IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613
Novel Model for Trust Management in Distributed IOT Manas Kumar Yogi1 Darapu Uma2 G.Vijay Kumar3 1,2,3 Assistant Professor 1,2,3 Department of Computer Science and Engineering 1,2,3 Pragati Engineering College, Surampalem, Andhra Pradesh, India Abstract— Trust in IOT is highly dependent on knowledge of internal state of the IOT which surrounds us. In distributed IOT internal states change rapidly and propagation of this state and their relevant information across the architecture of distributed IOT is challenging. In this model we propose a novel model for trust management. This model is applied to trust management for interaction between entities and also trust in the system from users view point. This model includes provision for finding reputation of entities which supply information from sensor storage. This paper will act as a quick guide to learn principle of trust management in distributed IOT architecture. Key words: trusted reputition, IoT, smart, distributed I. INTRODUCTION In modern age where smart devices have become as regular as basic human necessities, IoT provides a combination of services to us which were unimaginable and equally unconceivable just few years back. But now the reality is here. Pervasive computing is the backbone concept on which distributed IoT architecture is built and services are run with inherent smartness in the devices. User who are an participating entity in IoT feel the need of trust while interacting with data and service providers and also management of this trust level has to be optimized keeping in view attacks of various nature lurking in the IoT environment. The main goal of any protocol dealing with trust management is to provide near accurate trust assessment. We propose in this paper a trust management model to maximize the application performance operating in distributed IoT environment. We define both direct and indirect recommendations. Malicious nodes try to propagate distrust among the other interacting nodes there by weakening the service of smart devices. We venture the social relationships like ownership, friendship, community. The model harvests principles from already established models of trust which involve encounter based and activity based trust management protocols. smart devices which interact with each other can evaluate the behavior and rate the reputation after desired functionality is achieved by each device. II. PROPOSED MODEL Trust Management involves addressing the uncertainty of 2 aspects 1) Interaction between data providers 2) Interaction between service providers A. Uncertainty in Interaction Between Data Providers: Consider a scenario of distributed IOT where data providers A, B, C, D Denoted as supplies data to entity E1, E2, E3, E4 in regions. the following table gives information about trust level between the data providers and entities.
DP A
B
C
D
ENTITY E1 E2 E3 E4
đ?‘‡1đ??´ đ?‘‡1đ??ľ đ?‘‡1đ??ś đ?‘‡1đ??ˇ đ?‘‡2đ??´ đ?‘‡2đ??ľ đ?‘‡2đ??ś đ?‘‡2đ??ˇ đ?‘‡3đ??´ đ?‘‡3đ??ľ đ?‘‡3đ??ś đ?‘‡3đ??ˇ đ?‘‡4đ??´ đ?‘‡4đ??ľ đ?‘‡4đ??ś đ?‘‡4đ??ˇ Table 1: The trust level depends on crucial factors like reputation of data providers and recent user ratings. So we form an empirical formula like T=Rur 1.1 Wher R is reputation ur is user rating(recent) Depending on values of R,ur T value will vary. Reputation will be ranked from high to low values say on scale of 10-1 with 10 being highest value or highest reputation and 1 indicating least reputation. The next discussion is limited to calculation of user rating value. Time of user rating is important. We consider the top 20 user ratings into considerations and find the mean value of those ratings. We don’t consider the average of top 20 user ratings as there may be substantial time gap between the first and last user rating. So after obtaining values of R,ur we can represent their product as trust value of data provider. A copy of the table will be stored in each region each region will have a node which will store the table about trust information. If the trust value of any data provider changes over a period of time, the values will be updated in this node. The entities will just do a lookup operation for the trust values in the table present in the node and send a request appropriate to the context.a entity may send a request to a data provider in other region also in case, the trust value of data provider in its own region is less than data provider s in other region. B. Uncertainty in Interaction Between Service Providers: This aspect considers reputation among the service providers to decide service operations. A service provider can trust another service with good reputation. The concept of reputation is closely linked to that of trust; however, there is a clear and significant difference. Reputation is usually inspired by the past behaviors observed. Trust reflects the relying party’s subjective view of an entity’s trustworthiness, whereas reputation is a score which can be seen by the whole community. In our model we consider a concept of mutual rating of repute which can be applied to self rating also. Consider a situation in which service provider SP1 interacts with service provider SP2. We form a equation like: R(SP1)=R(SP2) + SR(SP1) 1.2 In above equation, R(SP1) indicates reputation of service provider 1 , R(SP2) indicates reputation of service provider 2 and SR(SP1) indicates self rating of reputation for service provider1.
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Novel Model for Trust Management in Distributed IOT (IJSRD/Vol. 4/Issue 05/2016/316)
Framing the reputation with fuzzy values may be less difficult but it leads to a degree of uncertainty .To reduce this uncertainty we can consider other factors like good mouthing and bad mouthing. These two factors counter self promotion to generate a high reputation by the concerned data provider. The reputation values of any service provider depends hugely on reliability, community interest honesty, cooperativeness. Any protocol which is implemented should be embedded with above mentioned factors for calculation of reputation value. III. FUTURE SCOPE The model we propose is generally setting of a skeletal trust management model for distributed IoT. The proposal is subjective and more precise mathematical model has to be developed. In case precision degree for uncertainty has to be obtained then the equations we have modelled are to be tested with reliable values obtained from a distributed IoT model. Our model serves as a readymade guide for small scale organisations which can extend this model to suit their organisational needs.
layer approach for efficient video dissemination in mobile multimedia IoT applications, Comput. Commun. 45 (0) (2014) 21–31. [8] J.P. Espada, V.G. Daz, R.G. Crespo, O.S. Martnez, B.P. G-Bustelo, J.M.C. Lovelle, Using extended web technologies to develop bluetooth multi-platform mobile applications for interact with smart things, Inf. Fusion 21 (0) (2014) 30–41. [9] J. An, X. Gui, W. Zhang, J. Jiang, J. Yang, Research on social relations cognitive model of mobile nodes in internet of things, J. Network Comput. Appl. 36 (2) (2013) 799–810. [10] T.-M. Gronli, P. Pourghomi, G. Ghinea, Towards NFC payments using a lightweight architecture for the web of things, Computing (2014).
IV. CONCLUSION Massive deployment with IoT devices will be successful only when we make interactions secure. This is possible only by enhancing the quality of trust among the participating entities. This paper considers the uncertainties while IoT interactions occur which try to reduce the trust level. Applying the principles of basic trust management in distributed environment along with reputation building factors for data providers and service providers we propose a model which can be used for further developing into a full scale robust protocol at each operating layer of distributed IoT architecture. REFERENCES [1] B. Niu, X. Zhu, H. Chi, H. Li, Privacy and authentication protocol for mobile rfid systems,Wireless Pers. Commun. 77 (3) (2014) 1713–1731. [2] Y.-S. Jeong, J. Lee, J.-B. Lee, J.-J. Jung, J. Park, An efficient and secure m-ips scheme of mobile devices for human-centric computing, J.Appl. Math. Special Issue 2014 (2014) 1–8. [3] J. Geng, X. Xiong, Research on mobile information access based on internet of things, Appl. Mech. Mater. 539 (2014) 460–463. [4] S. Kubler, K. Frmling, A. Buda, A standardized approach to deal with firewall and mobility policies in the iot, Pervasive MobileComput. (2014). [5] C. Perera, P. Jayaraman, A. Zaslavsky, D. Georgakopoulos, P.Christen, Mosden: An internet of things middleware for resource constrained mobile devices, in: Proceedings of the Annual Hawaii International Conference on System Sciences, Washington, DC,USA, 2014, pp. 1053–1062. [6] J. Montavont, D. Roth, T. Nol, Mobile {IPv6} in internet of things:analysis, experimentations and optimizations, Ad Hoc Netw. 14 (0) (2014) 15–25. [7] D. Rosario, Z. Zhao, A. Santos, T. Braun, E. Cerqueira, A beaconless opportunistic routing based on a cross-
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