Crowd monitoring in sports stadiums is important to enhance public safety and improve audience experience. Existing approaches mainly rely on cameras and microphones, which can cause significant disturbances and often raise privacy concerns. In this paper, we sense floor vibration, which provides a less disruptive and more non-intrusive way of crowd sensing, to predict crowd behavior. However, since the vibration-based crowd monitoring approach is newly developed, one main challenge is the lack of training data due to sports stadiums are usually large public spaces with complex physical activities.

Monitoring vital signs and activities in pig pens during the farrowing period is crucial for reducing pre-weaning piglet mortality and enhancing farm productivity. Traditional methods focus on either vital signs or activities separately, falling short in the dynamically changing farm environment. This paper introduces a multiscale vibration sensing method which dynamically adjusts sensor amplifier gain to detect both vital signs (e.g. heartbeats and respirations) and larger-scale activities (e.g. walking, eating, nursing, etc.). Preliminary trials demonstrate the system's potential to adapt to rapidly changing conditions by switching between high sensitivity for vital signs and reduced sensitivity for activity sensing depending on the detected vibration signal.

Assessing urban infrastructure and environment quality at scale remains a challenge. This work presents a multimodal sensing framework that integrates computer vision-based infrastructure analysis with mobile air quality monitoring to explore urban conditions beyond the camera's field of view. A vehicle-mounted system captures video data for semantic segmentation of roads and buildings while an air intake unit collects temperature, humidity, CO2, TVOC, and AQI levels. A preliminary test drive in Ann Arbor demonstrated expected correlations between CO2 and TVOC spikes in dense urban areas, providing a proof of concept for linking environmental sensing with visual urban analysis. Future work will refine sensor calibration, adaptive sampling strategies, and predictive modeling to improve accuracy and scalability.

Weight difference estimation is crucial in various applications, particularly for identifying items being picked up and put back when people interact with the shelf while shopping in autonomous stores, ensuring precise cost estimation. However, the conventional approach of estimating weight changes requires specialized weight-sensing shelves, which are densely deployed weight scales, incurring intensive sensor consumption and maintenance costs. Prior works explored the vibration-based weight sensing method, but they are limited to the object that can generate vibration through motion. This work demonstrates a system leveraging active vibration sensing for weight difference estimation on shelves at different locations. The main intuition of the system is that the weight placed on the shelf influences the dynamic vibration response of the shelf, thus altering the shelf vibration patterns. Our system achieves a mean absolute error 9.23 grams and mean absolute percentage error 7.9% on the real-store shelf layout.

This paper addresses challenges in monitoring human behavior in crowds through floor vibration sensing, overcoming limitations like subjective manual observation, visual occlusions, and audio interference. Our approach involves tackling limited-data vibration signal tasks by conducting pre-training across modalities, leveraging publicly available audio datasets. By leveraging self-supervised representation learning to pre-train on publicly available audio datasets, our approach reduces data requirements, improves robustness, and minimizes the need for human labeling efforts. Evaluation using in-game stadium vibration data with YouTube audio dataset demonstrates up to 5.8 × error reduction for crowd behavior.

Computer vision has been used more ubiquitously in recent years to understand and measure the environment around us, particularly in our neighborhoods. However, many city-wide sensing applications using vision require large labeling efforts, making various applications difficult on a wide scale. We propose a framework for labeling and self-training of in-car video to detect trash on the roads. Our approach requires minimal manual labeling to identify items not meant to be in the street, sidewalk, or public places, from a front-viewing car camera. Our system provides each frame of a video with a score indicating the amount of trash. To prevent overfitting, due to minimal available data, we remove data with high certainty of trash from the training dataset. The results show that our prediction with manually labeled ground truth yield an R2 of 0.66.

Recognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety. However, recognizing UAVs day and night with dual-vision cameras is nontrivial, since red–green–blue (RGB) images suffer from a low detection rate under an insufficient light condition, such as on cloudy or stormy days, while black-and-white infrared (IR) images struggle to capture UAVs that overlap with the background at night. In this paper, we propose a new optical flow-assisted graph-pooling residual network (OF-GPRN), which significantly enhances the UAV detection rate in day and night dual visions. The proposed OF-GPRN develops a new optical fusion to remove superfluous backgrounds, which improves RGB/IR imaging clarity. Furthermore, OF-GPRN extends optical fusion by incorporating a graph residual split attention network and a feature pyramid, which refines the perception of UAVs, leading to a higher success rate in UAV detection. A comprehensive performance evaluation is conducted using a benchmark UAV catch dataset. The results indicate that the proposed OF-GPRN elevates the UAV mean average precision (mAP) detection rate to 87.8%, marking a 17.9% advancement compared to the residual graph neural network (ResGCN)-based approach.

Precision Swine Farming has the potential to directly benefit swine health and industry profit by automatically monitoring the growth and health of pigs. We introduce the first system to use structural vibration to track animals and the first system for automated characterization of piglet group activities, including nursing, sleeping, and active times. PigSense uses physical knowledge of the structural vibration characteristics caused by pig-activity-induced load changes to recognize different behaviors of the sow and piglets. For our system to survive the harsh environment of the farrowing pen for three months, we designed simple, durable sensors for physical fault tolerance, then installed many of them, pooling their data to achieve algorithmic fault tolerance even when some do stop working. The key focus of this work was to create a robust system that can withstand challenging environments, has limited installation and maintenance requirements, and uses domain knowledge to precisely detect a variety of swine activities in noisy conditions while remaining flexible enough to adapt to future activities and applications. We provided an extensive analysis and evaluation of all-round swine activities and scenarios from our one-year field deployment across two pig farms in Thailand and the USA. To help assess the risk of crushing, farrowing sicknesses, and poor maternal behaviors, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for ingestion and excretion detection. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing, sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes allows the prediction of farrowing in advance, as well as status tracking during the farrowing process and on the occasion of farrowing issues. Furthermore, PigSense also predicts the daily pattern and weight gain in the lactation cycle with 89% accuracy, a metric that can be used to monitor the piglets’ growth progress over the lactation cycle.

Heart rate is a critical metric for human cardiovascular health. Most common methods for measuring human heart rate involve wearable devices (e.g., electrocardiography, smart watches). However, such devices can cause discomfort to some patients, especially the elderly or young children. This paper presents FreePulse, a heart rate monitoring system for seated subjects using ambient vibrations. FreePulse builds on our past work using vibrations in the building structures around us to measure human activities and health. As people’s hearts beat, they push on the surfaces the body is touching, creating vibrations in those structures. We combine structure response characterization with human pulse modelling to identify these pulse-induced vibrations from ambient vibration. In testing, FreePulse has shown up to 96% pulse rate accuracy on average, competitive with consumer-grade wearable devices.

The standard way to keep track of inventory is through human counting or with a hand-held device and inputting the data into the warehouse management system (WMS). WMS can be quite expensive for small warehouses and human counting is susceptible to errors. Alternatively, we propose an automated way to keep track of inventory by leveraging the surveillance cameras readily available in the warehouses. In practice, there are challenges such as incorrect stock numbers, overfitting issues, and a mundane labeling process. To identify and mitigate those challenges, we present real-world deployment to various warehouses for a period of 4 months with up to 97 percent counting accuracy.

Object classification is a vital technology that is widely used to track and identify misplaced and out-of-stock items in shopping centers. While there have been a number of studies utilizing various sensing modalities such as computer vision, RFID, and vibration sensors, these methods are limited in their use due to privacy concerns, scalability, and the inability to identify stationary objects. To overcome these limitations, we propose a novel active vibration-sensing approach for object classification by utilizing music as an excitation source. Different objects can induce different deformations of the surface and further change the surface structural response. Therefore, we leverage vibrations from music on a store shelf and measure the structural responses on the surface when different objects are placed. Our evaluation of a store shelf demonstrates that distinct object characteristics lead to unique vibration responses, enabling accurate classification of 98.6% accuracy in distinguishing five common store objects. This study provides a promising avenue for a reliable, privacy-preserving, and scalable object classification system in various settings beyond shopping centers.

IoT (Internet of Things) devices, such as network-enabled wearables, are carried by increasingly more people throughout daily life. Information from multiple devices can be aggregated to gain insights into a person’s behavior or status. For example, an elderly care facility could monitor patients for falls by combining fitness bracelet data with video of the entire class. For this aggregated data to be useful to each person, we need a multi-modality association of the devices’ physical ID (i.e., location, the user holding it, visual appearance) with a virtual ID (e.g., IP address/available services). Existing approaches for multi-modality association often require intentional interaction or direct line-of-sight to the device, which is infeasible for a large number of users or when the device is obscured by clothing. We present IDIoT , a calibration-free passive sensing approach that fuses motion sensor information with camera footage of an area to estimate the body location of motion sensors carried by a user. We characterize results across three baselines to highlight how different fusing methodology results better than earlier IMU-vision fusion algorithms. From this characterization, we determine IDIoT is more robust to errors such as missing frames or miscalibration that frequently occur in IMU-vision matching systems.

Monitoring cardiovascular risk factors requires a method for con-tinuous heart rate monitoring. However, this typically relies on a wearable device which must be issued to, then charged and worn by the user. Ambient sensors can address these challenges, but have only been usable in scenarios where the body is in proximity or contact with these sensors, such as on a bed. In this paper, we present SeatBeats, a system that can monitor heart rate using ambient vibrations in the surface a user sits on. The system relies on the surface's response to heart beat-induced vibrations and utilizes autocorrelation to capture and extract the repeated pattern beats. Our experiments in a real office environment show up to 96% accuracy relative to a smartwatch heart rate sensor.

In this paper, we demonstrate a new ultra-wideband (UWB) local-ization testbed, which tracks a UWB tag and estimates locations of obstacles based on channel impulse response measurements. An-chor nodes that are developed with off-the-shelf Decawave DW1000 UWB transceivers are deployed to cover the area of interest. The testbed is implemented and preliminary experiments are carried out to estimate the location of the object by analyzing channel impulse response strength of the UWB tag.

Pig vital sign monitoring (e.g., estimating the heart rate (HR) and respiratory rate (RR)) is essential to understand the stress level of the sow and detect the onset of parturition. It helps to maximize peri-natal survival and improve animal well-being in swine production. The existing approach mainly relies on manual measurement, which is labor-intensive and only provides a few points of information. Other sensing modalities such as wearables and cameras are developed to enable more continuous measurement, but are still limited due to animal discomfort, data transfer, and storage challenges. In this paper, we introduce PigV2, the first system to monitor pig heart rate and respiratory rate through ground vibrations. Our approach leverages the insight that both heartbeat and respiration generate ground vibrations when the sow is lying on the floor. We infer vital information by sensing and analyzing these vibrations. The main challenge in developing PigV2 is the overlap of vital- and non-vital-related information in the vibration signals, including pig movements, pig postures, pig-to-sensor distances, and so on. To address this issue, we first characterize their effects, extract their current status, and then reduce their impact by adaptively interpolating vital rates over multiple sensors. PigV2 is evaluated through a real-world deployment with 30 pigs. It has 3.4% and 8.3% average errors in monitoring the HR and RR of the sows, respectively.

We present a passive and non-intrusive sensing system for monitoring hand washing activity using structural vibration sensing. Proper hand washing is one of the most effective ways to limit the spread and transmission of disease, and has been especially critical during the COVID-19 pandemic. Prior approaches include direct observation and sensing-based approaches, but are limited in non-clinical settings due to operational restrictions and privacy concerns in sensitive areas such as restrooms. Our work introduces a new sensing modality for hand washing monitoring, which measures hand washing activity-induced vibration responses of sink structures, and uses those responses to monitor the presence and duration of hand washing. Primary research challenges are that vibration responses are similar for different activities, occur on different surfaces/structures, and tend to overlap/coincide. We overcome these challenges by extracting information about signal periodicity for similar activities through cepstrum-based features, leveraging hierarchical learning to differentiate activities on different surfaces, and denoting “primary/secondary” activities based on their relative frequency and importance. We evaluate our approach using real-world hand washing data across four different sink structures/locations, and achieve an average F1-score for hand washing activities of 0.95, which represents an 8.8X and 10.2X reduction in error over two different baseline approaches.

Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g., taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehicles like taxis usually prefer gathering at busy areas of a city where it is more likely to pick up riders. This leaves many parts of the city unsensed or less-sensed. In addition, due to the natural changes in a city and the movements of the vehicles, the sensed and unsensed areas change over time. Consequently, challenges of air pollution estimation with data collected by non-dedicated mobile platforms are twofold:i.data coverage is sparse;ii.data coverage changes over time. Therefore, the major research question is: how can we derive accurate and robust fine-grained field (e.g., air pollution) estimation given dynamic and sparse data collected from uncontrollable mobile sensing platforms? This paper presents adaptiveHMSS, an adaptivehybridmodel-enabledsensingsystem for fine-grained air pollution estimation with dynamic and sparse data collected from uncontrollable mobile sensing platforms, which is achieved by combining the advantages of aphysics guided modeland adata driven model. To address the challenge of sparse coverage, the physical understanding of the spatiotemporal correlation for air pollution distribution in thephysics guided modelis utilized to infer values at unsensed sparse areas. Meanwhile, thedata driven modelis adopted to estimate the air pollution influential factors (e.g., buildings) not included in thephysics guided model. To address the challenge of time-varying coverage, an adaptive model combination algorithm is designed to enable the system bias to either of the two models according to the amount of data collection and uncertainty of the model. To evaluate the system performance, we deployed 47 air pollution sensing devices on taxis and fixed locations in 2 cities for both controlled and uncontrolled experiments for over two weeks. The results show that with a resolution of$500 \;\mathrm m$by$500\;\mathrm m$by$1\;\mathrm {hour}$, our system achieves up to$3.2\times$error reduction when compared to the baseline approaches.

Automated monitoring of livestock behavior can help farmers economically by detecting changes in animal welfare. Prior approaches use video, which requires light and high storage capability, or motion detection, which has difficulty separating subtle activities. Wearable sensors can address these issues but are vulnerable to destruction by the animals. To the best of our knowledge, we present the first system that uses structural vibration to track animal behavior, and the first system to automatically detect piglet nursing. PigNet uses vibration sensors attached to a pig pen to sense the unique vibration patterns and changes in structural response caused by the animals' movement and position within the pen. Combined with our knowledge of pig behavior, we use this physical knowledge of vibration characteristics to detect pig activities and track piglet growth in a real farm environment. Our system is designed to be robust to the harsh environment, which can create unpredictable noise, as well as physically damage or disconnect sensor nodes. When deployed in a real-world farm environment, our system was able to achieve a daily pen-level status profile of up to 90% accuracy, which tracks nursing activity, sow lying activity, and changes in piglet growth over the weeks-long pre-weaning period.

Detecting a previously unknown person (newcomer detection) is critical for visitor management, intruder prevention, and access control in smart buildings. Biometrics have been used to detect newcomers, including face, fingerprint, voice, iris, etc. These approaches often require active participation of the users or require dense instrumentation. Prior work using footstep-induced floor vibration to identify people removes these requirements, but only functions for known people due to the high variability in footstep-induced vibrations and the limited number of predicted classes in supervised learning. To overcome the limitations, we introduce a newcomer detection system based on non-parametric Bayesian learning, which models the variability and distribution of consecutive footstep-induced floor vibration for newcomer walking patterns. Preliminary results from real-world experiments with 6 people show up to 92% accuracy in newcomer detection with an average of 4 consecutive footsteps.

In this paper, we present a practical secret key management for data relay security of bluetooth-connected drones. Time-varying received signal strengths between the drones and the ground sensing nodes are quantized to generate the secret key pairs, where the quantization interval is adjusted to reduce the number of mismatched secret key bits. To validate the key management performance, a multihop aerial relay system testbed is developed based on the MX400 drone platform and the bluetooth low energy radio transceiver.

Smartphones have become a common tool for researchers to collect, process, and analyze large quantities of data. This will lead to the creation of solutions that will mostly come in the form of smartphone apps, which will help solve real-life problems. One such real-life problem is the over-consumption of alcohol, since it can lead to many problems including fatality. Currently, there are very expensive or tedious alternative procedures for testing blood alcohol consumption in the market. This paper offers a cheaper alternative to address this problem by detecting if the user has consumed alcohol or not by using a smartphone. We describe an experiment and propose two features derived from accelerometer data that can help us distinguish between sober and intoxicated individuals.

Thanks to flexible deployment and excellent maneuverability, autonomous drones have been recently considered as an effective means to act as aerial data relays for wireless ground devices with limited or no cellular infrastructure, e.g., smart farming in a remote area. Due to the broadcast nature of wireless channels, data communications between the drones and the ground devices are vulnerable to eavesdropping attacks. This article develops BloothAir, which is a secure multi-hop aerial relay system based on Bluetooth Low Energy ( BLE ) connected autonomous drones. For encrypting the BLE communications in BloothAir, a channel-based secret key generation is proposed, where received signal strength at the drones and the ground devices is quantized to generate the secret keys. Moreover, a dynamic programming-based channel quantization scheme is studied to minimize the secret key bit mismatch rate of the drones and the ground devices by recursively adjusting the quantization intervals. To validate the design of BloothAir, we build a multi-hop aerial relay testbed by using the MX400 drone platform and the Gust radio transceiver, which is a new lightweight onboard BLE communicator specially developed for the drone. Extensive real-world experiments demonstrate that the BloothAir system achieves a significantly lower secret key bit mismatch rate than the key generation benchmarks, which use the static quantization intervals. In addition, the high randomness of the generated secret keys is verified by the standard NIST test, thereby effectively protecting the BLE communications in BloothAir from the eavesdropping attacks.

In this paper, a multi-hop airborne system is built based on Bluetooth 5 connected autonomous drones to relay real-time data of Internet of Things (IoT). A new lightweight Onboard Bluetooth Transceiver (OBT) is developed for reliable drone-to-drone and drone-to-ground communications. A graphical user interface is presented to monitor real-time flight trajectory of the drones and end-to-end data delivery. Outdoor experiments are conducted in real world to test autonomous flight control of the drones and received signal strength of the OBT communications.

This paper presents the Active Structural Occupant Detector, an active vibration sensing system that detects stationary occupants through injection of vibration signals into the floor. Many smart buildings require occupant detection in order to provide personalized services. Some examples include optimized energy usage and security. Several methods currently exist for occupant detection, each with their own drawbacks, such as installation requirements. Structural vibration sensing overcomes many of these drawbacks by measuring impulses created by occupants to infer their movements, but cannot detect stationary occupants. The ASOD utilizes active vibration sources, which inject acoustic waves into the structure then measure how the structure responds to them. Any occupants present interact with these waves, causing changes to the measured signal. By characterizing the changes in how the waves travel, we can predict the presence or lack of an occupant with up to a 97.7% accuracy, as demonstrated by experiments in a real- world environment.

Recent work has shown that two wearable devices worn on the same user can exploit gait as a secret source to generate a common key for secure pairing. The main threat of using gait comes from side-channel attackers who can use cameras to record the walking user and extract accelerations from the video to pair with legitimate devices. We propose a novel pre-step that uses a CNN-LSTM deep learning model to classify the acceleration measurement devices, i.e., between IMU vs. Camera. We prototype the pre-step and evaluate it using real subjects. Our results show that the proposed pre-step can achieve high classification success rates. The experiments with different cut-off frequencies show that the higher acceleration frequencies appear to contain more distinguishable features to classify camera from IMU.

Small unmanned autonomous vehicles (UAVs) swarms are becoming ubiquitous in a number of applications (e.g., surveying, monitoring, and situational awareness). Indoor environments may contain metal equipment that temporarily disrupts radio reception. During these momentary interruptions, a small UAV needs to be able to broadcast a 'heartbeat' to indicate that it is not damaged or lost. Considering alternative messaging modalities, we observe that light-based methods require line-of sight, which is not guaranteed when UAVs are moving through a cluttered environment, while a naive sound-based method is easily drowned out by the UAV's own loud motor and propeller noise.

Vehicular mobile crowdsensing (MCS) enables many smart city applications. Ridesharing vehicle fleets provide promising solutions to MCS due to the advantages of low cost, easy maintenance, high mobility, and long operational time. However, as nondedicated mobile sensing platforms, the first priorities of these vehicles are delivering passengers, which may lead to poor sensing coverage quality. Therefore, to help MCS derive good (large and balanced) sensing coverage quality, an actuation system is required to dispatch vehicles with a limited amount of monetary budget. This article presents PAS, a prediction-based actuation system for city-wide ridesharing vehicular MCS to achieve optimal sensing coverage quality with a limited budget. In PAS, two prediction models forecast probabilities of potential near-future vehicle routes and ride requests across the city. Based on prediction results, a prediction-based actuation planning algorithm is proposed to decide which vehicles to actuate and the corresponding routes. Experiments on city-scale deployments and physical feature-based simulations show that our PAS achieves up to 40% more improvement in sensing coverage quality and up to 20% higher ride request matching rate than baselines. In addition, to achieve a similar level of sensing coverage quality as the baseline, our PAS only needs 10% budget.

Vehicular crowd sensing systems are designed to achieve large spatio-temporal sensing coverage with low-cost in deployment and maintenance. For example, taxi platforms can be utilized for sensing city-wide air quality. However, the goals of vehicle agents are often inconsistent with the goal of the crowdsourcer. Vehicle agents like taxis prioritize searching for passenger ride requests (defined as task requests), which leads them to gather in busy regions. In contrast, sensing systems often need to sample data over the entire city with a desired distribution (e.g., Uniform distribution, Gaussian Mixture distribution, etc.) to ensure sufficient spatio-temporal information for further analysis. This inconsistency decreases the sensing coverage quality and thus impairs the quality of the collected information. A simple approach to reduce the inconsistency is to greedily incentivize the vehicle agents to different regions. However, incentivization brings challenges, including the heterogeneity of desired target distributions, limited budget to incentivize more vehicle agents, and the high computational complexity of optimizing incentivizing strategies. To this end, we present a vehicular crowd sensing system to efficiently incentivize the vehicle agents to match the sensing distribution of the sampled data to the desired target distribution with a limited budget. To make the system flexible to various desired target distributions, we formulate the incentivizing problem as a new type of non-linear multiple-choice knapsack problem, with the dissimilarity between the collected data distribution and the desired distribution as the objective function. To utilize the budget efficiently, we design a customized incentive by combining monetary incentives and potential task (ride) requests at the destination. Meanwhile, an efficient optimization algorithm, iLOCuS, is presented to plan the incentivizing policy for vehicle agents to decompose the sensing distribution into two distinct levels: time-location level and vehicle level, to approximate the optimal solution iteratively and reduce the dissimilarity objective. Our experimental results based on real-world data show that our system can reduce up to 26.99 percent of the dissimilarity between the sensed and target distributions compared to benchmark methods.

Large-scale micro-aerial vehicle (MAV) swarms provide promising solutions for situational awareness in applications such as environmental monitoring, urban surveillance, search and rescue, and so on. However, these scenarios do not provide localization infrastructure and limit cost and size of on-board capabilities of individual nodes, which makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this article, we present H-DrunkWalk , a collaborative and adaptive technique for heterogeneous MAV swarm navigation in environments not formerly preconditioned for operation. Working with heterogeneous MAV swarm, the H-DrunkWalk achieves high accuracy through collaboration but still maintains a low cost of the entire swarm. The heterogeneous MAV swarm consists of two types of nodes: (1) basic MAVs with limited sensing, communication, computing capabilities and (2) advanced MAVs with premium sensing, communication, computing capabilities. The key focus behind this networked MAV swarm research is to (1) rely on collaboration to overcome limitations of individual nodes and efficiently achieve system-wide sensing objectives and (2) fully take advantage of advanced MAVs to help basic MAVs improve their performance. The evaluations based on real MAV testbed experiments and large-scale physical-feature-based simulations show that compared to the traditional non-collaborative and non-adaptive method (dead reckoning with map bias), our system achieves up to 6× reductions in location estimation errors, and as much as 3× improvements in navigation success rate under the given time and accuracy constraints. In addition, by comprehensively considering the environment, heterogeneous structure, and quality of location estimation, our H-DrunkWalk brings 2× performance improvement (on average) as that of a hardware upgrade.

We present Deskbuddy, a vibration-based system that can track a user's activities through their desk. Tracking sitting and other office related activities let us remind the user to have healthier working habits, as well as giving information about how office spaces are used. Many solutions have been proposed for office activity tracking, but they either require the user to wear a device, or they use cameras or microphones, which can make subjects uncomfortable. Our demo includes a small vibration sensor that sits on a table that can detect four office related activities. We capture the signal from the vibration sensor, extract features, and perform classification on the resulting features. The full functionality of the system will be shown in a video. In order for our demo to be more effective in a crowded environment, we have re-trained it to detect only typing versus not typing.

Gait health monitoring is critical for condition diagnosis and fall prediction in elderly populations. Existing methods for gait health monitoring (e.g. direct observation and sensing) are not suitable for non-clinical environments due to qualitative assessments or operational limitations. Our method utilizes footstep-induced floor vibration sensing to provide a passive gait health monitoring platform that can be used in non-clinical environments (e.g. home settings) to provide gait health information in a timely manner. We decompose vibration responses to obtain signal peaks that correspond to temporal gait information and leverage foot dominance to learn a signal amplitude-footstep ground reaction force transfer function. Preliminary results show that temporal gait parameters can be estimated with up to 99% accuracy and gait balance symmetry can be estimated with as low as 10.4% error.

Secure pairing is an important problem especially due to large number of IoT devices. In this paper, we propose PosePair++, to enable a camera to securely pair with IoT devices which are equipped with IMU sensors. Existing context-based pairing approaches do not adequately address this problem due to differing sensing modalities. To address this challenge, we propose to translate the signals from heterogeneous sensing modalities to a common space, namely 2D acceleration. In this demo, we present PosePair++'s robustness against different types of attackers (i.e., attackers that observe the user's motion, or attackers performing mimicking attack).

Mobile crowd sensing (MCS) collects city-scale sensing data with low cost and high efficiency. One important goal of MCS is to ensure high quality of sensing coverage to provide sufficient information to data analysis end. However, the goal of the MCS may be inconsistent with the goal of vehicles. This inconsistency between goals results in a bad sensing coverage and decreases the quality of the collected information. Key challenges to resolve this inconsistency include the heterogeneous target desired spatio-temporal distributions, limited budget constraining the ability to incentivize more taxis, and high computational complexity.

The surveillance of air pollution is becoming a highly concerned issue for city residents and urban administrators. Fixed air quality stations as well as mobile gas sensors have been deployed for air quality monitoring but with sparse observations over the entire temporal-spatial space. Therefore, an inference algorithm is essential for comprehensive fine-grained air pollution sensing. Conventional physically-based models can hardly be applied to all the scenarios, while pure data-driven methods suffer from sampling bias and overfitting problems. This paper presents a hybrid algorithm for air pollution inference by guiding the data learning process with physical model. The quantitative combination of knowledge from observed dataset and a discretized convective-diffusion model is performed within a multi-task learning scheme. Evaluations show that, benefited from physical guidance, our hybrid method obtains higher extrapolation ability and more robustness, achieving the same performance with 1/8 sample amount and obtaining 31.9% less error in noisy synthesized environment. In a real-world deployment in Tianjin, our algorithm outperforms the pure data-driven model with 9.69% less inference error over a 9-day PM2.5data collection.

Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space. We then extend our sampling method to define a better initial training set, without the need for a trained model, by using Oriented FAST and Rotated BRIEF (ORB) feature descriptors. We validate MedAL on 3 medical image datasets and show that our method is robust to different dataset properties. MedAL is also efficient, achieving 80% accuracy on the task of Diabetic Retinopathy detection using only 425 labeled images, corresponding to a 32% reduction in the number of required labeled examples compared to the standard uncertainty sampling technique, and a 40% reduction compared to random sampling.

In the Internet of Things (IoT) paradigm, it is important to easily setup and control devices, which is achieved by pairing. In this work, we present a novel pairing scheme that utilizes heterogeneous sensing. The core idea is that devices with different sensing capabilities can still detect common information about their user. We demonstrate this idea through an example application consisting of a camera and IoT devices with inertial sensors. As the user holds a device and moves it around, the camera captures the human's pose and compares it to the IoT device motion. If the motion features are similar enough, the device can be successfully paired to the camera's network.

Indoor localization systems cannot rely on the same mechanisms, like GPS, that are used for outdoor or large-scale localization. Instead, autonomous or user-carried devices are often localized by measuring the time taken for an emitted signal to reach a known location; this signal can be sound, light, radio waves, or another similar sensed quantity. Autonomous mobile devices already contain motors, which produce sounds as a side effect of their operation, and so can potentially be included in a localization scheme without new hardware. In this paper, we briefly outline the challenges that need to be met for accurate detection and identification of motor-produced signals. We present a method for improving signal resolution for linear chirps that improves cross-correlation based signal detection by up to 2.8X.

Location-aware mobile crowdsourcing tasks like urban sensing always require exposing users' location, which lead to serious privacy breaches. In this poster, we propose a locally differentially private participants recruitment system to maximize spatial coverage of the mobile crowdsourcing task while preserving location privacy. Based on the mechanism of randomized response, our system preserves the privacy in a local way, which eliminates the need for a trusted server. With guaranteed location privacy protection, a heuristic algorithm is proposed to solve the maximum spatial coverage problem efficiently given the obfuscated reports. Extensive experiments on real-world user trajectories demonstrate the feasibility of our proposed system, which improves the spatial coverage by more than 10% on average compared with the state-of-the-art solutions.

Mobile sensing systems are deployed for urban air pollution monitoring to increase coverage over a city. However, the sampling irregularity brings great challenges for fine-grained pollution field recovery. To address this problem, we proposed a generative model based inference algorithm. By modeling the air pollution evolution and data sampling process separately, the temporal-spatial correlation of pollution field can be considered with irregular sampled data. We use a convolutional long-short term memory structure in the generative model and train it with the scattered observations from mobile sensing. Evaluations on synthesized data and a deployment in the city of Tianjin show that our algorithm accurately captures fine-grained PM2.5 pollution patterns and changes. The average inference error is 6.7μg/m3, which achieves 23.8% improvement over existing techniques.

No abstract available.

Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device's microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factor across differing sensor types. By focusing on the event timing, rather than the specific event sensor data, Perceptio creates event fingerprints that can be matched across a variety of IoT devices. We propose Perceptio based on the idea that devices co-located within a physically secure boundary (e.g., single family house) can observe more events in common over time, as opposed to devices outside. Devices make use of the observed contextual information to provide entropy for Perceptio's pairing protocol. We design and implement Perceptio, and evaluate its effectiveness as an autonomous secure pairing solution. Our implementation demonstrates the ability to sufficiently distinguish between legitimate devices (placed within the boundary) and attacker devices (placed outside) by imposing a threshold on fingerprint similarity. Perceptio demonstrates an average fingerprint similarity of 94.9% between legitimate devices while even a hypothetical impossibly well-performing attacker yields only 68.9% between itself and a valid device.

Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute , a novel occupant identification solution that makes use of existing and prevalent on-object sensors that are originally designed to monitor the status of objects to which they are attached. SenseTribute extracts richer information content from such on-object sensors and analyzes the data to accurately identify the person interacting with the objects. This approach is based on the physical phenomenon that different occupants interact with objects in different ways. Moreover, SenseTribute may not rely on users’ true identities, so the approach works even without labeled training data. However, resolution of information from a single on-object sensor may not be sufficient to differentiate occupants, which may lead to errors in identification. To overcome this problem, SenseTribute operates over a sequence of events within a user activity, leveraging recent work on activity segmentation. We evaluate SenseTribute using real-world experiments by deploying sensors on five distinct objects in a kitchen and inviting participants to interact with the objects. We demonstrate that SenseTribute can correctly identify occupants in 96% of trials without labeled training data, while per-sensor identification yields only 74% accuracy even with training data.

Incorrect muscle activation can lead to sub-optimal performance, muscle imbalance, and eventually bodily injury. Consequently, assessing muscle activation is important for both excelling in exercise, athletics, and professional sports in general. Existing techniques for assessing muscle activation, such as electromyography, are invasive, requiring needles inserted directly into the muscle or electrodes that have considerable placement requirements (shaving, gels, etc.). This makes them unsuitable for active environments. In addition, factors such as body motion noise that results from the high-impact movements encountered in active sports environments easily corrupt sensor data. This compounds the unsuitability of these systems in the sports and exercise arena. As a result, such systems have been explored mostly in clinical rather than sports-based scenarios. We present MyoVibe, a system for sensing and determining muscle activation in high-mobility, high-impact exercise scenarios. MyoVibe senses and interprets multiple muscle vibration signals obtained from a wearable network of accelerometers to determine muscle activation. By utilizing a diverse feature set combined with the simple yet effective motion artifact mitigation technique, MyoVibe can reduce inertial sensor noise in these high-mobility exercises. As a result, MyoVibe can detect muscle activation with greater than 97% accuracy.

No abstract available.

In this paper, we present Intercom, a simulator framework that provides separate components to address the interdependent aspects of IoT systems, such as sensing, physical interaction, wireless communication, and computation. We initially evaluate a scalable sensing and communication model, which simulates wireless signal strength measurements with an average error of 6.1dBm.

Unmanned aerial vehicle (UAV) swarms provide situation awareness in tasks such as emergency response, search and rescue, etc. However, most of these scenarios take place in GPS-denied environments, where accurately localizing each UAV is challenging. Heterogeneous UAV swarms, in which only a subset of the drones carry cameras, face the additional challenge of identifying each individual UAV to avoid sending position updates to the wrong drone, thus crashing. This work presents an identification mechanism based on the correlation between motion observed from external camera, and acceleration measured on each UAV's accelerometer.

Monitoring heartbeats takes an important role to ensure a person's health and well-being. Few of the existing systems are accurate, unobtrusive, robust and easy to install at the same time. Thus, we propose a completely unobtrusive system which can detect heartbeats during sleep by sensing the weak ballistic vibrations caused by heartbeats on any bed. The system, HB-Phone, is centered around the off-the-shelf geophone sensor and can be easily installed on an existing bed. In this demo, we demonstrate that our system can detect and extract heartbeats accurately and in real time, even with the presence of noise from the environment and gross body movements during sleep.

Touch surfaces are intuitive interfaces for computing devices. Most of the traditional touch interfaces (vision, IR, capacitive, etc.) have mounting requirements, resulting in specialized touch surfaces limited by their size, cost, and mobility. More recent work has shown that vibration-based touch sensing techniques can localize taps/knocks, which provides a low-cost flexible alternative. These surfaces are envisioned as intuitive inputs for applications such as interactive meeting tables, smart kitchen appliance control, etc. However, due to dispersive and reflective properties of various vibrating mediums, it is difficult to localize taps accurately on ubiquitous surfaces. Furthermore, no work has been done on tracking continuous swipe interactions through vibration sensing.

E-Loc is an indoor localization system, which, through using existing indoor electric wiring, detects occupants' location. While many indoor localization technologies require intensive infrastructural supports, E-Loc obtain locations by injecting a signal into the protected earth line of existing residential power network. Caused by human body inside a room, the electromagnetic character changes can be detected to deduce a resident's location. We evaluate our system through experiments inside multiple rooms and our system is able to reach meter-level accuracy.

Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person's heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person's respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal.

In this paper, given the scenario of a mobile sensing system for air pollution monitoring, we aim at the cause and influence of delay effect on measurement and present a filter-based solution to calibrate the sensing data. We also validate the idea and solution by a real-data experiment. It indicates that the solution decreases deviation on spatial measurement and can be applied in mobile sensing systems to improve the sensing data quality.

Low-cost sensors are widely used to realize large-scale deployment for sensing systems. In this paper, we discuss challenges in using industrial-grade gas sensors for air quality monitoring. To overcome variation due to system errors, we present a framework for individualized calibration. Within the framework, multiple regression and interpolation methods are prepared for alternative optimization on fitting sensors' response to gas concentration.

Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly changing operating environments with little access to situational information for first responders. In situ information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Static sensor networks that are pre-deployed or manually deployed have been proposed but are less practical due to need for large infrastructure, lack of adaptivity, and limited coverage. Controlled-mobility in sensor networks, that is, the capability of nodes to move as per network needs can provide the desired autonomy to overcome these limitations. In this article, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve three-dimensional sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario, we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy.

Heartbeat monitoring during sleep is critically important to ensuring the well-being of many people, ranging from patients to elderly. Technologies that support heartbeat monitoring should be unobtrusive, and thus solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. We tackle the challenge of accurate, low-cost and easy to deploy heartbeat monitoring by investigating whether off-the- shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. At the same time, lower-frequency movements such as respiration, can be naturally filtered out by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We have built a prototype sensor and conducted detailed experiments that involve 43 subjects (with IRB approval), which demonstrate that the geophone sensor is a compelling solution to long-term at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30% over 500 data samples when the subjects were still on the bed, and 3.87% over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25% over more than 181 hours' data.

Skeletal muscles are pivotal for sports and exercise. However, overexertion of skeletal muscles causes muscle fatigue which can lead to injury. Consequently, understanding skeletal muscle fatigue is important for injury prevention. Current ways to estimate exhaustion revolve around self-estimation or inference from such sensors as force sensors, electromyography e.t.c. These methods are not always reliable, especially during isotonic exercises. Toward this end, we present Burnout - a wearable system for quantifying skeletal muscle fatigue in an exercise setting. Burnout uses accelerometers to sense skeletal muscle vibrations. From these vibrations, Burnout obtains a region based feature (R- Feature), in the case of this work, the region mean power frequency (R-MPF) gradient to correlate the sensed vibrations to a known ground truth measure of skeletal muscle fatigue, i.e., Dimitrov's spectral fatigue index gradient. We evaluate Burnout on the biceps and quadriceps of 5 healthy participants through four different exercises, collected in a real world environment. Our results show that by using this R-MPF feature on our real world data set, Burnout is able to reduce the error of estimating the ground truth fatigue index gradient by up to 50% on average compared to using the standard MPF feature.

This paper presents a hybrid adaptive particle filter (HAP) with online feedback to dynamically reconstruct high spatial-temporal resolution air pollution information from sparse vehicular based sensors. To deal with data sparsity, we apply both spatial and temporal correlation of air dispersion to reduce data dimension requirement. HAP adaptively predicts when the accumulated prediction error is low and then uses data compensation for correction whenever the prediction error becomes high. The preliminary results based on the city scale deployments with 10 taxis show that our system achieves up to 50% reduction on system errors.

Resilient localization and navigation for autonomous Unmanned Aerial Vehicles (UAVs) still remains a challenge in certain scenarios, like GPS-deprived environments such as indoors or urban canyons. In this work, we explore a heterogeneous UAV swarm design, in which a small number of sensor and computationally powerful UAVs collaborate with the remaining resource-constrained UAVs to guarantee optimal localization accuracy.

We introduce a sensing system which leverages footstep-induced structural vibration for occupant localization. Such localization is important for many smart building applications, such as efficient building management, senior/health care, and security. Compared to other sensing approaches, footstep-induced vibration provides a sensing system which is sparse and non-intrusive. The main challenge of achieving high accuracy using such approach is frequency-dependent wave propagation characteristics, such as dispersion, in floor structure. These characteristics result in distortions in the shape of signal. To overcome such distortions, we decompose the vibration signals into different frequency components using wavelet transform and focus on specific components in all the sensors. In a set of experiments in a real structure, our approach results in average localization errors of 0.41 meters, a 4.4X reduction compared to a baseline approach using raw data.

Tracking multiple people in an indoor environment enables various smart building applications such as HVAC energy saving, patient/child monitoring, etc. Researchers have explored various sensing methods including vision, motion, and RF, which either require specific installation requirements or high deployment density. We introduce a passive sparse sensing method based on ambient structural vibration induced by foot strikes. Our system tracks multiple people based on the premise that human foot strikes have spatio-temporal variation, and hence do not fully overlap. The system achieved less than 0.4m accuracy in both one and two persons stepping conditions.

According to the World Health Organization (WHO), outdoor air pollution led to an estimated 3.7 million premature deaths worldwide in 2012. To address this problem, it is necessary for both residents and city administrations to understand air quality in their immediate environment with fine-grained temporal-spatial resolution. Currently both fixed and mobile systems are used to attempt to sense the pollution field. However, they generally are expensive, cover small areas and thus result in lower accuracy.

No abstract available.

Skeletal muscles are activated to generate the force needed for movement in most high motion sports and exercises. However, incorrect skeletal muscle activation during these sports and exercises, can lead to sub-optimal performance and injury. Existing techniques are susceptible to motion artifacts, particularly when used in high motion sports (e.g. jumping, cycling, etc.). They require limited body movement, or experts to manually interpret results, making them unsuitable in sports scenarios.

We describe a step-level indoor localization system which uses the ground vibration induced by human footsteps. Indoor localization is important for various smart building applications, including resources arrangement optimization, patient/customer tracking, etc. Geophones are used to measure the ground vibrations and time difference of arrival (TDoA) for different sensors are used to solve the multilateration localization problem. The advantages of this system include its sparsity and also its stability over time. Lesser dependency on instrument people is another upside of this system. The results of pilot tests show that this system can be successfully used for indoor localization.

The dynamic responses of the structure provide a variety of information about the structure as well as people inside. Compared with traditional sensing methods, structural sensing method serves more general sensing purposes due to the diversity of information it can infer from structural responses. For example, by sensing the structural vibration, a system can track and identify a person through vibration caused by their gaits [5, 6]. Such non-intrusive identification and tracking system enables various smart building applications, such as patient monitoring system at advanced hospitals and nursing homes.

Micro-aerial vehicle (MAV) swarms are a new class of mobile sensor networks with many applications, including search and rescue, urban surveillance, radiation monitoring, etc. These sensing applications require autonomously navigating a high number of low-cost, low-complexity MAV sensor nodes in hazardous environments. The lack of preexisting localization infrastructure and the limited sensing, computing, and communication abilities of individual nodes makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this paper, we present a collaborative and adaptive algorithm for resource-constrained MAV nodes to quickly and efficiently navigate to preassigned locations. Using radio fingerprints between flying and landed MAVs acting as radio beacons, the algorithm detects intersections in trajectories of mobile nodes. The algorithm combines noisy dead-reckoning measurements from multiple MAVs at detected intersections to improve the accuracy of the MAVs' location estimations. In addition, the algorithm plans intersecting trajectories of MAV nodes to aid the location estimation and provide desired performance in terms of timeliness and accuracy of navigation. We evaluate the performance of our algorithm through a real testbed implementation and large-scale physical feature based simulations. Our results show that, compared to existing autonomous navigation strategies, our algorithm achieves up to 6X reduction in location estimation errors, and as much as 3X improvement in navigation success rate under the given time and accuracy constraints.

The Chinese Spring Festival travel season (Chunyun) has been called the largest annual human migration in the world with approximately 3.6 billion trips in 2014. Understandably, all forms of transportation are stressed at or near saturation during this period and many people, especially lower-income individuals fail to get to their destinations. We present CountryRoads, a ridesharing system to address the transportation shortage during Chunyun. The CountryRoads system collects users' route information, and matches drivers and passengers as an online bipartite matching problem based on the proximity of the passenger's origin and destination and the route of the driver. The system is evaluated during four Chunyun periods from 2012 to 2015 with up to 17272 users and forms 4777 ridesharing tuples in 2015.

After a very successful edition of the IoT-App workshop, its second edition is conducted in conjunction with SenSys 2015. Again, the workshop succeeded in attracting a high number of high-quality submissions. The topics of the papers submitted feature most prominently urban sensing and smart home or city. Further directions are programming concepts for IoT devices as well as advances in machine learning, particularly deep learning for IoT devices.

We study the problem of deploying a high number of low-cost, low-complexity robots inside a known environment with the objective that at least one robotic platform reaches each of N preassigned goal locations. Our study is inspired by SensorFly, a micro-aerial vehicle successfully used for mobile sensor network applications. SensorFly nodes feature limited on-board sensors, so one has to rely on simple navigation strategies and increase performance through redundance in the team. We introduce a simple, fully scalable deployment algorithm exploiting the limited capabilities offered by the SensorFly platform, and we explore its performance by feeding the simulation system with parameters extracted from the real SensorFly platform.

Urban environment has significant impacts on the health of city dwellers. To understand these impacts, city planners have to obtain fine-grained environmental information, however such information is not available with traditional environmental systems. To address this problem, we present Gotcha, a taxi-based mobile sensing system for fine-grained environmental data acquisition. Gotcha utilizes taxi cabs to serve as a sensor that collects a variety of environmental information (such as concentrations of carbon-dioxide, carbon-monoxide, ozone, particulate matter, etc.). We aim to deploy our system in the city of Shenzhen on a fleet of 100 taxi cabs, and we present here our results from our initial deployment.

Heading information becomes widely used in ubiquitous computing applications for mobile devices. Digital magnetometers, also known as geomagnetic field sensors, provide absolute device headings relative to the earth's magnetic north. However, magnetometer readings are prone to significant errors in indoor environments due to the existence of magnetic interferences, such as from printers, walls, or metallic shelves. These errors adversely affect the performance and quality of user experience of the applications requiring device headings. In this paper, we propose Headio, a novel approach to provide reliable device headings in indoor environments. Headio achieves this by aggregating ceiling images of an indoor environment, and by using computer vision-based pattern detection techniques to provide directional references. To achieve zero-configured and energy-efficient heading sensing, Headio also utilizes multimodal sensing techniques to dynamically schedule sensing tasks. To fully evaluate the system, we implemented Headio on both Android and iOS mobile platforms, and performed comprehensive experiments in both small-scale controlled and large-scale public indoor environments. Evaluation results show that Headio constantly provides accurate heading detection performance in diverse situations, achieving better than 1 degree average heading accuracy, up to 33X improvement over existing techniques.

Poor posture and incorrect muscle usage are a leading cause of many injuries in sports and fitness. For this reason, non- invasive, fine-grained sensing and monitoring of human motion and muscles is important for mitigating injury and improving fitness efficacy. Current sensing systems either de- pend on invasive techniques or unscalable approaches whose accuracy is highly dependent on body sensor placement. As a result these systems are not suitable for use in active sports or fitness training where sensing needs to be scalable, accurate and un-inhibitive to the activity being performed. We present MARS, a system that detects both body motion and individual muscle group activity during physical human activity by only using unobtrusive, non-invasive in- ertial sensors. MARS not only accurately senses and recreates human motion down to the muscles, but also allows for fast personalized system setup by determining the individual identities of the instrumented muscles, obtained with minimal system training. In a real world human study con- ducted to evaluate MARS, the system achieves greater than 95% accuracy in identifying muscle groups.

Micro-aerial vehicle (MAV) swarms are emerging as a new class of mobile sensor networks with many potential applications such as urban surveillance, disaster response, radiation monitoring, etc., where the swarm is tasked with collaboratively covering a hazardous unknown environment. However, efficient collaborative coverage is challenging due to limited individual sensing, computing and communication resources of MAV sensor nodes, and lack of location infrastructure in the unknown application environment. We present SugarMap, a novel system that enables such resource-constrained MAV nodes to achieve efficient sensing coverage. The self-establishing system uses approximate motion models of mobile nodes in conjunction with radio signatures from self-deployed stationary anchor nodes to create a common coverage map. Consequently, the system coordinates node movements to reduce sensing overlap and increase the speed and efficiency of coverage. The system uses particle filters to account for uncertainty in sensors and actuation of MAV nodes, and incorporates redundancy to guarantee coverage. Through large-scale simulations and a real implementation on the SensorFly MAV sensing platform, we show that SugarMap provides better coverage than the existing coverage approaches for MAV swarms.

Recent developments in ubiquitous computing enable applications that leverage personal mobile devices, such as smartphones, as a means to interact with other devices in their close proximity. In this paper, we propose Spartacus, a mobile system that enables spatially-aware neighboring device interactions with zero prior configuration. Using built-in microphones and speakers on commodity mobile devices, Spartacus uses a novel acoustic technique based on the Doppler effect to enable users to accurately initiate an interaction with a neighboring device through a pointing gesture. To enable truly spontaneous interactions on energy-constrained mobile devices, Spartacus uses a continuous audio-based lower-power listening mechanism to trigger the gesture detection service. This eliminates the need for any manual action by the user. Experimental results show that Spartacus achieves an average 90% device selection accuracy within 3m for most interaction scenarios. Our energy consumption evaluations show that, Spartacus achieves about 4X lower energy consumption than WiFi Direct and 5.5X lower than the latest Bluetooth 4.0 protocols.

A system that helps people navigate in indoor environments on a fine-grained level can enable a variety of pervasive computing applications in retail environments. Existing indoor navigation systems rely on extensive RF tagging surveys and accurate floor plans. These prerequisites are often impractical in indoor environments. In this paper, we present SugarTrail, a system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require existing maps. By leveraging the structured movement patterns of shoppers in retail store environments, the system provides higher accuracy than existing radio finger-printing approaches. With minimal setup and active user participation, the system automatically learns user movement pathways in indoor environments from radiofrequency and magnetic signatures. These pathways are clustered and used to automatically build a navigable virtual roadmap of the environment. We present results from a campus testbed and from actual radio measurements collected in an operational supermarket to show that SugarTrail system can navigate users with a success rate of > 85% and an average accuracy of 0.7m.

We present MARS, a muscle activity recognition system that uses inertial sensors to capture the vibrations of active muscle. Specifically, we demonstrate how accelerometer data capturing these vibrations in the quadriceps, hamstrings and calf muscles of the human leg, can be leveraged to create muscle vibration signatures. We finally show that these vibration signatures can be used to distinguish these muscles from each other with greater than 85% precision and recall.

The SensorFly is a novel, low-cost, miniature (29g) controlled-mobile aerial sensor networking platform. Mobility permits a network of SensorFly nodes, unlike fixed networks, to be autonomous in deployment, maintenance and adapting to the environment, as required for emergency response situations such as fire monitoring or survivor search.

Incorrect muscle usage and muscle fatigue are a leading cause of many sports injures. As a result, sensing and monitoring muscles, as well as human motion, is important. Toward this end, we present the Muscle Activity Recognition System (MARS) system. MARS utilizes a system of small inertial sensors to deduce body motion and muscle fatigue. In this demo we demonstrate how MARS' sensors, placed on the major muscles of the lower body (hamstrings, quadriceps, calves), are used for motion capture and muscle fatigue determination. The system uses an animated human body model to display the motion of the subject, and highlight using different colors, the fatigue status of the muscles in use.

Energy saving is the main goal in most building energy monitoring applications. These systems, however, are operated by people. For this reason, an intuitive user interface is an essential element that will affect users' data understandability, thereby determining the system's usability. Traditional energy monitoring generally focuses on getting energy information by utilizing graphs or static text interfaces. However, these approaches are not related to the physical space. With the increase of information in energy monitoring systems, intuitive and efficient ways of displaying information are needed. In this demo, we present iCEnergy, a vision-based mobile information interface that provides power monitoring using augmented reality. Using existing system data, the system overlays an interactive "energy cloud" over corresponding devices in order to illustrate information about the physical environment. This approach aims to provide users with a comfortable interaction experience through its intuitive information display.

Future ubiquitous home environments can contain 10s or 100s of devices. Ubiquitous services running on these devices (i.e. localizing users, routing, security algorithms) will commonly require an accurate location of each device. In order to obtain these locations, existing techniques require either a manual survey, active sound sources, or estimation using wireless radios. These techniques, however, need additional hardware capabilities and are intrusive to the user. Non-intrusive, automatic localization of ubiquitous computing devices in the home has the potential to greatly facilitate device deployments.

Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly-changing operating environments with little access to situational information for first responders. In situ information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Cyber-physical controlled-mobile sensor networks have been proposed for emergency response situations. However, cost-effective development, analysis and evaluation of such cyber-physical systems require simulation frameworks that simultaneously model its many computational and physical components. Existing multi-sensor/robot simulation environments are inadequate for this purpose. This paper presents a simulator that incorporates a realistic indoor fire growth model (CFAST), with a radio path loss model, wireless network model, and mobility model of a controlled-mobile sensor network, to achieve a more comprehensive representation of such cyber-physical systems. A detailed example simulation scenario is presented along with analysis to illustrate the capabilities of the framework.

This demo presents PANDAA, a zero-configuration automatic spatial localization technique for networked devices based on ambient sound sensing. We will demonstrate that after initial placement of the devices, ambient sounds, such as human speech, music, footsteps, finger snaps, hand claps, or coughs and sneezes, can be used to autonomously resolve the spatial relative arrangement of devices, such as mobile phones, using trigonometric bounds and successive approximation.

Touchscreens enable users to interact directly and intuitively with computers by simply touching the display area without requiring any intermediate devices. There are various touchscreen technologies that generally utilize resistive or capacitive panels. Typical touchscreens are constrained by the fixed size and high cost panels. Many research efforts have been made towards achieving multitouch functionality using vision-based systems. However, existing approaches have limitations such as relying on pre-defined gestures [5], requiring users to wear a glove with a custom pattern [4], or using infrared light pens [2].

With the proliferation of mobile devices, an increasing number of sensing applications are using mobile sensor networks. These mobile networks are severely energy-constrained, and energy usage is one of the most common causes of failure in their deployments. In these networks, nodes that exhaust their energy before the targeted system lifetime degrade system performance; nodes that run past the system lifetime cannot fully utilize their stored energy. Although much work has focused on policies to reduce and regulate energy usage in fixed and dense networks, intermittently connected networks have been largely overlooked. Due to variations in hardware, software, node mobility, and environment, it is especially difficult for intermittently connected mobile networks to improve operations collectively in a dynamic environment. Here, we present and evaluate Collaborative Adaptive Targeted System Lifetime (CA-TSL), an adaptive policy that enforces a system-wide targeted lifetime in an intermittently connected system by adapting node energy usage to an estimated desired energy profile. For evaluation, we present both real-system and large-scale simulated results. Our approach improves sink data reception by an average of 50 percent, and an additional 30 percent when a density estimation technique is also employed. In addition, it reduces system lifetime variations by up to 5.5 ×.

The SensorFly system is a novel, low-cost, miniature controlled-mobile aerial sensor network. Mobility permits the network to be autonomous in deployment, maintenance and adapting to the environment, overcoming the reliance of traditionally fixed networks on human intervention, large infrastructure or inefficient random methods. We wish to demonstrate the novel hardware design, flight control and collaborative localization capabilities of the SensorFly system. To the best of our knowledge, this is the lightest realized flying sensor system in existence, with a weight of 30g and low mass production cost of ~$100. Developed under severe weight, cost, energy and processing power constraints, we show that this system is a viable and capable mobile sensor network platform.

As the field of sensor networks matures, research in this area is focusing not only on fixed networks, but also on mobile sensor networks. For many reasons, both technical and logistical, such networks will often be very sparse for all or part of their operation, sometimes functioning more as disruption-tolerant networks (DTNs). While much work has been done on localization methods for densely populated fixed networks, most of these methods are inefficient or ineffective for sparse mobile networks, where connections can be infrequent. While some mobile networks rely on fixed location beacons or per-node, onboard GPS, these methods are not always possible due to cost, power and other constraints. In this paper we present the Low-density Collaborative Ad-Hoc Localization Estimation (LOCALE) system for sparse sensor networks. In LOCALE, each node estimates its own position, and collaboratively refines that location estimate by updating its prediction based on neighbors it encounters. Nodes also estimate (as a probability density function) the likelihood their prediction is accurate. We evaluate LOCALE'S collaborative localization both through real implementations running on sensor nodes, as well as through simulations of larger systems. We consider scenarios of varying density (down to 0.02 neighbors per communication attempt), as well as scenarios that demonstrate LOCALE'S resilience in the face of extremely-inaccurate individual nodes. Overall, our algorithms yield up to a median of 21X better accuracy for location estimation compared to existing approaches. In addition, by allowing nodes to refine location estimates collaboratively, LOCALE also reduces the need for fixed location beacons (i.e. GPS- enabled beacon towers) by as much as 64X.

Wireless sensor networks are severely-energy constrained devices. Energy-related issues are one of the common failure modes in sensor deployments. One challenge in systemwide energy management is that individual nodes in a sensor network often have widely varying energy profiles due to the amount of data transmitted, hardware construction, and other environmental effects. These differences result in unpredictable node and system lifetimes. As a result, sensor network bit-rate and reliability may degrade prematurely. Our research explores and evaluates an easily implemented dynamic scheduling policy supported by a battery gauge aimed to solve this problem.The dynamic scheduling policy presented here operates in a slotted manner. The decision for each node to communicate is based on the available energy of that node. Our policy guarantees a minimum communication bandwidth, while allowing nodes with more energy to increase their available bandwidth by a factor related to the amount of "extra" energy they have. We present real-system results measured on test nodes in several different network scenarios. The results show our scheduling, when compared to a fixed schedule, guarantees a longer usable system lifetime by preventing premature degradation of connections. In addition to improving connectivity, it reduces data delay by as much as 50% for intermittently connected nodes, with no added communication overhead.

ZebraNet is a mobile, wireless sensor network in which nodes move throughout an environment working to gather and process information about their surroundings[10]. As in many sensor or wireless systems, nodes have critical resource constraints such as processing speed, memory size, and energy supply; they also face special hardware issues such as sensing device sample time, data storage/access restrictions, and wireless transceiver capabilities. This paper discusses and evaluates ZebraNet's system design decisions in the face of a range of real-world constraints.Impala---ZebraNet's middleware layer---serves as a light-weight operating system, but also has been designed to encourage application modularity, simplicity, adaptivity, and repairability. Impala is now implemented on ZebraNet hardware nodes, which include a 16-bit microcontroller, a low-power GPS unit, a 900MHz radio, and 4Mbits of non-volatile FLASH memory. This paper discusses Impala's operation scheduling and event handling model, and explains how system constraints and goals led to the interface designs we chose between the application, middleware, and firmware layers. We also describe Impala's network interface which unifies media access control and transport control into an efficient network protocol. With the minimum overhead in communication, buffering, and processing, it supports a range of message models, all inspired by and tailored to ZebraNet's application needs. By discussing design tradeoffs in the context of a real hardware system and a real sensor network application, this paper's design choices and performance measurements offer some concrete experiences with software systems issues for the mobile sensor design space. More generally, we feel that these experiences can guide design choices in a range of related systems.

The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for di#erent portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are di#erent from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them.