The Internet of Things (IoT) aims to enable a scenario where smart objects, inserted into information networks, supply smart services for human beings. The introduction of edge computing in IoT can reduce the decision-making latency, save bandwidth resources, and expand the cloud services to be allocated at the network’s edge. However, edge-based IoT systems currently face challenges in their decentralized trust management. Trust management is essential to obtain reliable mining and data fusion, improved user privacy and data security, and provisioning of services with context-awareness. In this survey, we first examine the edge-based IoT architectures currently reported in the literature. Then a complete review of trust requirements in edge-based IoT systems is produced. Also, we discuss about blockchain as a solution to solve several trust problems in IoT and analyze in detail the correlation between blockchain and edge computing. Finally, we provide a detailed analysis of performance aspects of trusted edge-based IoT systems and recommend promising research directions.

The introduction of the IoT technology and its pervasive penetration in our daily life implies that IoT smart objects can often participate in social events, and in this case their paradigm of interaction is commonly denoted as social IoT (SIoT). To make reliable transactions into a SIoT scenario, in this article, we introduce a multi-agent SIoT architecture, which integrates a reputation system based on a clustering of the SOs. In our framework, when a SO looks for a resource and detects a reliable partner having that resource, then the two SOs can interact to make a transaction, and at the end of the transaction each of them provides a feedback about the partner to the local reputation system of the edge server, to update the SOs’ reputation scores. Moreover, each edge server sends these reputation scores to the cloud, which updates the associated reputation values of the SOs, deriving from experiences coming from all the edge domains. This architecture provides a given object, moving from an edge domain to another one, with the possibility to have an updated value of its reputation, represented by the value stored in the cloud. We have validated our approach by a campaign of simulations, whose results seem particularly promising.

Learning software agents are able to assist Cloud providers in taking decisions about resource management at any level, as they are able to collect knowledge and improve their performances over time by means of learning strategies. On the other hand Cloud Federations allow providers to share computational infrastructures in order to build a distributed, interoperable multi-cloud context. In this work we present an evolutionary approach based on agent cloning, i.e. a mechanism of agent reproduction allowing providers to substitute an "unsatisfactory" agent acting in a "cloud context" with a clone of an existing agent having a suitable knowledge and a good reputation in the multi-cloud context. By this approach, cloud agents performances can be improved because they are substituted with agent clones that have shown a better behaviour.