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Using Sensor Redundancy in Vehicles ...

Ganesan, Arunkumaar.

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Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety./
Author:	Ganesan, Arunkumaar.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	168 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-11, Section: B.
Contained By:	Dissertations Abstracts International81-11B.
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28006473
ISBN:	9798643183969

Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety.
Ganesan, Arunkumaar.

Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 168 p.

Source: Dissertations Abstracts International, Volume: 81-11, Section: B.

Thesis (Ph.D.)--University of Michigan, 2020.

This item must not be sold to any third party vendors.

The average American spends around at least one hour driving every day. During that time the driver utilizes various sensors to enhance their commute. Approximately 77% of smartphone users rely on navigation apps daily. Consumer grade OBD dongles that collect vehicle sensor data to monitor safe driving habits are common.Existing sensing applications pertaining to our drive are often separate from each other and fail to learn from and utilize the information gained by other sensing streams and other drivers. In order to best leverage the widespread use of sensing capabilities, we have to unify and coordinate the diﬀerent sensing streams in a meaningful way.This dissertation explores and validates the following thesis: Sensing the same phenomenon from multiple perspectives can enhance vehicle safety, security and transportation.First, it presents ﬁndings from an exploratory study on unifying vehicular sensor streams. We explored combining sensory data from within one vehicle through pairwise correlation and across multiple vehicles through normal models built with principal component analysis and cluster analysis. Our ﬁndings from this exploratory study motivated the rest of this thesis work on using sensor redundancy for CAN-businjection detection and driving hazard detection.Second, we unify the phone sensors with vehicle sensors to detect CAN bus injection attacks that compromise vehicular sensor values. Speciﬁcally, we answer the question: Are phone sensors accurate enough to detect typical CAN bus injection attacks found in literature? Through extensive tests we found that phone sensors are sufficiently accurate to detect many CAN-bus injection attacks.Third, we construct GPS trajectories from multiple vehicles nearby to ﬁnd stationary and mobile driving hazards such as a bicyclist on the side of the road. Such a tool will eﬀectively extend the repertoire of current navigation assistant applications such as Google Maps which detect and warn drivers about upcoming stationary hazards.Finally, we present an easy-to-use tool to help developers and researchers quickly build and prototype data-collection apps that naturally exploit sensing redundancy.Overall, this thesis provides a unified basis for exploiting sensing redundancy existing inside a single vehicle as well as between different vehicles to enhance driving safety and security.

ISBN: 9798643183969Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Vehicular sensor redundancy

Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety.
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520 $a The average American spends around at least one hour driving every day. During that time the driver utilizes various sensors to enhance their commute. Approximately 77% of smartphone users rely on navigation apps daily. Consumer grade OBD dongles that collect vehicle sensor data to monitor safe driving habits are common.Existing sensing applications pertaining to our drive are often separate from each other and fail to learn from and utilize the information gained by other sensing streams and other drivers. In order to best leverage the widespread use of sensing capabilities, we have to unify and coordinate the diﬀerent sensing streams in a meaningful way.This dissertation explores and validates the following thesis: Sensing the same phenomenon from multiple perspectives can enhance vehicle safety, security and transportation.First, it presents ﬁndings from an exploratory study on unifying vehicular sensor streams. We explored combining sensory data from within one vehicle through pairwise correlation and across multiple vehicles through normal models built with principal component analysis and cluster analysis. Our ﬁndings from this exploratory study motivated the rest of this thesis work on using sensor redundancy for CAN-businjection detection and driving hazard detection.Second, we unify the phone sensors with vehicle sensors to detect CAN bus injection attacks that compromise vehicular sensor values. Speciﬁcally, we answer the question: Are phone sensors accurate enough to detect typical CAN bus injection attacks found in literature? Through extensive tests we found that phone sensors are sufficiently accurate to detect many CAN-bus injection attacks.Third, we construct GPS trajectories from multiple vehicles nearby to ﬁnd stationary and mobile driving hazards such as a bicyclist on the side of the road. Such a tool will eﬀectively extend the repertoire of current navigation assistant applications such as Google Maps which detect and warn drivers about upcoming stationary hazards.Finally, we present an easy-to-use tool to help developers and researchers quickly build and prototype data-collection apps that naturally exploit sensing redundancy.Overall, this thesis provides a unified basis for exploiting sensing redundancy existing inside a single vehicle as well as between different vehicles to enhance driving safety and security.
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