATE: 2016/3/21 1.
Autonomous cars D202, GROUP 4 ROCKY HE, RICHARD WONG, STUART PIERCE.
INTRODUCTION SELF-DRIVING CARS FUTURE OF AUTOMOBILES HYDROGEN POWERED CARS 1 GROUP 10 1)G.V.S.ABHISHEK 2)MOHIT AGARWAL 3)T.R.GOKUL.
PRESENTED BY:- P.SREENIVASULU ROLL NO:-12AT5A0420 IV-B.Tech ECE.
Telematics derived from the Greek words “Tele” and “matos”, Tele means (far away) and matos means (derivative of Greek word machinari), Combinedly telematics.
Mobile Applications for Asset/Fleet Management By Micrologic Group
Intelligent Transportation System
‘Adaptive Cruise Control’
Smart World: A Better World
Urban Traffic Condition Estimation: Let WiFi Do It
OCC and LiFi based Light Communication for 5G Revolution
HY-WIRE CAR PRESENTED BY idoldear.com.
What We Don’t Yet Know, What We Know & What We Can Do Now
Dejavu:An accurate Energy-Efficient Outdoor Localization System
OCC and LiFi based Light Communication for 5G Revolution
Accelerating the Introduction of
Course Project Topics for CSE5469
OCC and LiFi based Light Communication for 5G Revolution
Internet of Things.
Developing Vehicular Data Cloud Services in the IoT Environment
Arani Bhattacharya, Han Chen, Peter Milder, Samir R. Das
What is the optimal future architecture for spectrum monitoring?
Presentation transcript:

Mining Vehicular Context: The Full Spectrum Moustafa Youssef Wireless Research Center of E-JUST

Vehicular Context What the vehicle is doing – Location – Speed, acceleration – Braking – Fuel consumption Sensors – Cell phones – OBD Ultimate goal – Driverless cars (c) 2014, The Wireless Research Center, E-JUST.

Wider Spectrum Context User – Texting – Sleepy(ing) – Driver, passenger Vehicle – Car location – Speed, acceleration – Braking – Fuel consumption – Temperature, humidity, rain Environment – Awareness of surroundings (driverless cars) – Crowd-sensing: Traffic congestion, pollution, sound levels (c) 2014, The Wireless Research Center, E-JUST.

Wider Spectrum Sensors OBD Driver/passenger cell phone Special sensors – CO2 Unconventional sensors – Other users/cars – Sensor-less sensing - Device-free sensing [Youssef et al’07] (c) 2014, The Wireless Research Center, E-JUST.

Challenges Ubiquitous/zero cost – Cell phones/environment as a sensor? Heterogeneity of sensors – Different devices, different signals Scale – Millions (billions) of users – Data size – Data rate/bandwidth (c) 2014, The Wireless Research Center, E-JUST.

Challenges Noise in data – Cheap sensors Efficient computation/processing – Batch/opportunistic upload Vs. realtime processing – Offloading to cloud – Local clouds (cloudlets) Privacy/security (c) 2014, The Wireless Research Center, E-JUST.

Selected Projects

Projects Map++ – Crowd-sourced semantic-rich map construction Dejavu – Accurate Energy-efficient GPS Replacement ReVISE – Ubiquitous Car Type/Speed Estimation ARTS – Ubiquitous Traffic Monitoring

Map++ [SECON’14] Crowd-sensing for automatic map semantic identification (c) 2014, The Wireless Research Center, E-JUST.

Map++: Basic Idea Different semantics have unique sensor signature (c) 2014, The Wireless Research Center, E-JUST.

Dejavu [ACM SIGSPATIAL’13-Best Paper Award] Accurate energy-efficient outdoor localization GPS replacement Even more accurate in in-city driving conditions (c) 2014, The Wireless Research Center, E-JUST.

Dejavu: Basic Idea Dead-reckoning Unique anchors for error-resetting – Physical – Virtual (c) 2014, The Wireless Research Center, E-JUST.

ReVISE: Car Type/Speed [VTC’12] Ubiquitous transparent traffic state monitoring Device-free localization concept (c) 2014, The Wireless Research Center, E-JUST.

ReVISE: Car Type/Speed Current deployment: Road side – Car speed, human/car differentiation, car type (c) 2014, The Wireless Research Center, E-JUST.

ARTS [SIGSPATIAL’14] Accurate and reliable road traffic estimation – Cellular-based: single cell tower (c) 2014, The Wireless Research Center, E-JUST.

Conclusions Wide spectrum of context information – And sensors Number of challenges that need to be addressed Applications avalanche effect (c) 2014, The Wireless Research Center, E-JUST.

For More Information Project web site: Papers Media coverage Funded by: