[robotics-worldwide] [journals] International Journal of Distribu ted Sensor Networks Special Issue on Latent Knowledge Inference fro m Large Scale Sensor Networks

刘勇 yongliu at iipc.zju.edu.cn
Thu Dec 25 06:45:46 PST 2014

>>>> Apologies for multiple copies due to cross-posting. Please forward to colleagues who might be interested <<<<

Dear colleague,

the International Journal of Distributed Sensor Networks(IJDSN) invites you to submit a paper to the upcoming special issue on Latent Knowledge Inference from Large Scale Sensor Networks. Please find the Call for Papers attached below.

Latent Knowledge Inference from Large Scale Sensor Networks
Call for Papers

Recent advances in low cost sensors and cyber-physical system made it possible to deploy large scale sensor networks and fuse information collected from such infrastructures. This information obtained from the sensor networks can be regarded as the observations to the world, as when the scale of sensor network increases, the corresponding observation to the world will also become dense; thus, it can imply some additional knowledge that may be not directly correlated with the desired tasks of the sensor networks. As the additional knowledge cannot be fused from the sensor network directly, it is also called latent knowledge. For example, the information gathered from the small scale GPS sensor networks equipped in the vehicles can only be applied to fuse the traffic flow conditions of the roads, while it can infer the urban air quality when the scale of the network becomes huge. Another example is the case of smart meter which can measure the time series of energy consumption for the households. The information gathered from large scale of smart meters may allow inferring the daily routine and the number of persons in the household, even the economic status of the countries. Although the classical information fusion methods and models, such as JDL framework, Kalman Filter, and DS theory, have achieved many successes in varied applications, fusing information and obtaining latent knowledge from those large scale sensor networks will face new challenges, such as building new correlation models between the latent knowledge and the large scale sensor networks, real-time processing the huge volume of information, adapting for varied unstructured information, and so forth.

In this issue, we will focus on the fusion methods and models to obtain latent knowledge from large scale sensor networks including social scale sensor networks and large scale RFID networks. We invite investigators to contribute original research articles as well as review articles that will provide novel fusion methods and models for the latent knowledge inference from large scale sensor networks, the processing of strategies to treat the large scale sensor networks for further fusion, and the evaluation of fusion methods and models in large scale sensor networks. We are particularly interested in articles describing the new fusion methods or models for large scale sensor network; advances in social knowledge inferring from large scale sensor networks deployed in daily life; applications for social knowledge inference from daily very large scale sensor networks.

Potential topics include, but are not limited to:

New methods and models for information correlation with latent knowledge from large scale sensor networks
General latent knowledge inference methods from sensor networks
Perception of social knowledge from event patterns on the large scale sensor network
Visual analysis guided latent knowledge inference from scale sensor networks
Privacy protection under large scale sensor networks
Applications for latent knowledge inference from large scale sensor networks
Authors can submit their manuscripts via the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/ijdsn/lki/.

Manuscript Due	Friday, 15 May 2015
First Round of Reviews	Friday, 7 August 2015
Publication Date	Friday, 2 October 2015
Lead Guest Editor

Yong Liu, Zhejiang University, Hangzhou, China
Guest Editors

Nathan Kirchner, University of Technology, Sydney, Australia
Yun-Liang Jiang, Huzhou University, Huzhou, China
Anyi Liu, Indiana University-Purdue University Fort Wayne (IPFW), Fort Wayne, USA

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