## 3 Related Work  
The SIoT aims at extended device collaboration. _Service Oriented Architecture_ (_SOA_) is one of the dominant paradigms, where intelligent service/resource discovery is a core capability [5]. Most SIoT approaches for ranking services/resources with reference to a request/profile specification are based on (i) object social connectivity, (ii) object mobility patterns and/or (iii) preference similarity. This work exploits (i) and (iii), while (ii) is taken into account implicitly through dynamic relationship set-up and tear-down. In [18] a cosine similarity was computed combining metrics (ii) and (iii), improving results with reference to each single metric, although preferences and resources were described by means of a simplistic set of keywords, lacking proper semantics. Similarly, ref. [19] presented an automated service discovery process for mobile crowdsourcing applications. A large-scale IoT network was split into multiple virtual communities, grouping IoT devices that share common characteristics modeled using social relations. A Natural Language Processing  
Figure 1: Semantic Web of Things architecture for SIoT. (NLP) approach handled crowdsourcing textual requests and retrieved the list of IoT devices capable of accomplishing the requested tasks, although semantic processing was completely missing. Semantics-based approaches improve context-awareness as well as discovery capabilities in multi-agent systems [20]. In [21], a distributed OWL Knowledge Base (KB) management system has been exploited to enhance M2M social interactions in control networks. Unfortunately, supported inferences were very basic, limiting the applicability of the proposal. In [22], semantics-based situation detection and goal retrieval were exploited for matchmaking with task profiles for recommending activities to users, based on their current context. Nevertheless, social interactions occurred only between devices and users; furthermore, adopted rule-based reasoning could not retrieve approximate matches when exact ones did not exist. Likewise, ref. [23] focused on activities of daily living in smart homes, generating task-oriented recommendations for user's situational goals. A coastal monitoring system has been proposed in [24] leveraging the _Lysis_[25] SloT platform to virtualize physical IoT devices, supporting social relationships as well as semantic resource descriptions and queries in order to establish automatic device cooperation. A similar approach was adopted in [26] for Social Internet of Industrial Things scenarios. In both works, an ontology-based model describes social objects, but no formal framework is provided to exploit it for resource/service discovery. In [27], Near-Field Communication (NFC) technology-mediated social interactions between everyday objects and agents running on mobile devices. Ontology-based representations enabled both reactive and proactive behaviors by means of rule-based reasoning for planning toward situation-aware goals. Though interesting, the approach does not appear general enough to support a wide range of SloT scenarios. A cognitive reasoning-based framework for SloT services discovery in smart spaces was proposed in [28]. Unfortunately, the dynamic evolution of social relationships between smart object pairs was not exploited for service recommendation in the reasoning phase. Similarly, the LDP-CoAP framework in [9] provides a social collaborative protocol for resource discovery based on standard Semantic Web technologies, but relationships among smart objects are essentially static. Social connectivity management leverages the properties of things relationships. Several classifications exist in the literature. In [29] and subsequent works, _parental_ (same manufacturer), _co-location_ (same environment), _co-work_ (cooperation), _co-ownership_ (same owner) and _social_ (sporadic or frequent contact) were identified as basic object relationships. Similarly, ref.