The exponential growth of mobile videos has enabled a variety of video crowdsourcing applications. However, existing crowdsourcing approaches require all video files to be uploaded, wasting a large amount of bandwidth since not all crowdsourced videos are useful. Moreover, it is difficult for applications to find desired videos based on user-generated annotations, which can be inaccurate or miss important information. To address these issues, we present VideoMec, a video crowdsourcing system that automatically generates video descriptions based on various geographical and geometrical information, called metadata, from multiple embedded sensors in off-the-shelf mobile devices. With VideoMec, only a small amount of metadata needs to be uploaded to the server, hence reducing the bandwidth and energy consumption of mobile devices. Based on the uploaded metadata, VideoMec supports comprehensive queries for applications to find and fetch desired videos. For time-sensitive applications, it may not be possible to upload all desired videos in time due to limited wireless bandwidth and large video files. Thus, we formalize two optimization problems and propose efficient algorithms to select the most important videos to upload under bandwidth and time constraints. We have implemented a prototype of VideoMec, evaluated its performance, and demonstrated its effectiveness based on real experiments.
{"title":"VideoMec: A Metadata-Enhanced Crowdsourcing System for Mobile Videos","authors":"Yibo Wu, G. Cao","doi":"10.1145/3055031.3055089","DOIUrl":"https://doi.org/10.1145/3055031.3055089","url":null,"abstract":"The exponential growth of mobile videos has enabled a variety of video crowdsourcing applications. However, existing crowdsourcing approaches require all video files to be uploaded, wasting a large amount of bandwidth since not all crowdsourced videos are useful. Moreover, it is difficult for applications to find desired videos based on user-generated annotations, which can be inaccurate or miss important information. To address these issues, we present VideoMec, a video crowdsourcing system that automatically generates video descriptions based on various geographical and geometrical information, called metadata, from multiple embedded sensors in off-the-shelf mobile devices. With VideoMec, only a small amount of metadata needs to be uploaded to the server, hence reducing the bandwidth and energy consumption of mobile devices. Based on the uploaded metadata, VideoMec supports comprehensive queries for applications to find and fetch desired videos. For time-sensitive applications, it may not be possible to upload all desired videos in time due to limited wireless bandwidth and large video files. Thus, we formalize two optimization problems and propose efficient algorithms to select the most important videos to upload under bandwidth and time constraints. We have implemented a prototype of VideoMec, evaluated its performance, and demonstrated its effectiveness based on real experiments.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"8 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chris Xiaoxuan Lu, Hongkai Wen, Sen Wang, A. Markham, A. Trigoni
Sensor data acquired from multiple sensors simultaneously is featuring increasingly in our evermore pervasive world. Buildings can be made smarter and more efficient, spaces more responsive to users. A fundamental building block towards smart spaces is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit the unique vocal features as people interact with one another. As an example, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation (e.g. through a calendar or MAC address), can we learn to associate a specific identity with a particular voiceprint? Obviously enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. To address this problem, the standard approach is to perform a clustering step (e.g. of audio data) followed by a data association step, when identity-rich sensor data is available. In this paper we show that this approach is not robust to noise in either type of sensor stream; to tackle this issue we propose a novel algorithm that jointly optimises the clustering and association process yielding up to three times higher identification precision than approaches that execute these steps sequentially. We demonstrate the performance benefits of our approach in two case studies, one with acoustic and MAC datasets that we collected from meetings in a non-residential building, and another from an online dataset from recorded radio interviews.
{"title":"SCAN: Learning Speaker Identity from Noisy Sensor Data","authors":"Chris Xiaoxuan Lu, Hongkai Wen, Sen Wang, A. Markham, A. Trigoni","doi":"10.1145/3055031.3055073","DOIUrl":"https://doi.org/10.1145/3055031.3055073","url":null,"abstract":"Sensor data acquired from multiple sensors simultaneously is featuring increasingly in our evermore pervasive world. Buildings can be made smarter and more efficient, spaces more responsive to users. A fundamental building block towards smart spaces is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit the unique vocal features as people interact with one another. As an example, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation (e.g. through a calendar or MAC address), can we learn to associate a specific identity with a particular voiceprint? Obviously enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. To address this problem, the standard approach is to perform a clustering step (e.g. of audio data) followed by a data association step, when identity-rich sensor data is available. In this paper we show that this approach is not robust to noise in either type of sensor stream; to tackle this issue we propose a novel algorithm that jointly optimises the clustering and association process yielding up to three times higher identification precision than approaches that execute these steps sequentially. We demonstrate the performance benefits of our approach in two case studies, one with acoustic and MAC datasets that we collected from meetings in a non-residential building, and another from an online dataset from recorded radio interviews.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122462487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carmelo Di Franco, A. Prorok, Nikolay A. Atanasov, B. Kempke, P. Dutta, Vijay R. Kumar, George J. Pappas
We present a method for calibration-free, infrastructure-free localization in sensor networks. Our strategy is to estimate node positions and noise distributions of all links in the network simultaneously -- a strategy that has not been attempted thus far. In particular, we account for biased, NLOS range measurements from UWB devices that lead to multi-modal noise distributions, for which few solutions exist to date. Our approach circumvents cumbersome a-priori calibration, allows for rapid deployment in unknown environments, and facilitates adaptation to changing conditions. Our first contribution is a generalization of the classical multidimensional scaling algorithm to account for measurements that have multi-modal error distributions. Our second contribution is an online approach that iterates between node localization and noise parameter estimation. We validate our method in 3-dimensional networks, (i) through simulation to test the sensitivity of the algorithm on its design parameters, and (ii) through physical experimentation in a NLOS environment. Our setup uses UWB devices that provide time-of-flight measurements, which can lead to positively biased distance measurements in NLOS conditions. We show that our algorithm converges to accurate position estimates, even when initial position estimates are very uncertain, initial error models are unknown, and a significant proportion of the network links are in NLOS.
{"title":"Calibration-Free Network Localization Using Non-line-of-sight Ultra-wideband Measurements","authors":"Carmelo Di Franco, A. Prorok, Nikolay A. Atanasov, B. Kempke, P. Dutta, Vijay R. Kumar, George J. Pappas","doi":"10.1145/3055031.3055091","DOIUrl":"https://doi.org/10.1145/3055031.3055091","url":null,"abstract":"We present a method for calibration-free, infrastructure-free localization in sensor networks. Our strategy is to estimate node positions and noise distributions of all links in the network simultaneously -- a strategy that has not been attempted thus far. In particular, we account for biased, NLOS range measurements from UWB devices that lead to multi-modal noise distributions, for which few solutions exist to date. Our approach circumvents cumbersome a-priori calibration, allows for rapid deployment in unknown environments, and facilitates adaptation to changing conditions. Our first contribution is a generalization of the classical multidimensional scaling algorithm to account for measurements that have multi-modal error distributions. Our second contribution is an online approach that iterates between node localization and noise parameter estimation. We validate our method in 3-dimensional networks, (i) through simulation to test the sensitivity of the algorithm on its design parameters, and (ii) through physical experimentation in a NLOS environment. Our setup uses UWB devices that provide time-of-flight measurements, which can lead to positively biased distance measurements in NLOS conditions. We show that our algorithm converges to accurate position estimates, even when initial position estimates are very uncertain, initial error models are unknown, and a significant proportion of the network links are in NLOS.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133590015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The continuous fluctuation of electric network frequency (ENF) presents a fingerprint indicative of time, which we call natural timestamp. This paper studies the time accuracy of these natural timestamps obtained from powerline electromagnetic radiation (EMR), which is mainly excited by powerline voltage oscillations at the rate of the ENF. However, since the EMR signal is often weak and noisy, extracting the ENF is challenging, especially on resource-limited sensor platforms. We design an efficient EMR conditioning algorithm and evaluate the time accuracy of EMR natural timestamps on two representative classes of IoT platforms -- a high-end single-board computer with a customized EMR antenna and a low-end mote with a normal conductor wire acting as EMR antenna. Extensive measurements at five sites in a city, which are away from each other for up to 24 km, show that the high-end and low-end nodes achieve median time errors of about 50 ms and 150 ms, respectively. To demonstrate the use of the EMR natural timestamps, we discuss two applications, namely time recovery and run-time clock verification.
{"title":"Natural Timestamping Using Powerline Electromagnetic Radiation","authors":"Yang Li, Rui Tan, David K. Y. Yau","doi":"10.1145/3055031.3055075","DOIUrl":"https://doi.org/10.1145/3055031.3055075","url":null,"abstract":"The continuous fluctuation of electric network frequency (ENF) presents a fingerprint indicative of time, which we call natural timestamp. This paper studies the time accuracy of these natural timestamps obtained from powerline electromagnetic radiation (EMR), which is mainly excited by powerline voltage oscillations at the rate of the ENF. However, since the EMR signal is often weak and noisy, extracting the ENF is challenging, especially on resource-limited sensor platforms. We design an efficient EMR conditioning algorithm and evaluate the time accuracy of EMR natural timestamps on two representative classes of IoT platforms -- a high-end single-board computer with a customized EMR antenna and a low-end mote with a normal conductor wire acting as EMR antenna. Extensive measurements at five sites in a city, which are away from each other for up to 24 km, show that the high-end and low-end nodes achieve median time errors of about 50 ms and 150 ms, respectively. To demonstrate the use of the EMR natural timestamps, we discuss two applications, namely time recovery and run-time clock verification.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125104554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Philipp H. Kindt, Daniel Yunge, Gerhard Reinerth, S. Chakraborty
Recent results show that slotless, purely-interval based neighbor discovery protocols, in which time is assumed to be continuous, achieve significantly lower worst-case discovery latencies than time-slotted protocols. In slotted protocols, the discovery of device A by B and vice-versa occurs within the same slot, and hence the latencies for one-way and two-way discovery are identical.However, in purely interval-based protocols, these latencies are independent from each other, leading to longer mean latencies for two-way discovery. In this paper, we propose a cooperative approach to reduce this two-way discovery latency. In particular, each side broadcasts information on the time-period until its next reception phase takes place. The remote device adjusts its beacon schedule accordingly once a first packet is received. Compared to non-cooperative slotless protocols, this technique can reduce the two-way discovery latency by up to 43 %. We propose a theory to model such protocols and show that with an optimized schedule, our proposed protocol achieves considerably shorter mean latencies than all known protocols, while still guaranteeing worst-case latencies that are similar to the best known solutions. For example, compared to Searchlight-Striped, our proposed protocol achieves by up to 89 % lower mean latencies and by up to 86 % lower worst-case latencies.
{"title":"Griassdi: Mutually Assisted Slotless Neighbor Discovery","authors":"Philipp H. Kindt, Daniel Yunge, Gerhard Reinerth, S. Chakraborty","doi":"10.1145/3055031.3055074","DOIUrl":"https://doi.org/10.1145/3055031.3055074","url":null,"abstract":"Recent results show that slotless, purely-interval based neighbor discovery protocols, in which time is assumed to be continuous, achieve significantly lower worst-case discovery latencies than time-slotted protocols. In slotted protocols, the discovery of device A by B and vice-versa occurs within the same slot, and hence the latencies for one-way and two-way discovery are identical.However, in purely interval-based protocols, these latencies are independent from each other, leading to longer mean latencies for two-way discovery. In this paper, we propose a cooperative approach to reduce this two-way discovery latency. In particular, each side broadcasts information on the time-period until its next reception phase takes place. The remote device adjusts its beacon schedule accordingly once a first packet is received. Compared to non-cooperative slotless protocols, this technique can reduce the two-way discovery latency by up to 43 %. We propose a theory to model such protocols and show that with an optimized schedule, our proposed protocol achieves considerably shorter mean latencies than all known protocols, while still guaranteeing worst-case latencies that are similar to the best known solutions. For example, compared to Searchlight-Striped, our proposed protocol achieves by up to 89 % lower mean latencies and by up to 86 % lower worst-case latencies.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121104638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amr Alanwar, Yasser Shoukry, Supriyo Chakraborty, Bharathan Balaji, Paul D. Martin, P. Tabuada, Mani Srivastava
This demo abstract presents PrOLoc, a localization system thatcombines partially homomorphic encryption with a new way ofstructuring the localization problem to enable efficient and accurate computation of a target’s location while preserving the privacy of the observers.
{"title":"Demo Abstract: PrOLoc: Resilient Localization with Private Observers Using Partial Homomorphic Encryption","authors":"Amr Alanwar, Yasser Shoukry, Supriyo Chakraborty, Bharathan Balaji, Paul D. Martin, P. Tabuada, Mani Srivastava","doi":"10.1145/3055031.3055033","DOIUrl":"https://doi.org/10.1145/3055031.3055033","url":null,"abstract":"This demo abstract presents PrOLoc, a localization system thatcombines partially homomorphic encryption with a new way ofstructuring the localization problem to enable efficient and accurate computation of a target’s location while preserving the privacy of the observers.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Khasnobish, A. Sinharay, Raj Rakshit, T. Chakravarty
This paper demonstrates a system capable of capturing directionaltidal breathing (inhale/exhale cycles) pattern for application inpulmonary disease diagnostics. The work particularly highlightsintelligent usage of commercially available Analog Device’s phasegainIC and ultrasound to build such a system which otherwiserequires many fold, more complex circuits/electronics to achievesimilar sensitivity. This makes the system compact, affordable yetvery sensitive so that it can be reliably used for tidal breathing analysis,a new trend that emerged recently as opposed to traditionalSpirometric test (based on forced breathing) that is both tedious andpainful for patients with pulmonary obstructions. This work mayturn out quite useful in needy geographies as pulmonary ailmentslike Chronic Obstructive Pulmonary Disease (COPD) is rapidlybecoming an epidemic and requires immediate attention
{"title":"Demo Abstract: Phase-Gain IC Based Novel Design of Tidal Breathing Pattern Sensor for Pulmonary Disease Diagnostics","authors":"A. Khasnobish, A. Sinharay, Raj Rakshit, T. Chakravarty","doi":"10.1145/3055031.3055041","DOIUrl":"https://doi.org/10.1145/3055031.3055041","url":null,"abstract":"This paper demonstrates a system capable of capturing directionaltidal breathing (inhale/exhale cycles) pattern for application inpulmonary disease diagnostics. The work particularly highlightsintelligent usage of commercially available Analog Device’s phasegainIC and ultrasound to build such a system which otherwiserequires many fold, more complex circuits/electronics to achievesimilar sensitivity. This makes the system compact, affordable yetvery sensitive so that it can be reliably used for tidal breathing analysis,a new trend that emerged recently as opposed to traditionalSpirometric test (based on forced breathing) that is both tedious andpainful for patients with pulmonary obstructions. This work mayturn out quite useful in needy geographies as pulmonary ailmentslike Chronic Obstructive Pulmonary Disease (COPD) is rapidlybecoming an epidemic and requires immediate attention","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122097181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaohong Hao, Liwen Xu, N. Lane, Xin Liu, T. Moscibroda
Crowdsensing systems collect large-scale sensor data from mobile devices to provide a wide-area view of phenomena including traffic, noise and air pollution. Because such data often exhibits sparse structure, it is natural to apply compressive sensing (CS) for data sampling and recovery. However in practice, crowd participants are often distributed highly unevenly across the sensing area, and thus the numbers of observations collected over different areas may vary wildly -- an issue we call density disparity. Density disparity leads to inaccuracy in low density areas, and potentially undermines the recovery performance if conventional compressive sensing is applied directly, which equally treats data from areas of different density.To address this challenge, we propose a probabilistic accuracy estimator, based on which we devise two recovery algorithms: Threshold Recovery (TR) and Weighted Recovery (WR). As general-purpose recovery algorithms, TR and WR improve the performance of CS in the scenarios with density disparity, and also provide better guarantees in terms of $ell_2$-norm accuracy compared with conventional CS recovery algorithms. We also conduct extensive experiments based on synthetic and real-life datasets. Our results show that TR/WR typically reduce $ell_2$-norm error by more than 60% compared to state-of-the-art baselines.
{"title":"Density-Aware Compressive CrowdSensing","authors":"Xiaohong Hao, Liwen Xu, N. Lane, Xin Liu, T. Moscibroda","doi":"10.1145/3055031.3055081","DOIUrl":"https://doi.org/10.1145/3055031.3055081","url":null,"abstract":"Crowdsensing systems collect large-scale sensor data from mobile devices to provide a wide-area view of phenomena including traffic, noise and air pollution. Because such data often exhibits sparse structure, it is natural to apply compressive sensing (CS) for data sampling and recovery. However in practice, crowd participants are often distributed highly unevenly across the sensing area, and thus the numbers of observations collected over different areas may vary wildly -- an issue we call density disparity. Density disparity leads to inaccuracy in low density areas, and potentially undermines the recovery performance if conventional compressive sensing is applied directly, which equally treats data from areas of different density.To address this challenge, we propose a probabilistic accuracy estimator, based on which we devise two recovery algorithms: Threshold Recovery (TR) and Weighted Recovery (WR). As general-purpose recovery algorithms, TR and WR improve the performance of CS in the scenarios with density disparity, and also provide better guarantees in terms of $ell_2$-norm accuracy compared with conventional CS recovery algorithms. We also conduct extensive experiments based on synthetic and real-life datasets. Our results show that TR/WR typically reduce $ell_2$-norm error by more than 60% compared to state-of-the-art baselines.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134432552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amr Al-Anwar, Yasser Shoukry, Supriyo Chakraborty, Bharathan Balaji, Paul D. Martin, P. Tabuada, M. Srivastava
Aided by advances in sensors and algorithms, systems for localizing and tracking target objects or events have become ubiquitous in recent years. Most of these systems operate on the principle of fusing measurements of distance and/or direction to the target made by a set of spatially distributed observers using sensors that measure signals such as RF, acoustic, or optical. The computation of the target's location is done using multilateration and multiangulation algorithms, typically running at an aggregation node that, in addition to the distance/direction measurements, also needs to know the observers' locations. This presents a privacy risk for an observer that does not trust the aggregation node or other observers and could in turn lead to lack of participation. For example, consider a crowd-sourced sensing system where citizens are required to report security threats, or a smart car, stranded with a malfunctioning GPS, sending out localization requests to neighboring cars -- in both cases, observer (i.e., citizens and cars respectively) participation can be increased by keeping their location private. This paper presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable efficient and accurate computation of a target's location without requiring observers to make public their locations or measurements. Moreover, and unlike previously proposed perturbation based techniques, PrOLoc is also resilient to malicious active false data injection attacks. We present two realizations of our approach, provide rigorous theoretical guarantees, and also compare the performance of each against traditional methods. Our experiments on real hardware demonstrate that PrOLoc yields location estimates that are accurate while being at least 500times faster than state-of-art secure function evaluation techniques.
{"title":"PrOLoc: Resilient Localization with Private Observers Using Partial Homomorphic Encryption","authors":"Amr Al-Anwar, Yasser Shoukry, Supriyo Chakraborty, Bharathan Balaji, Paul D. Martin, P. Tabuada, M. Srivastava","doi":"10.1145/3055031.3055080","DOIUrl":"https://doi.org/10.1145/3055031.3055080","url":null,"abstract":"Aided by advances in sensors and algorithms, systems for localizing and tracking target objects or events have become ubiquitous in recent years. Most of these systems operate on the principle of fusing measurements of distance and/or direction to the target made by a set of spatially distributed observers using sensors that measure signals such as RF, acoustic, or optical. The computation of the target's location is done using multilateration and multiangulation algorithms, typically running at an aggregation node that, in addition to the distance/direction measurements, also needs to know the observers' locations. This presents a privacy risk for an observer that does not trust the aggregation node or other observers and could in turn lead to lack of participation. For example, consider a crowd-sourced sensing system where citizens are required to report security threats, or a smart car, stranded with a malfunctioning GPS, sending out localization requests to neighboring cars -- in both cases, observer (i.e., citizens and cars respectively) participation can be increased by keeping their location private. This paper presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable efficient and accurate computation of a target's location without requiring observers to make public their locations or measurements. Moreover, and unlike previously proposed perturbation based techniques, PrOLoc is also resilient to malicious active false data injection attacks. We present two realizations of our approach, provide rigorous theoretical guarantees, and also compare the performance of each against traditional methods. Our experiments on real hardware demonstrate that PrOLoc yields location estimates that are accurate while being at least 500times faster than state-of-art secure function evaluation techniques.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129741981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shijia Pan, C. G. Ramirez, Mostafa Mirshekari, Jonathon Fagert, Albert Jin Chung, C. C. Hu, John Paul Shen, H. Noh, Pei Zhang
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.In this paper, we present SurfaceVibe, a vibration-based interaction tracking system for multiple surface types. The system accounts for physics properties of different waves to allow two major interaction types: tap and swipe. For tap induced impulse-like surface waves, we design an algorithm that takes wave dispersion and reflection into account to achieve accurate localization on ubiquitous surfaces. For swipe induced body waves, SurfaceVibe segments signals into 'slip pulses' to localize, and then tracks the trajectory. We validate SurfaceVibe through experiments on different materials and varying surface/sensing area sizes in this paper. Our methods achieve up to 6X decrease in localization error for taps and 3X reduction in length estimation error for swipes compared to existing algorithms that do not take wave properties into account.
{"title":"SurfaceVibe: Vibration-Based Tap & Swipe Tracking on Ubiquitous Surfaces","authors":"Shijia Pan, C. G. Ramirez, Mostafa Mirshekari, Jonathon Fagert, Albert Jin Chung, C. C. Hu, John Paul Shen, H. Noh, Pei Zhang","doi":"10.1145/3055031.3055077","DOIUrl":"https://doi.org/10.1145/3055031.3055077","url":null,"abstract":"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.In this paper, we present SurfaceVibe, a vibration-based interaction tracking system for multiple surface types. The system accounts for physics properties of different waves to allow two major interaction types: tap and swipe. For tap induced impulse-like surface waves, we design an algorithm that takes wave dispersion and reflection into account to achieve accurate localization on ubiquitous surfaces. For swipe induced body waves, SurfaceVibe segments signals into 'slip pulses' to localize, and then tracks the trajectory. We validate SurfaceVibe through experiments on different materials and varying surface/sensing area sizes in this paper. Our methods achieve up to 6X decrease in localization error for taps and 3X reduction in length estimation error for swipes compared to existing algorithms that do not take wave properties into account.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116521648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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