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

https://doi.org/10.1016/j.jnca.2012.12.004Get rights and content

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.

Introduction

IoT brings great changes to our traditional thinking mode (Atzori et al., 2010) and forms a closed loop including context sensing, information processing and feedback control to the physical world, together with building the information bridge between things and things, things and people, and people and people, and finally generates a new kind of intelligent network. Compared with the Wireless Sensor Network (WSN), the sensing area of IoT is more extensive, and focuses more on people's daily lives and working environment. Therefore, it is not possible to deploy large number of sensor nodes like WSN to achieve the coverage of target area.

Interaction and communication between humans with smart mobile devices are a new trend of development in Internet of Things (IoT) (Gao et al., 2012). Many mobile devices become more and more powerful, such as iPhones and iPads. Different types of micro-sensor devices can be embedded and obtain information interested by users. This awareness information can bring us great convenience in daily life by its rational and effective use. For example, Alice wants to obtain the context information of the target region sometime (such as environmental information, traffic conditions), and provider Bob is currently in this area. So, our research goal is to establish interaction between them, and let Bob provide various services for Alice. In addition, some characteristics of human will inevitably bring new challenges to IoT in the following aspects:

  • Humans are not only the consumers of information, but also the participants. However, new awareness nodes, human's mobility, sociality and complexity in space and time will bring new technical challenges to the awareness and transmission of data. Moreover, human has some social natures (Gonzalez et al., 2009); their movement and activity pattern are not aimless and chaotic when they are engaged in social activities.

  • In the past studies (Campbell et al., 2008, Pan et al., 2005, Long and Huang, 2006, Lane et al., 2010, Boyd and Ellison, 2007), it is supposed that humans could interact with each other as long as their communication coverage range is reachable, without considering the trust problem. However, in the actual situation, a trust relationship exists between them, making people only respond to service requests from familiar nodes, but refuse strangers.

  • The emergence of smart mobile devices will greatly expand the scope of human communication. It has broken the constraints of communication in traditional networks. The communication range will be increased dramatically, and even any two nodes can interact with each other.

Therefore, the concept of mobile-aware computing based on the social relations cognitive model in IoT (An et al., 2011a) was proposed. It includes the following steps: firstly, using a variety of smart devices carried by mobile nodes,1 the virtual social network is formed. These devices can realize the mapping of the virtual society to the physical world with social network theory. Then, we can establish the trusted transmission chain for service requests by means of the trust relationship and social attributes of mobile-aware nodes, discover and choose appropriate candidate nodes which can provide the mobile-aware services in the target area.

As aforementioned, the completion of mobile-aware service needs initiators and providers. We know that the services mainly rely on the social attributes of nodes, whose essence is the evolution of the social relations between mobile nodes. By successfully quantifying the social relations of mobile nodes from physical and social dimensions, the communities can be constructed so as to further establish the trusted chain. The overall goal of service is to improve the real-time performance and reliability of the mobile awareness, and overcome the limitations in traditional network framework. Ultimately, it will be used to solve the problem of awareness hole in sparse network and improve the quality of mobile-aware service in IoT.

All the above points are rarely involved in past studies, so research needs to be conducted by new approaches. To sum up, the main contributions in this paper include:

  • This paper introduces a new concept, which is used to guide the completion of mobile-aware service, and summarizes the different social characteristics of mobile nodes in mobile-awareness of IoT, such as sociality, complexity, and so on.

  • This paper proposes the social relations cognitive model and defines the various Decision Factors (DF).

  • This paper considers the dynamic changes of the social relations between mobile nodes, and then uses the information entropy and rough sets method to study the weight distribution of social relations. The final experimental results prove the validity of the model.

The remainder of the paper is organized as follows: Section 2 reviews and summarizes the existing related work in mobile awareness. Section 3 introduces the system framework of social relations cognitive model, and has an in-depth study of social relations of mobile nodes. Section 4 is a detailed discussion of the modeling process. In Section 5, the feasibility and effectiveness of the cognitive model are analyzed by some experiments. Finally, Section 6 summarizes this article and proposes some future research plans.

Section snippets

Background

The concept of IoT is formally proposed by the International Telecommunication Union (ITU) (Atzori et al., 2010), and it is a conclusion and extension of the Pervasive Computing, Cyber Physical System (CPS), and Machine to Machine (M2M) in the macro sense. Presently, studies related to mobile awareness in IoT include the following aspects.

Analysis of mobile nodes social relations characteristic

Cognitive modeling of social relations belongs to social computing, which is a discipline combined with computer technology and sociology. Social computing uses computer technology to study the laws of society, and solves problems through the cooperation and communication between nodes. In the scenario of mobile awareness, the proposed service is mainly completed by mobile nodes, the network which is composed by these nodes is a complex network, and maintaining the topology of complex network

Calculation of the DF

The main goal of the cognitive model is to quantify the social relations reasonably. This paper considers a variety of elements that impact the social relations, and introduces L, I, S and F to depict the complexity, transitivity, uncertainties and other features of social relations from different aspects.

Definition 1

the social relations V (A, B) of nodes A and B (A,BN) can be defined as

V(A,B)=w1L(A,B)+w2I(A,B)+w3S(A,B)+w4(A,B)st.0wi1,i=14wi=1where L, I, S and F represent the different types of DF. w

Experimental results and analysis

The experiment was completed by combining the Ucinet with the prototype system developed by our research group. As shown in Fig. 5, the system can be divided into server-side and mobile-side. Server-side is responsible for collecting real-time kinds of information which are uploaded by the mobile terminal, mining and analyzing the social relations between mobile nodes, and realizing the related algorithms in this paper. Mobile-side uses the Android operating system to realize the automatic

Conclusion

The paper first analyzed the social elements affecting social relations between mobile nodes, and extracted different factors such as L, I, S and F, so as to quantify the social relations in valid and reasonable ways; secondly, through the introduction of rough sets and information entropy theory, we researched the different attributes of mobile nodes in depth, mined the variation regulars of their social attributes, and computed the weights of different attributes dynamically; finally, the

Acknowledgment

This work is supported by the National Natural Science Foundation of China (Nos. 60873071 and 91018011), the Important Projects of the National Science and Technology (No. 2012ZX03002001), and the Nation 863 Project (No. 2008AA01Z410).

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