Trust Management Model based on Fuzzy Approach for Ubiquitous Computing

Trust Management Model based on Fuzzy Approach for Ubiquitous Computing

Nalini A. Mhetre, Arvind V. Deshpande, Parikshit Narendra Mahalle
Copyright: © 2016 |Pages: 14
DOI: 10.4018/IJACI.2016070102
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Abstract

The current state of ubiquitous computing has been greatly influenced by emerging networking developments like Internet of Things (IoT), Future Internet etc. Adequate trust management is crucial to provide security. The entities involved in communication must be trusted for specific purposes depending on their role. Using trust model, devices can run trust computations and guide their behaviors. To this effect, a method is needed to evaluate the level of trust between devices. Trust models investigated so far discusses that devices face problems when communicating as transforming trust relationships from real to virtual world requires the negotiation of trust based on the security properties of devices. However, these models are developed in limited devices. This paper proposes a distributed trust model for device-to-device communication in ubiquitous computing. Mathematical model based on fuzzy rules to establish trust is presented. Fuzzy simulation of the model is presented to validate the findings. Simulation results show that proposed model calculates fuzzy trust values reliably.
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Introduction

In near future, the internet is going to make the world where physical things would consistently be incorporated into data systems which would provide smart services to users. These interconnected things, for example, sensors or portable gadgets would generate and gather volumes of information which can be further processed to discover helpful data to strengthen intelligent and ubiquitous services (Yan, 2014). Ubiquitous applications and services have been established and are spreading into the market which covers a variety of domains.

Trust management plays an important role in ubiquitous communication. It helps in decision making and to identify risks. Trust relationships occur naturally in many diverse contexts in open computing environments such as collaborative systems, e-commerce, interpersonal interactions, social networks, and semantic sensor web. The reputation of an entity is a derived measure from direct or indirect knowledge of earlier interactions of entities which are used to evaluate the level of trust put into an entity. Distributed trust decision making takes into account trust in the entities such as the information source, the processors of information, the elements of the communications network across which the information is transmitted, etc. (Cho, 2011). The trust is an inherent issue in ubiquitous computing environments as it impacts large-scale distributed systems (Ranganathan, 2004). Trust is complex notion with respect to the qualitative performance requirements of communication systems. There is no any universal concept of trust that is applicable to all domains. As per Webster’s dictionary, trust is assured reliance on the character, ability, strength or truth of someone or something or one in which confidence is placed (Webster’s Dictionary). It could be reasonable to say that trust is a measure of the degree of confidence one has on something and thus trust values are better described by the degree stating the confidence someone has on someone else doing an action, or it is a measure someone has in someone else. The computation of this measure is trust computation. In the context of this paper, trust is considered as a multi-valued notion and hence multi-valued logic i.e. fuzzy logic is used to deal with various degrees of trust. With modern forms of communication, the trust may be derived from on demand conditions and in a distributed way. Considering device level requirement, Trust management in ubiquitous computing for the security of devices is left unaddressed. This paper presents a generic trust model which computes trust score using fuzzy rule base and addresses above requirements in ubiquitous computing. This model is simulated using fuzzy inference system and validated with a mathematical model.

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