Biblio

Internet of Things (IoT) and 5G are emerging technologies that prompt a mobile service platform capable of provisioning billions of communication devices which enable ubiquitous computing and ambient intelligence. These novel approaches are guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. To achieve these limitations, ETSI has introduced the paradigm of Multi-Access Edge Computing (MEC) for creating efficient data processing architecture extending the cloud computing capabilities in the Radio Access Network (RAN). Despite the gained enhancements to the mobile network, MEC is subjected to security challenges raised from the heterogeneity of IoT services, intricacies in integrating virtualization technologies, and maintaining the performance guarantees of the mobile networks (i.e. 5G). In this paper, we are identifying the probable threat vectors in a typical MEC deployment scenario that comply with the ETSI standards. We analyse the identified threat vectors and propose solutions to mitigate them.

The internet-of-things (IoT) consists of embedded devices and their networks of communication as they form decentralized frameworks of ubiquitous computing services. Within such decentralized systems the potential for malicious actors to impact the system is significant, with far-reaching consequences. Hence this work addresses the challenge of providing IoT systems engineers with a framework to elicit privacy and security design considerations, specifically for indoor adaptive smart environments. It introduces a new ambient intelligence indoor adaptive environment framework (CORE) which leverages multiple forms of data, and aims to elicit the privacy and security needs of this representative system. This contributes both a new adaptive IoT framework, but also an approach to systematically derive privacy and security design requirements via a combined and modified OCTAVE-Allegro and Privacy-by-Design methodology. This process also informs the future developments and evaluations of the CORE system, toward engineering more secure and private IoT systems.

In this paper we propose a mechanism of prediction of domestic human activity in a smart home context. We use those predictions to adapt the behavior of home appliances whose impact on the environment is delayed (for example the heating). The behaviors of appliances are built by a reinforcement learning mechanism. We compare the behavior built by the learning approach with both a merely reactive behavior and a state-remanent behavior.