## 3 Related Work
[26] defined four types of relationships between industrial device assets: _parental_ (component-of), same functionality, replacement, and cooperation; friend discovery, however, was based on a purely syntactic match of attributes. The analysis in [30] was inspired by the literature on human SNS, instead, and resulted in an ontology allowing objects to manage their policies, friends and reputation. The ontology model developed in [31] included social relationships among events, people and objects in IoT environments. The _SIoTPredict_ framework [32] predicted future relationships among devices in a dynamic and heterogeneous SloT network with a Bayesian nonparametric learning model by exploiting the trajectories of IoT objects and their corresponding location. Trust management is one of the most relevant issues in the SloT [33; 34]. In this work, the focus is on application-level trust, particularly in social object relationships. Trust metrics can be retrieved from friends (recommendation), reliable intermediaries (reputation) or direct knowledge [5; 35]. [PERSON]. [36] introduced a _subjective_ and an _objective_ (i.e., shared) model, exploiting techniques mutated from peer-to-peer networks. The collaborative cognitive radio framework in [37] leveraged the SloT relationship types described above in [29] to improve both the reach of channel status queries and the trustworthiness of information through weights depending on the degree of friendship. A refinement was proposed by [38], where trust was defined as a function of reputation on past transactions, relationship type and energy status. Reputation combined direct and friend recommendation scores, while the relationship type between the two devices was derived from a correlation of trustworthiness with reference to mutual friends. Similarly, ref. [39] composed a _honesty_model out of spatial (object proximity), temporal (frequency and duration of interactions), relationship and credibility (cooperativeness and penalties) metrics. The credibility aspect is the closest to the proposal in this work. Ref. [40] adopted a similar approach, albeit in that case honesty referred to the absence of malicious behaviors. For this purpose, a trust propagation and aggregation scheme was introduced, conceptually similar to the objective trust model in [36]. The above works, however, aimed only at improving transaction success and did not consider dynamic social relationships. That issue was tackled by [41], exploiting Bayesian belief propagation networks. Similarly, ref. [42] proposed a hybrid trust management framework for SIoT networks. The _Probabilistic Neighbourhood Overlap_ method estimated tie strengths between nodes in a social graph generated by combining the social networks of both devices and their owners. In a SIoT network, malicious connections should be kept to a minimum. A node behaviors analysis module based on machine learning was exploited in the trust assessment process by [43], enabling not only the discovery of malicious node connections but also the identification of the kind of performed attack in order to prevent them in the future. The _ConTrust_[44] method proposed a context-dependent trust management technique for selecting a trustworthy service provider and allocating jobs in a SIoT environment. The trust model fused trust theory with social networks. It can be noted that all the above works focus either on service discovery and composition or on dynamic relationship and trust management, disregarding or trivially simulating the other aspect. Table 1 summarizes relevant features of some of the previously mentioned frameworks with respect to the approach proposed here.