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Ebrahimi M, Tadayon M H, Sayad Haghighi M. Trust Management in Internet of Things: Review, Analysis and Establishment of Evaluation Criteria. JSDP. 2021; 18 (2) :3-28
URL: http://jsdp.rcisp.ac.ir/article-1-1123-en.html
Iran Telecommunication Research Cente
Abstract:   (1112 Views)
In the complex Internet of Things (IoT) paradigm that things interact with each other as well as with human beings, one approach is to implement trust management systems in order to provide security for smart network applications. Trust, in general, overlaps with concepts such as privacy, security, and reliability. However, the high number of objects in IoT, along with its dynamic nature and existence of malicious entities, make IoT trust management quite challenging. These attributes rule out the possibility of using traditional best practices for IoT networks. Trust management algorithms have been implemented for a variety of applications in IoT environments. These algorithms are usually utilized to enhance the quality of received services in the presence of malicious entities. Such algorithms and methods have been proposed to secure IoT networks in different contexts, including traffic routing, smart cities, vehicular ad-hoc networks, healthcare ecosystems, and object authentication. In this paper, first, different state of the art trust computation methods are numerically evaluated to estimate trust in a common testbed. Finding the best approach to assign a precise value to the trust level of an object is a crucial matter. Therefore, the principal parameters that make trust computation methods different are extracted and then, the existing trust calculation approaches built upon them are categorized. Type of relationship, direct trust, indirect trust, combination of trust values, trust updating process, data storage, and social relationships are considered as the parameters to analyze trust computation models with. Type of relationship between trustor and trustee can be different. Either of them can be object or human. Moreover, trust is usually a combination of direct experiences and recommenders’ feedback. There are different update methods too. Trust estimation can be updated after each transaction, a definite time interval, or both of them. Depending on the storage and accessibility of data, algorithms can be built to be centralized, decentralized or semi-centralized. Moreover, social parameters can be involved in trust assessment, which is the subject of trust management in Social IoT. After analyzing each of these parameters’ effect on trust assessment, in the next part of the article, trust-related attacks are studied. Every method that can make trust management models resistant to attacks is explained.  We introduce relevant attacks and their countermeasures in direct, indirect, and hybrid trust calculation algorithms. More importantly, we study the methods of trust model evaluation and the effect of limited resources on the performance of trust calculation algorithms. In short, we conduct a comparative survey in which trust-related IoT works are studied from four perspectives: (1) Trust calculation principles, (2) Attack resistance, (3) The effect of resource limitation on model performance, and (4) Trust management evaluation framework. Through this, we find the advantages and disadvantages of existing algorithms and make a measure for the evaluation of IoT trust management systems. We provide comparative tables to show the differences between IoT trust models. A major contribution of this paper is establishing quantitative metrics to assess trust estimation models and reveal their strengths and weaknesses under different conditions.
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Type of Study: Research | Subject: Paper
Received: 2020/03/10 | Accepted: 2021/03/8 | Published: 2021/10/8 | ePublished: 2021/10/8

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