Data Management for Sensor Networks最新文献

DEMS: a data mining based technique to handle missing data in mobile sensor network applications 基于数据挖掘的技术，用于处理移动传感器网络应用中丢失的数据

Data Management for Sensor Networks Pub Date : 2010-09-13 DOI: 10.1145/1858158.1858167

L. Gruenwald, Md. Shiblee Sadik, Rahul Shukla, Hanqing Yang

{"title":"DEMS: a data mining based technique to handle missing data in mobile sensor network applications","authors":"L. Gruenwald, Md. Shiblee Sadik, Rahul Shukla, Hanqing Yang","doi":"10.1145/1858158.1858167","DOIUrl":"https://doi.org/10.1145/1858158.1858167","url":null,"abstract":"In Mobile Sensor Network (MSN) applications, sensors move to increase the area of coverage and/or to compensate for the failure of other sensors. In such applications, loss or corruption of sensor data, known as the missing sensor data phenomenon, occurs due to various reasons, such as power outage, network interference, and sensor mobility. A desirable way to address this issue is to develop a technique that can effectively and efficiently estimate the values of the missing sensor data in order to provide timely response to queries that need to access the missing data. There exists work that aims at achieving such a goal for applications in static sensor networks (SSNs), but little research has been done for those in MSNs, which are more complex than SSNs due to the mobility of mobile sensors. In this paper, we propose a novel data mining based technique, called Data Estimation for Mobile Sensors (DEMS), to handle missing data in MSN applications. DEMS mines the spatial and temporal relationships among mobile sensors with the help of virtual static sensors. DEMS converts mobile sensor readings into virtual static sensor readings and applies the discovered relationships on virtual static sensor readings to estimate the values of the missing sensor data. We also present the experimental results using both real life and synthetic datasets to demonstrate the efficacy of DEMS in terms of data estimation accuracy.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122445243","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: 24

Processing nested complex sequence pattern queries over event streams 处理事件流上嵌套的复杂序列模式查询

Data Management for Sensor Networks Pub Date : 2010-09-13 DOI: 10.1145/1858158.1858164

Mo Liu, M. Ray, Elke A. Rundensteiner, Daniel J. Dougherty, Chetan Gupta, Song Wang, Ismail Ari, Abhay Mehta

引用次数: 22

What's NExT?: Sensor + Cloud!? 接下来是什么?:传感器+云!

Data Management for Sensor Networks Pub Date : 2010-09-13 DOI: 10.1145/1858158.1858160

K. Tan

引用次数: 35

Provenance-based trustworthiness assessment in sensor networks 传感器网络中基于来源的可信度评估

Data Management for Sensor Networks Pub Date : 2010-09-13 DOI: 10.1145/1858158.1858162

Hyo-Sang Lim, Yang-Sae Moon, E. Bertino

{"title":"Provenance-based trustworthiness assessment in sensor networks","authors":"Hyo-Sang Lim, Yang-Sae Moon, E. Bertino","doi":"10.1145/1858158.1858162","DOIUrl":"https://doi.org/10.1145/1858158.1858162","url":null,"abstract":"As sensor networks are being increasingly deployed in decision-making infrastructures such as battlefield monitoring systems and SCADA (Supervisory Control and Data Acquisition) systems, making decision makers aware of the trustworthiness of the collected data is a crucial. To address this problem, we propose a systematic method for assessing the trustworthiness of data items. Our approach uses the data provenance as well as their values in computing trust scores, that is, quantitative measures of trustworthiness. To obtain trust scores, we propose a cyclic framework which well reflects the inter-dependency property: the trust score of the data affects the trust score of the network nodes that created and manipulated the data, and vice-versa. The trust scores of data items are computed from their value similarity and provenance similarity. The value similarity comes from the principle that \"the more similar values for the same event, the higher the trust scores\". The provenance similarity is based on the principle that \"the more different data provenances with similar values, the higher the trust scores\". Experimental results show that our approach provides a practical solution for trustworthiness assessment in sensor networks.","PeriodicalId":281465,"journal":{"name":"Data Management for Sensor Networks","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114510630","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: 213

Query-driven data collection and data forwarding in intermittently connected mobile sensor networks 间歇连接移动传感器网络中查询驱动的数据采集和数据转发

Data Management for Sensor Networks Pub Date : 2010-09-13 DOI: 10.1145/1858158.1858166

Wei Wu, Hock-Beng Lim, Kian-Lee Tan

引用次数: 19

Facilitating fine grained data provenance using temporal data model 使用时态数据模型促进细粒度的数据来源

Data Management for Sensor Networks Pub Date : 2010-09-13 DOI: 10.1145/1858158.1858163

M. R. Huq, A. Wombacher, P. Apers

引用次数: 23

PAO: power-efficient attribution of outliers in wireless sensor networks 无线传感器网络中异常值的功率效率属性

Data Management for Sensor Networks Pub Date : 2010-09-13 DOI: 10.1145/1858158.1858168

Nikos Giatrakos, Y. Kotidis, Antonios Deligiannakis

引用次数: 10

Alternating multiple tributaries + deltas 多个支流+三角洲交替形成

Data Management for Sensor Networks Pub Date : 2008-08-24 DOI: 10.1145/1402050.1402057

Goce Trajcevski, O. Ghica, P. Scheuermann, R. Tamassia, I. Cruz

引用次数: 5

Better tree - better fruits: using dominating set trees for MAX queries 更好的树-更好的果实:使用支配集树进行MAX查询

Data Management for Sensor Networks Pub Date : 2008-08-24 DOI: 10.1145/1402050.1402052

B. Malhotra, M. Nascimento, I. Nikolaidis

引用次数: 6

POT: an efficient top-k monitoring method for spatially correlated sensor readings POT:一种有效的空间相关传感器读数的top-k监测方法

Data Management for Sensor Networks Pub Date : 2008-08-24 DOI: 10.1145/1402050.1402053

YongHyun Cho, Jihoon Son, Y. Chung

引用次数: 24