2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)最新文献_第2页

Poster Abstract: Energy Efficient Mobile Data Collection from Sensor Networks with Range-Dependent Data Rates 摘要:基于距离相关数据速率的传感器网络节能移动数据采集

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00024

N. Annuar, N. Bergmann, R. Jurdak, B. Kusy

引用次数: 1

Moving Convolutional Neural Networks to Embedded Systems: The AlexNet and VGG-16 Case 将卷积神经网络移动到嵌入式系统:AlexNet和VGG-16案例

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00049

C. Alippi, Simone Disabato, M. Roveri

{"title":"Moving Convolutional Neural Networks to Embedded Systems: The AlexNet and VGG-16 Case","authors":"C. Alippi, Simone Disabato, M. Roveri","doi":"10.1109/IPSN.2018.00049","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00049","url":null,"abstract":"Execution of deep learning solutions is mostly restricted to high performing computing platforms, e.g., those endowed with GPUs or FPGAs, due to the high demand on computation and memory such solutions require. Despite the fact that dedicated hardware is nowadays subject of research and effective solutions exist, we envision a future where deep learning solutions -here Convolutional Neural Networks (CNNs)- are mostly executed by low-cost off-the shelf embedded platforms already available in the market. This paper moves in this direction and aims at filling the gap between CNNs and embedded systems by introducing a methodology for the design and porting of CNNs to limited in resources embedded systems. In order to achieve this goal we employ approximate computing techniques to reduce the computational load and memory occupation of the deep learning architecture by compromising accuracy with memory and computation. The proposed methodology has been validated on two well-know CNNs, i.e., AlexNet and VGG-16, applied to an image-recognition application and ported to two relevant off-the-shelf embedded platforms.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"24 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":"113965873","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: 94

Walkway Discovery from Large Scale Crowdsensing 大规模群众感知的人行道发现

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00009

Chu Cao, Zhidan Liu, Mo Li, Wenqiang Wang, Zheng Qin

{"title":"Walkway Discovery from Large Scale Crowdsensing","authors":"Chu Cao, Zhidan Liu, Mo Li, Wenqiang Wang, Zheng Qin","doi":"10.1109/IPSN.2018.00009","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00009","url":null,"abstract":"Most digital maps are designed for vehicles and miss a great number of walkways that can facilitate people's daily mobility as pedestrians. Despite of such a fact, most existing map updating approaches only focus on the motorways. To fill the gap, this paper presents VitalAlley, a walkway discovery and verification approach with mobility data from large scale crowdsensing. VitalAlley aims to identify the uncharted walkways from the big but noisy personal mobility data and incorporate these findings into existing incomplete road maps. The implementation of VitalAlley faces the major challenges due to the unstructured nature of the walkways themselves and the noise from crowdsensing data. VitalAlley leverages different aspects of individual mobility to model and estimate the walkable areas, based on which representative walkways that connect known road segments or points of interest are extracted. To verify the new-found walkways, we further propose image based auto-verification with the help of publicly accessible street image database from Google Street View. VitalAlley is implemented and evaluated with real world crowdsensing data from the Singapore National Science Experiment. As a result, 736 walkways (totaling 161 km in distance) are identified from the mobility dataset collected from 108,337 students in Singapore. We manually verify 224 walkways totaling 32.4 km over a 9 km^2 district through on-site inspection. The results suggest over 96% accuracy of VitalAlley in discovering the walkways.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"98 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":"124937481","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

Poster Abstract: Zero-Power Receiver for Touch Communication and Touch Sensing 摘要:用于触摸通信和触摸传感的零功率接收器

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00038

Raphael Strebel, M. Magno

引用次数: 0

Plug-and-Play Irrigation Control at Scale 即插即用灌溉控制的规模

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00008

Daniel A. Winkler, M. A. Carreira-Perpiñán, Alberto Cerpa

{"title":"Plug-and-Play Irrigation Control at Scale","authors":"Daniel A. Winkler, M. A. Carreira-Perpiñán, Alberto Cerpa","doi":"10.1109/IPSN.2018.00008","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00008","url":null,"abstract":"Lawns, also known as turf, cover an estimated 128,000km^2 in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain. With this consumption comes a large amount of environmental, economic, and social incentive to make turf irrigation systems as efficient as possible. Recent work introduced the concept of distributed control in irrigation systems, but existing control strategies either do not take advantage of the distributed control, or don't revise the strategy over time in response to collected data. In this work, we introduce PICS, a data-driven control strategy that self-improves over time, adapts to the local specific conditions and weather changes, and requires virtually no human input in both setup and maintenance providing a plug-and-play system that requires minimal pre-deployment efforts. In addition to substantial improvements in ease-of-use, we find across 4 weeks of large-scale irrigation system deployment that PICS improves system efficiency by 12.0% in comparison to industry best and 3.3% in comparison to academic state-of-the-art. Despite using less water, PICS also was found to improve quality of service by a factor of 4.0x compared to industry best and 2.5x compared to academic state of the art.","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":"125857654","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: 11

Data Fusion for Hybrid and Autonomous Time-of-Flight Positioning 混合与自主飞行时间定位的数据融合

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00054

Aymen Fakhreddine, D. Giustiniano, Vincent Lenders

引用次数: 5

On the Ability of Mobile Sensor Networks to Diffuse Information 移动传感器网络的信息扩散能力研究

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00011

Chen Gu, I. Downes, O. Gnawali, L. Guibas

引用次数: 1

Monitoring Meteorological Parameters with Crowdsourced Air Traffic Control Data 利用众包空中交通管制数据监测气象参数

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00010

Roman Trüb, Daniel Moser, Matthias Schäfer, Rui Pinheiro, Vincent Lenders

{"title":"Monitoring Meteorological Parameters with Crowdsourced Air Traffic Control Data","authors":"Roman Trüb, Daniel Moser, Matthias Schäfer, Rui Pinheiro, Vincent Lenders","doi":"10.1109/IPSN.2018.00010","DOIUrl":"https://doi.org/10.1109/IPSN.2018.00010","url":null,"abstract":"Up-to-date meteorological information about upper air conditions is crucial for accurate weather modeling and forecasting. Existing techniques to sense meteorological parameters in the atmosphere are costly and provide only limited temporal and spatial sensing resolutions. In this paper, we propose crowdsourcing air traffic control data as a new cost-efficient method to achieve a high temporal and spatial resolution, and large coverage. Our solution leverages Secondary Surveillance Radar Mode S and ADS-B transponder signals that are continuously transmitted by aircraft for air traffic control purposes. It builds on signals captured by the OpenSky Network, a global-scale sensor network crowdsourcing 15+ billions of transponder messages per day from aircraft up to an altitude of 13 km. Based on the decoded data, we infer meteorological conditions such as air temperature, wind speed, wind direction and atmospheric pressure. Our evaluation demonstrates that our approach is effective at estimating these parameters with high resolutions along the tracks of more than 50 percent of all aircraft monitored by the OpenSky Network. Our method delivers estimations for temperature with 0.11°C, wind speed with 0.09 m/s, wind direction with 1.00°, and air pressure with 0.10 hPa average deviation, making those measurements suitable for the assimilation in numerical weather models.","PeriodicalId":358074,"journal":{"name":"2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"13 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":"134221877","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: 18

Orientation-Aware RFID Tracking with Centimeter-Level Accuracy 具有厘米级精度的方向感知RFID跟踪

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00057

Chengkun Jiang, Yuan He, Xiaolong Zheng, Yunhao Liu

引用次数: 54

Battery-Free 802.15.4 Receiver 无电池802.15.4接收器

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2018-04-11 DOI: 10.1109/IPSN.2018.00045

C. Pérez-Penichet, Claro Noda, Ambuj Varshney, T. Voigt

引用次数: 15