2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)最新文献

Efficient cross-correlation via sparse representation in sensor networks 基于稀疏表示的传感器网络高效互相关

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-11-06 DOI: 10.1145/2426656.2426694

P. Misra, Wen Hu, Mingrui Yang, Sanjay Jha

{"title":"Efficient cross-correlation via sparse representation in sensor networks","authors":"P. Misra, Wen Hu, Mingrui Yang, Sanjay Jha","doi":"10.1145/2426656.2426694","DOIUrl":"https://doi.org/10.1145/2426656.2426694","url":null,"abstract":"Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. We propose cross-correlation via sparse representation: a new framework for ranging based on ℓ1-minimization. The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device, where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework, together with the proposed correlation domain achieved up to two order of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling. Furthermore, compared to cross-correlation results, 30-40% measurements are sufficient to obtain precise range estimates with an additional bias of only 2-6 cm for high accuracy application requirements, while 5% measurements are adequate to achieve approximately 100 cm precision for lower accuracy applications.","PeriodicalId":231003,"journal":{"name":"2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772866","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

Strawman: Resolving collisions in bursty low-power wireless networks 解决突发低功耗无线网络中的碰撞问题

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185729

Fredrik Österlind, L. Mottola, T. Voigt, N. Tsiftes, A. Dunkels

引用次数: 39

Poster abstract: Ambulatory real-time micro-sensor motion capture 海报摘要:动态实时微传感器运动捕捉

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1109/IPSN.2012.6920977

Shuai Huang, Shuyan Sun, Zhipei Huang, Jiankang Wu, X. Meng, Guanhong Tao, N. Zhang, Li Yang

{"title":"Poster abstract: Ambulatory real-time micro-sensor motion capture","authors":"Shuai Huang, Shuyan Sun, Zhipei Huang, Jiankang Wu, X. Meng, Guanhong Tao, N. Zhang, Li Yang","doi":"10.1109/IPSN.2012.6920977","DOIUrl":"https://doi.org/10.1109/IPSN.2012.6920977","url":null,"abstract":"Commercial optical human motion capture systems perform well in studio-like environments, but they do not provide solution in daily-life surroundings. Micro-sensor motion capture has shown its potentials because of its ubiquity and low cost. We present an ambulatory low-cost real-time motion capture system using wearable micro-sensors (accelerometers, magnetometers and gyroscopes), which can capture and reconstruct human motion in real-time almost every-where. It mainly consists of three parts: a sensor subsystem, a data fusion subsystem and an animation subsystem. The sensor subsystem collects human motion signals and transfers them into the data fusion subsystem. The data fusion subsystem performs sensor fusion to obtain motion information, i.e., the orientation and position of each body segment. Using the motion information from the data fusion subsystem, the animation subsystem drives the avatar in the 3D virtual world in order to reconstruct human motion. All the processes are accomplished in real-time. The experimental results show that our system can capture motions and drive animations in real-time vividly without drift and delay. And the output from our system can be made use of in film-making, sports training and argument reality applications, etc.","PeriodicalId":231003,"journal":{"name":"2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115104780","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

Demo abstract: Collaborative indoor sensing with the SensorFly aerial sensor network 演示摘要:基于SensorFly空中传感器网络的协同室内传感

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185720

Aveek Purohit, Frank Mokaya, Pei Zhang

引用次数: 12

On truth discovery in social sensing: A maximum likelihood estimation approach 社会感知中的真理发现:一种极大似然估计方法

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185737

Dong Wang, Lance M. Kaplan, H. Le, T. Abdelzaher

{"title":"On truth discovery in social sensing: A maximum likelihood estimation approach","authors":"Dong Wang, Lance M. Kaplan, H. Le, T. Abdelzaher","doi":"10.1145/2185677.2185737","DOIUrl":"https://doi.org/10.1145/2185677.2185737","url":null,"abstract":"This paper addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. A challenge in social sensing applications lies in the noisy nature of data. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown reliability together with their sensory measurements, this paper poses the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. The paper focuses on binary measurements. While some previous work approached the answer in a heuristic manner, we offer the first optimal solution to the above truth discovery problem. Optimality, in the sense of maximum likelihood estimation, is attained by solving an expectation maximization problem that returns the best guess regarding the correctness of each measurement. The approach is shown to outperform the state of the art fact-finding heuristics, as well as simple baselines such as majority voting.","PeriodicalId":231003,"journal":{"name":"2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129382605","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: 364

SunCast: Fine-grained prediction of natural sunlight levels for improved daylight harvesting SunCast:细粒度的自然阳光水平预测，以提高日光采集

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185738

Jiakang Lu, K. Whitehouse

引用次数: 37

Poster abstract: Efficient background subtraction for tracking in embedded camera networks 海报摘要:用于嵌入式摄像机网络跟踪的高效背景减法

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185698

Yiran Shenn, W. Hu, Mingrui Yang, Junbin Liu, C. Chou

引用次数: 2

TriopusNet: Automating wireless sensor network deployment and replacement in pipeline monitoring TriopusNet:自动化无线传感器网络部署和替换管道监测

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185686

Tsung-Te Lai, Wei-Ju Chen, Kuei-Han Li, Polly Huang, Hao-Hua Chu

{"title":"TriopusNet: Automating wireless sensor network deployment and replacement in pipeline monitoring","authors":"Tsung-Te Lai, Wei-Ju Chen, Kuei-Han Li, Polly Huang, Hao-Hua Chu","doi":"10.1145/2185677.2185686","DOIUrl":"https://doi.org/10.1145/2185677.2185686","url":null,"abstract":"This study presents TriopusNet, a mobile wireless sensor network system for autonomous sensor deployment in pipeline monitoring. TriopusNet works by automatically releasing sensor nodes from a centralized repository located at the source of the water pipeline. During automated deployment, TriopusNet runs a sensor deployment algorithm to determine node placement. While a node is flowing inside the pipeline, it performs placement by extending its mechanical arms to latch itself onto the pipe's inner surface. By continuously releasing nodes into pipes, the TriopusNet system builds a wireless network of interconnected sensor nodes. When a node runs at a low battery level or experiences a fault, the TriopusNet system releases a fresh node from the repository and performs a node replacement algorithm to replace the failed node with the fresh one. We have evaluated the TriopusNet system by creating and collecting real data from an experimental pipeline testbed. Comparing with the nonautomated static deployment, TriopusNet is able to use less sensor nodes to cover a sensing area in the pipes while maintaining network connectivity among nodes with high data collection rate. Experimental results also show that TriopusNet can recover from the network disconnection caused by a battery-depleted node and successfully replace the battery-depleted node with a fresh node.","PeriodicalId":231003,"journal":{"name":"2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129733183","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: 80

An effective coreset compression algorithm for large scale sensor networks 一种有效的大规模传感器网络核集压缩算法

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185739

Dan Feldman, Andrew Sugaya, D. Rus

{"title":"An effective coreset compression algorithm for large scale sensor networks","authors":"Dan Feldman, Andrew Sugaya, D. Rus","doi":"10.1145/2185677.2185739","DOIUrl":"https://doi.org/10.1145/2185677.2185739","url":null,"abstract":"The wide availability of networked sensors such as GPS and cameras is enabling the creation of sensor networks that generate huge amounts of data. For example, vehicular sensor networks where in-car GPS sensor probes are used to model and monitor traffic can generate on the order of gigabytes of data in real time. How can we compress streaming high-frequency data from distributed sensors? In this paper we construct coresets for streaming motion. The coreset of a data set is a small set which approximately represents the original data. Running queries or fitting models on the core-set will yield similar results when applied to the original data set. We present an algorithm for computing a small coreset of a large sensor data set. Surprisingly, the size of the coreset is independent of the size of the original data set. Combining map-and-reduce techniques with our coreset yields a system capable of compressing in parallel a stream of O(n) points using space and update time that is only O(log n). We provide experimental results and compare the algorithm to the popular Douglas-Peucker heuristic for compressing GPS data.","PeriodicalId":231003,"journal":{"name":"2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132031150","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: 31

Poster abstract: Multi-channel communication vs. adaptive routing for reliable communication in WSNs 海报摘要:多通道通信与自适应路由在无线传感器网络中的可靠通信

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2012-04-16 DOI: 10.1145/2185677.2185709

A. Gonga, O. Landsiedel, Pablo Soldati, M. Johansson

引用次数: 2