2,166 research outputs found
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs
The dissemination of vehicle position data all over the network is a
fundamental task in Vehicular Ad Hoc Network (VANET) operations, as
applications often need to know the position of other vehicles over a large
area. In such cases, inter-vehicular communications should be exploited to
satisfy application requirements, although congestion control mechanisms are
required to minimize the packet collision probability. In this work, we face
the issue of achieving accurate vehicle position estimation and prediction in a
VANET scenario. State of the art solutions to the problem try to broadcast the
positioning information periodically, so that vehicles can ensure that the
information their neighbors have about them is never older than the
inter-transmission period. However, the rate of decay of the information is not
deterministic in complex urban scenarios: the movements and maneuvers of
vehicles can often be erratic and unpredictable, making old positioning
information inaccurate or downright misleading. To address this problem, we
propose to use the Quality of Information (QoI) as the decision factor for
broadcasting. We implement a threshold-based strategy to distribute position
information whenever the positioning error passes a reference value, thereby
shifting the objective of the network to limiting the actual positioning error
and guaranteeing quality across the VANET. The threshold-based strategy can
reduce the network load by avoiding the transmission of redundant messages, as
well as improving the overall positioning accuracy by more than 20% in
realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European
Wireless 201
Fast Context Adaptation in Cost-Aware Continual Learning
In the past few years, DRL has become a valuable solution to automatically
learn efficient resource management strategies in complex networks with
time-varying statistics. However, the increased complexity of 5G and Beyond
networks requires correspondingly more complex learning agents and the learning
process itself might end up competing with users for communication and
computational resources. This creates friction: on the one hand, the learning
process needs resources to quickly convergence to an effective strategy; on the
other hand, the learning process needs to be efficient, i.e., take as few
resources as possible from the user's data plane, so as not to throttle users'
QoS. In this paper, we investigate this trade-off and propose a dynamic
strategy to balance the resources assigned to the data plane and those reserved
for learning. With the proposed approach, a learning agent can quickly converge
to an efficient resource allocation strategy and adapt to changes in the
environment as for the CL paradigm, while minimizing the impact on the users'
QoS. Simulation results show that the proposed method outperforms static
allocation methods with minimal learning overhead, almost reaching the
performance of an ideal out-of-band CL solution.Comment: arXiv admin note: text overlap with arXiv:2211.1691
An Adaptive Broadcasting Strategy for Efficient Dynamic Mapping in Vehicular Networks
In this work, we face the issue of achieving an efficient dynamic mapping in vehicular networking scenarios, i.e., obtaining an accurate estimate of the positions and trajectories of connected vehicles in a certain area. State-of-the-art solutions are based on the periodic broadcasting of the position information of the network nodes, with an inter-transmission period set by a congestion control scheme. However, the movements and maneuvers of vehicles can often be erratic, making transmitted data inaccurate or downright misleading. To address this problem, we propose to adopt a dynamic transmission scheme based on the actual positioning error, sending new data when the estimate overcomes a preset error threshold. Furthermore, the proposed method adapts the error threshold to the operational context according to an innovative congestion control algorithm that limits the collision probability among broadcast packet transmissions. This threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, and is shown to improve the overall positioning accuracy by more than 20% in realistic urban scenarios
A Multi-Agent Reinforcement Learning Architecture for Network Slicing Orchestration
The Network Slicing (NS) paradigm is one of the pillars of the future 5G networks and is gathering great attention from both industry and scientific communities. In a NS scenario, physical and virtual resources are partitioned among multiple logical networks, named slices, with specific characteristics. The challenge consists in finding efficient strategies to dynamically allocate the network resources among the different slices according to the user requirements. In this paper, we tackle the target problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agent training is carried out following the Advantage Actor Critic algorithm, which makes it possible to handle continuous action spaces. By means of extensive simulations, we show that our strategy yields better performance than an efficient empirical algorithm, while ensuring high adaptability to different scenarios without the need for additional training.acceptedVersio
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control
Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs)
in monitoring and remote area surveillance applications has become widespread
thanks to the price reduction and the increased capabilities of drones. The
drones in the swarm need to cooperatively explore an unknown area, in order to
identify and monitor interesting targets, while minimizing their movements. In
this work, we propose a distributed Reinforcement Learning (RL) approach that
scales to larger swarms without modifications. The proposed framework relies on
the possibility for the UAVs to exchange some information through a
communication channel, in order to achieve context-awareness and implicitly
coordinate the swarm's actions. Our experiments show that the proposed method
can yield effective strategies, which are robust to communication channel
impairments, and that can easily deal with non-uniform distributions of targets
and obstacles. Moreover, when agents are trained in a specific scenario, they
can adapt to a new one with minimal additional training. We also show that our
approach achieves better performance compared to a computationally intensive
look-ahead heuristic.Comment: Preprint of the paper published in IEEE Transactions on Cognitive
Communications and Networking ( Early Access
Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking
Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs)
have captured the interest of both the scientific and the industrial
communities. A typical scenario consists in the use of UAVs for surveillance or
target-search missions over a wide geographical area. In this case, it is
fundamental for the command center to accurately estimate and track the
trajectories of the UAVs by exploiting their periodic state reports. In this
work, we design an ad hoc tracking system that exploits the Long Range Wide
Area Network (LoRaWAN) standard for communication and an extended version of
the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone
movements in a 3D environment. Simulation results on a publicly available
dataset show that our system can reliably estimate the position and trajectory
of a UAV, significantly outperforming baseline tracking approaches.Comment: 6 pages, 6 figures, in review for IEEE WISARN 2020 (INFOCOM WORKSHOP)
2020 : IEEE WiSARN 2020 (INFOCOM WORKSHOP) 2020: 13th International Workshop
on Wireless Sensor, Robot and UAV Network
Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review
: The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events
Impact of protein-ligand solvation and desolvation on transition state thermodynamic properties of adenosine A2Aligand binding kinetics
Ligand-protein binding kinetic rates are growing in importance as parameters to consider in drug discovery and lead optimization. In this study we analysed using surface plasmon resonance (SPR) the transition state (TS) properties of a set of six adenosine A2Areceptor inhibitors, belonging to both the xanthine and the triazolo-triazine scaffolds. SPR highlighted interesting differences among the ligands in the enthalpic and entropic components of the TS energy barriers for the binding and unbinding events. To better understand at a molecular level these differences, we developed suMetaD, a novel molecular dynamics (MD)-based approach combining supervised MD and metadynamics. This method allows simulation of the ligand unbinding and binding events. It also provides the system conformation corresponding to the highest energy barrier the ligand is required to overcome to reach the final state. For the six ligands evaluated in this study their TS thermodynamic properties were linked in particular to the role of water molecules in solvating/desolvating the pocket and the small molecules. suMetaD identified kinetic bottleneck conformations near the bound state position or in the vestibule area. In the first case the barrier is mainly enthalpic, requiring the breaking of strong interactions with the protein. In the vestibule TS location the kinetic bottleneck is instead mainly of entropic nature, linked to the solvent behaviour
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