2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)最新文献

A Stitch in Time and Frequency Synchronization Saves Bandwidth 一针在时间和频率同步节省带宽

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00016

Anh Luong, Peter Hillyard, A. Abrar, Charissa Che, Anthony G. Rowe, T. Schmid, Neal Patwari

{"title":"A Stitch in Time and Frequency Synchronization Saves Bandwidth","authors":"Anh Luong, Peter Hillyard, A. Abrar, Charissa Che, Anthony G. Rowe, T. Schmid, Neal Patwari","doi":"10.1109/IPSN.2018.00016","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00016","url":null,"abstract":"We specify and evaluate a new software-defined clock network architecture, Stitch. We use Stitch to derive all subsystem clocks from a single local oscillator (LO) on an embedded platform, and enable efficient radio frequency synchronization (RFS) between two nodes' LOs. RFS uses the complex baseband samples from a low-power low-cost narrowband transceiver to drive the frequency difference between the two devices to less than 3 parts per billion (ppb). Recognizing that the use of a wideband channel to measure clock frequency offset for synchronization purposes is inefficient, we propose to use a separate narrowband radio to provide these measurements. However, existing platforms do not provide the ability to unify the local oscillator across multiple subsystems. We demonstrate Stitch with a reference hardware implementation on a research platform. We show that, with Stitch and RFS, we are able to achieve dramatic efficiency gains in ultra-wideband (UWB) time synchronization and ranging. We demonstrate the same UWB ranging accuracy in state-of-the-art systems but with 59% less utilization of the UWB channel.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125915737","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}

引用次数: 12

Glimpse.3D: A Motion-Triggered Stereo Body Camera for 3D Experience Capture and Preview Glimpse.3D:用于3D体验捕捉和预览的运动触发立体身体相机

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00046

Bashima Islam, Md Tamzeed Islam, S. Nirjon

{"title":"Glimpse.3D: A Motion-Triggered Stereo Body Camera for 3D Experience Capture and Preview","authors":"Bashima Islam, Md Tamzeed Islam, S. Nirjon","doi":"10.1109/IPSN.2018.00046","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00046","url":null,"abstract":"The Glimpse.3D is a body-worn camera that captures, processes, stores, and transmits 3D visual information of a real-world environment using a low-cost camera-based sensor system that is constrained by its limited processing capability, storage, and battery life. The 3D content is viewed on a mobile device such as a smartphone or a virtual reality headset. This system can be used in applications such as capturing and sharing 3D content in the social media, training people in different professions, and post-facto analysis of an event. Glimpse.3D uses off-the-shelf hardware and standard computer vision algorithms. Its novelty lies in the ability to optimally control camera data acquisition and processing stages to guarantee the desired quality of captured information and battery life. The design of the controller is based on extensive measurements and modeling of the relationships between the linear and angular motion of a body-worn camera and the quality of generated 3D point clouds as well as the battery life of the system. To achieve this, we 1) devise a new metric to quantify the quality of generated 3D point clouds, 2) formulate an optimization problem to find an optimal trigger point for the camera system that prolongs its battery life while maximizing the quality of captured 3D environment, and 3) make the model adaptive so that the system evolves and its performance improves over time.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128392279","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}

引用次数: 2

Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices 使用深度数据增强训练来解决可穿戴和智能手机传感设备中的软硬件异构问题

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00048

Akhil Mathur, Tianlin Zhang, S. Bhattacharya, Petar Velickovic, Leonid Joffe, N. Lane, F. Kawsar, P. Lio’

{"title":"Using Deep Data Augmentation Training to Address Software and Hardware Heterogeneities in Wearable and Smartphone Sensing Devices","authors":"Akhil Mathur, Tianlin Zhang, S. Bhattacharya, Petar Velickovic, Leonid Joffe, N. Lane, F. Kawsar, P. Lio’","doi":"10.1109/IPSN.2018.00048","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00048","url":null,"abstract":"A small variation in mobile hardware and software can potentially cause a significant heterogeneity or variation in the sensor data each device collects. For example, the microphone and accelerometer sensors on different devices can respond very differently to the same audio or motion phenomena. Other factors, like the instantaneous computational load on a smartphone, can cause key behavior like sensor sampling rates to fluctuate, further polluting the data. When sensing devices are deployed in unconstrained and real-world conditions, examples of sharply lower classification accuracy are observed due to what is collectively known as the sensing system heterogeneity. In this work, we take an unconventional approach and argue against solving individual forms of heterogeneity, e.g., improving OS behavior, or the quality/uniformity of components. Instead, we propose and build classifiers that themselves are more tolerant of these variations by leveraging deep learning and a data-augmented training process. Neither augmentation nor deep learning has previously been attempted to cope with sensor heterogeneity. We systematically investigate how these two machine learning methodologies can be adapted to solve such problems, and identify when and where they are able to be successful. We find that our proposed approach is able to reduce classifier errors on an average by 9% and 17% for a range of inertial-and audio-based mobile classification tasks.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122239275","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}

引用次数: 55

ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs 几率:在嵌入式gpu上使用深度传感器进行实时目标检测

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00051

Niluthpol Chowdhury Mithun, Sirajum Munir, Karen Guo, Charles Shelton

{"title":"ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs","authors":"Niluthpol Chowdhury Mithun, Sirajum Munir, Karen Guo, Charles Shelton","doi":"10.1109/IPSN.2018.00051","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00051","url":null,"abstract":"Detecting objects that are carried when someone enters or exits a room is very useful for a wide range of smart building applications including safety, security, and energy efficiency. While there has been a significant amount of work on object recognition using large-scale RGB image datasets, RGB cameras are too privacy invasive in many smart building applications and they work poorly in the dark. Additionally, deep object detection networks require powerful and expensive GPUs. We propose a novel system that we call ODDS (Object Detector using a Depth Sensor) that can detect objects in real-time using only raw depth data on an embedded GPU, e.g., NVIDIA Jetson TX1. Hence, our solution is significantly less privacy invasive (even if the sensor is compromised) and less expensive, while maintaining a comparable accuracy with state of the art solutions. Specifically, we resort to training a deep convolutional neural network using raw depth images, with curriculum based learning to improve accuracy by considering the complexity and imbalance in object classes and developing a sparse coding based technique that speeds up the system ~2x with minimal loss of accuracy. Based on a complete implementation and real-world evaluation, we see ODDS achieve 80.14% mean average precision in object detection in real-time (5-6 FPS) on a Jetson TX1.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127745864","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}

引用次数: 14

Enhancing Indoor Smartphone Location Acquisition Using Floor Plans 利用平面图增强室内智能手机位置获取

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00056

N. Rajagopal, Patrick Lazik, Nuno Pereira, Sindhura Chayapathy, B. Sinopoli, Anthony G. Rowe

{"title":"Enhancing Indoor Smartphone Location Acquisition Using Floor Plans","authors":"N. Rajagopal, Patrick Lazik, Nuno Pereira, Sindhura Chayapathy, B. Sinopoli, Anthony G. Rowe","doi":"10.1109/IPSN.2018.00056","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00056","url":null,"abstract":"Indoor localization systems typically determine a position using either ranging measurements, inertial sensors, environmental-specific signatures or some combination of all of these methods. Given a floor plan, inertial and signature-based systems can converge on accurate locations by slowly pruning away inconsistent states as a user walks through the space. In contrast, range-based systems are capable of instantly acquiring locations, but they rely on densely deployed beacons and suffer from inaccurate range measurements given non-line-of-sight (NLOS) signals. In order to get the best of both worlds, we present an approach that systematically exploits the geometry information derived from building floor plans to directly improve location acquisition in range-based systems. Our solving approach can disambiguate multiple feasible locations taking into account a mix of LOS and NLOS hypotheses to accurately localize with significantly fewer beacons. We demonstrate our geometry-aware solving approach using a new ultrasonic beacon platform that is able to perform direct time-of-flight ranges on commodity smartphones. The platform uses Bluetooth Low Energy (BLE) for time synchronization and ultrasound for measuring propagation distance. We evaluate our system's accuracy with multiple deployments in a university campus and show that our approach shifts the 80% accuracy point from 4-8m to 1m as compared to solvers that do not use the floor plan information. We are able to detect and remove NLOS signals with 91.5% accuracy.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130366704","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}

引用次数: 23

Magnitude-Based Angle-of-Arrival Estimation, Localization, and Target Tracking 基于震级的到达角估计、定位和目标跟踪

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00053

Chitra R. Karanam, Belal Korany, Y. Mostofi

{"title":"Magnitude-Based Angle-of-Arrival Estimation, Localization, and Target Tracking","authors":"Chitra R. Karanam, Belal Korany, Y. Mostofi","doi":"10.1109/IPSN.2018.00053","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00053","url":null,"abstract":"In this paper, we are interested in estimating the angle of arrival (AoA) of all the signal paths arriving at a receiver array using only the corresponding received signal magnitude measurements (or, equivalently, the received power measurements). Typical AoA estimation techniques require phase information, which is not available in some WiFi/Bluetooth receivers, and is further challenging to properly measure in a synthetic antenna array due to synchronization issues. In this paper, we then show that AoA estimation is possible with only the received signal magnitude measurements. More specifically, we first propose a framework, based on the spatial correlation of the received signal magnitude, to estimate the AoA of signal paths from fixed signal sources (both active transmitters and passive objects). Next, we extend our AoA estimation framework to a dual setting, and further utilize a particle filter, to show how a moving target (both active transmitters and passive robots/humans) can be tracked, based on only the received signal magnitude measurements of a small number of fixed receivers. We extensively validate our proposed framework with several experiments (total of 22), in both closed and open areas. More specifically, we first utilize a robot to emulate an antenna array, and estimate the AoA of active transmitters, as well as passive objects using only the received WiFi signal magnitude measurements. We next validate our tracking framework by using only three off-the-shelf WiFi devices as receivers, to track an active transmitter, a passive robot that writes the letters of IPSN on its path, and a walking human. Overall, our results show that AoA can be estimated, with a high accuracy, with only the received signal magnitude measurements, and can be utilized for high quality angular localization and tracking.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116912390","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}

引用次数: 56

Interference-Resilient Ultra-Low Power Aperiodic Data Collection 抗干扰超低功耗非周期数据采集

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00015

T. Istomin, M. Trobinger, A. Murphy, G. Picco

{"title":"Interference-Resilient Ultra-Low Power Aperiodic Data Collection","authors":"T. Istomin, M. Trobinger, A. Murphy, G. Picco","doi":"10.1109/IPSN.2018.00015","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00015","url":null,"abstract":"Aperiodic data collection received little attention in wireless sensor networks, compared to its periodic counterpart. The recent Crystal system uses synchronous transmissions to support aperiodic traffic with near-perfect reliability, low latency, and ultra-low power consumption. However, its performance is known under mild interference-a concern, as Crystal relies heavily on the (noise-sensitive) capture effect and targets aperiodic traffic where \"every packet counts\". We exploit a 49-node indoor testbed where, in contrast to existing evaluations using only naturally present interference to evaluate synchronous systems, we rely on JamLab to generate noise patterns that are not only more disruptive and extensive, but also reproducible. We show that a properly configured, unmodified Crystal yields perfect reliability (unlike Glossy) in several noise scenarios, but cannot sustain extreme ones (e.g., an emulated microwave oven near the sink) that instead are handled by routing-based approaches. We extend Crystal with techniques known to mitigate interference—channel hopping and noise detection-and demonstrate that these allow Crystal to achieve performance akin to the original even under multiple sources of strong interference.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116361792","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}

引用次数: 30

Charm: Exploiting Geographical Diversity through Coherent Combining in Low-Power Wide-Area Networks 魅力:在低功耗广域网中通过相干组合开发地理多样性

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00013

Adwait Dongare, Revathy Narayanan, Akshay Gadre, Anh Luong, Artur Balanuta, Swarun Kumar, Bob Iannucci, Anthony G. Rowe

{"title":"Charm: Exploiting Geographical Diversity through Coherent Combining in Low-Power Wide-Area Networks","authors":"Adwait Dongare, Revathy Narayanan, Akshay Gadre, Anh Luong, Artur Balanuta, Swarun Kumar, Bob Iannucci, Anthony G. Rowe","doi":"10.1109/IPSN.2018.00013","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00013","url":null,"abstract":"Low-Power Wide-Area Networks (LPWANs) are an emerging wireless platform which can support battery-powered devices lasting 10-years while communicating at low data-rates to gateways several kilometers away. Not all such devices will experience the promised 10 year battery life despite the high density of LPWAN gateways expected in cities. Transmission from devices located deep within buildings or in remote neighborhoods will suffer severe attenuation forcing the use of slow data-rates to reach even the closest gateway, thus resulting in battery drain. This paper presents Charm, a system that enhances both the battery life of client devices and the coverage of LPWANs in large urban deployments. Charm allows multiple LoRaWAN gateways to pool their received signals in the cloud, coherently combining them to detect weak signals that are not decodable at any individual gateway. Through a novel hardware and software design at the gateway, Charm carefully detects which chunks of the received signal need to be sent to the cloud, thereby saving uplink bandwidth. We present a scalable solution to decoding weak transmissions at city-scale by identifying the set of gateways whose signals need to be coherently combined over time. In evaluations over a test network and from simulations using traces from a large LoRaWAN deployment in Pittsburgh, Pennsylvania, Charm demonstrates a gain of up to 3x in range and 4x in client battery-life.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117296204","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}

引用次数: 100

Data-Driven Monitoring and Optimization of Classroom Usage in a Smart Campus 智能校园中数据驱动的教室使用监测与优化

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00050

Thanchanok Sutjarittham, H. Gharakheili, S. Kanhere, V. Sivaraman

{"title":"Data-Driven Monitoring and Optimization of Classroom Usage in a Smart Campus","authors":"Thanchanok Sutjarittham, H. Gharakheili, S. Kanhere, V. Sivaraman","doi":"10.1109/IPSN.2018.00050","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00050","url":null,"abstract":"Student enrollments world-wide are increasing each year, while lecture attendance continues to fall, due to diverse demands on student time and easy access to online content. The resulting underutilization of classrooms entails cost penalties, especially in campuses where real-estate is at a premium. This paper outlines our efforts to instrument a University campus with sensors to measure classroom attendance, in a cost-effective and scalable manner without endangering student privacy. We begin by undertaking a lab evaluation of several approaches to measuring class occupancy, and compare them in terms of cost, accuracy, and ease of deployment and operation. We then instrument 9 lecture halls of varying capacity across campus, collect and clean live data on occupancy spanning about 250 courses over 12 weeks during session, and draw insights into attendance patterns, including identification of canceled lectures and class tests; our occupancy data is released openly to the public. Lastly, we show how classroom allocation can be optimized based on attendance rather than enrollments, resulting in potential savings of 52% in room costs.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130081058","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}

引用次数: 16

Poster Abstract: Runtime Adaptation of PHY Settings for Dependable UWB Communications 摘要:运行时适应可靠的超宽带通信PHY设置

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00027

Bernhard Großwindhager, C. Boano, M. Rath, K. Römer

引用次数: 0