arXiv subjects

Andrea Tassi

Publications and source records attributed to Andrea Tassi.

At least 19 recordsLinked to original sources

arXiv2025

Optimized Split Computing Framework for Edge and Core Devices

With mobile networks expected to support services with stringent requirements that ensure high-quality user experience, the ability to apply Feed-Forward Neural Network (FFNN) models to User Equipment (UE) use cases has become critical. Given that UEs have limited resources, running FFNNs directly on UEs is an intrinsically challenging problem. This letter proposes an optimization framework for split computing applications where an FFNN model is partitioned into multiple sections, and executed by UEs, edge- and core-located nodes to reduce the required UE computational footprint while containing the inference time. An efficient heuristic strategy for solving the optimization problem is also provided. The proposed framework is shown to be robust in heterogeneous settings, eliminating the need for retraining and reducing the UE's memory (CPU) footprint by over 33.6% (60%).

cs.NI↗

arXiv2025

Lifecycle Management of Trustworthy AI Models in 6G Networks: The REASON Approach

Artificial Intelligence (AI) is expected to play a key role in 6G networks including optimising system management, operation, and evolution. This requires systematic lifecycle management of AI models, ensuring their impact on services and stakeholders is continuously monitored. While current 6G initiatives introduce AI, they often fall short in addressing end-to-end intelligence and crucial aspects like trust, transparency, privacy, and verifiability. Trustworthy AI is vital, especially for critical infrastructures like 6G. This paper introduces the REASON approach for holistically addressing AI's native integration and trustworthiness in future 6G networks. The approach comprises AI Orchestration (AIO) for model lifecycle management, Cognition (COG) for performance evaluation and explanation, and AI Monitoring (AIM) for tracking and feedback. Digital Twin (DT) technology is leveraged to facilitate real-time monitoring and scenario testing, which are essential for AIO, COG, and AIM. We demonstrate this approach through an AI-enabled xAPP use case, leveraging a DT platform to validate, explain, and deploy trustworthy AI models.

cs.NI↗

arXiv2024

On Optimization of Next-Generation Microservice-Based Core Networks

Next-generation mobile core networks are required to be scalable and capable of efficiently utilizing heterogeneous bare metal resources that may include edge servers. To this end, microservice-based solutions where control plane procedures are deconstructed in their fundamental building blocks are gaining momentum. This letter proposes an optimization framework delivering the partitioning and mapping of large-scale microservice graphs onto heterogeneous bare metal deployments while minimizing the total network traffic among servers. An efficient heuristic strategy for solving the optimization problem is also provided. Simulation results show that, with the proposed framework, a microservice-based core can consistently support the requested load in heterogeneous bare metal deployments even when alternative architecture fails. Besides, our framework ensures an overall reduction in the control plane-related network traffic if compared to current core architectures.

cs.NI↗

arXiv2022

Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps

Identifying the environment's structure, i.e., to detect core components as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation and motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable object), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE^2 ) of 2D occupancy maps, which may be cluttered and incomplete. ROSE^2 identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE^2 is tested in several real-world publicly-available cluttered maps obtained in different conditions. The results show how it can robustly identify the environment structure in 2D occupancy maps suffering from clutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation ROSE^2 consistently achieves higher performance than the state-of-the-art methods, against which it is compared.

cs.RO↗

arXiv2020

On Urban Traffic Flow Benefits of Connected and Automated Vehicles

Automated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.

physics.soc-ph↗

arXiv2019

Location Anomalies Detection for Connected and Autonomous Vehicles

Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres will have access to suitably anonymized CAV mobility information. Safe and secure operations will then be contingent on early detection of anomalies. In this paper, a novel unsupervised learning model based on deep autoencoder is proposed to detect the self-reported location anomaly in CAVs, using vehicle locations and the Received Signal Strength Indicator (RSSI) as features. Quantitative experiments on simulation datasets show that the proposed approach is effective and robust in detecting self-reported location anomalies.

cs.LG↗

arXiv2019

Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation

Future Connected and Autonomous Vehicles (CAVs) will be equipped with a large set of sensors. The large amount of generated sensor data is expected to be exchanged with other CAVs and the road-side infrastructure. Both in Europe and the US, Dedicated Short Range Communications (DSRC) systems, based on the IEEE 802.11p Physical Layer, are key enabler for the communication among vehicles. Given the expected market penetration of connected vehicles, the licensed band of 75 MHz, dedicated to DSRC communications, is expected to become increasingly congested. In this paper, we investigate the performance of a vehicular communication system, operated over the unlicensed bands 2.4 GHz - 2.5 GHz and 5.725 GHz - 5.875 GHz. Our experimental evaluation was carried out in a testing track in the centre of Bristol, UK and our system is a full-stack ETSI ITS-G5 implementation. Our performance investigation compares key communication metrics (e.g., packet delivery rate, received signal strength indicator) measured by operating our system over the licensed DSRC and the considered unlicensed bands. In particular, when operated over the 2.4 GHz - 2.5 GHz band, our system achieves comparable performance to the case when the DSRC band is used. On the other hand, as soon as the system, is operated over the 5.725 GHz - 5.875 GHz band, the packet delivery rate is 30% smaller compared to the case when the DSRC band is employed. These findings prove that operating our system over unlicensed ISM bands is a viable option. During our experimental evaluation, we recorded all the generated network interactions and the complete data set has been publicly available.

cs.NI↗

arXiv2019

A Dataset of Full-Stack ITS-G5 DSRC Communications over Licensed and Unlicensed Bands Using a Large-Scale Urban Testbed

A dataset of measurements of ETSI ITS-G5 Dedicated Short Range Communications (DSRC) is presented. Our dataset consists of network interactions happening between two On-Board Units (OBUs) and four Road Side Units (RSUs). Each OBU was fitted onto a vehicle driven across the FLOURISH Test Track in Bristol, UK. Each RSU and OBU was equipped with two transceivers operating at different frequencies. During our experiments, each transceiver broadcasts Cooperative Awareness Messages (CAMs) over the licensed DSRC band, and over the unlicensed Industrial, Scientific, and Medical radio (ISM) bands 2.4GHz-2.5GHz and 5.725GHz-5.875GHz. Each transmitted and received CAM is logged along with its Received Signal Strength Indicator (RSSI) value and accurate positioning information. The Media Access Control layer (MAC) layer Packet Delivery Rates (PDRs) and RSSI values are also empirically calculated across the whole length of the track for any transceiver. The dataset can be used to derive realistic approximations of the PDR as a function of RSSI values under urban environments and for both the DSRC and ISM bands -- thus, the dataset is suitable to calibrate (simplified) physical layers of full-stack vehicular simulators where the MAC layer PDR is a direct function of the RSSI. The dataset is not intended to be used for signal propagation modelling. The dataset can be found at https://doi.org/10.5523/bris.eupowp7h3jl525yxhm3521f57 , and it has been analyzed in the following paper: I. Mavromatis, A. Tassi, and R. J. Piechocki, "Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation," IEEE PIMRC 2019. [Online]. Available: arXiv:1904.00464.

cs.NI↗

arXiv2019

Efficient Millimeter-Wave Infrastructure Placement for City-Scale ITS

Millimeter Waves (mmWaves) will play a pivotal role in the next-generation of Intelligent Transportation Systems (ITSs). However, in deep urban environments, sensitivity to blockages creates the need for more sophisticated network planning. In this paper, we present an agile strategy for deploying road-side nodes in a dense city scenario. In our system model, we consider strict Quality-of-Service (QoS) constraints (e.g. high throughput, low latency) that are typical of ITS applications. Our approach is scalable, insofar that takes into account the unique road and building shapes of each city, performing well for both regular and irregular city layouts. It allows us not only to achieve the required QoS constraints but it also provides up to $50\%$ reduction in the number of nodes required, compared to existing deployment solutions.

cs.NI↗

arXiv2019

Secure Data Offloading Strategy for Connected and Autonomous Vehicles

Connected and Automated Vehicles (CAVs) are expected to constantly interact with a network of processing nodes installed in secure cabinets located at the side of the road -- thus, forming Fog Computing-based infrastructure for Intelligent Transportation Systems (ITSs). Future city-scale ITS services will heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog Computing network. Due to the broadcast nature of the medium, CAVs' communications can be vulnerable to eavesdropping. This paper proposes a novel data offloading approach where the Random Linear Network Coding (RLNC) principle is used to ensure the probability of an eavesdropper to recover relevant portions of sensor data is minimized. Our preliminary results confirm the effectiveness of our approach when operated in a large-scale ITS networks.

cs.NI↗

arXiv2018

On Intercept Probability Minimization under Sparse Random Linear Network Coding

This paper considers a network where a node wishes to transmit a source message to a legitimate receiver in the presence of an eavesdropper. The transmitter secures its transmissions employing a sparse implementation of Random Linear Network Coding (RLNC). A tight approximation to the probability of the eavesdropper recovering the source message is provided. The proposed approximation applies to both the cases where transmissions occur without feedback or where the reliability of the feedback channel is impaired by an eavesdropper jamming the feedback channel. An optimization framework for minimizing the intercept probability by optimizing the sparsity of the RLNC is also presented. Results validate the proposed approximation and quantify the gain provided by our optimization over solutions where non-sparse RLNC is used.

cs.IT↗

arXiv2018

Poster: Parallel Implementation of the OMNeT++ INET Framework for V2X Communications

The field of parallel network simulation frameworks is evolving at a great pace. That is also because of the growth of Intelligent Transportation Systems (ITS) and the necessity for cost-effective large-scale trials. In this contribution, we will focus on the INET Framework and how we re-factor its single-thread code to make it run in a multi-thread fashion. Our parallel version of the INET Framework can significantly reduce the computation time in city-scale scenarios, and it is completely transparent to the user. When tested in different configurations, our version of INET ensures a reduction in the computation time of up to 43%.

cs.NI↗

arXiv2018

A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges

As we move towards autonomous vehicles, a reliable Vehicle-to-Everything (V2X) communication framework becomes of paramount importance. In this paper we present the development and the performance evaluation of a real-world vehicular networking testbed. Our testbed, deployed in the heart of the City of Bristol, UK, is able to exchange sensor data in a V2X manner. We will describe the testbed architecture and its operational modes. Then, we will provide some insight pertaining the firmware operating on the network devices. The system performance has been evaluated under a series of large-scale field trials, which have proven how our solution represents a low-cost high-quality framework for V2X communications. Our system managed to achieve high packet delivery ratios under different scenarios (urban, rural, highway) and for different locations around the city. We have also identified the instability of the packet transmission rate while using single-core devices, and we present some future directions that will address that.

cs.NI↗

arXiv2018

Efficient V2V Communication Scheme for 5G MmWave Hyper-Connected CAVs

Connected and Autonomous Vehicles (CAVs) require continuous access to sensory data to perform complex high-speed maneuvers and advanced trajectory planning. High priority CAVs are particularly reliant on extended perception horizon facilitated by sensory data exchange between CAVs. Existing technologies such as the Dedicated Short Range Communications (DSRC) are ill-equipped to provide advanced cooperative perception service. This creates the need for more sophisticated technologies such as the 5G Millimetre-Waves (mmWaves). In this work, we propose a distributed Vehicle-to-Vehicle (V2V) mmWaves association scheme operating in a heterogeneous manner. Our system utilises the information exchanged within the DSRC frequency band to bootstrap the best CAV pairs formation. Using a Stable Fixtures Matching Game, we form V2V multipoint-to-multipoint links. Compared to more traditional point-to-point links, our system provides almost twice as much sensory data exchange capacity for high priority CAVs while doubling the mmWaves channel utilisation for all the vehicles in the network.

cs.NI↗

arXiv2018

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.

cs.NI↗

arXiv2018

Multi-Radio 5G Architecture for Connected and Autonomous Vehicles: Application and Design Insights

Connected and Autonomous Vehicles (CAVs) will play a crucial role in next-generation Cooperative Intelligent Transportation Systems (C-ITSs). Not only is the information exchange fundamental to improve road safety and efficiency, but it also paves the way to a wide spectrum of advanced ITS applications enhancing efficiency, mobility and accessibility. Highly dynamic network topologies and unpredictable wireless channel conditions entail numerous design challenges and open questions. In this paper, we address the beneficial interactions between CAVs and an ITS and propose a novel architecture design paradigm. Our solution can accommodate multi-layer applications over multiple Radio Access Technologies (RATs) and provide a smart configuration interface for enhancing the performance of each RAT.

cs.NI↗

arXiv2017

High-Speed Data Dissemination over Device-to-Device Millimeter-Wave Networks for Highway Vehicular Communication

Gigabit-per-second connectivity among vehicles is expected to be a key enabling technology for sensor information sharing, in turn, resulting in safer Intelligent Transportation Systems (ITSs). Recently proposed millimeter-wave (mmWave) systems appear to be the only solution capable of meeting the data rate demand imposed by future ITS services. In this poster, we assess the performance of a mmWave device-to-device (D2D) vehicular network by investigating the impact of system and communication parameters on end-users.

cs.NI↗

arXiv2017

Agile Calibration Process of Full-Stack Simulation Frameworks for V2X Communications

Computer simulations and real-world car trials are essential to investigate the performance of Vehicle-to-Everything (V2X) networks. However, simulations are imperfect models of the physical reality and can be trusted only when they indicate agreement with the real-world. On the other hand, trials lack reproducibility and are subject to uncertainties and errors. In this paper, we will illustrate a case study where the interrelationship between trials, simulation, and the reality-of-interest is presented. Results are then compared in a holistic fashion. Our study will describe the procedure followed to macroscopically calibrate a full-stack network simulator to conduct high-fidelity full-stack computer simulations.

cs.PF↗