For better lateral dynamic stability of an A-double vehicle (comprising a tractor, semitrailer, dolly, and another semitrailer) at high speeds, it is a practical and economical way to control the dolly, a linking structure for long combination vehicles (LCVs). This paper proposes a simple differential brake control strategy based on the feedback of the yaw rate of the dolly. A stability analysis approach is employed by exploring the relationship between damping ratios and speeds, and the critical speed of an A-double vehicle is found. Next, the relationship between the A-double vehicle's damping ratios and the feedback parameter employed in this differential brake control approach is examined, showing that a more favorable feedback value can raise the vehicle's minimum damping ratio. Finally, the method of selecting feedback parameters is also provided, considering the adhesion utilization demanded by the dolly. The time-domain simulation confirms that the stability analysis utilizing the damping ratio is accurate. Using MATLAB/Simulink at various speeds, the effectiveness of this control strategy is tested under single lane-change operations. According to these simulation results, the A-double vehicle using the differential control strategy has a higher critical speed and maintains stability during single lane-change operations at high speeds. This verified that the simple differential brake control strategy has the potential to significantly increase the lateral dynamic stability of the A -double vehicle at high speeds during single-lane change operations economically and practically.

In this paper, we address a critical task of dynamically detecting the simultaneous behavior of driving and texting using smartphone as the sensor. We propose, design, and implement TEXIVE which achieves the goal of detecting texting operations during driving utilizing irregularities and rich micro-movements of users. Without relying on any external infrastructures and additional devices, and no need to bring any modification to vehicles, TEXIVE is able to successfully detect dangerous operations with good sensitivity, specificity, and accuracy by leveraging the inertial sensors integrated in regular smartphones. To validate our approach, we conduct extensive experiments involving in a number of volunteers on various of vehicles and smartphones. Our evaluation results show that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.

Visible light communication (VLC) has been shown to have several advantages over traditional wireless communication. In this paper, we envision an LED-light-to-smartphone VLC protocol for delivering messages to a group of randomly arriving smartphone receivers. Our goal is to increase the throughput for large message delivery, as well as to reduce the delay of message broadcast. Key challenges for implementing such a VLC message broadcast protocol are: 1) the imperfect synchronization among receivers and the transmitter; 2) the receivers' arbitrary arrival times; and 3) the diversity of receivers' smartphones (e.g., location, capability, and frame-rates). In this paper, we propose a new modulation scheme and design link-layer protocols for improving the network data rate. We carefully design and implement our protocol, Martian, which allows smooth communication from the LED lights to a group of smartphone embedded cameras. Across several phone models, Martian can achieve data rate of about 1.6 kb/s even with NLOS -light. It also has a stable and small delay for broadcasting messages to the randomly arriving receivers.

Visible light communication (VLC) has been shown to have several advantages over traditional wireless communication. We envision a LED-to-smartphone VLC protocol for delivering messages to a group of unsynchronized mobile device receivers. We carefully design and implement our protocol, Martian, which allows smooth communication from the LED lights to a group of camera-enabled mobile devices. Across several phone models, Martian can achieve data rate of about 1.6kbps even with NLOS-light. Our intensive evaluations indicate that, the data rate reaches 4.2kbps on iPhone 6. This is a significant improvement compared with the 88bps data rate claimed by state-of-art design.

Recently a number of systems have been developed to implement and improve the visual communication over screen-camera links. In this paper we study an opposite problem: how to prevent unauthorized users from videotaping a video played on a screen, such as in a theater, while do not affect the viewing experience of legitimate audiences. We propose and develop a light-weight hardware-free system, called Kaleido, that ensures these properties by taking advantage of the limited disparities between the screen-eye channel and the screen-camera channel. Kaleido does not require any extra hardware and is purely based on re-encoding the original video frame into multiple frames used for displaying. We extensively test our system Kaleido using a variety of smartphone cameras. Our experiments confirm that Kaleido preserves the high-quality screen-eye channel while reducing the secondary screen-camera channel quality significantly.

Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 × less energy in tracking compared to WiFi-based tracking.

With the increased popularity of smartphones, various security threats and privacy leakages targeting them are discovered and investigated. In this work, we present SilentSense, a framework to authenticate users silently and transparently by exploiting dynamics mined from the user touch behavior biometrics and the micro-movement of the device caused by user's screen-touch actions. We build a “touch-based biometrics” model of the owner by extracting some principle features, and then verify whether the current user is the owner or guest/attacker. When using the smartphone, some unique operating dynamics of the user is detected and learnt by collecting the sensor data and touch events silently. When users are mobile, the micro-movement of mobile devices caused by touch is suppressed by that due to the large scale user-movement which will render the touch-based biometrics ineffective. To address this, we integrate a movement-based biometrics for each user with previous touch-based biometrics. We conduct extensive evaluations of our approaches on the Android smartphone, we show that the user identification accuracy is over 99%.

The ever increasing popularity of social networks and the ever easier photo taking and sharing experience have led to unprecedented concerns on privacy infringement. Inspired by the fact that the Robot Exclusion Protocol, which regulates web crawlers' behavior according a per-site deployed robots.txt, and cooperative practices of major search service providers, have contributed to a healthy web search industry, in this paper, we propose Privacy Expressing and Respecting Protocol (PERP) that consists of a Privacy.tag -- a physical tag that enables a user to explicitly and flexibly express their privacy deal, and Privacy Respecting Sharing Protocol (PRSP) -- a protocol that empowers the photo service provider to exert privacy protection following users' policy expressions, to mitigate the public's privacy concern, and ultimately create a healthy photo-sharing ecosystem in the long run. We further design an exemplar Privacy.Tag using customized yet compatible QR-code, and implement the Protocol and study the technical feasibility of our proposal. Our evaluation results confirm that PERP and PRSP are indeed feasible and incur negligible computation overhead.

In this work, we address a critical task of detecting the user behavior of driving and texting simultaneously using smartphones. We propose, design, and implement TEXIVE which achieves the goal of distinguishing drivers and passengers, and detecting texting operations during driving utilizing irregularities and rich micro-movements of users. Without relying on any external infrastructures and additional devices, and no need to bring any modification to vehicles, TEXIVE is able to successfully detect dangerous operations with good sensitivity, specificity and accuracy. We conduct experimental study of TEXIVE with the help of a number of volunteers using various vehicles and smartphones. Our results indicate that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.

We present SmartLoc, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.

In this work, we present SilentSense, a framework to authenticate users silently and transparently by exploiting the user touch behavior biometrics and leveraging the integrated sensors to capture the micro-movement of the device caused by user's screen-touch actions. By tracking the fine-detailed touch actions of the user, we build a "touch-based biometrics" model of the owner by extracting some principle features, and then verify whether the current user is the owner or guest/attacker. When using the smartphone, the unique operating pattern of the user is detected and learnt by collecting the sensor data and touch events silently. When users are mobile, the micro-movement of mobile devices caused by touch is suppressed by that due to the large scale user-movement which will render the touch-based biometrics ineffective. To address this, we integrate a movement-based biometrics for each user with previous touch-based biometrics. We conduct extensive evaluations of our approaches on the Android smartphone, we show that the user identification accuracy is over 99%.

As a large scale real sensor network system, GreenOrbs reveals that locating sensor nodes in the forest still faces great challenges because of volatile and fluctuating environmental factors. In this paper, we present a novel localization scheme, EARL, which provides accurate reference nodes and good ranging quality. We exam the range quality along routing paths by taking complex environmental factors into account, such as forest density, temperature and humidity. To improve localization accuracy, we use power scanning technique to judge the accuracy reference nodes and further calibrate the bad nodes through reverse-localization. To overcome the error propagation, we assign different weights to the range measurement according to the ranging quality. We implemented our localization scheme in GreenOrbs testbed, and evaluate through extensive experiments. The results demonstrate that EARL outperforms the current localization approaches with better accuracy. The localization accuracy achieved by our method is around 20% higher than best existing methods.

Due to advances in low power micro-sensor technology, energy harvesting techniques, we can now build large scale solar-powered sensor networks to support long-running operations. Solar powered sensors often harvest variable amounts of energy in different weather conditions. Then a primary requirement for an efficient and a long-running solar-powered sensor system is to adapt to changing environment conditions and resources, and to gather as much valuable data as possible. Sensing and collecting data at a constant rate, without taking into account energy availability or data deliverability, will either drain the battery or waste resources. In this work, we design and test a highly efficient and robust solar-powered system SmartMote; and we further present an energy and value of information (VoI) aware routing strategy, that balances the rates of sensing with packet delivery for SmartMote. SmartMote achieves fairness and near maximum utility across the network. We deploy SmartMote in a forest with 100 sensors in order to monitor the humidity, temperature and luminance intensity. Our experimental results corroborate our design.

In wireless sensor networks, most of existing data aggregation scheduling methods try to aggregate the data from all the nodes at all time-instances. It is neither energy efficient nor practical because of the link unreliability and spatio-temporal data correlation. This paper proposes a lossy data aggregation scheme to allow estimated aggregation at the root by selectively letting some nodes sample at some time slots. Firstly, all nodes sample data synchronously and the error between the real value and estimated one is guaranteed to being bounded respectively with and without the link unreliability. And the error bound is analyzed when the confidence is given a priori. Then we also design an algorithm to assign the confidence level among the parents such that each parent can calculate the minimum number of needed leaves based on the assigned confidence level. Secondly, all nodes sample data asynchronously, under which we analyze the probability that the error could be bounded under a given confidence level. Then a new algorithm is designed to implement data aggregation under a synchronization. We also present the experiment based on a real test-bed to evaluate our schemes.

Using solar panels to power wireless sensor nodes is feasible in most of WSNs applications. We present an efficient solar-charging system and a remote energy-profile monitoring system which can monitor the dynamic charging procedure of wireless sensor nodes in different environments. We design and implement dynamic routing policies according to the current available energy of nodes for WSNs powered by solar panels.