## 7 Conclusions  
This paper has introduced a novel comprehensive framework combining the Semantic Web of Things and the Social Internet of Things to enable dynamic autonomous management of smart object networks in Edge Computing. While basically general-purpose, the approach has been illustrated by means of a smart mobility case study. Key features of the proposal include the following: a decentralized collaborative service discovery protocol, grounded on Linked Data Notifications on top of LDP-CoAP and on non-standard inference services in Description Logics over service descriptions in Web Ontology Language; a dynamic social relationship management protocol, based on the friends' usefulness as a service and device recommender. Experimental evaluation has validated the sustainability and efficiency of the proposal for object networks of various sizes and topologies, extending the preliminary results obtained in previous works about processing time and memory consumption of the service discovery procedure [7; 9] and non-standard reasoning algorithms [17] on resource-constrained IoT devices. The dynamic relationship management based on a close SWoT-SIoT integration allows devices to increase their number of friends even in small or poorly connected networks, leading to higher request satisfaction rates and reduced network load, since the most effective service/resource providers are queried directly. As the network scale grows, the framework remains able to reach steady-state conditions fairly quickly, with a balanced number of friends for each de  
Figure 13: Comparison of dynamic (this paper) vs. static [9] relationship management. vice, leading to consistently high request satisfaction and low network load. Experimental comparisons have shown improvements with reference to static relationship management in [9]: in random networks, friendships are 50% to 250% higher, request satisfaction is 100% to 150% higher, and the number of activated services is 100% to 150% higer, all with a 100% lower amount of exchanged data; in Barabasi-Albert networks, established friendships are 100% to 400% higher, like values are 10% higher, while the number of activated services is 10% to 50% higher in small networks and decreases up to \(-\)50% in large networks, implying that the discovery of the best services is more effective, as demonstrated also by \(-\)60% exchanged data; in strongly connected (Dorogotsev-Mendes) networks, friendships are 50% to 150% higher, like values are 2% higher on average, the number of activated services increases up to 30% in small networks (implying more matches are found) and decreases up to \(-\)55% in medium- and large-size networks (implying requests are satisfied equally well or better with fewer services, as the high number of available services allows a more effective selection), with a 15% to 30% decrease in exchanged data. Future work includes expanding on the type of social relationships in order to diversify supported interaction patterns. Autonomic management of weights in the model described in Section 4.3 is also under early investigation, based on discovery performance as well as computational and network load. As the collaborative discovery protocol represents a form of dialogue among participating social objects, ref. [53] emphasizes that this aspect of the proposal is under scrutiny by means of a more explicit formalization exploiting computational argumentation frameworks. From an infrastructure standpoint, integration into the cloud-to-thing continuum can enable leveraging a large-scale distributed architecture for collaborative tasks such as environmental monitoring and federated learning [54]. Finally, increasing the Technology Readiness Level (TRL) of the proposal will be achieved through practical implementations in real-world use cases spanning from smart mobility to building management systems, smart grid, precision agriculture, and last-mile logistics. Conceptualization, M.R., E.D.S. and G.L. ; methodology, F.S. and G.L. ; software, G.L. and G.C. ; validation, C.F. and A.P.