Mario Marchese

Ali Krayani, Khalid Khan, Lucio Marcenaro et al.

This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world model to understand the environment and implicitly discover the optimizer's policy. UAV equipped with the world model can make real-time autonomous decisions and engage in online planning using active inference. During planning, UAV can score different policies based on the expected surprise, allowing it to choose among alternative futures. Additionally, UAV can anticipate the outcomes of its actions using the world model and assess the expected surprise in a self-supervised manner. Our method enables quicker adaptation to new situations and better performance than traditional RL, leading to broader generalizability.

Divya Kanapram, Damian Campo, Mohamad Baydoun et al.

This paper presents a novel approach to detect abnormalities in dynamic systems based on multisensory data and feature selection. The proposed method produces multiple inference models by considering several features of the observed data. This work facilitates the obtainment of the most precise features for predicting future instances and detecting abnormalities. Growing neural gas (GNG) is employed for clustering multisensory data into a set of nodes that provide a semantic interpretation of data and define local linear models for prediction purposes. Our method uses a Markov Jump particle filter (MJPF) for state estimation and abnormality detection. The proposed method can be used for selecting the optimal set features to be shared in networking operations such that state prediction, decision-making, and abnormality detection processes are favored. This work is evaluated by using a real dataset consisting of a moving vehicle performing some tasks in a controlled environment.

Nikos Fotiou, Vasilios A Siris, George C. Polyzos et al.

HTTPS enhances end-user privacy and is often preferred or enforced by over-the-top content providers, but renders inoperable all intermediate network functions operating above the transport layer, including caching, content/protocol optimization, and security filtering tools. These functions are crucial for the optimization of integrated satellite-terrestrial networks. Additionally, due to the use of end-to-end and per-session encryption keys, the advantages of a satellite's wide-area broadcasting capabilities are limited or even negated completely. This paper investigates two solutions for authorized TLS interception that involve TLS splitting. We present how these solutions can be incorporated into integrated satellite-terrestrial networks and we discuss their trade-offs in terms of deployment, performance, and privacy. Furthermore, we design a solution that leverages satellite broadcast transmission even in the presence of TLS (i.e. with the use of HTTPS) by exploiting application layer encryption in the path between the satellite terminal and the TLS server. Our findings indicate that even if no other operation than TLS splitting is performed, TLS handshake time, which involves roundtrips through possibly a Geosynchronous satellite, can be reduced by up to 94%. Moreover, by combining an application layer encryption solution with TLS splitting, broadcast transmissions can be exploited