Research on social relations cognitive model of mobile nodes in Internet of Things
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,B∈N) can be defined as
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).
References (32)
- et al.
The Internet of Things: a survey
Computer Networks.
(2010) - et al.
M-Dimension: multi-characteristics based routing protocol in human associated delay-tolerant networks with improved performance over one dimensional classic models
Journal of Network and Computer Applications
(2012) - et al.
Analysis of ratings on trust inference in open environments
Performance Evaluation
(2008) Rough set approach to knowledge-based decision support
European Journal of Operational Research
(1997)- et al.
Dynamical Shannon entropy and information Tsallis entropy in complex systems
Physica A-Statistical Mechanics and Its Applications.
(2004) - et al.
An algorithm to discover service nodes for mobility-aware in the Internet of Things
Journal of Xi'an Jiaotong University
(2011) - An J, Gui X, Zhang W, Jiang J. Nodes Social Relations Cognition for Mobility-Aware in the Internet of Things. In:...
- et al.
Location-based services: back to the future
IEEE Pervasive Computing
(2008) - et al.
Social network sites: definition, history, and scholarship
Journal of Computer-Mediated Communication
(2007) - et al.
The rise of people-centric sensing
IEEE Internet Computing
(2008)
Inferring friendship network structure by using mobile phone data
Proceedings of the National Academy of Sciences
Understanding individual human mobility patterns
Nature
Where do social relations come from? A study of personal networks in the Toulouse area of France
Social Networks
A survey of mobile phone sensing
Communications Magazine, IEEE
Computational social science
Science
Cited by (52)
STSIR: An individual-group game-based model for disclosing virus spread in Social Internet of Things
2023, Journal of Network and Computer ApplicationsGarbage in garbage out: The precarious link between IoT and blockchain in food supply chains
2022, Journal of Industrial Information IntegrationCitation Excerpt :IoT is a cornerstone in the emerging Industry 4.0 landscape thanks to its capability to enable autonomous data collection and sharing with minimal human intervention [10,11]. However, multiple survey studies into IoT [12-16] indicate that while the IoT technology promises great opportunities for promoting industrial automation and intelligence across various domains, including cities and homes, environment monitoring, health, energy, and business, it inherently comes with a multitude of challenges due to their architectural styles and computational complexities. Data provenance and integrity in the IoT-run environment is one of the most concerning challenges surrounding IoT [17-19].
Manufacturing service composition model based on synergy effect: A social network analysis approach
2018, Applied Soft Computing JournalCitation Excerpt :When L(i) = L(j), it means a co-location relationship. An et al. [42] gave the definition and calculation of distance factor through the similarity function of position information. Distance is a complex concept containing geographical, cultural and social elements.
Secure IoT structural design for smart homes
2018, Smart Cities Cybersecurity and PrivacyDesign and Implement Technique for Security of IoT-5G System using RSA Algorithm
2024, International Journal of Intelligent Systems and Applications in Engineering