9 research outputs found
Mining users' significant driving routes with low-power sensors
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.This research has been funded by the EPSRC Innovation
and Knowledge Centre for Smart Infrastructure and Construction
project (EP/K000314).This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2668332.266834
Smart Sensing Systems for the Daily Drive
When driving, you might sometimes wonder, "Are there any disruptions on my regular route that might delay me, and will I be able to find a parking space when I arrive?" Two smartphone-based prototype systems can help answer these questions. The first is ParkSense, which can be used to sense on-street parking-space occupancy when coupled with electronic parking payment systems. The second system can sense and recognize a user's repeated car journeys, which can be used to provide personalized alerts to the user. Both systems aim to minimize the impact of sensing tasks on the device's lifetime so that the user can continue to use the device for its primary purpose. This department is part of a special issue on smart vehicle spaces
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
The pervasiveness and availability of mobile phone data offer the opportunity
of discovering usable knowledge about crowd behaviors in urban environments.
Cities can leverage such knowledge in order to provide better services (e.g.,
public transport planning, optimized resource allocation) and safer cities.
Call Detail Record (CDR) data represents a practical data source to detect and
monitor unusual events considering the high level of mobile phone penetration,
compared with GPS equipped and open devices. In this paper, we provide a
methodology that is able to detect unusual events from CDR data that typically
has low accuracy in terms of space and time resolution. Moreover, we introduce
a concept of unusual event that involves a large amount of people who expose an
unusual mobility behavior. Our careful consideration of the issues that come
from coarse-grained CDR data ultimately leads to a completely general framework
that can detect unusual crowd events from CDR data effectively and efficiently.
Through extensive experiments on real-world CDR data for a large city in
Africa, we demonstrate that our method can detect unusual events with 16%
higher recall and over 10 times higher precision, compared to state-of-the-art
methods. We implement a visual analytics prototype system to help end users
analyze detected unusual crowd events to best suit different application
scenarios. To the best of our knowledge, this is the first work on the
detection of unusual events from CDR data with considerations of its temporal
and spatial sparseness and distinction between user unusual activities and
daily routines.Comment: 18 pages, 6 figure
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Mechanisms for improving information quality in smartphone crowdsensing systems
Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs.
Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii
Mining users' significant driving routes with low-power sensors
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, can open doors for 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 orWiFi 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. Copyright 2014 ACM
Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices
Vehicle trajectories are an important source of information for estimating traffic flow characteristics. Lately, several studies have focused on identifying a vehicleās trajectory in traffic network using data from mobile devices. However, these studies predominantly employed GPS coordinate information for tracking a vehicleās speed and position in the transportation network. Considering the known limitations of GPS, such as, connectivity issues at urban canyons and underpasses, low precision of localization, high power consumption of device while GPS is in use, this research focuses on developing alternate methods for identifying a vehicleās trajectory at an intersection and at a urban grid network using sensor data other than GPS in order to minimize GPS dependency. In particular, accelerometer and gyroscope data collected using smartphoneās inertial sensors, and speed data collected using an on-board diagnostics (OBD) device, are utilized to develop algorithms for maneuver (i.e., left/right turn and through), trip direction, and trajectory identification.
Different algorithms using threshold of gyroscope and magnetometer readings, and machine learning techniques such as k-medoids clustering and dynamic time warping are developed for maneuver identification and their accuracy is tested on collected field data. It is found that, clustering based on maximum and minimum value of gyroscope readings is effective for maneuver identification. For trip direction identification at an intersection, two different methods are developed and tested. The first method utilizes accelerometer, gyroscope and OBD speed data, and the 2nd method employs magnetometer and acceleration data. The results demonstrate that the developed method using accelerometer, gyroscope and OBD speed data are effective in identifying a vehicleās direction. An effective algorithm is developed using OBD speed information, maneuver and trip direction identification algorithms to identify vehicleās trajectory at a grid network. Techniques for noise removal and orientation correction to transfer the raw data from phoneās local coordinate to global coordinate system are also demonstrated.
Overall, this research eliminates the need for continuous GPS connectivity for trajectory identification. This research can be incorporated in methods developed by researchers to estimate traffic flow, delays, and queue lengths at intersections. This information can lead to better signal timings, travel recommendations, and traffic updates
Compressed Sensing in Resource-Constrained Environments: From Sensing Mechanism Design to Recovery Algorithms
Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of CS in resource-constrained environments.
First, we try to solve the problem on how to design sensing mechanisms that could better adapt to the resource-limited smartphone platform. We propose the compressed phone sensing (CPS) framework where two challenging issues are studied, the energy drainage issue due to continuous sensing which may impede the normal functionality of the smartphones and the requirement of active user inputs for data collection that may place a high burden on the user.
Second, we propose a CS reconstruction algorithm to be used in VSNs for recovery of frames/images. An efficient algorithm, NonLocal Douglas-Rachford (NLDR), is developed. NLDR takes advantage of self-similarity in images using nonlocal means (NL) filtering. We further formulate the nonlocal estimation as the low-rank matrix approximation problem and solve the constrained optimization problem using Douglas-Rachford splitting method.
Third, we extend the NLDR algorithm to surveillance video processing in VSNs and propose recursive Low-rank and Sparse estimation through Douglas-Rachford splitting (rLSDR) method for recovery of the video frame into a low-rank background component and sparse component that corresponds to the moving object. The spatial and temporal low-rank features of the video frame, e.g., the nonlocal similar patches within the single video frame and the low-rank background component residing in multiple frames, are successfully exploited