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检索条件"任意字段=13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014"

共 45 条记录，以下是31-40 订阅

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CSense: A Stream-processing Toolkit for Robust and High-Rate Mobile Sensing Applications

CSense: A Stream-Processing Toolkit for Robust and High-Rate...

引用

13th ieee/acm international Symposium on information processing in sensor networks (ipsn)

作者： Lai, Farley Hasan, Syed Shabih Laugesen, Austin Chipara, Octav Univ Iowa Dept Comp Sci Aging Mind & Brain Initiat Iowa City IA 52242 USA

ISBN: (纸本)9781479931460

this paper presents CSense - a stream-processing toolkit for developing robust and high-rate mobile sensing application in Java. CSense addresses the needs of these systems by providing a new programming model that supports flexible application configuration, a high-level concurrency model, memory management, and compiler analyses and optimizations. Our compiler includes a novel flow analysis that optimizes the exchange of data across components from an application-wide perspective. A mobile sensing application benchmark indicates that flow analysis may reduce CPU utilization by as much as 45%. Static analysis is used to detect a range of programming errors including application composition errors, improper use of memory management, and data races. We identify that memory management and concurrency limit the scalability of stream processing systems. We incorporate memory pools, frame conversion optimizations, and custom synchronization primitives to develop a scalable run-time. CSense is evaluated on Galaxy Nexus phones running Android. Empirical results indicate that our run-time achieves 19 times higher steam processing rate compared to a realistic baseline implementation. We demonstrate the versatility of CSense by developing three mobile sensing applications.

关键词： Dataflow computing runtime environment embedded software

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Poster Abstract: information-maximizing Data Collection in Social Sensing using Named-Data

Poster Abstract: Information-maximizing Data Collection in S...

引用

13th ieee/acm international Symposium on information processing in sensor networks (ipsn)

作者： Wang, Shiguang Abdelzaher, Tarek Gajendran, Santhosh Herga, Ajith Kulkarni, Sachin Li, Shen Liu, Hengchang Suresh, Chethan Sreenath, Abhishek Dron, William Leung, Alice Govindan, Ramesh Hancock, John Univ Illinois Urbana IL 61801 USA BBN Raytheon Technol Cambridge MA 02138 USA Univ Southern Calif Los Angeles CA 90089 USA ArtisTech Inc Fairfax VA 22030 USA

ISBN: (纸本)9781479931460

this poster describes the information funnel, a data collection protocol for social sensing that maximizes a measure of delivered information utility. We argue that information-centric networking (ICN), where data objects are named instead of hosts, is especially suited for utility-maximizing transport in resource-constrained environments, because data names can expose similarities between named objects that can be leveraged for minimizing redundancy, hence maximizing utility. We implement the funnel on the recently proposed named-data networking (NDN) stack, an instance of ICN. With proper name space design, a protocol prioritizes transmission of data items over bottlenecks to maximize information utility, with very weak assumptions on the utility function. this prioritization is achieved merely by comparing data names, without knowing application-level name semantics, which makes it generalizable across a wide range of applications. Evaluation results show the information funnel improves the utility of the collected data objects compared with state-of-the-art solutions.

关键词： Semantics

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SIPS: Solar Irradiance Prediction System

SIPS: Solar Irradiance Prediction System

引用

13th ieee/acm international Symposium on information processing in sensor networks (ipsn)

作者： Achleitner, Stefan Kamthe, Ankur Liu, Tao Cerpa, Alberto E. Penn State Univ Comp Sci & Engn University Pk PA 16802 USA Univ Calif Elect Engn & Comp Sci Merced 95344 CA USA

ISBN: (纸本)9781479931460

there is high interest in up-scaling capacities of renewable energy sources such as wind and solar. However, variability and uncertainty in power output is a major concern and forecasting is, therefore, a top priority. Advancements in forecasting can potentially limit the impact of fluctuations in solar power generation, specifically in cloudy days when the variability and dynamics are the largest. We propose SIPS, Solar Irradiance Prediction System, a novel sensing infrastructure using wireless sensor networks (WSNs) to enable sensing of solar irradiance for solar power generation forecasting. In this paper, we report the findings of a deployment of a hierarchical WSN system consisting of 19 TelosB nodes equipped with solar irradiance sensors, and 5 MicaZ nodes equipped with GPS boards, deployed in the vicinity of a 1 MW solar array. We evaluate different irradiance sensor types and the performance of different novel prediction methods using SIPS' data and show that the spatial-temporal cross-correlations between sensor node readings and solar array output power exists and can be exploited to improve prediction accuracy. Using this data for short-term solar forecasting for cloudy days with very high dynamics in solar output power generation -the worst case scenario for prediction-, we get an average of 97.24% accuracy in our prediction for short time horizon forecasting and 240% reduction of predicted normalized root mean square error (NRMSE) compared to state-of-the-art methods that do not use SIPS data.

关键词： wireless sensor networks solar energy forecasting sensor data processing forecasting algorithms

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Aquatic Debris Monitoring Using Smartphone-Based Robotic sensors

Aquatic Debris Monitoring Using Smartphone-Based Robotic Sen...

引用

13th ieee/acm international Symposium on information processing in sensor networks (ipsn)

作者： Wang, Yu Tan, Rui Xing, Guoliang Wang, Jianxun Tan, Xiaobo Liu, Xiaoming Chang, Xiangmao Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Illinios Singapore Singapore Singapore Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479931460

Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. this paper presents the design and implementation of SOAR - a vision-based surveillance robot system that integrates an off-the-shelf Android smartphone and a gliding robotic fish for debris monitoring. SOAR features real-time debris detection and coverage-based rotation scheduling algorithms. the image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. the rotation scheduling algorithm provides effective coverage of sporadic debris arrivals despite camera's limited angular view. Moreover, SOAR is able to dynamically offload computation-intensive processing tasks to the cloud for battery power conservation. We have implemented a SOAR prototype and conducted extensive experimental evaluation. the results show that SOAR can accurately detect debris in the presence of various environment and system dynamics, and the rotation scheduling algorithm enables SOAR to capture debris arrivals with reduced energy consumption.

关键词： Robotic sensor aquatic debris smartphone computer vision object detection

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Optimal sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs

Optimal Sensor Placement and Measurement of Wind for Water Q...

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13th ieee/acm international Symposium on information processing in sensor networks (ipsn)

作者： Du, Wan Xing, Zikun Li, Mo He, Bingsheng Chua, Lloyd Hock Chye Miao, Haiyan Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Nanyang Technol Univ Sch Civil & Environm Engn Singapore Singapore ASTAR Inst High Performance Comp IHPC Singapore Singapore

ISBN: (纸本)9781479931460

We collaborate with environmental scientists to study the hydrodynamics and water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. the output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. the in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

关键词： sensor placement Spatial prediction Wind measurements Water Quality Urban reservoir

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Lightweight Map Matching for Indoor Localisation using Conditional Random Fields

Lightweight Map Matching for Indoor Localisation using Condi...

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13th ieee/acm international Symposium on information processing in sensor networks (ipsn)

作者： Xiao, Zhuoling Wen, Hongkai Markham, Andrew Trigoni, Niki Univ Oxford Dept Comp Sci Oxford OX1 3QD England

ISBN: (纸本)9781479931460

Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Although indoor maps are becoming increasingly available, there is no practical and reliable indoor map matching solution available at present. We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Key to our approach is expressing the tracking problem as a conditional random field (CRF), a technique which has had great success in areas such as natural language processing, but has yet to be considered for indoor tracking. Unlike directed graphical models like Hidden Markov Models, CRFs capture arbitrary constraints that express how well observations support state transitions, given map constraints. Extensive experiments in multiple sites show how MapCraft outperforms state-of-the art approaches, demonstrating excellent tracking error and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position of a user from accelerometer and magnetometer measurements only (i.e. gyro-and WiFi-free). We believe that such an energy-efficient approach will enable always-on background localisation, enabling a new era of location-aware applications to be developed.

关键词： INDOOR TRACKS maps tracking problem MAPPING(MAthEMATICAL) Location awareness Smartphone Conditional Natural Language processing INDOORS Corticotropin-Releasing Hormone Energy efficiency accelerometers

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Demonstration Abstract: PiMi Air Box - A Cost-effective sensor for Participatory Indoor Quality Monitoring

Demonstration Abstract: PiMi Air Box - A Cost-effective Sens...

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13th ieee/acm international Symposium on information processing in sensor networks (ipsn)

作者： Li, Linglong Zheng, Yixin Zhang, Lin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781479931460

Ultra-fine particles with aerodynamic diameter smaller than 2.5 microns, namely Particulate Matter 2.5 (PM 2.5), are capable of penetrating the lung cells and circulating the circulatory system, and compose a major health threat to people. Although the government is publishing the outdoor PM2.5 concentration on an hourly basis, the indoor PM 2.5 concentration, to which most people expose for most of their everyday life time, remains unsupervised. the high cost of the professional PM 2.5 measuring equipments, which utilize filtering and direct mass measuring methodology, prevents the indoor air quality to be monitored pervasively. We designed and implemented PiMi air box, a cost-effective portable sensor, which is able to estimate the PM 2.5 mass concentration with satisfactory accuracy. the PiMi air boxes adopt the low-cost optical particle counting technology and convert the particle counts into PM 2.5 mass concentration via empirical diameter-distribution and density of particulate matters. the errors introduced by the individuality of the low-cost particle counters are offset during a machine-learning-based calibration procedure for each single unit. the PiMi air box enjoys a stunning cost reduction by a factor of 1,000 comparing to professional equipments, and still maintains an satisfactory level of accuracy for everyday life air quality measurement. Together with embedded Bluetooth connectivity and SmartPhone APPs, PiMi air box is well-suited for massive crowd-sourced indoor air-quality monitoring research.

关键词： Indoor air quality Cost-effective PM 2.5 sensors

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conference organization

Conference organization

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international Symposium on information processing in sensor networks (ipsn)

Provides a listing of current committee members and society officers.

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Demonstration abstract: Crowdsourced indoor localization and navigation with Anyplace

Demonstration abstract: Crowdsourced indoor localization and...

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international Symposium on information processing in sensor networks (ipsn)

作者： Lambros Petrou George Larkou Christos Laoudias Demetrios Zeinalipour-Yazti Christos G. Panayiotou Department of Computer Science University of Cyprus Nicosia Cyprus KIOS Research Center for Intelligent Systems and Networks University of Cyprus Nicosia Cyprus

In this demonstration paper, we present the Anyplace system that relies on the abundance of sensory data on smartphones (e.g., WiFi signal strength and inertial measurements) to deliver reliable indoor geolocation information. Our system features two highly desirable properties, namely crowdsourcing and scalability. Anyplace implements a set of crowdsourcing-supportive mechanisms to handle the enormous amount of crowdsensed data, filter incorrect user contributions and exploit WiFi data from heterogeneous mobile devices. Moreover, Anyplace follows a big-data architecture for efficient and scalable storage and retrieval of localization and mapping data.

关键词： Navigation ieee 802.11 Standards Buildings Smart phones Crowdsourcing Servers Computer architecture

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PiLoc: A self-calibrating participatory indoor localization system

PiLoc: A self-calibrating participatory indoor localization ...

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international Symposium on information processing in sensor networks (ipsn)

作者： Chengwen Luo Hande Hong Mun Choon Chan School of Computing National University of Singapore Singapore

While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. the key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths. We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.

关键词： Trajectory Legged locomotion Correlation Calibration ieee 802.11 Standards Market research Educational institutions

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