2023
Journal article
Open Access
Towards a fully-observable Markov decision process with generative models for integrated 6G-non-terrestrial networks
Machumilane A., Cassara P., Gotta A.
The upcoming sixth generation (6G) mobile networks require integration between terrestrial mobile networks and non-terrestrial networks (NTN) such as satellites and high altitude platforms (HAPs) to ensure wide and ubiquitous coverage, high connection density, reliable communications and high data rates. The main challenge in this integration is the requirement for line-of-sight (LOS) communication between the user equipment (UE) and the satellite. In this paper, we propose a framework based on actorcritic reinforcement learning and generative models for LOS estimation and traffic scheduling on multiple links connecting a user equipment to multiple satellites in 6G-NTN integrated networks. The agent learns to estimate the LOS probabilities of the available channels and schedules traffic on appropriate links to minimise end-to-end losses with minimal bandwidth. The learning process is modelled as a partially observable Markov decision process (POMDP), since the agent can only observe the state of the channels it has just accessed. As a result, the learning agent requires a longer convergence time compared to the satellite visibility period at a given satellite elevation angle. To counteract this slow convergence, we use generative models to transform a POMDP into a fully observable Markov decision process (FOMDP). We use generative adversarial networks (GANs) and variational autoencoders (VAEs) to generate synthetic channel states of the channels that are not selected by the agent during the learning process, allowing the agent to have complete knowledge of all channels, including those that are not accessed, thus speeding up the learning process. The simulation results show that our framework enables the agent to converge in a short time and transmit with an optimal policy for most of the satellite visibility period, which significantly reduces end-to-end losses and saves bandwidth. We also show that it is possible to train generative models in real time without requiring prior knowledge of the channel models and without slowing down the learning process or affecting the accuracy of the models.Source: IEEE open journal of the Communications Society 4 (2023): 1913–1930. doi:10.1109/OJCOMS.2023.3307209
DOI: 10.1109/ojcoms.2023.3307209
Metrics:
Machumilane A., Cassara P., Gotta A.
The upcoming sixth generation (6G) mobile networks require integration between terrestrial mobile networks and non-terrestrial networks (NTN) such as satellites and high altitude platforms (HAPs) to ensure wide and ubiquitous coverage, high connection density, reliable communications and high data rates. The main challenge in this integration is the requirement for line-of-sight (LOS) communication between the user equipment (UE) and the satellite. In this paper, we propose a framework based on actorcritic reinforcement learning and generative models for LOS estimation and traffic scheduling on multiple links connecting a user equipment to multiple satellites in 6G-NTN integrated networks. The agent learns to estimate the LOS probabilities of the available channels and schedules traffic on appropriate links to minimise end-to-end losses with minimal bandwidth. The learning process is modelled as a partially observable Markov decision process (POMDP), since the agent can only observe the state of the channels it has just accessed. As a result, the learning agent requires a longer convergence time compared to the satellite visibility period at a given satellite elevation angle. To counteract this slow convergence, we use generative models to transform a POMDP into a fully observable Markov decision process (FOMDP). We use generative adversarial networks (GANs) and variational autoencoders (VAEs) to generate synthetic channel states of the channels that are not selected by the agent during the learning process, allowing the agent to have complete knowledge of all channels, including those that are not accessed, thus speeding up the learning process. The simulation results show that our framework enables the agent to converge in a short time and transmit with an optimal policy for most of the satellite visibility period, which significantly reduces end-to-end losses and saves bandwidth. We also show that it is possible to train generative models in real time without requiring prior knowledge of the channel models and without slowing down the learning process or affecting the accuracy of the models.Source: IEEE open journal of the Communications Society 4 (2023): 1913–1930. doi:10.1109/OJCOMS.2023.3307209
DOI: 10.1109/ojcoms.2023.3307209
Metrics:
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| ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
A federated channel modeling system using generative neural networks
Bano S., Cassarà P., Tonellotto N., Gotta A.
The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different regions.Source: VTC2023-Spring - IEEE 97th Vehicular Technology Conference, Florence, Italy, 20-23/06/2023
DOI: 10.1109/vtc2023-spring57618.2023.10199491
Metrics:
Bano S., Cassarà P., Tonellotto N., Gotta A.
The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different regions.Source: VTC2023-Spring - IEEE 97th Vehicular Technology Conference, Florence, Italy, 20-23/06/2023
DOI: 10.1109/vtc2023-spring57618.2023.10199491
Metrics:
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ISTI Repository | ieeexplore.ieee.org 
| CNR ExploRA
2023
Journal article
Open Access
Learning-based traffic scheduling in non-stationary multipath 5G non-terrestrial networks
Machumilane A., Gotta A., Cassarà P., Amato G., Gennaro C.
In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent's retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.Source: Remote sensing (Basel) 15 (2023). doi:10.3390/rs15071842
DOI: 10.3390/rs15071842
Metrics:
Machumilane A., Gotta A., Cassarà P., Amato G., Gennaro C.
In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent's retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.Source: Remote sensing (Basel) 15 (2023). doi:10.3390/rs15071842
DOI: 10.3390/rs15071842
Metrics:
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Remote Sensing | ISTI Repository | www.mdpi.com | ZENODO | CNR ExploRA
2023
Journal article
Open Access
Artificial intelligence of things at the edge: scalable and efficient distributed learning for massive scenarios
Bano S., Tonellotto N., Cassarà P., Gotta A.
Federated Learning (FL) is a distributed optimization method in which multiple client nodes collaborate to train a machine learning model without sharing data with a central server. However, communication between numerous clients and the central aggregation server to share model parameters can cause several problems, including latency and network congestion. To address these issues, we propose a scalable communication infrastructure based on Information-Centric Networking built and tested on Apache Kafka®. The proposed architecture consists of a two-tier communication model. In the first layer, client updates are cached at the edge between clients and the server, while in the second layer, the server computes global model updates by aggregating the cached models. The data stored in the intermediate nodes at the edge enables reliable and effective data transmission and solves the problem of intermittent connectivity of mobile nodes. While many local model updates provided by clients can result in a more accurate global model in FL, they can also result in massive data traffic that negatively impacts congestion at the edge. For this reason, we couple a client selection procedure based on a congestion control mechanism at the edge for the given architecture of FL. The proposed algorithm selects a subset of clients based on their resources through a time-based backoff system to account for the time-averaged accuracy of FL while limiting the traffic load. Experiments show that our proposed architecture has an improvement of over 40% over the network-centric based FL architecture, i.e., Flower. The architecture also provides scalability and reliability in the case of mobile nodes. It also improves client resource utilization, avoids overflow, and ensures fairness in client selection. The experiments show that the proposed algorithm leads to the desired client selection patterns and is adaptable to changing network environments.Source: Computer communications 205 (2023): 45–57. doi:10.1016/j.comcom.2023.04.010
DOI: 10.1016/j.comcom.2023.04.010
Project(s): TEACHING
Metrics:
Bano S., Tonellotto N., Cassarà P., Gotta A.
Federated Learning (FL) is a distributed optimization method in which multiple client nodes collaborate to train a machine learning model without sharing data with a central server. However, communication between numerous clients and the central aggregation server to share model parameters can cause several problems, including latency and network congestion. To address these issues, we propose a scalable communication infrastructure based on Information-Centric Networking built and tested on Apache Kafka®. The proposed architecture consists of a two-tier communication model. In the first layer, client updates are cached at the edge between clients and the server, while in the second layer, the server computes global model updates by aggregating the cached models. The data stored in the intermediate nodes at the edge enables reliable and effective data transmission and solves the problem of intermittent connectivity of mobile nodes. While many local model updates provided by clients can result in a more accurate global model in FL, they can also result in massive data traffic that negatively impacts congestion at the edge. For this reason, we couple a client selection procedure based on a congestion control mechanism at the edge for the given architecture of FL. The proposed algorithm selects a subset of clients based on their resources through a time-based backoff system to account for the time-averaged accuracy of FL while limiting the traffic load. Experiments show that our proposed architecture has an improvement of over 40% over the network-centric based FL architecture, i.e., Flower. The architecture also provides scalability and reliability in the case of mobile nodes. It also improves client resource utilization, avoids overflow, and ensures fairness in client selection. The experiments show that the proposed algorithm leads to the desired client selection patterns and is adaptable to changing network environments.Source: Computer communications 205 (2023): 45–57. doi:10.1016/j.comcom.2023.04.010
DOI: 10.1016/j.comcom.2023.04.010
Project(s): TEACHING
Metrics:
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ISTI Repository | Computer Communications | CNR ExploRA
2023
Patent
Unknown
System and method for supporting an operator for navigation
Sebastiani L., Di Summa Maria, Viganò G. P., Sacco M., Cassarà P, Gotta A., Figari M., Martelli M., Zaccone R.
Source: EP3907470B1, Europeo
Sebastiani L., Di Summa Maria, Viganò G. P., Sacco M., Cassarà P, Gotta A., Figari M., Martelli M., Zaccone R.
Source: EP3907470B1, Europeo
See at: CNR ExploRA
2023
Conference article
Open Access
Traffic scheduling in non-stationary multipath non-terrestrial networks: a reinforcement learning approach
Machumilane A., Gotta A., Cassarà P., Gennaro C., Amato G.
In Non-Terrestrial Networks (NTNs), where LEO satellites and User Equipment (UE) move relative to each other, Line-of-Sight (LOS) tracking, and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous LOS estimation and channel impairment compensation are crucial for a UE to access a satellite and maintain connectivity. In this paper, we propose a Actor-Critic (AC)-Reinforcement Learning (RL) framework for traffic scheduling in NTN scenarios where the channel state is non-stationary due to the variability of LOS, which depends on the current satellite elevation. We deploy the framework as an agent in a Multi-Path Routing (MPR) scheme where the UE can access more than one satellite simultaneously to improve link reliability and throughput. We study how the agent schedules traffic on multiple satellite links by adopting the AC version of RL. The agent continuously trains based on variations in satellite elevation angles, handoffs, and relative LOS probabilities. We compare the agent retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis considering the dense urban area of Chicago, where high-rise buildings significantly affect the LOS. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1 times the satellite visibility time at certain elevations, which guarantees efficient use of satellite visibility.Source: ICC 2023 - IEEE International Conference on Communications, pp. 4094–4099, Rome, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279788
DOI: 10.5281/zenodo.8430896
DOI: 10.5281/zenodo.8430897
Metrics:
Machumilane A., Gotta A., Cassarà P., Gennaro C., Amato G.
In Non-Terrestrial Networks (NTNs), where LEO satellites and User Equipment (UE) move relative to each other, Line-of-Sight (LOS) tracking, and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous LOS estimation and channel impairment compensation are crucial for a UE to access a satellite and maintain connectivity. In this paper, we propose a Actor-Critic (AC)-Reinforcement Learning (RL) framework for traffic scheduling in NTN scenarios where the channel state is non-stationary due to the variability of LOS, which depends on the current satellite elevation. We deploy the framework as an agent in a Multi-Path Routing (MPR) scheme where the UE can access more than one satellite simultaneously to improve link reliability and throughput. We study how the agent schedules traffic on multiple satellite links by adopting the AC version of RL. The agent continuously trains based on variations in satellite elevation angles, handoffs, and relative LOS probabilities. We compare the agent retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis considering the dense urban area of Chicago, where high-rise buildings significantly affect the LOS. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1 times the satellite visibility time at certain elevations, which guarantees efficient use of satellite visibility.Source: ICC 2023 - IEEE International Conference on Communications, pp. 4094–4099, Rome, Italy, 28/05-01/06/2023
DOI: 10.1109/icc45041.2023.10279788
DOI: 10.5281/zenodo.8430896
DOI: 10.5281/zenodo.8430897
Metrics:
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ZENODO | ZENODO | ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2023
Journal article
Open Access
E-Navigation: a distributed decision support system with extended reality for bridge and ashore seafarers
Cassarà P., Di Summa M., Gotta A., Martelli M.
A distributed decision support system has been developed to assist seafarers during several navigation tasks, for instance, in avoiding a collision with a detected obstacle in the sea and envisioning a future autonomous navigation system. In this paper, the decision support system is based on the results of a customized simulation model representing the ship's behavior, including hydrodynamics, propulsion, and control effects. Sensors monitor and collect the parameters of the environment and the ship onboard. The telemetry and the calculated route are visualized on a wearable visor exploiting augmented reality. Such context information is also replicated ashore through a narrow-band satellite link using an IoT publish-subscribe communication paradigm to allow one or more remote seafarers to supervise the situation in a virtual reality environment. Overall, the potential of the proposed system is presented and discussed for application in the context of autonomous navigation.Source: IEEE transactions on intelligent transportation systems (Online) 24 (2023): 13384–13395. doi:10.1109/TITS.2023.3311817
DOI: 10.1109/tits.2023.3311817
Metrics:
Cassarà P., Di Summa M., Gotta A., Martelli M.
A distributed decision support system has been developed to assist seafarers during several navigation tasks, for instance, in avoiding a collision with a detected obstacle in the sea and envisioning a future autonomous navigation system. In this paper, the decision support system is based on the results of a customized simulation model representing the ship's behavior, including hydrodynamics, propulsion, and control effects. Sensors monitor and collect the parameters of the environment and the ship onboard. The telemetry and the calculated route are visualized on a wearable visor exploiting augmented reality. Such context information is also replicated ashore through a narrow-band satellite link using an IoT publish-subscribe communication paradigm to allow one or more remote seafarers to supervise the situation in a virtual reality environment. Overall, the potential of the proposed system is presented and discussed for application in the context of autonomous navigation.Source: IEEE transactions on intelligent transportation systems (Online) 24 (2023): 13384–13395. doi:10.1109/TITS.2023.3311817
DOI: 10.1109/tits.2023.3311817
Metrics:
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ISTI Repository | IEEE Transactions on Intelligent Transportation Systems | ieeexplore.ieee.org | CNR ExploRA
2022
Journal article
Open Access
A path-aware scheduler for Air-to-ground multipath multimedia delivery in real time
Machumilane A., Gotta A., Cassarà P., Bacco M.
The use of multipath techniques in transmission has emerged in the last years thanks to their potential in increasing throughput. They can also be used as a means to counteract errors or losses in transmission, thus increasing reliability. In this work, we focus on the challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) via multiple wireless channels. We propose a lightweight scheduler capable of dynamically selecting the paths to be used and of determining the necessary redundancy rate to protect the multimedia flow. Our scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The results we present show that the proposed scheduler can be used to target a very low loss rate by dynamically adapt to varying channel conditions in terms of losses and experienced delay.Source: IEEE communications magazine (Online) 60 (2022): 54–58. doi:10.1109/MCOM.001.2100904
DOI: 10.1109/mcom.001.2100904
Metrics:
Machumilane A., Gotta A., Cassarà P., Bacco M.
The use of multipath techniques in transmission has emerged in the last years thanks to their potential in increasing throughput. They can also be used as a means to counteract errors or losses in transmission, thus increasing reliability. In this work, we focus on the challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) via multiple wireless channels. We propose a lightweight scheduler capable of dynamically selecting the paths to be used and of determining the necessary redundancy rate to protect the multimedia flow. Our scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The results we present show that the proposed scheduler can be used to target a very low loss rate by dynamically adapt to varying channel conditions in terms of losses and experienced delay.Source: IEEE communications magazine (Online) 60 (2022): 54–58. doi:10.1109/MCOM.001.2100904
DOI: 10.1109/mcom.001.2100904
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Journal article
Open Access
Orbital edge offloading on mega-LEO satellite constellations for equal access to computing
Cassarà P., Gotta A., Marchese M., Patrone F.
Mega-LEO satellite constellations are becoming a concrete reality. Companies such as SpaceX, Virgin Orbit, and OneWeb have already started launching hundreds of LEO satellites and are turning their services on. Even if the aim of such LEO satellite constellations is just, for now, to offer worldwide Internet access equality, their deployment proves their feasibility and suggests usefulness for further purposes. In this article, we shed some light on the possible integration of the in-network computing paradigm in mega-LEO satellite constellations. Terrestrial and/or non-terrestrial nodes can benefit from offloading the computing to an orbital edge (OE) platform reachable through the satellite constellation, exploiting its fast and distributed computational capability. In this context, a preliminary analysis highlights that task offloading strategies can lead to performance improvements that open up novel challenges in the design and setup of OE platforms.Source: IEEE communications magazine (Print) 60 (2022): 32–36. doi:10.1109/MCOM.001.2100818
DOI: 10.1109/mcom.001.2100818
Metrics:
Cassarà P., Gotta A., Marchese M., Patrone F.
Mega-LEO satellite constellations are becoming a concrete reality. Companies such as SpaceX, Virgin Orbit, and OneWeb have already started launching hundreds of LEO satellites and are turning their services on. Even if the aim of such LEO satellite constellations is just, for now, to offer worldwide Internet access equality, their deployment proves their feasibility and suggests usefulness for further purposes. In this article, we shed some light on the possible integration of the in-network computing paradigm in mega-LEO satellite constellations. Terrestrial and/or non-terrestrial nodes can benefit from offloading the computing to an orbital edge (OE) platform reachable through the satellite constellation, exploiting its fast and distributed computational capability. In this context, a preliminary analysis highlights that task offloading strategies can lead to performance improvements that open up novel challenges in the design and setup of OE platforms.Source: IEEE communications magazine (Print) 60 (2022): 32–36. doi:10.1109/MCOM.001.2100818
DOI: 10.1109/mcom.001.2100818
Metrics:
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ISTI Repository | IEEE Communications Magazine | ieeexplore.ieee.org | CNR ExploRA
2022
Journal article
Open Access
Federated feature selection for cyber-physical systems of systems
Cassarà P., Gotta A., Valerio L.
Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present infor- mative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and com- munication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant at- tributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data.Source: IEEE transactions on vehicular technology 71 (2022): 9937–9950. doi:10.1109/TVT.2022.3178612
DOI: 10.1109/tvt.2022.3178612
DOI: 10.5281/zenodo.6901226
DOI: 10.48550/arxiv.2109.11323
DOI: 10.5281/zenodo.6901227
Project(s): TEACHING, HumanE-AI-Net , MARVEL , SoBigData-PlusPlus
Metrics:
Cassarà P., Gotta A., Valerio L.
Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present infor- mative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and com- munication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant at- tributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data.Source: IEEE transactions on vehicular technology 71 (2022): 9937–9950. doi:10.1109/TVT.2022.3178612
DOI: 10.1109/tvt.2022.3178612
DOI: 10.5281/zenodo.6901226
DOI: 10.48550/arxiv.2109.11323
DOI: 10.5281/zenodo.6901227
Project(s): TEACHING
, HumanE-AI-Net , MARVEL , SoBigData-PlusPlus Metrics:
See at:
arXiv.org e-Print Archive | ZENODO | ZENODO | ieeexplore.ieee.org | ISTI Repository | ISTI Repository | IEEE Transactions on Vehicular Technology | doi.org | CNR ExploRA
2022
Conference article
Open Access
Exploring Machine Learning for classification of QUIC flows over satellite
Secchi R., Cassarà P., Gotta A.
Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.Source: ICC 2022 - IEEE International Conference on Communications, pp. 4709–4714, Seoul, Korea, 16-20/05/2022
DOI: 10.1109/icc45855.2022.9838463
Metrics:
Secchi R., Cassarà P., Gotta A.
Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.Source: ICC 2022 - IEEE International Conference on Communications, pp. 4709–4714, Seoul, Korea, 16-20/05/2022
DOI: 10.1109/icc45855.2022.9838463
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Conference article
Open Access
A novel approach to distributed model aggregation using Apache Kafka
Bano S., Carlini E., Cassarà P., Coppola M., Dazzi P., Gotta A.
Multi-Access Edge Computing (MEC) is attracting a lot of interest because it complements cloud-based approaches. Indeed, MEC is opening up in the direction of reducing both interaction delays and data sharing, called Cyber-Physical Systems (CPSs). In the near fu-ture, edge technologies will be a fundamental tool to better support time-dependent and data-intensive applications. In this context, this work explores existing and emerging platforms for MEC and human-centric applications, and proposes a suitable architecture that can be used in the context of autonomous vehicle systems.The proposed architecture will support scalable communication among sensing devices and edge/cloud computing platforms, as well as orchestrate services for computing, storage, and learning with the use of an Information-centric paradigm such as Apache KafkaSource: FRAME '22 - 2nd Workshop on Flexible Resource and Application Management on the Edge, pp. 33–36, Minneapolis, Minnesota, USA, 27/06-01/07/2022
DOI: 10.1145/3526059.3533621
Project(s): TEACHING
Metrics:
Bano S., Carlini E., Cassarà P., Coppola M., Dazzi P., Gotta A.
Multi-Access Edge Computing (MEC) is attracting a lot of interest because it complements cloud-based approaches. Indeed, MEC is opening up in the direction of reducing both interaction delays and data sharing, called Cyber-Physical Systems (CPSs). In the near fu-ture, edge technologies will be a fundamental tool to better support time-dependent and data-intensive applications. In this context, this work explores existing and emerging platforms for MEC and human-centric applications, and proposes a suitable architecture that can be used in the context of autonomous vehicle systems.The proposed architecture will support scalable communication among sensing devices and edge/cloud computing platforms, as well as orchestrate services for computing, storage, and learning with the use of an Information-centric paradigm such as Apache KafkaSource: FRAME '22 - 2nd Workshop on Flexible Resource and Application Management on the Edge, pp. 33–36, Minneapolis, Minnesota, USA, 27/06-01/07/2022
DOI: 10.1145/3526059.3533621
Project(s): TEACHING
Metrics:
See at:
ZENODO | dl.acm.org | doi.org | CNR ExploRA
2022
Conference article
Open Access
FedTCS: federated learning with time-based client selection to optimize edge resources
Bano S., Tonellotto N., Cassarà P., Gotta A.
Client sampling in federated learning (FL) is a significant problem, especially in massive cross-device scenarios where communication with all devices is not possible. In this work, we study the client selection problem using a time-based back-off system in federated learning for a MEC-based network infrastructure. In the FL paradigm, where a group of nodes can jointly train a machine learning model with the help of a central server, client selection is expected to have a significant impact in FL applications deployed in future 6G networks, given the increasing number of connected devices. Our timer settings are based on an exponential distribution to obtain an expected number of clients for the FL process. Empirical results show that our technique is scalable and robust for a large number of clients and keeps data queues stable at the edge.Source: AI6G 2022 - First International Workshop on Artificial Intelligence in Beyond 5G and 6G Wireless Networks, Padua, Italy, 18/06/2022
Project(s): TEACHING
Bano S., Tonellotto N., Cassarà P., Gotta A.
Client sampling in federated learning (FL) is a significant problem, especially in massive cross-device scenarios where communication with all devices is not possible. In this work, we study the client selection problem using a time-based back-off system in federated learning for a MEC-based network infrastructure. In the FL paradigm, where a group of nodes can jointly train a machine learning model with the help of a central server, client selection is expected to have a significant impact in FL applications deployed in future 6G networks, given the increasing number of connected devices. Our timer settings are based on an exponential distribution to obtain an expected number of clients for the FL process. Empirical results show that our technique is scalable and robust for a large number of clients and keeps data queues stable at the edge.Source: AI6G 2022 - First International Workshop on Artificial Intelligence in Beyond 5G and 6G Wireless Networks, Padua, Italy, 18/06/2022
Project(s): TEACHING
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
KafkaFed: two-tier federated learning communication architecture for internet of vehicles
Bano S., Tonellotto N., Cassarà P., Gotta A.
In the current era of the Internet of Vehicles (IoV), vehicle to vehicle data sharing can provide customized applications for Connected and Autonomous Vehicles (CAVs). The advancement of Deep Learning (DL) methodologies is one of the key driving forces for CAVs, allowing elaborating a massive amount of data by the resource-constrained onboard devices. In a traditional centralized DL approach, vehicle data are transmitted to the cloud for the training of models. This approach leads to significant communication overhead, high delays, and data privacy concerns. Conversely, Federated Learning (FL) performs the training using the local models in a distributed fashion and mitigates the data privacy risks by sharing only the model parameters with the server, optimizing the FL to be used with resources-constrained devices. In this paper, we propose the design of a scalable communication infrastructure to support the FL procedure based on Information-Centric Networking (ICN) using Apache Kafka, called KafkaFed. The ICN-based infrastructure allows to overcome the shortcomings of current client-server architectures for FL, in which routing is content-based or name-based to achieve efficient data retrieval for mobile nodes. In ICN, data are stored at intermediate nodes to provide efficient and reliable data delivery. A proof of concept of the KafkaFed communication architecture is developed and tested in an emulated environment. The performance of the proposed framework compared to the client server-based FL architecture, i.e., FLOWER showed a boost of almost 40% with just 32 clients in addition to several other advantages of scalability, reliability, and securitySource: PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 515–520, Pisa, Italy, 21-25 March 2022
DOI: 10.1109/percomworkshops53856.2022.9767510
Project(s): TEACHING
Metrics:
Bano S., Tonellotto N., Cassarà P., Gotta A.
In the current era of the Internet of Vehicles (IoV), vehicle to vehicle data sharing can provide customized applications for Connected and Autonomous Vehicles (CAVs). The advancement of Deep Learning (DL) methodologies is one of the key driving forces for CAVs, allowing elaborating a massive amount of data by the resource-constrained onboard devices. In a traditional centralized DL approach, vehicle data are transmitted to the cloud for the training of models. This approach leads to significant communication overhead, high delays, and data privacy concerns. Conversely, Federated Learning (FL) performs the training using the local models in a distributed fashion and mitigates the data privacy risks by sharing only the model parameters with the server, optimizing the FL to be used with resources-constrained devices. In this paper, we propose the design of a scalable communication infrastructure to support the FL procedure based on Information-Centric Networking (ICN) using Apache Kafka, called KafkaFed. The ICN-based infrastructure allows to overcome the shortcomings of current client-server architectures for FL, in which routing is content-based or name-based to achieve efficient data retrieval for mobile nodes. In ICN, data are stored at intermediate nodes to provide efficient and reliable data delivery. A proof of concept of the KafkaFed communication architecture is developed and tested in an emulated environment. The performance of the proposed framework compared to the client server-based FL architecture, i.e., FLOWER showed a boost of almost 40% with just 32 clients in addition to several other advantages of scalability, reliability, and securitySource: PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 515–520, Pisa, Italy, 21-25 March 2022
DOI: 10.1109/percomworkshops53856.2022.9767510
Project(s): TEACHING
Metrics:
See at:
ISTI Repository | ZENODO | doi.org | ieeexplore.ieee.org | CNR ExploRA
2022
Conference article
Open Access
Federated semi-supervised classification of multimedia flows for 3D networks
Bano S., Machumilane A., Valerio L., Cassarà P., Gotta A.
Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.Source: MELECON 2022 - 21st IEEE Mediterranean Electrotechnical Conference, pp. 165–170, Palermo, Italy, 14-16 June 2022
DOI: 10.1109/melecon53508.2022.9843104
Project(s): TEACHING
Metrics:
Bano S., Machumilane A., Valerio L., Cassarà P., Gotta A.
Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.Source: MELECON 2022 - 21st IEEE Mediterranean Electrotechnical Conference, pp. 165–170, Palermo, Italy, 14-16 June 2022
DOI: 10.1109/melecon53508.2022.9843104
Project(s): TEACHING
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Conference article
Open Access
AI-as-a-Service toolkit for human-centered intelligence in autonomous driving
De Caro V., Bano S., Machumilane A., Gotta A., Cassarà P., Carta A., Semola R., Sardianos C., Chronis C., Varlamis I., Tserpes K., Lomonaco V., Gallicchio C., Bacciu D.
This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving emulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.Source: PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 91–93, Pisa, Italy, 21-25 March 2022
DOI: 10.1109/percomworkshops53856.2022.9767501
DOI: 10.48550/arxiv.2202.01645
Project(s): TEACHING
Metrics:
De Caro V., Bano S., Machumilane A., Gotta A., Cassarà P., Carta A., Semola R., Sardianos C., Chronis C., Varlamis I., Tserpes K., Lomonaco V., Gallicchio C., Bacciu D.
This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving emulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.Source: PerCom Workshops - 2022 IEEE International Conference on Pervasive Computing and Communications, pp. 91–93, Pisa, Italy, 21-25 March 2022
DOI: 10.1109/percomworkshops53856.2022.9767501
DOI: 10.48550/arxiv.2202.01645
Project(s): TEACHING
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | doi.org | doi.org | ieeexplore.ieee.org | ZENODO | CNR ExploRA
2022
Conference article
Open Access
Actor-critic scheduling for path-aware air-to-ground multipath multimedia delivery
Machumilane A., Gotta A., Cassarà P., Gennaro C., Amato G.
Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths. The scheduler acting as an RL agent learns in real-time the optimal policy for path selection, path rate allocation and redundancy estimation for flow protection. The scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.Source: VTC2022-Spring - 95th IEEE Vehicular Technology Conference, Helsinki, Finland, 19-22 June 2022
DOI: 10.1109/vtc2022-spring54318.2022.9860760
Project(s): TEACHING
Metrics:
Machumilane A., Gotta A., Cassarà P., Gennaro C., Amato G.
Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths. The scheduler acting as an RL agent learns in real-time the optimal policy for path selection, path rate allocation and redundancy estimation for flow protection. The scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.Source: VTC2022-Spring - 95th IEEE Vehicular Technology Conference, Helsinki, Finland, 19-22 June 2022
DOI: 10.1109/vtc2022-spring54318.2022.9860760
Project(s): TEACHING
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2021
Conference article
Open Access
TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence
Bacciu D., Akarmazyan S., Armengaud E., Bacco M., Bravos G., Calandra C., Carlini E., Carta A., Cassarà P., Coppola M., Davalas C., Dazzi P., Degennaro M. C., Di Sarli D., Dobaj J., Gallicchio C., Girbal S., Gotta A., Groppo R., Lomonaco V., Macher G., Mazzei D., Mencagli G., Michail D., Micheli A., Peroglio R., Petroni S., Potenza R., Pourdanesh F., Sardianos C., Tserpes K., Tagliabò F., Vatl J., Varlamis I., Veledar O.
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges.Source: IEEE COINS 2021 - IEEE International Conference on Omni-layer Intelligent systems, Online conference, 23-26/08/2021
DOI: 10.1109/coins51742.2021.9524099
DOI: 10.5281/zenodo.5293769
DOI: 10.5281/zenodo.5293768
Project(s): TEACHING
Metrics:
Bacciu D., Akarmazyan S., Armengaud E., Bacco M., Bravos G., Calandra C., Carlini E., Carta A., Cassarà P., Coppola M., Davalas C., Dazzi P., Degennaro M. C., Di Sarli D., Dobaj J., Gallicchio C., Girbal S., Gotta A., Groppo R., Lomonaco V., Macher G., Mazzei D., Mencagli G., Michail D., Micheli A., Peroglio R., Petroni S., Potenza R., Pourdanesh F., Sardianos C., Tserpes K., Tagliabò F., Vatl J., Varlamis I., Veledar O.
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges.Source: IEEE COINS 2021 - IEEE International Conference on Omni-layer Intelligent systems, Online conference, 23-26/08/2021
DOI: 10.1109/coins51742.2021.9524099
DOI: 10.5281/zenodo.5293769
DOI: 10.5281/zenodo.5293768
Project(s): TEACHING
Metrics:
See at:
arXiv.org e-Print Archive | ieeexplore.ieee.org | xplorestaging.ieee.org | CNR ExploRA
2021
Journal article
Open Access
An outlook on the future marine traffic management system for autonomous ships
Martelli M., Virdis A., Gotta A., Cassarà P., Di Summa M.
In the shipping digitalisation process, the peak will be reached with the advent of a wholly autonomous and at the same time safe and reliable ship. Full autonomy could be obtained by two linked Artificial-Intelligence systems representing the ship navigator and the ship engineer that possess sensing and analysis skills, situational awareness, planning, and control capabilities. Many efforts have been made in developing onboard systems; however, the shore facilities are not ready yet to deal with these new technologies. The paper aims to present the innovative technologies and methodologies needed to develop a futuristic Vessel Traffic System. The proposed systems will aim at faultless data acquisition and processing, provide input to decision-making systems, and suggest evasive manoeuvre; to deal with hazards and systems failure without human intervention onboard. The system is composed of three different and interacting layers. The first is an artificially intelligent tool to detect and control autonomous ships, thanks to situation recognition and obstacle avoidance strategies. The second is an orchestration and management platform designed to coordinate the sensing/actuation infrastructure and the AI algorithms' results made available by multiple ships, mustering edge, and distributed computing techniques to fulfil the specific harsh requirements of the sea environment. The final part is a holistic guidance-navigation-control framework to manage autonomous ships' navigation in a crowded area. Eventually, a cyber-physical scenario, using both a ship digital-twin and a real model-scale ship, is suggested to test and validate the innovative system without the availability of a full-scale scenario.Source: IEEE access 9 (2021): 157316–157328. doi:10.1109/ACCESS.2021.3130741
DOI: 10.1109/access.2021.3130741
Metrics:
Martelli M., Virdis A., Gotta A., Cassarà P., Di Summa M.
In the shipping digitalisation process, the peak will be reached with the advent of a wholly autonomous and at the same time safe and reliable ship. Full autonomy could be obtained by two linked Artificial-Intelligence systems representing the ship navigator and the ship engineer that possess sensing and analysis skills, situational awareness, planning, and control capabilities. Many efforts have been made in developing onboard systems; however, the shore facilities are not ready yet to deal with these new technologies. The paper aims to present the innovative technologies and methodologies needed to develop a futuristic Vessel Traffic System. The proposed systems will aim at faultless data acquisition and processing, provide input to decision-making systems, and suggest evasive manoeuvre; to deal with hazards and systems failure without human intervention onboard. The system is composed of three different and interacting layers. The first is an artificially intelligent tool to detect and control autonomous ships, thanks to situation recognition and obstacle avoidance strategies. The second is an orchestration and management platform designed to coordinate the sensing/actuation infrastructure and the AI algorithms' results made available by multiple ships, mustering edge, and distributed computing techniques to fulfil the specific harsh requirements of the sea environment. The final part is a holistic guidance-navigation-control framework to manage autonomous ships' navigation in a crowded area. Eventually, a cyber-physical scenario, using both a ship digital-twin and a real model-scale ship, is suggested to test and validate the innovative system without the availability of a full-scale scenario.Source: IEEE access 9 (2021): 157316–157328. doi:10.1109/ACCESS.2021.3130741
DOI: 10.1109/access.2021.3130741
Metrics:
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ieeexplore.ieee.org | ISTI Repository | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Air-to-ground real-time multimedia delivery: a multipath testbed
Bacco M., Cassarà P., Gotta A.
In this work, we focus our attention on real-time multimedia flows from Unmanned Aerial Vehicles (UAVs) to the ground, presenting and analysing the data collected in field trials during a real testbed. The objective is assessing whether a video feed of reasonable quality can be provided to the pilot of a UAV to enable Beyond Visual Line of Sight (BVLoS) operations, by exploiting the multiple cellular operators available in the area. Three cellular networks have been jointly used in a multihoming/multipath setup, leveraging the variable coverage offered in both urban and suburban environments. Taking into account both Quality of Service (QoS) and Quality of Experience (QoE) metrics, the target parameters measured in this testbed are: latency, packet error rate, and video quality, which accounts for frames integrity, continuity, and fluidity. Data collected on the field allow evaluating both QoS and QoE in the presence of a multipath architecture, showing how the latter, in the presence of network diversity, offer the possibility to improve the QoE at the receiver. We also design a framework to characterize the error model and to map it into a QoE model, therefore providing an analytical characterisation of a multipath channel.Source: Vehicular communications (Print) 33 (2021). doi:10.1016/j.vehcom.2021.100443
DOI: 10.1016/j.vehcom.2021.100443
Metrics:
Bacco M., Cassarà P., Gotta A.
In this work, we focus our attention on real-time multimedia flows from Unmanned Aerial Vehicles (UAVs) to the ground, presenting and analysing the data collected in field trials during a real testbed. The objective is assessing whether a video feed of reasonable quality can be provided to the pilot of a UAV to enable Beyond Visual Line of Sight (BVLoS) operations, by exploiting the multiple cellular operators available in the area. Three cellular networks have been jointly used in a multihoming/multipath setup, leveraging the variable coverage offered in both urban and suburban environments. Taking into account both Quality of Service (QoS) and Quality of Experience (QoE) metrics, the target parameters measured in this testbed are: latency, packet error rate, and video quality, which accounts for frames integrity, continuity, and fluidity. Data collected on the field allow evaluating both QoS and QoE in the presence of a multipath architecture, showing how the latter, in the presence of network diversity, offer the possibility to improve the QoE at the receiver. We also design a framework to characterize the error model and to map it into a QoE model, therefore providing an analytical characterisation of a multipath channel.Source: Vehicular communications (Print) 33 (2021). doi:10.1016/j.vehcom.2021.100443
DOI: 10.1016/j.vehcom.2021.100443
Metrics:
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ISTI Repository | www.sciencedirect.com | CNR ExploRA