With a subtle rise in the vehicular traffic, it has become necessary for the users to be more aware of their surroundings. Vehicular AdHoc Network (VANET) provides a solution to connect these vehicles with the data centers to leverage this information for the benefit of the users. It renders end-to-end details for traffic flow monitoring and dynamic route scheduling of vehicles to prevent unforeseen incidents. Current researches suggest that numerous IoV architectures authenticate the vehicles enabling inter-vehicular communication. To improve the quality of service, this work leverage vehicle-to-roadside communication, and priority based batch-verification system. Further, to ensure safer computations of time-critical information and faster processing of event-driven messages, vehicular edge computing based hierarchical framework is proposed. The security analysis proves that this framework can substantially improve power consumption and mitigate security issues such as traceability, identity privacy-preserving, replay attacks, denial of service attacks, and impersonate attacks.

OBJECTIVE: While non-invasive, cuffless blood pressure (BP) measurement has demonstrated relevancy in controlled environments, ambulatory measurement is important for hypertension diagnosis and control. We present both in-lab and ambulatory BP estimation results from a diverse cohort of participants. METHODS: Participants (N=1125, aged 21-85, 49.2% female, multiple hypertensive categories) had BP measured in-lab over a 24-hour period with a subset also receiving ambulatory measurements. Radial tonometry, photoplethysmography (PPG), electrocardiography (ECG), and accelerometry signals were collected simultaneously with auscultatory or oscillometric references for systolic (SBP) and diastolic blood pressure (DBP). Predictive models to estimate BP using a variety of sensor-based feature groups were evaluated against challenging baselines. RESULTS: Despite limited availability, tonometry-derived features showed superior performance compared to other feature groups and baselines, yieldingprediction errors of 0.32 ±9.8 mmHg SBP and 0.54 ±7.7 mmHg DBP in-lab, and 0.86 ±8.7 mmHg SBP and 0.75 ±5.9 mmHg DBP for 24-hour averages. SBP error standard deviation (SD) was reduced in normotensive (in-lab: 8.1 mmHg, 24-hr: 7.2 mmHg) and younger (in-lab: 7.8 mmHg, 24-hr: 6.7 mmHg) subpopulations. SBP SD was further reduced 15-20% when constrained to the calibration posture alone. CONCLUSION: Performance for normotensive and younger participants was superior to the general population across all feature groups. Reference type, posture relative to calibration, and controlled vs. ambulatory setting all impacted BP errors. SIGNIFICANCE: Results highlight the need for demographically diverse populations and challenging evaluation settings for BP estimation studies. We present the first public dataset of ambulatory tonometry and cuffless BP over a 24-hour period to aid in future cardiovascular research.

Time of use tiered pricing schedules encourage shifting electricity demand from peak to off-peak hours. Charging times for electric vehicles (EV) can be shifted into overnight hours, which are usually off-peak. EVs can also be used as energy storage devices, available during certain peak hours to power a house with electricity stored during off-peak hours. Studies suggest both techniques are practical, but were based on simulated demand patterns or large commercial fleets. To investigate feasibility on a per home basis, we collected data from 15 EV homes using the Lab of Things sensing infrastructure. We evaluate a scheme that powers homes with their car battery during expensive electricity periods and then charges the battery during cheaper periods. We show an average potential savings of $10.91/month for shifting charging times, and an additional $13.58/month for powering the home from the EV, even accounting for the inefficiencies of electric conversion.

The different electronic devices we use on a daily basis produce distinct electromagnetic radiation due to differences in their underlying electrical components. We present MagnifiSense, a low-power wearable system that uses three passive magneto-inductive sensors and a minimal ADC setup to identify the device a person is operating. MagnifiSense achieves this by analyzing near-field electromagnetic radiation from common components such as the motors, rectifiers, and modulators. We conducted a staged, in-the-wild evaluation where an instrumented participant used a set of devices in a variety of settings in the home such as cooking and outdoors such as commuting in a vehicle. MagnifiSense achieves a classification accuracy of 82.6% using a model-agnostic classifier and 94.0% using a model-specific classifier. In a 24-hour naturalistic deployment, MagnifiSense correctly identified 25 of the total 29 events, while achieving a low false positive rate of 0.65% during 20.5 hours of non-activity.

Electricity and appliance usage information can often reveal the nature of human activities in a home. For instance, sensing the use of vacuum cleaner, a microwave oven, and kitchen appliances can give insights into a person's current activities. Instead of putting a sensor on each appliance, our technique is based on the idea that appliance usage can be sensed by their manifestations in an environment's existing electrical infrastructure. Prior approaches using this technique could only detect an appliance's on-off states; that is, they only sense “what” is being used, but not “how” it is used. In this paper, we introduce DOSE, a significant advancement for inferring operating states of electronic devices from a single sensing point in a home. When an electronic device is in operation, it generates time-varying Electromagnetic Interference (EMI) based upon its operating states (e.g., vacuuming on a rug vs. hardwood floor). This EMI noise is coupled to the power line and can be picked up from a single sensing hardware attached to the wall outlet in a house. Unlike prior data-driven approaches, we employ domain knowledge of the device's circuitry for semi-supervised model training to avoid tedious labeling process. We evaluated DOSE in a residential house for 2 months and found that operating states for 16 appliances could be estimated with an average accuracy of 93.8%. These fine-grained electrical characteristics affords rich feature sets of electrical events and have the potential to support various applications such as in-home activity inference, energy disaggregation and device failure detection.

Sensing respiration rate has many applications in monitoring various health conditions, such as sleep apnea and chronic obstructive pulmonary disease. In this paper, we present WiBreathe, a wireless, high fidelity and non-invasive breathing monitor that leverages wireless signals at 2.4 GHz to estimate an individual's respiration rate. Our work extends past approaches of using wireless signals for respiratory monitoring by using only a single transmitter-receiver pair at the same frequency range of commodity Wi-Fi signals to estimate the respiratory rate of an individual. This is done irrespective of whether they are in line of sight or not (e.g., through walls). Furthermore, we demonstrate the capability of WiBreathe in detecting multiple people and by extension, their respiration rates. We evaluate our approach in various natural environments and show that we can track breathing with the accuracy of 1.54 breaths per minute when compared to a clinical respiratory chest band.

In this paper, we present a significant improvement over past work on non-contact end-user deployable sensor for real time whole home power consumption. The technique allows users to place a single device consisting of magnetic pickups on the outside of a power or breaker panel to infer whole home power consumption without the need for professional installation of current transformers (CTs). The new approach does not require precise placement on the breaker panel, a key requirement in previous approaches. This is enabled through a self-calibration technique using a neural network that dynamically learns the transfer function despite the placement of the sensor and the construction of the breaker panel itself. We also demonstrate the ability to actually infer true power using this technique, unlike past solutions that have only been able to capture apparent power. We have evaluated our technique in six homes and one industrial building, including one seven-day deployment. Our results show we can estimate true power consumption with an average accuracy of 95.0% during naturalistic energy use in the home.

Current solutions for enabling touch interaction on existing non-touch LCD screens require adding additional sensors to the interaction surface. We present uTouch, a system that detects and classifies touches and hovers without any modification to the display, and without adding any sensors to the user. Our approach utilizes existing signals in an LCD that are amplified when a user brings their hand near or touches the LCD's front panel. These signals are coupled onto the power lines, where they appear as electromagnetic interference (EMI) which can be sensed using a single device connected elsewhere on the power line infrastructure. We validate our approach with an 11 user, 8 LCD study, and demonstrate a real-time system.

Mobile and embedded electronics are pervasive in today's environment. As such, it is necessary to have a natural and intuitive way for users to indicate the intent to connect to these devices from a distance. We present DopLink, an ultrasonic-based device selection approach. It utilizes the already embedded audio hardware in smart devices to determine if a particular device is being pointed at by another device (i.e., the user waves their mobile phone at a target in a pointing motion). We evaluate the accuracy of DopLink in a controlled user study, showing that, within 3 meters, it has an average accuracy of 95% for device selection and 97% for finding relative device position. Finally, we show three applications of DopLink: rapid device pairing, home automation, and multi-display synchronization.

Input modalities such as speech and gesture allow users to interact with computers without holding or touching a physical device, thus enabling at-a-distance interaction. It remains an open problem, however, to incorporate haptic feedback into such interaction. In this work, we explore the use of air vortex rings for this purpose. Unlike standard jets of air, which are turbulent and dissipate quickly, vortex rings can be focused to travel several meters and impart perceptible feedback. In this paper, we review vortex formation theory and explore specific design parameters that allow us to generate vortices capable of imparting haptic feedback. Applying this theory, we developed a prototype system called AirWave. We show through objective meas urements that AirWave can achieve spatial resolution of less than 10 cm at a distance of 2.5 meters. We further demonstrate through a user study that this can be used to direct tactile stimuli to different regions of the human body.

This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two- bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%.

Haptic devices that provide robust and realistic force feedback are generally grounded to counterweight the applied force, prohibiting their use in mobile devices. Many ungrounded force-feedback devices rely on the gyro effect to produce torques on the human body, but their active control systems render them extremely bulky for implementation in small mobile devices. We present GyroTab, a relatively flat handheld system that utilizes the gyro effect to provide torque feedback. GyroTab relies on the user to produce an input torque and provides feedback by opposing that torque, making its feedback reactive to the user's motion. We describe the implementation of GyroTab, discuss the kinds of feedback it generates, and explore some of the psychophysical results we obtained from a study with the device.

Gesture is becoming an increasingly popular means of interacting with computers. However, it is still relatively costly to deploy robust gesture recognition sensors in existing mobile platforms. We present SoundWave, a technique that leverages the speaker and microphone already embedded in most commodity devices to sense in-air gestures around the device. To do this, we generate an inaudible tone, which gets frequency-shifted when it reflects off moving objects like the hand. We measure this shift with the microphone to infer various gestures. In this note, we describe the phenomena and detection algorithm, demonstrate a variety of gestures, and present an informal evaluation on the robustness of this approach across different devices and people.

Wearable sensor systems have been used in the ubiquitous computing community and elsewhere for applications such as activity and gesture recognition, health and wellness monitoring, and elder care. Although the power consumption of accelerometers has already been highly optimized, this work introduces a novel sensing approach which lowers the power requirement for motion sensing by orders of magnitude. We present an ultra-low-power method for passively sensing body motion using static electric fields by measuring the voltage at any single location on the body. We present the feasibility of using this sensing approach to infer the amount and type of body motion anywhere on the body and demonstrate an ultra-low-power motion detector used to wake up more power-hungry sensors. The sensing hardware consumes only 3.3 μW, and wake-up detection is done using an additional 3.3 μW (6.6 μW total).

We conduct an extensive study of information leakage over the powerline infrastructure from eight televisions (TVs) spanning multiple makes, models, and underlying technologies. In addition to being of scientific interest, our findings contribute to the overall debate of whether or not measurements of residential powerlines reveal significant information about the activities within a home. We find that the power supplies of modern TVs produce discernible electromagnetic interference (EMI) signatures that are indicative of the video content being displayed. We measure the stability of these signatures over time and across multiple instances of the same TV model, as well as the robustness of these signatures in the presence of other noisy electronic devices connected to the same powerline.

We present Interactive Generator (InGen), a self-powered wireless rotary input device capable of generating haptic or force feedback without the need for any external power source. Our approach uses a modified servomotor to perform three functions: (1) generating power for wireless communication and embedded electronics, (2) sensing the direction and speed of rotation, and (3) providing force feedback during rotation. While InGen is rotating, the device is capable of providing the sensation of detents or bumps, changes in stiffness, and abrupt stops using only power that is harvested during interaction. We describe the device in detail, demonstrate an initial 'TV remote control' application, and end with a discussion of our experiences developing the prototype and application. To the best of our knowledge, InGen is the first self-powered device, which also provides haptic feedback during operation. More broadly, this work demonstrates a new class of input sys-tems that uses human-generated power to provide feedback to the user and wirelessly communicate sensed information.

We present HeatWave, a system that uses digital thermal imaging cameras to detect, track, and support user interaction on arbitrary surfaces. Thermal sensing has had limited examination in the HCI research community and is generally under-explored outside of law enforcement and energy auditing applications. We examine the role of thermal imaging as a new sensing solution for enhancing user surface interaction. In particular, we demonstrate how thermal imaging in combination with existing computer vision techniques can make segmentation and detection of routine interaction techniques possible in real-time, and can be used to complement or simplify algorithms for traditional RGB and depth cameras. Example interactions include (1) distinguishing hovering above a surface from touch events, (2) shape-based gestures similar to ink strokes, (3) pressure based gestures, and (4) multi-finger gestures. We close by discussing the practicality of thermal sensing for naturalistic user interaction and opportunities for future work.

In this paper, we describe LightWave, a sensing approach that turns ordinary compact fluorescent light (CFL) bulbs into sensors of human proximity. Unmodified CFL bulbs are shown to be sensitive proximity transducers when they are illuminated. This approach utilizes predictable variations in electromagnetic noise resulting from the change in impedance due to the proximity of a human body to the bulb. The electromagnetic noise can be sensed from any point along a home's electrical wiring. This allows users to perform gestures near any CFL lighting fixture, even when multiple lamps are operational. Gestures can be sensed using a single interface device plugged into any electrical outlet. We experimentally show that we can reliably detect hover gestures (waving a hand close to a lamp), touches on lampshades, and touches on the glass part of the bulb itself. Additionally, we show that touches anywhere along the body of a metal lamp can be detected. These basic detectable signals can then be combined to form complex gesture sequences for a variety of applications. We also show that CFLs can function as more general-purpose sensors for distributed human motion detection and ambient temperature sensing.

We present the design, development, and evaluation of an end-user installable, whole house power consumption sensing system capable of gathering accurate real-time power use that does not require installing a current transformer around the electrical feeds in a home. Rather, our sensor system offers contactless operation by simply placing it on the outside of the breaker panel in a home. Although there are a number of existing commercial systems for gathering energy use in a home, almost none can easily and safely be installed by a homeowner (especially for homes in the U.S.). Our approach leverages advances in magnetoresistive materials and circuit design to allow contactless operation by reliably sensing the magnetic field induced by the 60 Hz current and a closed loop circuit allows us to precisely infer the power consumption in real-time. The contribution of this work is an enabling technology for researchers in the fields of Ubiquitous Computing and Human-Computer Interaction wanting to conduct practical large-scale deployments of end-user-deployable energy monitoring applications. We discuss the technical details, the iterative design, and end-user evaluations of our sensing approach.

This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.

Haptic feedback provides an additional interaction channel when auditory and visual feedback may not be appropriate. We present a novel haptic feedback system that changes its elasticity to convey information for eyes-free interaction. SqueezeBlock is an electro-mechanical system that can realize a virtual spring having a programmatically controlled spring constant. It also allows for additional haptic modalities by altering the Hooke's Law linear-elastic force- displacement equation, such as non-linear springs, size changes, and spring length (range of motion) variations. This ability to program arbitrarily spring constants also allows for "click" and button-like feedback. We present several potential applications along with results from a study showing how well participants can distinguish between several levels of stiffness, size, and range of motion. We conclude with implications for interaction design.