2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)最新文献_第5页

TinySDM: Software Defined Measurement in Wireless Sensor Networks TinySDM:无线传感器网络中的软件定义测量

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460723

Chenhong Cao, Luyao Luo, Yi Gao, Wei Dong, Chun Chen

{"title":"TinySDM: Software Defined Measurement in Wireless Sensor Networks","authors":"Chenhong Cao, Luyao Luo, Yi Gao, Wei Dong, Chun Chen","doi":"10.1109/IPSN.2016.7460723","DOIUrl":"https://doi.org/10.1109/IPSN.2016.7460723","url":null,"abstract":"Network measurement, which provides detailed information about the behaviors of operational networks, is essential for network management in wireless sensor networks. In the literature, there have been many approaches focusing on measuring individual aspect of the network, e.g., per-packet routing path and per-hop delay. However, there lacks a general support for conducting different measurement tasks. When managing an operational network, a network operator often needs to switch the current measurement task to a different one, in order to diagnose the observed symptoms. In this paper, we propose TinySDM, a software-defined measurement architecture for WSNs. TinySDM provides a general support for conducting different measurement tasks. TinySDM defines a set of carefully selected hooks that allow the users to easily execute their own measurement tasks. In addition, TinySDM provides a C- like language called TinyCode Language (TCL) to enable easy customization of measurement tasks. By only transmitting the binary code of the measurement task, TinySDM significantly reduces the size of the disseminated data compared with existing reprogramming approaches. We implement TinySDM on the TinyOS/TelosB platform and evaluate its performance extensively in a testbed with 60 nodes. We also use TCL to implement four specific measurement tasks. Results show that TinySDM is flexible, efficient and easily programmable.","PeriodicalId":137855,"journal":{"name":"2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127742828","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: 21

Poster Abstract: A Heterogeneous System Architecture for Event-Triggered Wireless Sensing 摘要:事件触发无线传感的异构系统架构

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460692

Felix Sutton, Reto Da Forno, David Gschwend, R. Lim, Tonio Gsell, J. Beutel, L. Thiele

引用次数: 3

Demonstration Abstract: Accelerating Embedded Deep Learning Using DeepX 摘要:利用DeepX加速嵌入式深度学习

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460666

N. Lane, S. Bhattacharya, Petko Georgiev, Claudio Forlivesi, F. Kawsar

引用次数: 1

Poster Abstract: WSNLOC.EU - An Introduction to WSN Localization Tasks Repository 摘要:WSNLOC。EU - WSN本地化任务库介绍

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460718

M. Marks, E. Niewiadomska-Szynkiewicz

引用次数: 0

CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks 交叉:对抗低功耗无线网络中的交叉技术干扰

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460663

Anwar Hithnawi, Su Li, Hossein Shafagh, J. Gross, S. Duquennoy

{"title":"CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks","authors":"Anwar Hithnawi, Su Li, Hossein Shafagh, J. Gross, S. Duquennoy","doi":"10.1109/IPSN.2016.7460663","DOIUrl":"https://doi.org/10.1109/IPSN.2016.7460663","url":null,"abstract":"Low-power wireless devices suffer notoriously from Cross- Technology Interference (CTI). To enable co-existence, researchers have proposed a variety of interference mitigation strategies. Existing solutions, however, are designed to work with the limitations of currently available radio chips. In this paper, we investigate how to exploit physical layer properties of 802.15.4 signals to better address CTI. We present CrossZig, a cross-layer solution that takes advantage of physical layer information and processing to improve low-power communication under CTI. To this end, CrossZig utilizes physical layer information to detect presence of CTI in a corrupted packet and to apply an adaptive packet recovery which incorporates a novel cross-layer based packet merging and an adaptive FEC coding. We implement a prototype of CrossZig for the low-power IEEE 802.15.4 in a software-defined radio platform. We show the adaptability and the performance gain of CrossZig through experimental evaluation considering both micro-benchmarking and system performance under various interference patterns. Our results demonstrate that CrossZig can achieve a high accuracy in error localization (94.3% accuracy) and interference type identification (less than 5% error rate for SINR ranges below 3 dB). Moreover, our system shows consistent performance improvements under interference from various interfering technologies.","PeriodicalId":137855,"journal":{"name":"2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125412402","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: 40

Indoor Tracking Using Crowdsourced Maps 使用众包地图进行室内跟踪

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460679

Jiang Dong, Yu Xiao, Zhonghong Ou, Yong Cui, Antti Ylä-Jääski

引用次数: 13

Sensor-Assisted Face Recognition System on Smart Glass via Multi-View Sparse Representation Classification 基于多视图稀疏表示分类的智能玻璃传感器辅助人脸识别系统

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460721

Weitao Xu, Yiran Shen, N. Bergmann, Wen Hu

{"title":"Sensor-Assisted Face Recognition System on Smart Glass via Multi-View Sparse Representation Classification","authors":"Weitao Xu, Yiran Shen, N. Bergmann, Wen Hu","doi":"10.1109/IPSN.2016.7460721","DOIUrl":"https://doi.org/10.1109/IPSN.2016.7460721","url":null,"abstract":"Face recognition is one of the most popular research problems on various platforms. New research issues arise when it comes to resource constrained devices, such as smart glasses, due to the overwhelming computation and energy requirements of the accurate face recognition methods. In this paper, we propose a robust and efficient sensor-assisted face recognition system on smart glasses by exploring the power of multimodal sensors including the camera and Inertial Measurement Unit (IMU) sensors. The system is based on a novel face recognition algorithm, namely Multi-view Sparse Representation Classification (MVSRC), by exploiting the prolific information among multi-view face images. To improve the efficiency of MVSRC on smart glasses, we propose a novel sampling optimization strategy using the less expensive inertial sensors. Our evaluations on public and private datasets show that the proposed method is up to 10% more accurate than the state-of-the-art multi-view face recognition methods while its computation cost is in the same order as an efficient benchmark method (e.g., Eigenfaces). Finally, extensive real-world experiments show that our proposed system improves recognition accuracy by up to 15% while achieving the same level of system overhead compared to the existing face recognition system (OpenCV algorithms) on smart glasses.","PeriodicalId":137855,"journal":{"name":"2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"584 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123138282","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

Poster Abstract: Wake-Up Receivers for Energy Efficient and Low Latency Communication 摘要:用于节能低延迟通信的唤醒接收器

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460717

Fayçal Ait Aoudia, M. Magno, M. Gautier, O. Berder, L. Benini

引用次数: 6

Poster Abstract: Personal Energy Footprint in Shared Building Environment 摘要:共享建筑环境中的个人能源足迹

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460710

Xiaoqi Chen, Rishikanth Chandrasekaran, Fengyi Song, Xiaofan Jiang

引用次数: 1

Harmonium: Asymmetric, Bandstitched UWB for Fast, Accurate, and Robust Indoor Localization Harmonium:用于快速，准确和稳健的室内定位的非对称，带缝超宽带

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2016-04-11 DOI: 10.1109/IPSN.2016.7460675

B. Kempke, P. Pannuto, P. Dutta

{"title":"Harmonium: Asymmetric, Bandstitched UWB for Fast, Accurate, and Robust Indoor Localization","authors":"B. Kempke, P. Pannuto, P. Dutta","doi":"10.1109/IPSN.2016.7460675","DOIUrl":"https://doi.org/10.1109/IPSN.2016.7460675","url":null,"abstract":"We introduce Harmonium, a novel ultra-wideband RF localization architecture that achieves decimeter-scale accuracy indoors. Harmonium strikes a balance between tag simplicity and processing complexity to provide fast and accurate indoor location estimates. Harmonium uses only commodity components and consists of a small, inexpensive, lightweight, and FCC-compliant ultra-wideband transmitter or tag, fixed infrastructure anchors with known locations, and centralized processing that calculates the tag's position. Anchors employ a new frequency-stepped narrowband receiver architecture that rejects narrowband interferers and extracts high-resolution timing information without the cost or complexity of traditional ultra-wideband approaches. In a complex indoor environment, 90% of position estimates obtained with Harmonium exhibit less than 31 cm of error with an average 9 cm of inter-sample noise. In non-line-of-sight conditions (i.e. through-wall), 90% of position error is less than 42 cm. The tag draws 75 mW when actively transmitting, or 3.9 mJ per location fix at the 19 Hz update rate. Tags weigh 3 g and cost $4.50 USD at modest volumes. Harmonium introduces a new design point for indoor localization and enables localization of small, fast objects such as micro quadrotors, devices previously restricted to expensive optical motion capture systems.","PeriodicalId":137855,"journal":{"name":"2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127297753","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: 25