Guest editors:
Sun Yunchuan, yunch@bnu.edu.cn, Beijing Normal University, China
Klaus Marius Hansen, klausmh@diku.dk, University of Copenhagen, Denmark
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The Internet of Things (IoT) provides a promising vision of the future world where the virtual world of information technology integrates seamlessly with the real world of things. It is regarded as an ideal platform for people, as well as smart things, to communicate easily with each other not only in business and industries but also in our daily scenarios. Automation and intelligence are the primary characteristics of this promising environment where knowledge and information play key roles. Applications and systems tend to become more complex than before to manage and operate in the converged world of large scale Internet of Things (IoT), Internet of Services (IoS), Internet of People (IoP). The knowledge bases in the Internet of Things can acquire knowledge from domain experts as well as knowledge mining tools, which can retrieve new rules and models from massive information generated from people and things in future context of the Internet of Things. The models of knowledge management, knowledge refinement, knowledge sharing, and self-managing models of pervasive service are the keys to make the systems efficiently and effectively.
The special issue aims at bringing together research efforts focused on the development of Knowledge-driven and Semantics-centered methods, self-managing models for pervasive services, tools and techniques for the handling of various aspects of Internet of Things. In this special issue, we solicit high-quality contributions with consolidated and thoroughly evaluated application-oriented research results in the area of knowledge engineering and self-managing pervasive services for the Internet of Things that are worthy of archival publication in the Knowledge-based Journal. It is intended to (i) provide a summary of research that advances knowledge engineering and self-managing pervasive services for the Internet of Things, and (ii) serve as a comprehensive collection of some of the current state-of-the-art technologies within this context. The special issue will collaborate with several international workshops/conferences: IIKI2012 (http://ireg.bnu.edu.cn/IIKI2012/),
SKG2012 (http://www.knowledgegrid.net/SKG2012), EPS2012 (https://sites.google.com/site/eps2012workshop), and SeMaPS2012 (https://sites.google.com/site/semapsworkshop).
The solicited papers include, but not limited to, the topical areas listed below:
 Knowledge representation models in IoT
 Knowledge retrieving and sharing mechanisms in IoT
 Enterprise knowledge management in IoT
 Reasoning algorithms for the knowledge systems in IoT
 Interoperability including Semantic interoperability in IoT
 Software engineering and approaches on self-management pervasive computing environments
 Business information processing and business models in IoT
 Integration of heterogeneous information for IoT
 Management information systems of IoT
 Technologies of data management and integration in IoT
 Privacy protection and security issues of IoT
 Service oriented computing in IoT
 Intelligent applications of IoT
 Approaches and tools for building pervasive service systems
 Data Mining in IoT
 Context Awareness in IoT
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Submission details:
Authors should submit their papers via EES: http://www.ees.elsevier.com/knosys. From the popup menu "Choose Article Type" select "KEIoT" when you submit a contribution. Authors should specify in the cover letter of their submissions that the paper is to be contributed in the special issue, and that it has not submitted or published before and original in its contents. Contributions not from the mentioned conference are also welcomed. Papers from conference proceedings should be submitted as at least, more than 35% update compare to the published version. It is recommended that a submission does not exceed 20 pages and submission instructions for authors can be found at http://www.elsevier.com/wps/find/journaldescription.cws_home/525448/authorinstructions.