2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)最新文献

Poster abstract: Connected wireless camera network deployment with visibility coverage 海报摘要:联网无线摄像机网络部署与可见性覆盖

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2020-07-20 DOI: 10.1145/3399431

Hua Huang, Chien-Chun Ni, Xiaomeng Ban, Jie Gao, Shan Lin

引用次数: 2

Energy-efficient Low Power Listening for wireless sensor networks in noisy environments 噪声环境下无线传感器网络的节能低功耗监听

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461415

M. Sha, Gregory Hackmann, Chenyang Lu

{"title":"Energy-efficient Low Power Listening for wireless sensor networks in noisy environments","authors":"M. Sha, Gregory Hackmann, Chenyang Lu","doi":"10.1145/2461381.2461415","DOIUrl":"https://doi.org/10.1145/2461381.2461415","url":null,"abstract":"Low Power Listening (LPL) is a common MAC-layer technique for reducing energy consumption in wireless sensor networks, where nodes periodically wakeup to sample the wireless channel to detect activity. However, LPL is highly susceptible to false wakeups caused by environmental noise being detected as activity on the channel, causing nodes to spuriously wakeup in order to receive nonexistent transmissions. In empirical studies in residential environments, we observe that the false wakeup problem can significantly increase a node's duty cycle, compromising the benefit of LPL. We also find that the energy-level threshold used by the Clear Channel Assessment (CCA) mechanism to detect channel activity has a significant impact on the false wakeup rate. We then design AEDP, an adaptive energy detection protocol for LPL, which dynamically adjusts a node's CCA threshold to improve network reliability and duty cycle based on application-specified bounds. Empirical experiments in both controlled tests and real-world environments showed AEDP can effectively mitigate the impact of noise on radio duty cycles, while maintaining satisfactory link reliability.","PeriodicalId":162698,"journal":{"name":"2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"161 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114037917","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: 86

In-pavement wireless Weigh-In-Motion 路面无线动态称重

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461397

Ravneet Bajwa, R. Rajagopal, Erdem Coleri, P. Varaiya, Christopher Flores

{"title":"In-pavement wireless Weigh-In-Motion","authors":"Ravneet Bajwa, R. Rajagopal, Erdem Coleri, P. Varaiya, Christopher Flores","doi":"10.1145/2461381.2461397","DOIUrl":"https://doi.org/10.1145/2461381.2461397","url":null,"abstract":"Truck weight data is used in many areas of transportation such as weight enforcement and pavement condition assessment. This paper describes a wireless sensor network (WSN) that estimates the weight of moving vehicles from pavement vibrations caused by vehicular motion. The WSN consists of: acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicle's arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The paper also describes a novel algorithm that estimates a vehicle's weight from pavement vibration and vehicle detection data, and calculates pavement deflection in the process. A prototype of the system has been deployed near a conventional Weigh-In-Motion (WIM) system on I-80 W in Pinole, CA. Weights of 52 trucks at different speeds and loads were estimated by the system under different pavement temperatures and varying environmental conditions, adding to the challenges the system must overcome. The error in load estimates was less than 10% for gross weight and 15% for individual axle weights. Different states have different requirements for WIM but the system described here outperformed the nearby conventional WIM, and meets commonly used standards in United States. The system also opens up exciting new opportunities for WSNs in pavement engineering and intelligent transportation.","PeriodicalId":162698,"journal":{"name":"2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116597842","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

Acoustic shockwave-based bearing estimation 基于声冲击波的方位估计

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461409

J. Sallai, P. Völgyesi, Á. Lédeczi, Ken Pence, T. Bapty, S. Neema, James R. Davis

引用次数: 14

Poster abstract - Exploiting nonlinear data similarities: A multi-scale nearest-neighbor approach for adaptive sampling in wireless pollution sensor networks 海报摘要-利用非线性数据相似性:无线污染传感器网络中自适应采样的多尺度最近邻方法

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461440

Manik Gupta, E. Bodanese, Lamling Venus Shum, S. Hailes

引用次数: 0

Matchstick: A room-to-room thermal model for predicting indoor temperature from wireless sensor data 火柴棍:从无线传感器数据预测室内温度的房间到房间的热模型

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461389

Carl Ellis, M. Hazas, James Scott

引用次数: 14

Demo abstract - skitracker: Measuring skiing performance using a body-area network 演示摘要- skitracker:使用身体区域网络测量滑雪性能

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461427

Thomas Homewood, Christer Norström, P. Gunningberg

引用次数: 2

FlockLab: A testbed for distributed, synchronized tracing and profiling of wireless embedded systems FlockLab:一个用于分布式、同步跟踪和分析无线嵌入式系统的测试平台

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461402

R. Lim, F. Ferrari, Marco Zimmerling, C. Walser, P. Sommer, J. Beutel

{"title":"FlockLab: A testbed for distributed, synchronized tracing and profiling of wireless embedded systems","authors":"R. Lim, F. Ferrari, Marco Zimmerling, C. Walser, P. Sommer, J. Beutel","doi":"10.1145/2461381.2461402","DOIUrl":"https://doi.org/10.1145/2461381.2461402","url":null,"abstract":"Testbeds are indispensable for debugging and evaluating wireless embedded systems. While existing testbeds provide ample opportunities for realistic, large-scale experiments, they are limited in their ability to closely observe and control the distributed operation of resource-constrained nodes-access to the nodes is restricted to the serial port. This paper presents FlockLab, a testbed that overcomes this limitation by allowing multiple services to run simultaneously and synchronously against all nodes under test in addition to the traditional serial port service: tracing of GPIO pins to record logical events occurring on a node, actuation of GPIO pins to trigger actions on a node, and high-resolution power profiling. FlockLab's accurate timing information in the low microsecond range enables logical events to be correlated with power samples, thus providing a previously unattained level of visibility into the distributed behavior of wireless embedded systems. In this paper, we describe FlockLab's design, benchmark its performance, and demonstrate its capabilities through several real-world test cases.","PeriodicalId":162698,"journal":{"name":"2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128747584","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: 244

Sensemaking for mobile health 为移动健康创造意义

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1109/IPSN.2013.6917550

D. Estrin

{"title":"Sensemaking for mobile health","authors":"D. Estrin","doi":"10.1109/IPSN.2013.6917550","DOIUrl":"https://doi.org/10.1109/IPSN.2013.6917550","url":null,"abstract":"Mobile health (mHealth) leverages the power and ubiquity of mobile and cloud technologies to support patients and clinicians in monitoring and understanding symptoms, side effects and treatment outside the clinical setting; thereby closing the feedback loops of self-care, clinical-care, and personal-evidence-creation. However, to realize this promise, we must develop new data capture, processing and modeling techniques to convert the digital exhaust emitted by mobile phone use into behavioral biomarkers. This calls for a modular layered sensemaking framework in which low level state classifications of raw data (e.g., estimated activity states such as sitting, walking, driving from continuous accelerometer and location traces), are used to derive mid-level semantic features (e.g., total number of ambulatory minutes, number of hours spent out of house), that can then be mapped to particular behavioral biomarkers for specific diseases (e.g., chronic pain, GI disfunction, MS, fatigue, depression, etc). The techniques needed to derive these markers will range from simple functions to machine learning classifiers, and will need to fuse diverse data types, but all will need to cope with noisy, erratic data sources. We are working to build an open architecture and community to speed the rate and robustness of innovation in this space, both academic and commercial (http://openmhealth.org).","PeriodicalId":162698,"journal":{"name":"2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130079430","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: 3

Aerial robot swarms 空中机器人群

2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2013-04-08 DOI: 10.1145/2461381.2461384

Vijay R. Kumar

引用次数: 1