Mining users' significant driving routes with low-power sensors

Abstract

While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, opens doors to the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones.In this paper, we present a passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer. This approach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone orientation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recognition capabilities of this approach with GPS trajectory analysis and show that it achieves similar performance. Moreover, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor.

OS-level Side Channels without Procfs: Exploring Cross-App Information Leakage on iOS

“…We will show how this monitoring app can utilize some OS-level sidechannel attack vectors on iOS (to be discussed shortly) to breach user privacy. Out of the scope are CPU cache side channels [66], electronic magnetic side channels [24], [39], [40], and mobile sensor based side channels [50], [52], [53], [57]. As they explore leakage through micro-architectures, electronic magnetic emission, or device orientation, which are not specific to iOS.…”

“…Besides location leakage through GPS [44], [55], accelerometers [23], [41], [47], [53], [59], magnetometer [57], gyroscope [50], [52], [59], and orientation sensor [29], [63] have also been exploited to infer the user's location, movement, and even keystrokes (thus PIN and passwords). These papers all studied sensor-based side channels on Android.…”

Sensor Guardian: prevent privacy inference on Android sensors

“…Miluzzo et al [52] uses motion sensors to infer the location of users' taps on the screen. More stealthily, [9,[53][54][55][56] use unrestricted sensors, including gyroscope, accelerometer, magnetometer, and barometer to infer users' location, driving patterns, and traveled routes. Xu et al, Cai and Chen, Shen et al, Aviv et al, and Owusu et al [1,2,8,46,57] use motion sensors to infer users' keystroke on the smartphone's screen because typing on different locations on the screen may cause changes on the device's motion status, which is reflected on the motion sensors.…”

Smart Sensing Systems for the Daily Drive

“…4 The key insight that this system relies on is the fact when a car travels along a particular route, it executes a certain set of maneuvers (turns and bends) that form a unique signature of the route. These turns and bends can be easily detected by the phone carried inside the vehicle using its embedded gyroscope.…”