http://www.journals.elsevier.com/information-fusion/
Call for papers for a special issue of
Information Fusion
An International Journal on Multi-Sensor, Multi-Source Information Fusion
An Elsevier Publication
On “Fusion - An Aide to Data Mining in Internet of Things”

Editor-in-Chief: Belur V. Dasarathy, Ph. D, FIEEE
belur.d@gmail.com http://belur.no-ip.com (http://belur.tripod.com)

The Information Fusion Journal is planning a special issue on advanced academic and industrial research that explicitly demonstrates the role of Fusion as an Aide to Data Mining in Internet of Things”

The Internet of Things (IoT) is expected to create a world where physical objects are seamlessly integrated into information networks in order to provide advanced and intelligent services for human-beings. The number of interconnected “things” or devices has already overtaken the world’s human population count and it is projected to reach 24 billion by 2020. Various applications and services of IoT have been emerging in different markets, e.g. surveillance, health care, security, transport, and so on. The future of IoT is indeed promising and challenging.

Being involved with a large number of wireless sensor devices, IoT generates a large amount of data, which are massive, multi-source, heterogeneous, dynamic and sparse. Accordingly, data fusion is an important tool in the manipulation and management of these data in order to improve processing efficiency and provide advanced intelligence. By exploiting the synergy among the data sets, data fusion can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences in any stages of data processing in IoT.

Data mining can be viewed as front end to the fusion process and as such is an essential and integral part of IoT. Relying on cloud computing, mining data in the whole IoT networks is de-manding and expected to forecast user needs and behaviors, decide service strategies, control the “things” in reverse, and more importantly provide “only here, only now and only me” services to human-beings. Data mining has already evolved from a traditional method of data analysis or pat-tern discovery to an important segment in IoT. Data fusion and mining have become indispensible parts of IoT and work together to support intelligent and efficient IoT services and applications.

However, the massive, heterogeneous and non-synchronous data sensed or collected by IoT intro-duces significant challenges to the data fusion and mining, for example, holographic information fusion; security, authenticity and reliability of data aggregation; data privacy preservation; data synchronization and mining trust; and huge data process efficiency. The challenge of how to achieve efficient, accurate, trustworthy, distributed and parallel data fusion and mining in IoT has become critically important and significantly impacts its future success. In recent years, Data Fu-sion and Mining has gained special attention and has spurred serious studies in both academia and industry in order to support successful deployment of IoT applications and services in practice.

Manuscripts (which should be original and not previously published either in full or in part or presented even in a more or less similar form at any other forum) covering original unpublished research illustrative of “Fusion as aide to Data Mining in Internet of Things” that clearly delineate the role of information fusion are invited. Absolutely no cut and pastes from prior publications (of text and/or figures or tables or other illustrations) will be permitted. This is a mandatory requirement. All such reproduced material should be excluded by generous use of citations to the relevant prior publications wherever necessary within the text of the Journal submission. Such re-lated papers, if any, should also be submitted online along with the m/s designating them as com-panion files. (Submissions will be evaluated for overlap with published literature using CROSS-CHECK and high scores may result in the manuscript being rejected without formal review.)

The manuscript will be judged solely on the basis of new contributions excluding the contri-butions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein.

Topics appropriate for this special issue include, but are not necessarily limited to:
• Data collection, extraction and abstraction in IoT
• Data synchronization and aggregation in IoT
• Clustering and classification in IoT
• Data trust, security and privacy in IoT
• Security, authenticity and reliability of data aggregation
• Efficient and trustworthy huge data process
• Information fusion and data mining algorithms
• Neural network computing, learning automata
• Modeling related to information fusion and data mining in IoT
• Data mining and fusion in social networking and cloud computing
• Smart grid and smart metering
• Smart education
• Data fusion and mining in pervasive computing
• Applications and services of data fusion and mining in IoT

Manuscripts should be submitted electronically online at http://ees.elsevier.com/inffus
The corresponding author will have to create a user profile if one has not been established before at Elsevier.

Guest Editors
Jun Liu, liukeen@mail.xjtu.edu.cn, Xi’an Jiaotong University, China.
Zheng Yan, zheng.yan@aalto.fi, Aalto University, Finland / Xidian University, China.
Laurence T. Yang, ltyang@ieee.org, St. Francis Xavier University, Canada.

Deadline for Submission: September 30th, 2013