Advances on data, information, and knowledge in the internet of things

Abstract

The internet of things (IoT) is a dynamic smart networked system with connected sensors, processors, and actuators that are designed to sense and interact with the physical world. Achieving the benefits of IoT requires the fusion and management of large scale heterogeneous data using knowledge-based decision systems and the integration of different technologies. Many challenges exist, including identifying things correctly and safely in IoT, modeling and integrating variety and volumes of data, acquiring knowledge automatically from the big data, and ensuring security and privacy, etc. This theme issue aims to explore these challenges through papers which address: (1) data models and big data and information processing in IoT; (2) how collaboration and interaction in IoT can be facilitated leveraging the best practices developed in social computing, social and community intelligence, and wireless sensor networks; and (3) the security and privacy issues in IoT and mobile computing. This issue is in collaboration with the international workshop on Identification, Information, and Knowledge in the Internet of Things (IIKI2013) held in October 2014, Beijing, China (http://ireg.bnu.edu.cn/IIKI2014/). Following a strict review process, we accept seventeen papers for this theme issue finally. Each of the papers was peer-reviewed by at least two experts in the field. In the following, we provide a brief introduction to each paper. Data dissemination is a challenging issue for mobile social activities in IoT. The paper titled ‘‘strengthen nodal cooperation for data dissemination in mobile social networks’’ by Guoliang Liu et al. proposes an incentive scheme to stimulate the users in a network to be more cooperative for data dissemination by considering selfishness factors of the users. Each node’s ability to fetch messages of a specific kind of interest is evaluated, and every single user can rent other nodes to help with obtaining the interested messages by paying credits. Extensive simulations on real traces are implemented to evaluate the proposed incentive scheme. The paper ‘‘self-Universum support vector machine’’ (SUSVM) by Dalian Liu et al. provides a theoretical explanation for an improved twin support vector machine based on the concept of Universum, which takes the positive class and negative class as Universum separately for the binary classification problem. Furthermore, SUSVM is improved by a special formulate of linear programming, which leads to the better generalization performance and less computational time. Data management and information organization play key roles in IoT realization. Yunchuan Sun et al. propose an extensible and active semantic information organization model for IoT in the paper titled ‘‘An extensible and active semantic model of information organizing for the Internet of Things.’’ The proposed model is well defined and involves two layers: the object layer and the event layer, which are both discussed in detail including the Y. Sun (&) R. Bie Beijing Normal University, Beijing, China e-mail: yunch@bnu.edu.cn