68 research outputs found
Characterizing Power Consumption of Dual-Frequency GNSS of a Smartphone
Location service is one of the most widely used features on a smartphone.
More and more apps are built based on location services. As such, demand for
accurate positioning is ever higher. Mobile brand Xiaomi has introduced Mi 8,
the world's first smartphone equipped with a dual-frequency GNSS chipset which
is claimed to provide up to decimeter-level positioning accuracy. Such
unprecedentedly high location accuracy brought excitement to industry and
academia for navigation research and development of emerging apps. On the other
hand, there is a significant knowledge gap on the energy efficiency of
smartphones equipped with a dual-frequency GNSS chipset. In this paper, we
bridge this knowledge gap by performing an empirical study on power consumption
of a dual-frequency GNSS phone. To the best our knowledge, this is the first
experimental study that characterizes the power consumption of a smartphone
equipped with a dual-frequency GNSS chipset and compares the energy efficiency
with a single-frequency GNSS phone. We demonstrate that a smartphone with a
dual-frequency GNSS chipset consumes 37% more power on average outdoors, and
28% more power indoors, in comparison with a singe-frequency GNSS phone.Comment: Published in IEEE Global Communications Conference (GLOBECOM
DeepWalking: Enabling Smartphone-based Walking Speed Estimation Using Deep Learning
Walking speed estimation is an essential component of mobile apps in various
fields such as fitness, transportation, navigation, and health-care. Most
existing solutions are focused on specialized medical applications that utilize
body-worn motion sensors. These approaches do not serve effectively the general
use case of numerous apps where the user holding a smartphone tries to find his
or her walking speed solely based on smartphone sensors. However, existing
smartphone-based approaches fail to provide acceptable precision for walking
speed estimation. This leads to a question: is it possible to achieve
comparable speed estimation accuracy using a smartphone over wearable sensor
based obtrusive solutions?
We find the answer from advanced neural networks. In this paper, we present
DeepWalking, the first deep learning-based walking speed estimation scheme for
smartphone. A deep convolutional neural network (DCNN) is applied to
automatically identify and extract the most effective features from the
accelerometer and gyroscope data of smartphone and to train the network model
for accurate speed estimation. Experiments are performed with 10 participants
using a treadmill. The average root-mean-squared-error (RMSE) of estimated
walking speed is 0.16m/s which is comparable to the results obtained by
state-of-the-art approaches based on a number of body-worn sensors (i.e., RMSE
of 0.11m/s). The results indicate that a smartphone can be a strong tool for
walking speed estimation if the sensor data are effectively calibrated and
supported by advanced deep learning techniques.Comment: 6 pages, 9 figures, published in IEEE Global Communications
Conference (GLOBECOM
Experimental Study on Low Power Wide Area Networks (LPWAN) for Mobile Internet of Things
In the past decade, we have witnessed explosive growth in the number of
low-power embedded and Internet-connected devices, reinforcing the new
paradigm, Internet of Things (IoT). The low power wide area network (LPWAN),
due to its long-range, low-power and low-cost communication capability, is
actively considered by academia and industry as the future wireless
communication standard for IoT. However, despite the increasing popularity of
`mobile IoT', little is known about the suitability of LPWAN for those mobile
IoT applications in which nodes have varying degrees of mobility. To fill this
knowledge gap, in this paper, we conduct an experimental study to evaluate,
analyze, and characterize LPWAN in both indoor and outdoor mobile environments.
Our experimental results indicate that the performance of LPWAN is surprisingly
susceptible to mobility, even to minor human mobility, and the effect of
mobility significantly escalates as the distance to the gateway increases.
These results call for development of new mobility-aware LPWAN protocols to
support mobile IoT.Comment: To appear at 2017 IEEE 85th Vehicular Technology Conference (VTC'17
Spring
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
A traffic monitoring system is an integral part of Intelligent Transportation
Systems (ITS). It is one of the critical transportation infrastructures that
transportation agencies invest a huge amount of money to collect and analyze
the traffic data to better utilize the roadway systems, improve the safety of
transportation, and establish future transportation plans. With recent advances
in MEMS, machine learning, and wireless communication technologies, numerous
innovative traffic monitoring systems have been developed. In this article, we
present a review of state-of-the-art traffic monitoring systems focusing on the
major functionality--vehicle classification. We organize various vehicle
classification systems, examine research issues and technical challenges, and
discuss hardware/software design, deployment experience, and system performance
of vehicle classification systems. Finally, we discuss a number of critical
open problems and future research directions in an aim to provide valuable
resources to academia, industry, and government agencies for selecting
appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces
L-Platooning: A Protocol for Managing a Long Platoon with DSRC
Vehicle platooning is an automated driving technology that enables a group of
vehicles to travel very closely together as a single unit to improve fuel
efficiency and driver safety and reduces CO2 emission. The significant benefits
of platooning attracted huge interests from academia and industry, especially
from logistics companies for utilizing platoons of "long-body" trailer trucks
because of the huge cost savings. In this paper, we demonstrate that existing
DSRC-based platooning solutions, however, fail to support formation of such
"long" platoons consisting of typical trailer trucks because of the limited
communication range of DSRC. To address this problem, we propose L-Platooning,
the first platooning protocol that enables seamless, reliable, and rapid
formation of a long platoon. We introduce a novel concept called Virtual Leader
that refers to a vehicle that acts like a platoon leader to extend the coverage
of the original platoon leader. A virtual leader election algorithm is
developed to effectively designate a virtual leader based on the novel metric
called the Virtual Leader Quality Index (VLQI) which quantifies the
effectiveness of a vehicle serving as a platoon leader. We also develop
mechanisms for L-Platooning to support the vehicle join and leave maneuvers
specifically for a long platoon. Through extensive simulations using the
combination of Veins (Plexe) and SUMO, we demonstrate that L-Platooning enables
long-body trailer trucks to form a long platoon effectively and maintain the
desired inter-vehicle distance precisely. We also show that L-Platooning
handles seamlessly the vehicle join and leave maneuvers for a long platoon.Comment: Published in IEEE Transactions on Intelligent Transportation System
Adaptive Multi-Class Audio Classification in Noisy In-Vehicle Environment
With ever-increasing number of car-mounted electric devices and their
complexity, audio classification is increasingly important for the automotive
industry as a fundamental tool for human-device interactions. Existing
approaches for audio classification, however, fall short as the unique and
dynamic audio characteristics of in-vehicle environments are not appropriately
taken into account. In this paper, we develop an audio classification system
that classifies an audio stream into music, speech, speech+music, and noise,
adaptably depending on driving environments including highway, local road,
crowded city, and stopped vehicle. More than 420 minutes of audio data
including various genres of music, speech, speech+music, and noise are
collected from diverse driving environments. The results demonstrate that the
proposed approach improves the average classification accuracy up to 166%, and
64% for speech, and speech+music, respectively, compared with a non-adaptive
approach in our experimental settings
UBAT: On Jointly Optimizing UAV Trajectories and Placement of Battery Swap Stations
Unmanned aerial vehicles (UAVs) have been widely used in many applications.
The limited flight time of UAVs, however, still remains as a major challenge.
Although numerous approaches have been developed to recharge the battery of
UAVs effectively, little is known about optimal methodologies to deploy
charging stations. In this paper, we address the charging station deployment
problem with an aim to find the optimal number and locations of charging
stations such that the system performance is maximized. We show that the
problem is NP-Hard and propose UBAT, a heuristic framework based on the ant
colony optimization (ACO) to solve the problem. Additionally, a suite of
algorithms are designed to enhance the execution time and the quality of the
solutions for UBAT. Through extensive simulations, we demonstrate that UBAT
effectively performs multi-objective optimization of generation of UAV
trajectories and placement of charging stations that are within 8.3% and 7.3%
of the true optimal solutions, respectively.Comment: Accepted for publication in ICRA, 202
An Experimental Study on Direction Finding of Bluetooth 5.1: Indoor vs Outdoor
The Bluetooth Special Interest Group (Bluetooth SIG) introduced a new feature
for highly accurate localization called the Direction Finding in the Bluetooth
Core Specification 5.1. Since this new localization feature is relatively new,
despite the significant interest of industry and academia in the accurate
positioning of Bluetooth devices/tags, there are only a handful of experimental
studies conducted to evaluate the performance of the new technology.
Furthermore, these experimental works are constrained to only indoor
environments or performed with hardware emulation of Bluetooth 5.1 via
Universal Software Radio Peripherals (USRPs). In this paper, we perform an
experimental study on the positioning accuracy of the direction finding using
COTS Bluetooth 5.1 devices in booth indoor and outdoor environments to provide
insights on the performance gap under these different experimental settings.
Our results demonstrate that the average angular error in an outdoor
environment is 0.28 degrees, significantly improving the angular error measured
in an indoor environment by 73%. It is also demonstrated that the average
positioning accuracy measured in an outdoor environment is 22cm which is 39.7%
smaller than that measured in an indoor environment
D-ACC: Dynamic Adaptive Cruise Control for Highways with Ramps Based on Deep Q-Learning
An Adaptive Cruise Control (ACC) system allows vehicles to maintain a desired
headway distance to a preceding vehicle automatically. It is increasingly
adopted by commercial vehicles. Recent research demonstrates that the effective
use of ACC can improve the traffic flow through the adaptation of the headway
distance in response to the current traffic conditions. In this paper, we
demonstrate that a state-of-the-art intelligent ACC system performs poorly on
highways with ramps due to the limitation of the model-based approaches that do
not take into account appropriately the traffic dynamics on ramps in
determining the optimal headway distance. We then propose a dynamic adaptive
cruise control system (D-ACC) based on deep reinforcement learning that adapts
the headway distance effectively according to dynamically changing traffic
conditions for both the main road and ramp to optimize the traffic flow.
Extensive simulations are performed with a combination of a traffic simulator
(SUMO) and vehicle-to-everything communication (V2X) network simulator (Veins)
under numerous traffic scenarios. We demonstrate that D-ACC improves the
traffic flow by up to 70% compared with a state-of-the-art intelligent ACC
system in a highway segment with a ramp.Comment: Accepted for Publication in IEEE International Conference on Robotics
and Automation (ICRA) 202
SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone
The number of pedestrian accidents continues to keep climbing. Distraction
from smartphone is one of the biggest causes for pedestrian fatalities. In this
paper, we develop SaferCross, a mobile system based on the embedded sensors of
smartphone to improve pedestrian safety by preventing distraction from
smartphone. SaferCross adopts a holistic approach by identifying and developing
essential system components that are missing in existing systems and
integrating the system components into a "fully-functioning" mobile system for
pedestrian safety. Specifically, we create algorithms for improving the
accuracy and energy efficiency of pedestrian positioning, effectiveness of
phone activity detection, and real-time risk assessment. We demonstrate that
SaferCross, through systematic integration of the developed algorithms,
performs situation awareness effectively and provides a timely warning to the
pedestrian based on the information obtained from smartphone sensors and Direct
Wi-Fi-based peer-to-peer communication with approaching cars. Extensive
experiments are conducted in a department parking lot for both component-level
and integrated testing. The results demonstrate that the energy efficiency and
positioning accuracy of SaferCross are improved by 52% and 72% on average
compared with existing solutions with missing support for positioning accuracy
and energy efficiency, and the phone-viewing event detection accuracy is over
90%. The integrated test results show that SaferCross alerts the pedestrian
timely with an average error of 1.6sec in comparison with the ground truth
data, which can be easily compensated by configuring the system to fire an
alert message a couple of seconds early.Comment: Published in IEEE Access, 202
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