VENET 2018 - Special issue on “Data Fusion in Heterogeneous Networks”

A heterogeneous network is a network composed of multiple types of objects and links based on different networking infrastructures. It involves the Internet, Internet of Things, mobile cellular networks, Mobile Ad Hoc Networks (MANET), Vehicular Networks (VENET), Wireless Sensor Networks (WSN) and others. It also includes their up-layer application networks such as social networks, crowdsourcing, Human-Machine Networks, which are gigantic and interconnected. The heterogeneous network holds such characteristics as networking pervasiveness, structure heterogeneity, data diversification and high complexity. Huge volumes of data are generated in the heterogeneous networks. Similar examples are present everywhere, ranging from social media to scientific, engineering or medical systems, and to online e-commerce systems. The data types can be unstructured text documents, multi-lingual data, networking statistics, and online multi-modal data such as images, text, and audios.
The heterogeneous networks are not only ubiquitous but also form a critical component of modern information infrastructure for knowledge retrieval and knowledge discovery. In such a networking environment, data fusion is definitely indispensable and plays a significance role. It provides a main vehicle to information fusion due to the pervasiveness of networking and various modes of information as well as data transmitted. However, the particular characteristics of heterogeneous networks introduce new challenges on data fusion. Heterogeneous data fusion, incentive for data collection, judgement on data veracity, and data trust management are hard to be overcome by existing solutions in the context of heterogeneous networks. Hence, suitable techniques are badly needed to manage and refine data fusion in the heterogeneous networks.
This special issue aims to bring together researchers and practitioners to discuss various aspects of data fusion in heterogeneous networks, explore key technologies, proposed and investigate technology enablers and innovate new solutions for overcoming major challenges in this research area.
Topics central to this special issue include (but are not necessarily limited to):
· Machine learning, data mining and fusion for heterogeneous networks
· Novel datasets and benchmarks for heterogeneous big data analytics
· Information network learning and generation
· New models of heterogeneous network fusion
· Multimodal data fusion via machine learning methods
· Graph (Network) embedding in heterogeneous networks
· Data fusion in next generation communication networks
· Data fusion trust in large scale heterogeneous networks
· Security related data fusion and mining in heterogeneous networks
Manuscripts should be submitted electronically at: http://ees.elsevier.com/inffus/
All manuscripts and any supplementary material should be submitted via the Elsevier Editorial System (http://ees.elsevier.com/inffus/). Each paper will go through a rigorous peer-review process by at least three international researchers.
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 “DataFusion-HN” 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 related papers, if any, should also be submitted online along with the m/s designating them as companion 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 contributions 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.
Guest Editors
Please email inquiries concerning this special issue to:
Prof. Zheng Yan, Xidian University, China and Aalto University, Finland, Email: zheng.yan-AT-aalto.fi
Prof. Jun Liu, Xi’an Jiaotong University, China, Email: liukeen-AT-mail.xjtu.edu.cn
Prof. Laurence T. Yang, St Francis Xavier University, Canada, Email: ltyang-AT-gmail.com
Prof. Witold Pedrycz, University of Alberta, Canada, Email: pedrycz-AT-ee.ualberta.ca
Deadline for Submission: June 1, 2018