Clarifying Trust in Social Internet of Things

Abstract

A social approach can be exploited for the Internet of Things (IoT) to manage a large number of connected objects. These objects operate as autonomous agents to request and provide information and services to users. Establishing trustworthy relationships among the objects greatly improves the effectiveness of node interaction in the social IoT and helps nodes overcome perceptions of uncertainty and risk. However, there are limitations in the existing trust models. In this paper, a comprehensive model of trust is proposed that is tailored to the social IoT. The model includes ingredients such as trustor, trustee, goal, trustworthiness evaluation, decision, action, result, and context. Building on this trust model, we clarify the concept of trust in the social IoT in five aspects such as (1) mutuality of trustor and trustee, (2) inferential transfer of trust, (3) transitivity of trust, (4) trustworthiness update, and (5) trustworthiness affected by dynamic environment. With network connectivities that are from real-world social networks, a series of simulations are conducted to evaluate the performance of the social IoT operated with the proposed trust model. An experimental IoT network is used to further validate the proposed trust model.Comment: 14 pages, 16 figures. Submitted to IEEE Transactions on Knowledge and Data Engineerin

Improving IoT Technology Adoption through Improving Consumer Trust

“…The success of the IoT essentially depends on the consumer perceptions about IoT products' security and the level of their trust [2]. Moreover, good participation is usually related to a high enough level of trust [8]. In the global rush to promote IoT technologies, the industry is lagging behind on investing in consumer trust.…”

“…Nonetheless, the consumer's adoption is greatly dependent on establishing their trust. Most previous research on this concept has concentrated on the technical issues of IoT usage but neglected the IoT users and their perceptions about the technology [8,9]. Consequently, a deep understanding of the factors that lead to customers' trust in IoT-enabled products and services could help developers to implement efficient and widely adopted IoT services [10].…”

T‐D2D: A trust model for service offloading in device‐to‐device communication

“…Beside the aforementioned areas, several works proposed security solutions specially designed for the IoT or D2D context with regard to confidentiality, data reliability, privacy, access control, and authentication, which are mostly surveyed in previous studies. [22][23][24][25][26][27][28][29][30][31] More related to our paper, some other survey papers review existing works focusing especially on trust models in the IoT 6,9,10,[32][33][34] or D2D 11,35,36 context. In the following, we review these works, regardless of whether they use or not social-related information.…”

“…For this reason, the proposed T-D2D uses an attenuating factor, denoted as , to reduce the impact of recommendations while the direct trust becomes rich over time. The attenuating factor is calculated using Equation (10).…”

“…Although a number of works has already proposed trust models to face with on-off or bad-mouthing attacks, these works are not capable to detect light or dispersed on-off conducts especially when dishonest recommenders exist in the environment. 6,[9][10][11] To be more explicit, most of the existing works with emphasis on on-off attacks attempt to increase the impact of misbehavior in trust calculation, avoiding an attacker to compensate its misbehavior with normal conducts. 12,13 However, these works are not capable to detect light attacks with relatively more significant normal conducts than misbehavior.…”