# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 1 Introduction  
As the 4 th industrial revolution and the development of future social interconnection technology, internet of things (IoT), following the internet, brings tremendous changes in people's lives [1; 2; 3]. With the continuous intelligence of hardware devices and the maturity of edge computing technology, IoT will have greater scalability [4; 5]. Integrating the concept of socialization into the IoT system, the social internet of things (SIoT) [6; 7], as a new service paradigm, improves the interoperability among IoT objects and enhances the service efficiency in industry applications. The objects will establish the relationship with each other and collaborate on services without human intervention, which make the objects more autonomous in the process of IoT service. Moreover, the structure of SIoT boosts the network navigability and scalability, which enhances the service discovery and resource acquisition. Currently, the SIoT paradigm has been widely applied in various application scenarios, such as vehicular social networks [8; 9; 10; 11], mobile crowdsensing [12; 13; 14; 15; 16], data-driven smart city [17; 18; 19; 20], etc. In SIoT, each object (e.g., intelligent sensors, smartphone, and video camera) can be a service requester (SR) or service provider (SP), according to its own motivations. The SR will broadcast the service request, such as collecting sensing tasks or urban noise data, and provide some rewards to the SP. On the other hand, the SP will provide the specific service, such as sharing information or computation resources to the SR, to receive some rewards from the SR. Each IoT object can autonomously determine which service to initiate and which object to delegate within a given set of candidate objects. By this method, the service discovery, interaction, and execution will be optimally implemented. Although the SIoT paradigm will improve the quality of services to a certain extent, it also may suffer from various types of attacks due to the presence of malicious objects [21]. Some malicious objects may launch bad-mouthing or cheating attacks to affect the decision process of service delegation [22]. To address this issue, in recent years, some works in the literature have presented various trust models to solve the problems of trust establishment and relationship maintenance among objects in SIoT [23; 24]. Trust is a complex and comprehensive concept in SIoT [25; 26]. Specifically, trust not only reflects the security and reliability at the IoT system level, but also reflects the degree of cooperation between two IoT objects when establishing an interactive relationship. The establishment of trust will stimulate cooperation and improve security in the process of service [27; 28; 29]. [PERSON] and [PERSON] introduced a systematic socio-cognitive trust theory [27]. They proposed a layered model for trust, which consists of five basic ingredients: trustor, trustee, task, goal, and context. They also proposed and analyzed the important characteristics, including integrated, socio-cognitive, multi-factor and multi-dimensional, dynamic, non-prescriptive, etc. The proposed trust theory can be used as a theoretical foundation for analyzing the trust issue of SIoT. [PERSON] et al. combined the fuzzy logic method to solve the trustworthiness convergence issue and proposed a lightweight mechanism for service discovery based on directed acyclic graph (DAG) [28]. On this basis, [PERSON] et al. proposed a trustworthiness inference framework which combines a kernel-based nonlinear multivariate grey prediction model and fuzzy logic method to quantify the trust [29]. [PERSON] et al. presented a classified catalog of friendliness and trust in SIoT. They described the key ingredients and challenges of friendliness- and trust-based approaches, which contributes to the analysis of the effectiveness of the trust model [30]. [PERSON] and [PERSON] proposed a hybrid trust management framework based on probabilistic neighborhood overlap, which considers the resource-constrained IoT devices [31].