Research on social relations cognitive model of mobile nodes in Internet of Things

Authors:
Jian An;Xiaolin Gui;Wendong Zhang;Jinhua Jiang;Jianwei Yang
Affiliations:
Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China and The Key Laboratory of Computer Network, Xi'an 710049, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China and The Key Laboratory of Computer Network, Xi'an 710049, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China and The Key Laboratory of Computer Network, Xi'an 710049, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China and The Key Laboratory of Computer Network, Xi'an 710049, China;Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China and The Key Laboratory of Computer Network, Xi'an 710049, China
Venue:
Journal of Network and Computer Applications
Year:
2013

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Abstract

Interaction and communication between humans with smart mobile devices are a new trend of development in Internet of Things (IoT). With the powerful sensing capability of smart device and human mobility, various services could be provided by building a trusted chain between service requesters and suppliers. The cognition of social relations between mobile nodes is the basis of final mobile-aware services. It involves many decision factors, such as time, space and activity patterns. Using social network theory, a new cognitive model for social relations of mobile nodes in IoT is proposed. Firstly, nodes' social relations are reasoned and quantified from multiple perspectives based on the summary of social characteristics of mobile nodes and the definition of different decision factors. Then the location factor, interconnection factor, service evaluation factor and feedback aggregation factor are defined to solve the shortcomings in existing quantitative models. Finally, the weight distribution is set up by information entropy and rough set theory for these decision factors; it can overcome the shortage of traditional methods, in which the weight is set up by subjective ways and hence their dynamic adaptability is poor. We compare our cognitive model to existing models using MIT dataset by defining a variety of test indicators, such as network overall density (NOD), the degree center potential (DCP), the network distribution index (EI), etc. Simulation results show that, the cognitive model has better internal structure and significant validity in network analysis, and thus can provide mobile-aware service effectively in dynamic environment.