A Framework For IncidentDetection AndNotification InVehicular AdHoc Networks                     A Framework For Incident D...
OutlineA Framework For IncidentDetection AndNotification In                     1   IntroductionVehicular AdHoc Networks   ...
Vehicular Ad Hoc NetworksA Framework For IncidentDetection And           In the US, Vehicular Ad Hoc Networks(VANETs) are ...
Vehicular Ad Hoc NetworksA Framework For IncidentDetection And           In the US, Vehicular Ad Hoc Networks(VANETs) are ...
Vehicular Ad Hoc NetworksA Framework For IncidentDetection And           In the US, Vehicular Ad Hoc Networks(VANETs) are ...
Vehicular Ad Hoc NetworksA Framework For IncidentDetection And           In the US, Vehicular Ad Hoc Networks(VANETs) are ...
Vehicular Ad Hoc NetworksA Framework For IncidentDetection And           In the US, Vehicular Ad Hoc Networks(VANETs) are ...
Collision Warning SystemsA Framework For IncidentDetection And            Congested highways due to traffic incidents cost ...
Collision Warning SystemsA Framework For IncidentDetection And            Congested highways due to traffic incidents cost ...
Collision Warning SystemsA Framework For IncidentDetection And            Congested highways due to traffic incidents cost ...
Collision Warning SystemsA Framework For IncidentDetection And            Congested highways due to traffic incidents cost ...
Collision Warning SystemsA Framework For IncidentDetection And            Congested highways due to traffic incidents cost ...
Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor...
Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor...
Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor...
Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor...
Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor...
Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor...
Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor...
Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.  S. Ola...
Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.  S. Ola...
Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.  S. Ola...
Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.  S. Ola...
Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.  S. Ola...
System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In           The first component of our frame...
System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In           The first component of our frame...
System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In           The first component of our frame...
System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In           The first component of our frame...
System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In           The first component of our frame...
Belts modelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.  S. Olari...
Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.       ...
Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.       ...
Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.       ...
Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.       ...
Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.       ...
Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: ...
Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: ...
Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: ...
Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: ...
Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: ...
Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: ...
Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof.  S. Ola...
Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof...
Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof...
Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof...
Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof...
Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In                       1   The pr...
Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In                       1   The pr...
Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In                       1   The pr...
Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In                       1   The pr...
Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In                       1   The pr...
Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And                         Let Pr[I] to be the a pri...
Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And                         Let Pr[I] to be the a pri...
Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And                         Let Pr[I] to be the a pri...
Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And            We are interested here in two types of...
Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And              We are interested here in two types ...
Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And              We are interested here in two types ...
Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And              We are interested here in two types ...
Blocking incident detectionA Framework          Assume that an accident has occurred at For IncidentDetection And        p...
Blocking incident detectionA Framework          Assume that an accident has occurred at For IncidentDetection And        p...
Blocking incident detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotification In...
Blocking incident detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotification In...
Blocking incident detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotification In...
Blocking incident detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotification In...
Blocking incident detection: Updating                     ProbabilitiesA Framework For Incident            For each positi...
Blocking incident detection: Updating                     ProbabilitiesA Framework For Incident            For each positi...
Blocking incident detection: Updating                     ProbabilitiesA Framework For Incident            For each positi...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            The main d...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            The main d...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            The main d...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            The main d...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            Similar to...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            Similar to...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            Similar to...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            Similar to...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            For each p...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            For each p...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            For each p...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For Incident            For each p...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotificati...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotificati...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotificati...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotificati...
Non Blocking Incidents detection: Updating                     ProbabilitiesA Framework For IncidentDetection AndNotificati...
Incident detectionA Framework For IncidentDetection AndNotification InVehicular Ad                         While updating t...
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks
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A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks

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PhD defense by Mahmoud Abuelela
Old Dominion University
Department of Computer Science
March 25, 2011

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A Framework for Incident Detection And Notification in Vehicular Ad Hoc Networks

  1. 1. A Framework For IncidentDetection AndNotification InVehicular AdHoc Networks A Framework For Incident Detection And M. AbuelelaAdvisor: Prof. S. Olariu Notification In Vehicular Ad Hoc NetworksIntroductionInfrastructure M. AbuelelaIncidentDetection Advisor: Prof. S. OlariuWarming upProbabilistictechnique Department of Computer ScienceData Old Dominion UniversityDissemination Norfolk, VA, USATraffic analysisData disseminationin undivided roadsData disseminationin divided roads March 2011Securing DataDisseminationBelts Clustering
  2. 2. OutlineA Framework For IncidentDetection AndNotification In 1 IntroductionVehicular AdHoc Networks 2 System Infrastructure M. AbuelelaAdvisor: Prof. S. Olariu 3 Automatic Incident DetectionIntroduction Warming upInfrastructure Probabilistic techniqueIncidentDetection 4 Data DisseminationWarming upProbabilistic Traffic analysistechnique Data dissemination in undivided roadsDataDissemination Data dissemination in divided roadsTraffic analysisData disseminationin undivided roads 5 Securing Data DisseminationData disseminationin divided roads Belts ClusteringSecuring DataDisseminationBelts Clustering
  3. 3. Vehicular Ad Hoc NetworksA Framework For IncidentDetection And In the US, Vehicular Ad Hoc Networks(VANETs) are usingNotification InVehicular Ad 75 MHz of spectrum in the 5.850 to 5.925 GHz bandHoc Networks specially allocated by the US Federal Communications M. AbuelelaAdvisor: Prof. Commission(FCC) S. Olariu VANETs have a number of specific characteristics that setIntroduction them apart from traditional Mobile Ad Hoc NetworksInfrastructure (MANETs)IncidentDetection 1 Movement pattern : nodes in MANETs are assumed toWarming upProbabilistic be slow and can move everywhere.technique 2 Scale: VANETs involve thousands of fast-movingDataDissemination vehicles over tens of miles of roadways and streetsTraffic analysis 3 Connectivity: while MANETs may experience transientData disseminationin undivided roads periods of loss of connectivity, in VANETs, extendedData disseminationin divided roads periods of disconnection are the norm rather than theSecuring Data exceptionDisseminationBelts Clustering
  4. 4. Vehicular Ad Hoc NetworksA Framework For IncidentDetection And In the US, Vehicular Ad Hoc Networks(VANETs) are usingNotification InVehicular Ad 75 MHz of spectrum in the 5.850 to 5.925 GHz bandHoc Networks specially allocated by the US Federal Communications M. AbuelelaAdvisor: Prof. Commission(FCC) S. Olariu VANETs have a number of specific characteristics that setIntroduction them apart from traditional Mobile Ad Hoc NetworksInfrastructure (MANETs)IncidentDetection 1 Movement pattern : nodes in MANETs are assumed toWarming upProbabilistic be slow and can move everywhere.technique 2 Scale: VANETs involve thousands of fast-movingDataDissemination vehicles over tens of miles of roadways and streetsTraffic analysis 3 Connectivity: while MANETs may experience transientData disseminationin undivided roads periods of loss of connectivity, in VANETs, extendedData disseminationin divided roads periods of disconnection are the norm rather than theSecuring Data exceptionDisseminationBelts Clustering
  5. 5. Vehicular Ad Hoc NetworksA Framework For IncidentDetection And In the US, Vehicular Ad Hoc Networks(VANETs) are usingNotification InVehicular Ad 75 MHz of spectrum in the 5.850 to 5.925 GHz bandHoc Networks specially allocated by the US Federal Communications M. AbuelelaAdvisor: Prof. Commission(FCC) S. Olariu VANETs have a number of specific characteristics that setIntroduction them apart from traditional Mobile Ad Hoc NetworksInfrastructure (MANETs)IncidentDetection 1 Movement pattern : nodes in MANETs are assumed toWarming upProbabilistic be slow and can move everywhere.technique 2 Scale: VANETs involve thousands of fast-movingDataDissemination vehicles over tens of miles of roadways and streetsTraffic analysis 3 Connectivity: while MANETs may experience transientData disseminationin undivided roads periods of loss of connectivity, in VANETs, extendedData disseminationin divided roads periods of disconnection are the norm rather than theSecuring Data exceptionDisseminationBelts Clustering
  6. 6. Vehicular Ad Hoc NetworksA Framework For IncidentDetection And In the US, Vehicular Ad Hoc Networks(VANETs) are usingNotification InVehicular Ad 75 MHz of spectrum in the 5.850 to 5.925 GHz bandHoc Networks specially allocated by the US Federal Communications M. AbuelelaAdvisor: Prof. Commission(FCC) S. Olariu VANETs have a number of specific characteristics that setIntroduction them apart from traditional Mobile Ad Hoc NetworksInfrastructure (MANETs)IncidentDetection 1 Movement pattern : nodes in MANETs are assumed toWarming upProbabilistic be slow and can move everywhere.technique 2 Scale: VANETs involve thousands of fast-movingDataDissemination vehicles over tens of miles of roadways and streetsTraffic analysis 3 Connectivity: while MANETs may experience transientData disseminationin undivided roads periods of loss of connectivity, in VANETs, extendedData disseminationin divided roads periods of disconnection are the norm rather than theSecuring Data exceptionDisseminationBelts Clustering
  7. 7. Vehicular Ad Hoc NetworksA Framework For IncidentDetection And In the US, Vehicular Ad Hoc Networks(VANETs) are usingNotification InVehicular Ad 75 MHz of spectrum in the 5.850 to 5.925 GHz bandHoc Networks specially allocated by the US Federal Communications M. AbuelelaAdvisor: Prof. Commission(FCC) S. Olariu VANETs have a number of specific characteristics that setIntroduction them apart from traditional Mobile Ad Hoc NetworksInfrastructure (MANETs)IncidentDetection 1 Movement pattern : nodes in MANETs are assumed toWarming upProbabilistic be slow and can move everywhere.technique 2 Scale: VANETs involve thousands of fast-movingDataDissemination vehicles over tens of miles of roadways and streetsTraffic analysis 3 Connectivity: while MANETs may experience transientData disseminationin undivided roads periods of loss of connectivity, in VANETs, extendedData disseminationin divided roads periods of disconnection are the norm rather than theSecuring Data exceptionDisseminationBelts Clustering
  8. 8. Collision Warning SystemsA Framework For IncidentDetection And Congested highways due to traffic incidents cost over $75Notification InVehicular Ad billion a year in lost worker productivity, over 8.4 billionHoc Networks gallons of fuel and an average of 119 persons died each day. M. AbuelelaAdvisor: Prof. S. Olariu A number of VANETs based systems and prototypes have been proposed to alert drivers about possible trafficIntroduction incidents.Infrastructure Most of these techniques give individual vehicle theIncidentDetection responsibility for inferring/notifying about the presence of anWarming upProbabilistic incidenttechniqueData This invites a host of serious and well-documented securityDisseminationTraffic analysis attacks.Data disseminationin undivided roadsData dissemination Several routing and data dissemination protocols have beenin divided roads proposed in literature where very few of them took lack ofSecuring DataDissemination connectivity into considerationBelts Clustering
  9. 9. Collision Warning SystemsA Framework For IncidentDetection And Congested highways due to traffic incidents cost over $75Notification InVehicular Ad billion a year in lost worker productivity, over 8.4 billionHoc Networks gallons of fuel and an average of 119 persons died each day. M. AbuelelaAdvisor: Prof. S. Olariu A number of VANETs based systems and prototypes have been proposed to alert drivers about possible trafficIntroduction incidents.Infrastructure Most of these techniques give individual vehicle theIncidentDetection responsibility for inferring/notifying about the presence of anWarming upProbabilistic incidenttechniqueData This invites a host of serious and well-documented securityDisseminationTraffic analysis attacks.Data disseminationin undivided roadsData dissemination Several routing and data dissemination protocols have beenin divided roads proposed in literature where very few of them took lack ofSecuring DataDissemination connectivity into considerationBelts Clustering
  10. 10. Collision Warning SystemsA Framework For IncidentDetection And Congested highways due to traffic incidents cost over $75Notification InVehicular Ad billion a year in lost worker productivity, over 8.4 billionHoc Networks gallons of fuel and an average of 119 persons died each day. M. AbuelelaAdvisor: Prof. S. Olariu A number of VANETs based systems and prototypes have been proposed to alert drivers about possible trafficIntroduction incidents.Infrastructure Most of these techniques give individual vehicle theIncidentDetection responsibility for inferring/notifying about the presence of anWarming upProbabilistic incidenttechniqueData This invites a host of serious and well-documented securityDisseminationTraffic analysis attacks.Data disseminationin undivided roadsData dissemination Several routing and data dissemination protocols have beenin divided roads proposed in literature where very few of them took lack ofSecuring DataDissemination connectivity into considerationBelts Clustering
  11. 11. Collision Warning SystemsA Framework For IncidentDetection And Congested highways due to traffic incidents cost over $75Notification InVehicular Ad billion a year in lost worker productivity, over 8.4 billionHoc Networks gallons of fuel and an average of 119 persons died each day. M. AbuelelaAdvisor: Prof. S. Olariu A number of VANETs based systems and prototypes have been proposed to alert drivers about possible trafficIntroduction incidents.Infrastructure Most of these techniques give individual vehicle theIncidentDetection responsibility for inferring/notifying about the presence of anWarming upProbabilistic incidenttechniqueData This invites a host of serious and well-documented securityDisseminationTraffic analysis attacks.Data disseminationin undivided roadsData dissemination Several routing and data dissemination protocols have beenin divided roads proposed in literature where very few of them took lack ofSecuring DataDissemination connectivity into considerationBelts Clustering
  12. 12. Collision Warning SystemsA Framework For IncidentDetection And Congested highways due to traffic incidents cost over $75Notification InVehicular Ad billion a year in lost worker productivity, over 8.4 billionHoc Networks gallons of fuel and an average of 119 persons died each day. M. AbuelelaAdvisor: Prof. S. Olariu A number of VANETs based systems and prototypes have been proposed to alert drivers about possible trafficIntroduction incidents.Infrastructure Most of these techniques give individual vehicle theIncidentDetection responsibility for inferring/notifying about the presence of anWarming upProbabilistic incidenttechniqueData This invites a host of serious and well-documented securityDisseminationTraffic analysis attacks.Data disseminationin undivided roadsData dissemination Several routing and data dissemination protocols have beenin divided roads proposed in literature where very few of them took lack ofSecuring DataDissemination connectivity into considerationBelts Clustering
  13. 13. Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. Olariu Many incident detection algorithms exist from simple patternIntroduction recognition to AI techniques.InfrastructureIncident Most developed techniques rely mainly on trafficDetectionWarming up measurements acquired at inductive loop detectors (ILDs) orProbabilistictechnique video detection cameras installed at regular spacing alongData the freeways.DisseminationTraffic analysis All of these techniques assign cars a passive role in theData disseminationin undivided roads detection process.Data disseminationin divided roads Also, It is fundamentally difficult to detect non-blockingSecuring Data accidents and those that occur under light load, as theDisseminationBelts Clustering deviation from normal traffic patterns may be negligible
  14. 14. Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. Olariu Many incident detection algorithms exist from simple patternIntroduction recognition to AI techniques.InfrastructureIncident Most developed techniques rely mainly on trafficDetectionWarming up measurements acquired at inductive loop detectors (ILDs) orProbabilistictechnique video detection cameras installed at regular spacing alongData the freeways.DisseminationTraffic analysis All of these techniques assign cars a passive role in theData disseminationin undivided roads detection process.Data disseminationin divided roads Also, It is fundamentally difficult to detect non-blockingSecuring Data accidents and those that occur under light load, as theDisseminationBelts Clustering deviation from normal traffic patterns may be negligible
  15. 15. Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. Olariu Many incident detection algorithms exist from simple patternIntroduction recognition to AI techniques.InfrastructureIncident Most developed techniques rely mainly on trafficDetectionWarming up measurements acquired at inductive loop detectors (ILDs) orProbabilistictechnique video detection cameras installed at regular spacing alongData the freeways.DisseminationTraffic analysis All of these techniques assign cars a passive role in theData disseminationin undivided roads detection process.Data disseminationin divided roads Also, It is fundamentally difficult to detect non-blockingSecuring Data accidents and those that occur under light load, as theDisseminationBelts Clustering deviation from normal traffic patterns may be negligible
  16. 16. Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. Olariu Many incident detection algorithms exist from simple patternIntroduction recognition to AI techniques.InfrastructureIncident Most developed techniques rely mainly on trafficDetectionWarming up measurements acquired at inductive loop detectors (ILDs) orProbabilistictechnique video detection cameras installed at regular spacing alongData the freeways.DisseminationTraffic analysis All of these techniques assign cars a passive role in theData disseminationin undivided roads detection process.Data disseminationin divided roads Also, It is fundamentally difficult to detect non-blockingSecuring Data accidents and those that occur under light load, as theDisseminationBelts Clustering deviation from normal traffic patterns may be negligible
  17. 17. Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructure False positive alarms have been unacceptably high forIncident operational purposes. For example, 2% false positives perDetectionWarming up ILD means simply thousands false alarms daily from many ofProbabilistictechnique them which discredit the otherwise useful AIDDataDissemination Traffic signs are localized with the incidents locations.Traffic analysisData disseminationin undivided roads Thus, driver may not have many choices to avoid the incidentData disseminationin divided roads (usually there is a single "detour" to avoid the incident)Securing DataDisseminationBelts Clustering
  18. 18. Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructure False positive alarms have been unacceptably high forIncident operational purposes. For example, 2% false positives perDetectionWarming up ILD means simply thousands false alarms daily from many ofProbabilistictechnique them which discredit the otherwise useful AIDDataDissemination Traffic signs are localized with the incidents locations.Traffic analysisData disseminationin undivided roads Thus, driver may not have many choices to avoid the incidentData disseminationin divided roads (usually there is a single "detour" to avoid the incident)Securing DataDisseminationBelts Clustering
  19. 19. Automatic Incident DetectionA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructure False positive alarms have been unacceptably high forIncident operational purposes. For example, 2% false positives perDetectionWarming up ILD means simply thousands false alarms daily from many ofProbabilistictechnique them which discredit the otherwise useful AIDDataDissemination Traffic signs are localized with the incidents locations.Traffic analysisData disseminationin undivided roads Thus, driver may not have many choices to avoid the incidentData disseminationin divided roads (usually there is a single "detour" to avoid the incident)Securing DataDisseminationBelts Clustering
  20. 20. Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueData 1 System architecture: an infrastructure that collectsDisseminationTraffic analysis information from passing vehicles.Data disseminationin undivided roads 2 Incident detection engine: uses accumulated information toData disseminationin divided roads detect potential incidents and traffic delaysSecuring DataDissemination 3 Notification dissemination: once an accident detected,Belts Clustering approaching drivers need to be alerted about its existence.
  21. 21. Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueData 1 System architecture: an infrastructure that collectsDisseminationTraffic analysis information from passing vehicles.Data disseminationin undivided roads 2 Incident detection engine: uses accumulated information toData disseminationin divided roads detect potential incidents and traffic delaysSecuring DataDissemination 3 Notification dissemination: once an accident detected,Belts Clustering approaching drivers need to be alerted about its existence.
  22. 22. Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueData 1 System architecture: an infrastructure that collectsDisseminationTraffic analysis information from passing vehicles.Data disseminationin undivided roads 2 Incident detection engine: uses accumulated information toData disseminationin divided roads detect potential incidents and traffic delaysSecuring DataDissemination 3 Notification dissemination: once an accident detected,Belts Clustering approaching drivers need to be alerted about its existence.
  23. 23. Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueData 1 System architecture: an infrastructure that collectsDisseminationTraffic analysis information from passing vehicles.Data disseminationin undivided roads 2 Incident detection engine: uses accumulated information toData disseminationin divided roads detect potential incidents and traffic delaysSecuring DataDissemination 3 Notification dissemination: once an accident detected,Belts Clustering approaching drivers need to be alerted about its existence.
  24. 24. Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueData 1 System architecture: an infrastructure that collectsDisseminationTraffic analysis information from passing vehicles.Data disseminationin undivided roads 2 Incident detection engine: uses accumulated information toData disseminationin divided roads detect potential incidents and traffic delaysSecuring DataDissemination 3 Notification dissemination: once an accident detected,Belts Clustering approaching drivers need to be alerted about its existence.
  25. 25. System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In The first component of our framework is an architecture forVehicular AdHoc Networks the notification of traffic incidents, NOTICE for short. M. Abuelela Using sensor belts embedded in the roadway, each messageAdvisor: Prof. S. Olariu is associated with physical vehicleIntroduction NOTICE moves the responsibility for making decisions aboutInfrastructure traffic-related information to the infrastructure rather thanIncident leaving those decisions with the vehicles, which may haveDetectionWarming up incomplete or incorrect knowledge.Probabilistictechnique Existing automatic incident detection techniques areDataDissemination enhanced with cars and drivers inputs while preserving theTraffic analysis privacy of drivers.Data disseminationin undivided roadsData disseminationin divided roads Drivers are alerted early enough about traffic incidents toSecuring Data take the appropriate decisions.DisseminationBelts Clustering
  26. 26. System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In The first component of our framework is an architecture forVehicular AdHoc Networks the notification of traffic incidents, NOTICE for short. M. Abuelela Using sensor belts embedded in the roadway, each messageAdvisor: Prof. S. Olariu is associated with physical vehicleIntroduction NOTICE moves the responsibility for making decisions aboutInfrastructure traffic-related information to the infrastructure rather thanIncident leaving those decisions with the vehicles, which may haveDetectionWarming up incomplete or incorrect knowledge.Probabilistictechnique Existing automatic incident detection techniques areDataDissemination enhanced with cars and drivers inputs while preserving theTraffic analysis privacy of drivers.Data disseminationin undivided roadsData disseminationin divided roads Drivers are alerted early enough about traffic incidents toSecuring Data take the appropriate decisions.DisseminationBelts Clustering
  27. 27. System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In The first component of our framework is an architecture forVehicular AdHoc Networks the notification of traffic incidents, NOTICE for short. M. Abuelela Using sensor belts embedded in the roadway, each messageAdvisor: Prof. S. Olariu is associated with physical vehicleIntroduction NOTICE moves the responsibility for making decisions aboutInfrastructure traffic-related information to the infrastructure rather thanIncident leaving those decisions with the vehicles, which may haveDetectionWarming up incomplete or incorrect knowledge.Probabilistictechnique Existing automatic incident detection techniques areDataDissemination enhanced with cars and drivers inputs while preserving theTraffic analysis privacy of drivers.Data disseminationin undivided roadsData disseminationin divided roads Drivers are alerted early enough about traffic incidents toSecuring Data take the appropriate decisions.DisseminationBelts Clustering
  28. 28. System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In The first component of our framework is an architecture forVehicular AdHoc Networks the notification of traffic incidents, NOTICE for short. M. Abuelela Using sensor belts embedded in the roadway, each messageAdvisor: Prof. S. Olariu is associated with physical vehicleIntroduction NOTICE moves the responsibility for making decisions aboutInfrastructure traffic-related information to the infrastructure rather thanIncident leaving those decisions with the vehicles, which may haveDetectionWarming up incomplete or incorrect knowledge.Probabilistictechnique Existing automatic incident detection techniques areDataDissemination enhanced with cars and drivers inputs while preserving theTraffic analysis privacy of drivers.Data disseminationin undivided roadsData disseminationin divided roads Drivers are alerted early enough about traffic incidents toSecuring Data take the appropriate decisions.DisseminationBelts Clustering
  29. 29. System Infrastructure: NOTICEA Framework For IncidentDetection AndNotification In The first component of our framework is an architecture forVehicular AdHoc Networks the notification of traffic incidents, NOTICE for short. M. Abuelela Using sensor belts embedded in the roadway, each messageAdvisor: Prof. S. Olariu is associated with physical vehicleIntroduction NOTICE moves the responsibility for making decisions aboutInfrastructure traffic-related information to the infrastructure rather thanIncident leaving those decisions with the vehicles, which may haveDetectionWarming up incomplete or incorrect knowledge.Probabilistictechnique Existing automatic incident detection techniques areDataDissemination enhanced with cars and drivers inputs while preserving theTraffic analysis privacy of drivers.Data disseminationin undivided roadsData disseminationin divided roads Drivers are alerted early enough about traffic incidents toSecuring Data take the appropriate decisions.DisseminationBelts Clustering
  30. 30. Belts modelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roads Every two consecutive belts A and B share a secretData disseminationin divided roads symmetric key µ(A, B, t) known only between A and B.Securing DataDissemination Adjacent belts are connected using wires under the medianBelts Clustering
  31. 31. Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. Car’s EDR is a tamper-resistant device much like the S. Olariu well-known black-boxes on commercial aircraft.Introduction All of the vehicle’s sub-assemblies, including the GPS unit,Infrastructure speedometer, gas tank reading, and sensors for outsideIncident temperature, feed their own readings into the EDRDetectionWarming upProbabilistic Cars are equipped with automatic lane detections and feedstechnique its output to its EDRDataDissemination Given a time interval I , the EDR stores information such asTraffic analysisData dissemination the highest and lowest speed during I, the position and timein undivided roadsData dissemination of the strongest deceleration during I, as well as locationin divided roads lane changes informationSecuring DataDissemination Drivers can provide input to the EDR, using a simple menu,Belts Clustering either through a dashboard console or through verbal input
  32. 32. Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. Car’s EDR is a tamper-resistant device much like the S. Olariu well-known black-boxes on commercial aircraft.Introduction All of the vehicle’s sub-assemblies, including the GPS unit,Infrastructure speedometer, gas tank reading, and sensors for outsideIncident temperature, feed their own readings into the EDRDetectionWarming upProbabilistic Cars are equipped with automatic lane detections and feedstechnique its output to its EDRDataDissemination Given a time interval I , the EDR stores information such asTraffic analysisData dissemination the highest and lowest speed during I, the position and timein undivided roadsData dissemination of the strongest deceleration during I, as well as locationin divided roads lane changes informationSecuring DataDissemination Drivers can provide input to the EDR, using a simple menu,Belts Clustering either through a dashboard console or through verbal input
  33. 33. Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. Car’s EDR is a tamper-resistant device much like the S. Olariu well-known black-boxes on commercial aircraft.Introduction All of the vehicle’s sub-assemblies, including the GPS unit,Infrastructure speedometer, gas tank reading, and sensors for outsideIncident temperature, feed their own readings into the EDRDetectionWarming upProbabilistic Cars are equipped with automatic lane detections and feedstechnique its output to its EDRDataDissemination Given a time interval I , the EDR stores information such asTraffic analysisData dissemination the highest and lowest speed during I, the position and timein undivided roadsData dissemination of the strongest deceleration during I, as well as locationin divided roads lane changes informationSecuring DataDissemination Drivers can provide input to the EDR, using a simple menu,Belts Clustering either through a dashboard console or through verbal input
  34. 34. Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. Car’s EDR is a tamper-resistant device much like the S. Olariu well-known black-boxes on commercial aircraft.Introduction All of the vehicle’s sub-assemblies, including the GPS unit,Infrastructure speedometer, gas tank reading, and sensors for outsideIncident temperature, feed their own readings into the EDRDetectionWarming upProbabilistic Cars are equipped with automatic lane detections and feedstechnique its output to its EDRDataDissemination Given a time interval I , the EDR stores information such asTraffic analysisData dissemination the highest and lowest speed during I, the position and timein undivided roadsData dissemination of the strongest deceleration during I, as well as locationin divided roads lane changes informationSecuring DataDissemination Drivers can provide input to the EDR, using a simple menu,Belts Clustering either through a dashboard console or through verbal input
  35. 35. Vehicles ModelA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. Car’s EDR is a tamper-resistant device much like the S. Olariu well-known black-boxes on commercial aircraft.Introduction All of the vehicle’s sub-assemblies, including the GPS unit,Infrastructure speedometer, gas tank reading, and sensors for outsideIncident temperature, feed their own readings into the EDRDetectionWarming upProbabilistic Cars are equipped with automatic lane detections and feedstechnique its output to its EDRDataDissemination Given a time interval I , the EDR stores information such asTraffic analysisData dissemination the highest and lowest speed during I, the position and timein undivided roadsData dissemination of the strongest deceleration during I, as well as locationin divided roads lane changes informationSecuring DataDissemination Drivers can provide input to the EDR, using a simple menu,Belts Clustering either through a dashboard console or through verbal input
  36. 36. Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  37. 37. Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  38. 38. Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  39. 39. Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  40. 40. Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  41. 41. Belt To Car CommunicationsA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  42. 42. Our FrameworkA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueData 1 System architecture: an infrastructure that collectsDisseminationTraffic analysis information from passing vehicles.Data disseminationin undivided roads 2 Incident detection engine: uses accumulated information toData disseminationin divided roads detect potential incidents and traffic delaysSecuring DataDissemination 3 Notification dissemination: once an accident detected,Belts Clustering approaching drivers need to be alerted about its existence.
  43. 43. Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroduction A belt is responsible for collecting and managing InformationInfrastructure about lane changes from passing vehiclesIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  44. 44. Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroduction A belt is responsible for collecting and managing InformationInfrastructure about lane changes from passing vehiclesIncidentDetection Each belt maintains a table RoadImage[m][n] where m isWarming upProbabilistic number of lanes and n is the distance between twotechnique consecutive belts.DataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  45. 45. Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroduction A belt is responsible for collecting and managing InformationInfrastructure about lane changes from passing vehiclesIncidentDetection Each belt maintains a table RoadImage[m][n] where m isWarming upProbabilistic number of lanes and n is the distance between twotechnique consecutive belts.DataDissemination When a belt receives an EDR from a passing vehicles, it willTraffic analysisData dissemination increment RoadImage[i][j] for all positions (lane = i, positionin undivided roadsData dissemination = j) that the car has passed over.in divided roadsSecuring DataDisseminationBelts Clustering
  46. 46. Warming Up: Basic IdeaA Framework For IncidentDetection AndNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroduction A belt is responsible for collecting and managing InformationInfrastructure about lane changes from passing vehiclesIncidentDetection Each belt maintains a table RoadImage[m][n] where m isWarming upProbabilistic number of lanes and n is the distance between twotechnique consecutive belts.DataDissemination When a belt receives an EDR from a passing vehicles, it willTraffic analysisData dissemination increment RoadImage[i][j] for all positions (lane = i, positionin undivided roadsData dissemination = j) that the car has passed over.in divided roadsSecuring Data RoadImage[i][j] = x, means that x cars have passed overDisseminationBelts Clustering the location (lane = i; position = j) recently.
  47. 47. Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In 1 The previous technique still produces many false positiveVehicular AdHoc Networks alarms, specially if we are interested in short detection time. M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  48. 48. Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In 1 The previous technique still produces many false positiveVehicular AdHoc Networks alarms, specially if we are interested in short detection time. M. Abuelela 2 It does not consider the relation between positions at whichAdvisor: Prof. S. Olariu a vehicle has changed lane and the location of the accident. Whenever a change lane being made, a belt assumed thatIntroduction having an incident at each location is equiprobable.InfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  49. 49. Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In 1 The previous technique still produces many false positiveVehicular AdHoc Networks alarms, specially if we are interested in short detection time. M. Abuelela 2 It does not consider the relation between positions at whichAdvisor: Prof. S. Olariu a vehicle has changed lane and the location of the accident. Whenever a change lane being made, a belt assumed thatIntroduction having an incident at each location is equiprobable.InfrastructureIncident 3 The value of the detection threshold is very important yetDetectionWarming up difficult to determine for each road.ProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  50. 50. Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In 1 The previous technique still produces many false positiveVehicular AdHoc Networks alarms, specially if we are interested in short detection time. M. Abuelela 2 It does not consider the relation between positions at whichAdvisor: Prof. S. Olariu a vehicle has changed lane and the location of the accident. Whenever a change lane being made, a belt assumed thatIntroduction having an incident at each location is equiprobable.InfrastructureIncident 3 The value of the detection threshold is very important yetDetectionWarming up difficult to determine for each road.Probabilistictechnique 4 More importantly, it is very difficult to incorporate otherDataDissemination parameters, drivers inputs or declaration, in the detectionTraffic analysis process.Data disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  51. 51. Problems With The Warming Up TechniqueA Framework For IncidentDetection AndNotification In 1 The previous technique still produces many false positiveVehicular AdHoc Networks alarms, specially if we are interested in short detection time. M. Abuelela 2 It does not consider the relation between positions at whichAdvisor: Prof. S. Olariu a vehicle has changed lane and the location of the accident. Whenever a change lane being made, a belt assumed thatIntroduction having an incident at each location is equiprobable.InfrastructureIncident 3 The value of the detection threshold is very important yetDetectionWarming up difficult to determine for each road.Probabilistictechnique 4 More importantly, it is very difficult to incorporate otherDataDissemination parameters, drivers inputs or declaration, in the detectionTraffic analysis process.Data disseminationin undivided roadsData disseminationin divided roads 5 Calibration complexity and lack of universality (orSecuring Data transferability)DisseminationBelts Clustering
  52. 52. Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And Let Pr[I] to be the a priori probability (or belief) of an incidentNotification In I at a given position on the roadVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  53. 53. Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And Let Pr[I] to be the a priori probability (or belief) of an incidentNotification In I at a given position on the roadVehicular AdHoc Networks When a number of evidences E s, correlated in both time M. AbuelelaAdvisor: Prof. and position, about an existing incident are collected S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  54. 54. Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And Let Pr[I] to be the a priori probability (or belief) of an incidentNotification In I at a given position on the roadVehicular AdHoc Networks When a number of evidences E s, correlated in both time M. AbuelelaAdvisor: Prof. and position, about an existing incident are collected S. Olariu A belt computes the posteriori probability of an incident I atIntroduction the given location asInfrastructureIncident Pr[I] · Pr[E|I]Detection Bel(I) = = α · Pr[I] · Pr[E|I]Warming up Pr[E]Probabilistictechnique where Pr[E|I] is the likelihood, E represents any evidenceDataDissemination such as changing lanes or passing over a road anomaly andTraffic analysis α is computed by the law of total probability asData disseminationin undivided roadsData dissemination 1in divided roads .Securing Data Pr[I] · Pr[E|I] + Pr[I] · Pr[E|I]DisseminationBelts Clustering
  55. 55. Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And We are interested here in two types of incidentsNotification InVehicular AdHoc Networks M. AbuelelaAdvisor: Prof. S. OlariuIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  56. 56. Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And We are interested here in two types of incidentsNotification InVehicular AdHoc Networks Blocking incidents : Incidents that blocks one M. Abuelela or more lanes and approaching vehicles haveAdvisor: Prof. S. Olariu to change lane to avoid them. Traffic accidents are good examples for this categoryIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  57. 57. Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And We are interested here in two types of incidentsNotification InVehicular AdHoc Networks Blocking incidents : Incidents that blocks one M. Abuelela or more lanes and approaching vehicles haveAdvisor: Prof. S. Olariu to change lane to avoid them. Traffic accidents are good examples for this categoryIntroduction Non-blocking incidents : Those that driversInfrastructure may pass over if (s)he did not notice them.Incident Potholes are very good example for thatDetectionWarming up where a driver may pass over, decelerate ,Probabilistictechnique take it between wheels or change lane toData avoid.DisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  58. 58. Probabilistic Technique: Basic IdeaA Framework For IncidentDetection And We are interested here in two types of incidentsNotification InVehicular AdHoc Networks Blocking incidents : Incidents that blocks one M. Abuelela or more lanes and approaching vehicles haveAdvisor: Prof. S. Olariu to change lane to avoid them. Traffic accidents are good examples for this categoryIntroduction Non-blocking incidents : Those that driversInfrastructure may pass over if (s)he did not notice them.Incident Potholes are very good example for thatDetectionWarming up where a driver may pass over, decelerate ,Probabilistictechnique take it between wheels or change lane toData avoid.DisseminationTraffic analysisData dissemination Without loss of generality, we will consider a couple ofin undivided roadsData dissemination evidences in each case. (lane change and driver input) forin divided roads blocking incidents. (lane change, declaration, driver inputSecuring DataDissemination and pothole sensing) for non blocking ones.Belts Clustering
  59. 59. Blocking incident detectionA Framework Assume that an accident has occurred at For IncidentDetection And position p, let X be the random variable thatNotification InVehicular Ad keeps track of the position at which vehiclesHoc Networks change lanes. We show that M. AbuelelaAdvisor: Prof. S. Olariu 1 −(x−p)2 fX (x) = √ e 2 2ΠIntroductionInfrastructureIncidentDetectionWarming upProbabilistictechniqueDataDisseminationTraffic analysisData disseminationin undivided roadsData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  60. 60. Blocking incident detectionA Framework Assume that an accident has occurred at For IncidentDetection And position p, let X be the random variable thatNotification InVehicular Ad keeps track of the position at which vehiclesHoc Networks change lanes. We show that M. AbuelelaAdvisor: Prof. S. Olariu 1 −(x−p)2 fX (x) = √ e 2 2ΠIntroductionInfrastructureIncident Similarly, Let Y be a random variable that keeps track of theDetectionWarming up position at which a driver would provide input after anProbabilistictechnique incident We show thatDataDissemination 1 −(p−y )2Traffic analysis fY (y ) = I(D) · √ e 2Data dissemination 2πin undivided roadsData disseminationin divided roads Where I(D) is an indicator function that returns 1 if the driverSecuring Data provided an input about the incident and returns 0 otherwise.DisseminationBelts Clustering
  61. 61. Blocking incident detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In Each belt maintains a table TempProb[m][n] that keeps theVehicular AdHoc Networks probabilities of having a blocking incidents on different M. Abuelela positionsAdvisor: Prof. S. Olariu All probabilities are initialized to a very small value representing the original probability of having an incident onIntroduction that roadInfrastructureIncident Let us assume that a belt has received an EDR from aDetectionWarming up passing car xProbabilistictechnique For each position p = (lane = i, position = j) that a car x hasDataDissemination passed over, a belt setsTraffic analysisData disseminationin undivided roads TempProb[i][j] = Initial probabilityData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  62. 62. Blocking incident detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In Each belt maintains a table TempProb[m][n] that keeps theVehicular AdHoc Networks probabilities of having a blocking incidents on different M. Abuelela positionsAdvisor: Prof. S. Olariu All probabilities are initialized to a very small value representing the original probability of having an incident onIntroduction that roadInfrastructureIncident Let us assume that a belt has received an EDR from aDetectionWarming up passing car xProbabilistictechnique For each position p = (lane = i, position = j) that a car x hasDataDissemination passed over, a belt setsTraffic analysisData disseminationin undivided roads TempProb[i][j] = Initial probabilityData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  63. 63. Blocking incident detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In Each belt maintains a table TempProb[m][n] that keeps theVehicular AdHoc Networks probabilities of having a blocking incidents on different M. Abuelela positionsAdvisor: Prof. S. Olariu All probabilities are initialized to a very small value representing the original probability of having an incident onIntroduction that roadInfrastructureIncident Let us assume that a belt has received an EDR from aDetectionWarming up passing car xProbabilistictechnique For each position p = (lane = i, position = j) that a car x hasDataDissemination passed over, a belt setsTraffic analysisData disseminationin undivided roads TempProb[i][j] = Initial probabilityData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  64. 64. Blocking incident detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In Each belt maintains a table TempProb[m][n] that keeps theVehicular AdHoc Networks probabilities of having a blocking incidents on different M. Abuelela positionsAdvisor: Prof. S. Olariu All probabilities are initialized to a very small value representing the original probability of having an incident onIntroduction that roadInfrastructureIncident Let us assume that a belt has received an EDR from aDetectionWarming up passing car xProbabilistictechnique For each position p = (lane = i, position = j) that a car x hasDataDissemination passed over, a belt setsTraffic analysisData disseminationin undivided roads TempProb[i][j] = Initial probabilityData disseminationin divided roadsSecuring DataDisseminationBelts Clustering
  65. 65. Blocking incident detection: Updating ProbabilitiesA Framework For Incident For each position p = (lane = i, position = j) that a car hasDetection AndNotification In avoided, the following is performedVehicular AdHoc Networks The belt computes the posteriori probability of having a M. AbuelelaAdvisor: Prof. blocking incident at p that forced x’s driver to change lane S. Olariu before p asIntroduction TempProb[i][j] = α · TempProb[i][j] · Py ,jInfrastructureIncident where Py ,j is the probability of changing lane at position yDetection given that an accident occurred at position j and α can be computed by the law of total probability as described before.Warming upProbabilistictechniqueData If x’s EDR shows a driver input about position p, we alsoDisseminationTraffic analysis update the posteriori probability asData disseminationin undivided roadsData dissemination TempProb[i][j] = α · TempProb[i][j] · Pz,jin divided roadsSecuring Data where Pz,j is the probability that the driver provided input atDisseminationBelts Clustering position z given that an incident did exist at position j.
  66. 66. Blocking incident detection: Updating ProbabilitiesA Framework For Incident For each position p = (lane = i, position = j) that a car hasDetection AndNotification In avoided, the following is performedVehicular AdHoc Networks The belt computes the posteriori probability of having a M. AbuelelaAdvisor: Prof. blocking incident at p that forced x’s driver to change lane S. Olariu before p asIntroduction TempProb[i][j] = α · TempProb[i][j] · Py ,jInfrastructureIncident where Py ,j is the probability of changing lane at position yDetection given that an accident occurred at position j and α can be computed by the law of total probability as described before.Warming upProbabilistictechniqueData If x’s EDR shows a driver input about position p, we alsoDisseminationTraffic analysis update the posteriori probability asData disseminationin undivided roadsData dissemination TempProb[i][j] = α · TempProb[i][j] · Pz,jin divided roadsSecuring Data where Pz,j is the probability that the driver provided input atDisseminationBelts Clustering position z given that an incident did exist at position j.
  67. 67. Blocking incident detection: Updating ProbabilitiesA Framework For Incident For each position p = (lane = i, position = j) that a car hasDetection AndNotification In avoided, the following is performedVehicular AdHoc Networks The belt computes the posteriori probability of having a M. AbuelelaAdvisor: Prof. blocking incident at p that forced x’s driver to change lane S. Olariu before p asIntroduction TempProb[i][j] = α · TempProb[i][j] · Py ,jInfrastructureIncident where Py ,j is the probability of changing lane at position yDetection given that an accident occurred at position j and α can be computed by the law of total probability as described before.Warming upProbabilistictechniqueData If x’s EDR shows a driver input about position p, we alsoDisseminationTraffic analysis update the posteriori probability asData disseminationin undivided roadsData dissemination TempProb[i][j] = α · TempProb[i][j] · Pz,jin divided roadsSecuring Data where Pz,j is the probability that the driver provided input atDisseminationBelts Clustering position z given that an incident did exist at position j.
  68. 68. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident The main difference between detecting blocking andDetection AndNotification In non-blocking incidents is that a driver may pass over aVehicular AdHoc Networks non-blocking incident if he could not avoid it while he has to M. Abuelela change lane to avoid the blocking one.Advisor: Prof. S. Olariu Thus, not every driver will be able to, or have to, change lane to avoid passing over a pothole for example.IntroductionInfrastructure let Z be a random variable that keeps track of the position atIncident which vehicles change lanes to avoid passing over a pothole.Detection We can writeWarming up 1 −(z−p)2Probabilistic fZ (z) = I(D). √ e 2technique 2πDataDisseminationTraffic analysis Where I(D) is an indicator function returning 1 if the driverData disseminationin undivided roads could change lane to avoid the pothole and 0 otherwiseData disseminationin divided roads To enhance the detection process, vehicles are assumed toSecuring DataDissemination be equipped with a sensing device that can detect when theyBelts Clustering pass over a speed bump or a pothole.
  69. 69. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident The main difference between detecting blocking andDetection AndNotification In non-blocking incidents is that a driver may pass over aVehicular AdHoc Networks non-blocking incident if he could not avoid it while he has to M. Abuelela change lane to avoid the blocking one.Advisor: Prof. S. Olariu Thus, not every driver will be able to, or have to, change lane to avoid passing over a pothole for example.IntroductionInfrastructure let Z be a random variable that keeps track of the position atIncident which vehicles change lanes to avoid passing over a pothole.Detection We can writeWarming up 1 −(z−p)2Probabilistic fZ (z) = I(D). √ e 2technique 2πDataDisseminationTraffic analysis Where I(D) is an indicator function returning 1 if the driverData disseminationin undivided roads could change lane to avoid the pothole and 0 otherwiseData disseminationin divided roads To enhance the detection process, vehicles are assumed toSecuring DataDissemination be equipped with a sensing device that can detect when theyBelts Clustering pass over a speed bump or a pothole.
  70. 70. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident The main difference between detecting blocking andDetection AndNotification In non-blocking incidents is that a driver may pass over aVehicular AdHoc Networks non-blocking incident if he could not avoid it while he has to M. Abuelela change lane to avoid the blocking one.Advisor: Prof. S. Olariu Thus, not every driver will be able to, or have to, change lane to avoid passing over a pothole for example.IntroductionInfrastructure let Z be a random variable that keeps track of the position atIncident which vehicles change lanes to avoid passing over a pothole.Detection We can writeWarming up 1 −(z−p)2Probabilistic fZ (z) = I(D). √ e 2technique 2πDataDisseminationTraffic analysis Where I(D) is an indicator function returning 1 if the driverData disseminationin undivided roads could change lane to avoid the pothole and 0 otherwiseData disseminationin divided roads To enhance the detection process, vehicles are assumed toSecuring DataDissemination be equipped with a sensing device that can detect when theyBelts Clustering pass over a speed bump or a pothole.
  71. 71. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident The main difference between detecting blocking andDetection AndNotification In non-blocking incidents is that a driver may pass over aVehicular AdHoc Networks non-blocking incident if he could not avoid it while he has to M. Abuelela change lane to avoid the blocking one.Advisor: Prof. S. Olariu Thus, not every driver will be able to, or have to, change lane to avoid passing over a pothole for example.IntroductionInfrastructure let Z be a random variable that keeps track of the position atIncident which vehicles change lanes to avoid passing over a pothole.Detection We can writeWarming up 1 −(z−p)2Probabilistic fZ (z) = I(D). √ e 2technique 2πDataDisseminationTraffic analysis Where I(D) is an indicator function returning 1 if the driverData disseminationin undivided roads could change lane to avoid the pothole and 0 otherwiseData disseminationin divided roads To enhance the detection process, vehicles are assumed toSecuring DataDissemination be equipped with a sensing device that can detect when theyBelts Clustering pass over a speed bump or a pothole.
  72. 72. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident Similar to the TempProb table, a belt maintains another tableDetection AndNotification In PermProb[m][n]Vehicular AdHoc Networks Moreover, each belt maintains a list ManMade that stores M. Abuelela information about man-made road anomalies like rail-roadAdvisor: Prof. S. Olariu crossings and speed bump positions. This information is very helpful to automatically filter out these anomalies whenIntroduction reported by cars.InfrastructureIncident For each position p(i, j) that car x has avoided, assumingDetectionWarming up that x’s driver has made the last lane change at positionProbabilistictechnique y j.Data x’s driver might have changed lanes because of a pothole atDisseminationTraffic analysis p, so, the belt computes the posteriori probability asData disseminationin undivided roadsData dissemination permprob[i][j] = α · permprob[i][j] · Py ,jin divided roadsSecuring Data Where Py ,j is the probability of changing lane at position yDisseminationBelts Clustering given that a pothole does exist at position j.
  73. 73. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident Similar to the TempProb table, a belt maintains another tableDetection AndNotification In PermProb[m][n]Vehicular AdHoc Networks Moreover, each belt maintains a list ManMade that stores M. Abuelela information about man-made road anomalies like rail-roadAdvisor: Prof. S. Olariu crossings and speed bump positions. This information is very helpful to automatically filter out these anomalies whenIntroduction reported by cars.InfrastructureIncident For each position p(i, j) that car x has avoided, assumingDetectionWarming up that x’s driver has made the last lane change at positionProbabilistictechnique y j.Data x’s driver might have changed lanes because of a pothole atDisseminationTraffic analysis p, so, the belt computes the posteriori probability asData disseminationin undivided roadsData dissemination permprob[i][j] = α · permprob[i][j] · Py ,jin divided roadsSecuring Data Where Py ,j is the probability of changing lane at position yDisseminationBelts Clustering given that a pothole does exist at position j.
  74. 74. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident Similar to the TempProb table, a belt maintains another tableDetection AndNotification In PermProb[m][n]Vehicular AdHoc Networks Moreover, each belt maintains a list ManMade that stores M. Abuelela information about man-made road anomalies like rail-roadAdvisor: Prof. S. Olariu crossings and speed bump positions. This information is very helpful to automatically filter out these anomalies whenIntroduction reported by cars.InfrastructureIncident For each position p(i, j) that car x has avoided, assumingDetectionWarming up that x’s driver has made the last lane change at positionProbabilistictechnique y j.Data x’s driver might have changed lanes because of a pothole atDisseminationTraffic analysis p, so, the belt computes the posteriori probability asData disseminationin undivided roadsData dissemination permprob[i][j] = α · permprob[i][j] · Py ,jin divided roadsSecuring Data Where Py ,j is the probability of changing lane at position yDisseminationBelts Clustering given that a pothole does exist at position j.
  75. 75. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident Similar to the TempProb table, a belt maintains another tableDetection AndNotification In PermProb[m][n]Vehicular AdHoc Networks Moreover, each belt maintains a list ManMade that stores M. Abuelela information about man-made road anomalies like rail-roadAdvisor: Prof. S. Olariu crossings and speed bump positions. This information is very helpful to automatically filter out these anomalies whenIntroduction reported by cars.InfrastructureIncident For each position p(i, j) that car x has avoided, assumingDetectionWarming up that x’s driver has made the last lane change at positionProbabilistictechnique y j.Data x’s driver might have changed lanes because of a pothole atDisseminationTraffic analysis p, so, the belt computes the posteriori probability asData disseminationin undivided roadsData dissemination permprob[i][j] = α · permprob[i][j] · Py ,jin divided roadsSecuring Data Where Py ,j is the probability of changing lane at position yDisseminationBelts Clustering given that a pothole does exist at position j.
  76. 76. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident For each position p = (lane = i, position = j) that a car x hasDetection AndNotification In passed over:Vehicular AdHoc Networks 1 If x’s EDR has a record for a suspected pothole at M. Abuelela position p, p ∈ ManMade , then the belt updates /Advisor: Prof. S. Olariu posteriori probability asIntroduction PermProb[i][j] = α·PermProb[i][j]·Pr[Detection|Pothole]InfrastructureIncident 2 If x’s EDR shows that x has significantly deceleratedDetectionWarming up around position p, p ∈ ManMade, this means that there /Probabilistictechnique might be a pothole at position p that the driver wantedData to avoid or reduce its effect on his car.DisseminationTraffic analysis PermProb[i][j] = α·PermProb[i][j]·Pr[Decelerate|Pothole]Data dissemination Where Pr[Reduce|Pothole] is the probability that thein undivided roadsData dissemination driver slows down when he sees a pothole.in divided roadsSecuring DataDisseminationBelts Clustering If x’s EDR shows nothing about position p, two options exist. Either that position is clear or the driver could avoid the
  77. 77. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident For each position p = (lane = i, position = j) that a car x hasDetection AndNotification In passed over:Vehicular AdHoc Networks 1 If x’s EDR has a record for a suspected pothole at M. Abuelela position p, p ∈ ManMade , then the belt updates /Advisor: Prof. S. Olariu posteriori probability asIntroduction PermProb[i][j] = α·PermProb[i][j]·Pr[Detection|Pothole]InfrastructureIncident 2 If x’s EDR shows that x has significantly deceleratedDetectionWarming up around position p, p ∈ ManMade, this means that there /Probabilistictechnique might be a pothole at position p that the driver wantedData to avoid or reduce its effect on his car.DisseminationTraffic analysis PermProb[i][j] = α·PermProb[i][j]·Pr[Decelerate|Pothole]Data dissemination Where Pr[Reduce|Pothole] is the probability that thein undivided roadsData dissemination driver slows down when he sees a pothole.in divided roadsSecuring DataDisseminationBelts Clustering If x’s EDR shows nothing about position p, two options exist. Either that position is clear or the driver could avoid the
  78. 78. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident For each position p = (lane = i, position = j) that a car x hasDetection AndNotification In passed over:Vehicular AdHoc Networks 1 If x’s EDR has a record for a suspected pothole at M. Abuelela position p, p ∈ ManMade , then the belt updates /Advisor: Prof. S. Olariu posteriori probability asIntroduction PermProb[i][j] = α·PermProb[i][j]·Pr[Detection|Pothole]InfrastructureIncident 2 If x’s EDR shows that x has significantly deceleratedDetectionWarming up around position p, p ∈ ManMade, this means that there /Probabilistictechnique might be a pothole at position p that the driver wantedData to avoid or reduce its effect on his car.DisseminationTraffic analysis PermProb[i][j] = α·PermProb[i][j]·Pr[Decelerate|Pothole]Data dissemination Where Pr[Reduce|Pothole] is the probability that thein undivided roadsData dissemination driver slows down when he sees a pothole.in divided roadsSecuring DataDisseminationBelts Clustering If x’s EDR shows nothing about position p, two options exist. Either that position is clear or the driver could avoid the
  79. 79. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For Incident For each position p = (lane = i, position = j) that a car x hasDetection AndNotification In passed over:Vehicular AdHoc Networks 1 If x’s EDR has a record for a suspected pothole at M. Abuelela position p, p ∈ ManMade , then the belt updates /Advisor: Prof. S. Olariu posteriori probability asIntroduction PermProb[i][j] = α·PermProb[i][j]·Pr[Detection|Pothole]InfrastructureIncident 2 If x’s EDR shows that x has significantly deceleratedDetectionWarming up around position p, p ∈ ManMade, this means that there /Probabilistictechnique might be a pothole at position p that the driver wantedData to avoid or reduce its effect on his car.DisseminationTraffic analysis PermProb[i][j] = α·PermProb[i][j]·Pr[Decelerate|Pothole]Data dissemination Where Pr[Reduce|Pothole] is the probability that thein undivided roadsData dissemination driver slows down when he sees a pothole.in divided roadsSecuring DataDisseminationBelts Clustering If x’s EDR shows nothing about position p, two options exist. Either that position is clear or the driver could avoid the
  80. 80. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In In contrast to our technique for accident detection, potholeVehicular AdHoc Networks probabilities are never re-initialized after receiving an EDR M. Abuelela from a passing vehicle.Advisor: Prof. S. Olariu However, if a belt continues updating pothole probabilities with the arrival of each EDR showing lane changes, falseIntroduction positive alarms will be eventually generated.InfrastructureIncident Therefore, it is important to re-initialize pothole probabilitiesDetectionWarming up after some detection duration to avoid the detection of falseProbabilistictechnique potholesDataDissemination If this detection duration is very short, shorter than the meanTraffic analysis detection time, we may never detect any pothole.Data disseminationin undivided roadsData disseminationin divided roads On the other hand, If it is very long, false alarms would beSecuring Data generated.DisseminationBelts Clustering
  81. 81. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In In contrast to our technique for accident detection, potholeVehicular AdHoc Networks probabilities are never re-initialized after receiving an EDR M. Abuelela from a passing vehicle.Advisor: Prof. S. Olariu However, if a belt continues updating pothole probabilities with the arrival of each EDR showing lane changes, falseIntroduction positive alarms will be eventually generated.InfrastructureIncident Therefore, it is important to re-initialize pothole probabilitiesDetectionWarming up after some detection duration to avoid the detection of falseProbabilistictechnique potholesDataDissemination If this detection duration is very short, shorter than the meanTraffic analysis detection time, we may never detect any pothole.Data disseminationin undivided roadsData disseminationin divided roads On the other hand, If it is very long, false alarms would beSecuring Data generated.DisseminationBelts Clustering
  82. 82. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In In contrast to our technique for accident detection, potholeVehicular AdHoc Networks probabilities are never re-initialized after receiving an EDR M. Abuelela from a passing vehicle.Advisor: Prof. S. Olariu However, if a belt continues updating pothole probabilities with the arrival of each EDR showing lane changes, falseIntroduction positive alarms will be eventually generated.InfrastructureIncident Therefore, it is important to re-initialize pothole probabilitiesDetectionWarming up after some detection duration to avoid the detection of falseProbabilistictechnique potholesDataDissemination If this detection duration is very short, shorter than the meanTraffic analysis detection time, we may never detect any pothole.Data disseminationin undivided roadsData disseminationin divided roads On the other hand, If it is very long, false alarms would beSecuring Data generated.DisseminationBelts Clustering
  83. 83. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In In contrast to our technique for accident detection, potholeVehicular AdHoc Networks probabilities are never re-initialized after receiving an EDR M. Abuelela from a passing vehicle.Advisor: Prof. S. Olariu However, if a belt continues updating pothole probabilities with the arrival of each EDR showing lane changes, falseIntroduction positive alarms will be eventually generated.InfrastructureIncident Therefore, it is important to re-initialize pothole probabilitiesDetectionWarming up after some detection duration to avoid the detection of falseProbabilistictechnique potholesDataDissemination If this detection duration is very short, shorter than the meanTraffic analysis detection time, we may never detect any pothole.Data disseminationin undivided roadsData disseminationin divided roads On the other hand, If it is very long, false alarms would beSecuring Data generated.DisseminationBelts Clustering
  84. 84. Non Blocking Incidents detection: Updating ProbabilitiesA Framework For IncidentDetection AndNotification In In contrast to our technique for accident detection, potholeVehicular AdHoc Networks probabilities are never re-initialized after receiving an EDR M. Abuelela from a passing vehicle.Advisor: Prof. S. Olariu However, if a belt continues updating pothole probabilities with the arrival of each EDR showing lane changes, falseIntroduction positive alarms will be eventually generated.InfrastructureIncident Therefore, it is important to re-initialize pothole probabilitiesDetectionWarming up after some detection duration to avoid the detection of falseProbabilistictechnique potholesDataDissemination If this detection duration is very short, shorter than the meanTraffic analysis detection time, we may never detect any pothole.Data disseminationin undivided roadsData disseminationin divided roads On the other hand, If it is very long, false alarms would beSecuring Data generated.DisseminationBelts Clustering
  85. 85. Incident detectionA Framework For IncidentDetection AndNotification InVehicular Ad While updating the probabilities using an EDR of a passingHoc Networks car, a belt also checks for incidents M. AbuelelaAdvisor: Prof. If TempProb[i][j] T1 or PermProb[i][j] T2 for any i and j, S. Olariu an alarm is raised.Introduction The larger the threshold is, the more conservative the belt is,Infrastructure the longer the time needed to detect incidents, the lessIncidentDetection incident detection rate and fewer false alarms reported.Warming upProbabilistictechnique One of the strong points about our proposed techniques isData that the threshold is not a magic number that is very hard toDissemination set like most traditional AID techniquesTraffic analysis Different threshold may be used for different sections/partsData disseminationin undivided roadsData disseminationin divided roads of the roadSecuring DataDisseminationBelts Clustering

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