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These papers relate to the design or evaluation of devices that can sense the physical world.Papers are listed in reverse-chronological order;
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Methods: Study participants included adults receiving MOUD at a large outpatient treatment program. We predicted NPOU (EMA-based), medication nonadherence (Electronic Health Record [EHR]- and EMA-based), and treatment retention (EHR-based) using context-sensitive EMAs (e.g., stress, pain, social setting). We used recurrent deep learning models with 7-day sliding windows to predict the next-day outcomes, using Area Under the ROC Curve (AUC) for assessment. We employed SHapley additive ExPlanations (SHAP) to understand feature latency and importance.
Results: Participants comprised 62 adults with 14,322 observations. Model performance varied across EMA subtypes and outcomes with AUCs spanning 0.59-0.97. Recent substance use was the best performing predictor for EMA-based NPOU (AUC=0.97) and medication nonadherence (AUC=0.68); life-contextual factors performed best for EHR-based medication nonadherence (AUC=0.89) and retention (AUC=0.80). SHAP revealed varying latencies between predictors and outcomes.
Conclusions: Findings support the effectiveness of EMA and deep learning for forecasting actionable outcomes in persons receiving MOUD. These insights will enable the development of personalized dynamic risk profiles and just-in-time adaptive interventions (JITAIs) to mitigate high-risk OUD outcomes.
In this paper we present MOAT, a system that leverages Wi-Fi sniffers to analyze the physical properties of a device's wireless transmissions to infer whether that device is located inside or outside of a home. MOAT can adaptively self-update to accommodate changes in the home indoor environment to ensure robust long-term performance. Notably, MOAT does not require prior knowledge of the home's layout or cooperation from target devices, and is easy to install and configure.
We evaluated MOAT in four different homes with 21 diverse commercial smart devices and achieved an overall balanced accuracy rate of up to 95.6%. Our novel periodic adaptation technique allowed our approach to maintain high accuracy even after rearranging furniture in the home. MOAT is a practical and efficient first step for monitoring and managing devices in a smart home.
Objective: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time.
Methods: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions—one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics.
Results: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet’s count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669.
Conclusions: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults.
In this thesis, we present an end-to-end solution for providing information provenance for mHealth data, which begins by securing mHealth data at its source: the mHealth device. To this end, we devise a memory-isolation method that combines compiler-inserted code and Memory Protection Unit (MPU) hardware to protect application code and data on ultra-low-power micro-controllers. Then we address the security of mHealth data outside of the source (e.g., data that has been uploaded to smartphone or remote-server) with our health-data system, Amanuensis, which uses Blockchain and Trusted Execution Environment (TEE) technologies to provide confidential, yet verifiable, data storage and computation for mHealth data. Finally, we look at identity privacy and data freshness issues introduced by the use of blockchain and TEEs. Namely, we present a privacy-preserving solution for blockchain transactions, and a freshness solution for data access-control lists retrieved from the blockchain.
We envision a solution called the SPLICEcube whose goal is to detect smart devices, locate them in three dimensions within the home, securely monitor their network traffic, and keep an inventory of devices and important device information throughout the device’s lifecycle. The SPLICEcube system consists of the following components: 1) a main cube, which is a centralized hub that incorporates and expands on the functionality of the home router, 2) a database that holds network data, and 3) a set of support cubelets that can be used to extend the range of the network and assist in gathering network data.
To deliver this vision of identifying, securing, and managing smart devices, we introduce an architecture that facilitates intelligent research applications (such as network anomaly detection, intrusion detection, device localization, and device firmware updates) to be integrated into the SPLICEcube. In this thesis, we design a general-purpose Wi-Fi architecture that underpins the SPLICEcube. The architecture specifically showcases the functionality of the cubelets (Wi-Fi frame detection, Wi-Fi frame parsing, and transmission to cube), the functionality of the cube (routing, reception from cubelets, information storage, data disposal, and research application integration), and the functionality of the database (network data storage). We build and evaluate a prototype implementation to demonstrate our approach is scalable to accommodate new devices and extensible to support different applications. Specifically, we demonstrate a successful proof-of-concept use of the SPLICEcube architecture by integrating a security research application: an "Inside-Outside detection" system that classifies an observed Wi-Fi device as being inside or outside the home.
Methods: This paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes.
Discussion: Results may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals' daily lives and their MOUD treatment response.
Clinical Trial Registration: Identifier: NCT04535583.
To address this problem, in this paper, we investigate the use of vibration, generated by a smartRing, as an out-of-band communication channel to unobtrusively share a secret with a smartThing. This exchanged secret can be used to bootstrap a secure wireless channel over which the smartphone (or another trusted device) and the smartThing can communicate. We present the design, implementation, and evaluation of this system, which we call VibeRing. We describe the hardware and software details of the smartThing and smartRing. Through a user study we demonstrate that it is possible to share a secret with various objects quickly, accurately and securely as compared to several existing techniques. Overall, we successfully exchange a secret between a smartRing and various smartThings, at least 85.9% of the time. We show that VibeRing can perform this exchange at 12.5 bits/second at a bit error rate of less than 2.5%. We also show that VibeRing is robust to the smartThing’s constituent material as well as the holding style. Finally, we demonstrate that a nearby adversary cannot decode or modify the message exchanged between the trusted devices.
In the first part of the thesis, we discuss our work on accurate sensing and detection of different states-of-vulnerability. We start by discussing our work on advancing the field of physiological stress sensing. We took the first step towards testing the reproducibility and validity of our methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Our thorough exploration of reproducibility in a controlled environment provides a critical foundation for deeper study of such methods, and is a prerequisite for tackling reproducibility in free-living conditions.
Next, we present our work on detecting at-risk indicators for patients undergoing Opioid Use Disorder (OUD) treatment. We conducted a 12-week study with 59 patients undergoing an OUD treatment and collected sensor data, like location, physical activity, sleep, and heart rate, from smartphones and wearables. We used the data collected to formulate low- level contextual features and high-level behavioral features and explored the feasibility of detecting self-reported stress, craving, and mood of the participants. Our results show that adaptive, personalized models can detect different at-risk behaviors with the area under the receiver operating characteristic (AUROC) values of up to 0.85.
In the second part of this dissertation, we discuss our contributions in the domain of state-of-receptivity for digital health interventions. We start by conducting a study with 189 participants in Switzerland to explore participant receptivity towards actual physical activity behavior change interventions and report novel and significant results, e.g., being more receptive to interventions leads to higher goal completion likelihood. We further built machine-learning models to predict state-of-receptivity and deployed those models in a real-world study with participants in the United States to evaluate their effectiveness. Our results show that participants were more receptive to interventions delivered at moments detected as ‘receptive’ by our models.
In addition to receptivity in daily living conditions, we explored how participants interact with affective health interventions while driving. We analyzed longitudinal data from 10 participants driving in their day-to-day lives for two months. In this exploratory work, we found that several high-level trip factors (traffic flow, trip length, and vehicle occupancy) and in-the-moment factors (road type, average speed, and braking behavior) showed significant associations with the participant’s decision to start or cancel an intervention. Based on our analysis, we provide solid recommendations on delivering interventions to maximize responsiveness and effectiveness and minimize the burden on the drivers.
Overall, this dissertation makes significant contributions to the respective sub-fields by addressing fundamental challenges, advancing the current state-of-the-art, and contribut- ing new knowledge, thereby laying a solid foundation for designing, implementing, and delivering future digital health interventions.
First, we developed Auracle, a wearable earpiece that can automatically detect eating episodes. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the head. This audio data is then processed by a custom circuit board. We collected data with 14 participants for 32 hours in free-living conditions and achieved accuracy exceeding 92.8% and F1 score exceeding77.5% for eating detection with 1-minute resolution.
Second, we adapted Auracle for measuring children’s eating behavior, and improved the accuracy and robustness of the eating-activity detection algorithms. We used this improved prototype in a laboratory study with a sample of 10 children for 60 total sessions and collected 22.3 hours of data in both meal and snack scenarios. Overall, we achieved 95.5% accuracy and 95.7% F1 score for eating detection with 1-minute resolution.
Third, we developed a computer-vision approach for eating detection in free-living scenarios. Using a miniature head-mounted camera, we collected data with 10 participants for about 55 hours. The camera was fixed under the brim of a cap, pointing to the mouth of the wearer and continuously recording video (but not audio) throughout their normal daily activity. We evaluated performance for eating detection using four different Convolutional Neural Network (CNN) models. The best model achieved 90.9% accuracy and 78.7%F1 score for eating detection with 1-minute resolution. Finally, we validated the feasibility of deploying the 3D CNN model in wearable or mobile platforms when considering computation, memory, and power constraints.
Methods: A 12-week pilot in 28 older rural adults with obesity (body mass index [BMI] ≥ 30 kg/m2) was conducted at a community aging center. The intervention consisted of individualized, weekly dietitian visits focusing on behavior therapy and caloric restriction with twice weekly physical therapist-led group strengthening training classes in a community-based aging center. All participants were provided a Fitbit Flex 2. An aerobic activity prescription outside the strength training classes was provided.
Results: Mean age was 72.9 ± 5.3 years (82% female). Baseline BMI was 37.1 kg/m2, and waist circumference was 120.0 ± 33.0 cm. Mean weight loss (pre/post) was 4.6 ± 3.2 kg (4.9 ± 3.4%; p < .001). Of the 40 eligible participants, 33 (75%) enrolled, and the completion rate was high (84.8%). Objective measures of physical function improved at follow-up: 6-minute walk test improved: 35.7 ± 41.2 m (p < .001); gait speed improved: 0.10 ± 0.24 m/s (p = .04); and five-times sit-to-stand improved by 2.1 seconds (p < .001). Subjective measures of late-life function improved (5.2 ± 7.1 points, p = .003), as did Patient-Reported Outcome Measurement Information Systems mental and physical health scores (5.0 ± 5.7 and 4.4 ± 5.0, both p < .001). Participants wore their Fitbit 93.9% of all intervention days, and were overall satisfied with the trial (4.5/5.0, 1–5 low–high) and with Fitbit (4.0/5.0).
Conclusions: A multicomponent obesity intervention incorporating a wearable device is feasible and acceptable to older adults with obesity, and potentially holds promise in enhancing health.
Objective: This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis.
Methods: Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes.
Results: The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics.
Conclusions: Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.
Methods: We conducted a convergent, parallel mixed-methods study using semi-structured interviews, focus groups, and self-reported questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting. We developed codes informed by thematic analysis and assessed the quantitative data using descriptive statistics.
Results: All groups expressed that mHealth could improve health behaviors. Older adults were optimistic that mHealth could track health. Participants believed they could improve patient insight into health, motivating change and assuring accountability. Barriers to using technology were described, including infrastructure.
Conclusions: Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist.
We present an authentication method for desktops called Seamless Authentication using Wristbands (SAW), which addresses the lack of intentionality limitation of proximity-based methods. SAW uses a low-effort user input step for explicitly conveying user intentionality, while keeping the overall usability of the method better than password-based methods. In SAW, a user wears a wristband that acts as the user’s identity token, and to authenticate to a desktop, the user provides a low-effort input by tapping a key on the keyboard multiple times or wiggling the mouse with the wristband hand. This input to the desktop conveys that someone wishes to log in to the desktop, and SAW verifies the user who wishes to log in by confirming the user’s proximity and correlating the received keyboard or mouse inputs with the user’s wrist movement, as measured by the wristband. In our feasibility user study (n=17), SAW proved quick to authenticate (within two seconds), with a low false-negative rate of 2.5% and worst-case false-positive rate of 1.8%. In our user perception study (n=16), a majority of the participants rated it as more usable than passwords.
The app implements an activity-level detection model we developed using a Linear Support Vector Machine (SVM). We trained our model using data from a user study, where subjects performed common physical activities (sit, stand, lay down, walk and run). We obtained accuracies up to 99.2% and 98.5% with 10-fold cross validation and leave-one-subject-out (LOSO) cross-validation respectively. We ran a week-long field study to evaluate the utility, usability and battery life of the ActivityAware system where 5 older adults wore the Amulet as it monitored their activity level. The utility evaluation showed that the app was somewhat useful in achieving the daily physical activity goal. The usability feedback showed that the ActivityAware system has the potential to be used by people for monitoring their activity levels. Our energy-efficiency evaluation revealed a battery life of at least 1 week before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for epidemiological studies, and eventually for the development of interventions that could improve the health of older adults.
We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime.
First, we present the findings of a user study we conducted to understand people’s authentication behavior: things they authenticate to, how and when they authenticate, authentication errors they encounter and why, and their opinions about authentication. In our study, participants performed about 39 authentications per day on average; the majority of these authentications were to personal computers (desktop, laptop, smartphone, tablet) and with passwords, but the number of authentications to other things (e.g., car, door) was not insignificant. We saw a high failure rate for desktop and laptop authentication among our participants, affirming the need for a more usable authentication method. Overall, we found that authentication was a noticeable part of all our participants’ lives and burdensome for many participants, but they accepted it as cost of security, devising their own ways to cope with it.
Second, we propose a new approach to authentication, called bilateral authentication, that leverages wrist-wearable technology to enable seamless authentication for things that people use with their hands, while wearing a smart wristband. In bilateral authentication two entities (e.g., user’s wristband and the user’s phone) share their knowledge (e.g., about user’s interaction with the phone) to verify the user’s identity. Using this approach, we developed a seamless authentication method for desktops and smartphones. Our authentication method offers quick and effortless authentication, continuous user verification while the desktop (or smartphone) is in use, and automatic deauthentication after use. We evaluated our authentication method through four in-lab user studies, evaluating the method’s usability and security from the system and the user’s perspective. Based on the evaluation, our authentication method shows promise for reducing users’ authentication burden for desktops and smartphones.
We built a user-friendly, mobile health-data collection system using wireless medical sensors that interface with an Android application. The data-collection system was designed to support minimally trained, non-clinical health workers to gather data about blood pressure and body weight using off-the-shelf medical sensors. This system comprises a blood-pressure cuff, a weighing scale and a portable point-of-sales printer. With this system, we introduced a new method to record contextual information associated with a blood-pressure reading using a tablet’s touchscreen and accelerometer. This contextual information can be used to verify that a patient’s lower arm remained well-supported and stationary during her blood-pressure measurement. In a preliminary user study, we found that a binary support vector machine classifier could be used to distinguish lower-arm movements from stationary arms with 90% accuracy. Predetermined thresholds for the accelerometer readings suffice to determine whether the tablet, and therefore the arm that rested on it, remained supported. Together, these two methods can allow mHealth applications to guide untrained patients (or health workers) in measuring blood pressure correctly.
Usability is a particularly important design and deployment challenge in remote, rural areas, given the limited resources for technology training and support. We conducted a field study to assess our system’s usability in Kolar town, India, where we logged health worker interactions with the app’s interface using an existing usability toolkit. Researchers analyzed logs from this toolkit to evaluate the app’s user experience and quantify specific usability challenges in the app. We have recorded experiential notes from the field study in this document.
Our recognition method uses bioimpedance, a measurement of how tissue responds when exposed to an electrical current. By collecting bioimpedance samples using a small wearable device we designed, our system can determine that (a)the wearer is indeed the expected person and (b) the device is physically on the wearer’s body. Our recognition method works with 98% balanced-accuracy under a cross-validation of a day’s worth of bioimpedance samples from a cohort of 8 volunteer subjects. We also demonstrate that our system continues to recognize a subset of these subjects even several months later. Finally, we measure the energy requirements of our system as implemented on a Nexus S smart phone and custom-designed module for the Shimmer sensing platform.
To address this problem we propose ZEBRA. In ZEBRA, a user wears a bracelet (with a built-in accelerometer, gyroscope, and radio) on her dominant wrist. When the user interacts with a computer terminal, the bracelet records the wrist movement, processes it, and sends it to the terminal. The terminal compares the wrist movement with the inputs it receives from the user (via keyboard and mouse), and confirms the continued presence of the user only if they correlate. Because the bracelet is on the same hand that provides inputs to the terminal, the accelerometer and gyroscope data and input events received by the terminal should correlate because their source is the same -- the user’s hand movement. In our experiments ZEBRA performed continuous authentication with 85% accuracy in verifying the correct user and identified all adversaries within 11 s. For a different threshold that trades security for usability, ZEBRA correctly verified 90% of users and identified all adversaries within 50 s.
In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work.
These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors.
We present a wearable sensor to passively recognize people. Our sensor uses the unique electrical properties of a person’s body to recognize their identity. More specifically, the sensor uses bioimpedance -- a measure of how the body’s tissues oppose a tiny applied alternating current -- and learns how a person’s body uniquely responds to alternating current of different frequencies. In this paper we demonstrate the feasibility of our system by showing its effectiveness at accurately recognizing people in a household 90% of the time.
In order for such a vision to be successful, these devices will need to seamlessly interoperate with no interaction required of the user. As difficult as it is for users to manage their wireless area networks, it will be even more difficult for a user to manage their wireless body-area network in a truly pervasive world. As such, we believe these wearable devices should form a wireless body-area network that is passive in nature. This means that these pervasive wearable devices will require no configuration, yet they will be able form a wireless body-area network by (1) discovering their peers, (2) recognizing they are attached to the same body, (3) securing their communications, and (4) identifying to whom they are attached. While we are interested in all aspects of these passive wireless body-area networks, we focus on the last requirement: identifying who is wearing a device.
We conducted focus groups to understand the privacy concerns that patients have when they use mHealth devices. We conducted a user study to understand how willing patients are to share their personal health information that was collected using an mHealth device. To the best of our knowledge, ours is the first study that explores users’ privacy concerns by giving them the opportunity to actually share the information collected about them using mHealth devices. We found that patients tend to share more information with third parties than the public and prefer to keep certain information from their family and friends. Finally, based on these discoveries, we propose some guidelines to developing defaults for sharing settings in mHealth systems.
We provide a method to probabilistically detect this situation. Because accelerometers are relatively cheap and require little power, we imagine that the cellphone and each sensor will have a companion accelerometer embedded with the sensor itself. We extract standard features from these companion accelerometers, and use a pair-wise statistic -- coherence, a measurement of how well two signals are related in the frequency domain -- to determine how well features correlate for different locations on the body. We then use these feature coherences to train a classifier to recognize whether a pair of sensors -- or a sensor and a cellphone -- are on the same body. We evaluate our method over a dataset of several individuals walking around with sensors in various positions on their body and experimentally show that our method is capable of achieving an accuracies over 80%.
This poster describes a simple, flexible, and novel approach to protecting both the confidentiality and integrity medical sensing and data processing on vulnerable mobile phones, using plug-in smart cards---even a phone compromised by malware. We describe our design, implementation, and initial experimental results using real smart cards and Android smartphones.
We propose DEAMON (Distributed Energy-Aware MONitoring), an energy-efficient distributed algorithm for long-term sensor monitoring. Our approach assumes only that mobile nodes are tasked to report sensor data under conditions specified by a Boolean expression, and that a network of nearby sensor nodes contribute to monitoring subsets of the task’s sensors. Our algorithm to select sensor nodes and to monitor the sensing condition conserves energy of all nodes by limiting sensing and communication operations. We evaluate DEAMON with a stochastic analysis and with simulation results, and show that it should significantly reduce energy consumption.
We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder.
We propose SenseRight, the first architecture for high-integrity people-centric sensing. The SenseRight approach, which extends and enhances AnonySense, assures integrity of both the sensor data (through use of tamper-resistant sensor devices) and the sensor context (through a time-constrained protocol), maintaining anonymity if desired.
We propose AnonySense, a general-purpose architecture for leveraging users’ mobile devices for measuring context, while maintaining the privacy of the users. AnonySense features multiple layers of privacy protection---a framework for nodes to receive tasks anonymously, a novel blurring mechanism based on tessellation and clustering to protect users’ privacy against the system while reporting context, and k-anonymous report aggregation to improve the users’ privacy against applications receiving the context. We outline the architecture and security properties of AnonySense, and focus on evaluating our tessellation and clustering algorithm against real mobility traces.
In this paper we present a data-dissemination service, PACK, which allows applications to specify customized data-reduction policies. These policies define how to discard or summarize data flows wherever buffers overflow on the dissemination path, notably at the mobile hosts where applications often reside. The PACK service provides an overlay infrastructure to support mobile data sources and sinks, using application-specific data-reduction policies where necessary along the data path. We uniformly apply the data-stream “packing” abstraction to buffer overflow caused by network congestion, slow receivers, and the temporary disconnections caused by end-host mobility. We demonstrate the effectiveness of our approach with an application example and experimental measurements.
In this paper, we motivate and describe our graph abstraction, and discuss a variety of critical design issues. We also sketch our Solar system, an implementation that represents one point in the design space for our graph abstraction.
In this paper, we motivate and describe our graph abstraction, and discuss a variety of critical design issues. We also sketch our Solar system, an implementation that represents one point in the design space for our graph abstraction.