arXiv subjects

Mario Marchese

Publications and source records attributed to Mario Marchese.

6 recordsLinked to original sources

arXiv2024

Self-Supervised Path Planning in UAV-aided Wireless Networks based on Active Inference

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.

cs.RO↗

arXiv2020

Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles

The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness in a network of intelligent agents. A specific collective self awareness functionality is considered, namely, agent centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent. Data driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent, is used to allow the agents in the network to each synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithms reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Real data sets are also used acquired by autonomous vehicles performing different tasks in a controlled environment.

cs.LG↗

arXiv2020

Dynamic Bayesian Approach for decision-making in Ego-Things

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.

cs.LG↗

arXiv2019

Exploiting Satellite Broadcast despite HTTPS

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

cs.CR↗

arXiv2017

Enhancing Contention Resolution ALOHA using Combining Techniques

Recently, random access protocols have acquired a new wave of interest, not only from the satellite communication community, but also from researchers active in fields like Internet of Things and machine-to-machine. Asynchronous (slot- and frame-wise) ALOHA-like random access protocols, are very attractive for such applications, enabling low complexity transmitters and avoiding time synchronization requirements. Evolutions of ALOHA employ time diversity through proactive replication of packets, but the time diversity is not fully exploited at the receiver. Combining techniques, as selection combining and maximal-ratio combining, are beneficial and are adopted in the enhanced contention resolution ALOHA (ECRA) scheme, presented here. A tight approximation of the packet loss rate for asynchronous random access, including ECRA, well suited for the low channel load region is derived. Finally, ECRA is evaluated in terms of spectral efficiency, throughput and packet loss rate in comparison with recent protocols, showing that it is able to largely outperform both slotted synchronous and asynchronous schemes.

cs.IT↗

arXiv2016

Detection and Combining Techniques for Asynchronous Random Access with Time Diversity

Asynchronous random access (RA) protocols are particularly attractive for their simplicity and avoidance of tight synchronization requirements. Recent enhancements have shown that the use of successive interference cancellation (SIC) can largely boost the performance of these schemes. A further step forward in the performance can be attained when diversity combining techniques are applied. In order to enable combining, the detection and association of the packets to their transmitters has to be done prior to decoding. We present a solution to this problem, that articulates into two phases. Non-coherent soft-correlation as well as interference-aware soft-correlation are used for packet detection. We evaluate the detection capabilities of both solutions via numerical simulations. We also evaluate numerically the spectral efficiency achieved by the proposed approach, highlighting its benefits.

cs.IT↗