201 research outputs found
Collaborative Reinforcement Learning for Multi-Service Internet of Vehicles
Internet of Vehicles (IoV) is a recently introduced paradigm aiming at extending the Internet of Things (IoT) toward the vehicular scenario in order to cope with its specific requirements. Nowadays, there are several types of vehicles, with different characteristics, requested services, and delivered data types. In order to efficiently manage such heterogeneity, Edge Computing facilities are often deployed in the urban environment, usually co-located with the Roadside Units (RSUs), for creating what is referenced as Vehicular Edge Computing (VEC). In this paper, we consider a joint network selection and computation offloading optimization problem in multi-service VEC environments, aiming at minimizing the overall latency and the consumed energy in an IoV scenario. Two novel collaborative Q-learning based approaches are proposed, where Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication paradigms are exploited, respectively. In the first approach, we define a collaborative Q-learning method in which, through V2I communications, several vehicles participate in the training process of a centralized Q-agent. In the second approach, by exploiting the V2V communications, each vehicle is made aware of the surrounding environment and the potential offloading neighbors, leading to better decisions in terms of network selection and offloading. In addition to the tabular method, an advanced deep learning-based approach is also used for the action value estimation, allowing to handle more complex vehicular scenarios. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy with respect to some benchmark solutions
A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks
Vehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy
Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems
Supported by some of the major revolutionary technologies, such as Internet of Vehicles (IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks (VNs) are changing drastically and converging rapidly into one of the most complex, highly intelligent, and advanced networking systems, mostly known as Intelligent Transportation System (ITS). Recently, distributed ML techniques, such as Federated Learning (FL) have gained huge popularity mainly for their advantages in terms of intelligence sharing and privacy concerns. VNs are a natural contender for exploiting FL for solving challenging problems; however, their limited resources, dynamic nature, high speed, and reduced latency requirements often become the bottleneck. V2X communication technologies allow vehicular terminals (VTs) to share their valuable local environment parameters and become aware of their surroundings. Such information can be utilized to build a more sustainable and affordable FL platform for serving VTs. Gaining from recently introduced 3D architectures, integrating terrestrial and aerial edge computing layers, we present here a distributed FL platform able to distribute the FL process on a 3D fashion while reducing the overall communication cost for providing vehicular services. The framework is defined as a constrained optimization problem for reducing the overall FL process cost through a proper network selection between various nodes. We have modeled the FL network selection problem as a sequential decision-making process through a Markov Decision Process (MDP) with time-dependent state transition probabilities. A computation-efficient value iteration algorithm is adapted for solving the MDP. Comparison with various benchmark methods shows the overall improvement in terms of latency, energy, and FL performance
Energy Efficient Adaptive Network Coding Schemes for Satellite Communications
In this paper, we propose novel energy efficient adaptive network coding and
modulation schemes for time variant channels. We evaluate such schemes under a
realistic channel model for open area environments and Geostationary Earth
Orbit (GEO) satellites. Compared to non-adaptive network coding and adaptive
rate efficient network-coded schemes for time variant channels, we show that
our proposed schemes, through physical layer awareness can be designed to
transmit only if a target quality of service (QoS) is achieved. As a result,
such schemes can provide remarkable energy savings.Comment: Lecture Notes of the Institute for Computer Sciences, Social
Informatics and Telecommunications Engineering, 24 March 201
Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications
This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed intelligence network for Internet of Things (IoT) devices. The heterogeneous nature of clients and data presents challenges for effective federated learning (FL) techniques, prompting our exploration of federated transfer learning (FTL) on Raspberry Pi, Odroid, and virtual machine platforms. Our study provides a detailed examination of the design, implementation, and evaluation of the FTL framework, specifically adapted to the unique constraints of various IoT platforms. By measuring the accuracy of FTL across diverse clients, we reveal its superior performance over traditional FL, particularly in terms of faster training and higher accuracy, due to the use of transfer learning (TL). Real-world measurements further demonstrate improved resource efficiency with lower average load, memory usage, temperature, power, and energy consumption when FTL is implemented compared to FL. Our experiments also showcase FTL’s robustness in scenarios where users leave the server’s communication coverage, resulting in fewer clients and less data for training. This adaptability underscores the effectiveness of FTL in environments with limited data, clients, and resources, contributing valuable information to the intersection of edge computing and DL for the 6G IoT
Adaptive Network Coding Schemes for Satellite Communications
In this paper, we propose two novel physical layer aware adaptive network
coding and coded modulation schemes for time variant channels. The proposed
schemes have been applied to different satellite communications scenarios with
different Round Trip Times (RTT). Compared to adaptive network coding, and
classical non-adaptive network coding schemes for time variant channels, as
benchmarks, the proposed schemes demonstrate that adaptation of packet
transmission based on the channel variation and corresponding erasures allows
for significant gains in terms of throughput, delay and energy efficiency. We
shed light on the trade-off between energy efficiency and delay-throughput
gains, demonstrating that conservative adaptive approaches that favors less
transmission under high erasures, might cause higher delay and less throughput
gains in comparison to non-conservative approaches that favor more transmission
to account for high erasures.Comment: IEEE Advanced Satellite Multimedia Systems Conference and the 14th
Signal Processing for Space Communications Workshop (ASMS/SPSC), 201
Wireless Communication Protocols for Distributed Computing Environments
The distributed computing is an approach relying on the presence of multiple devices that can interact among them in order to perform a pervasive and parallel computing. This chapter deals with the communication protocol aiming to be used in a distributed computing scenario; in particular the considered computing infrastructure is composed by elements (nodes) able to consider specific application requests for the implementation of a service in a distributed manner according to the pervasive grid computing principle (Priol & Vanneschi, 2008; Vanneschi & Veraldi, 2007). In the classical grid computing paradigm, the processing nodes are high performance computers or multicore workstations, usually organized in clusters and interconnected through broadband wired communication networks with small delay (e.g., fiber optic, DSL lines). The pervasive grid computing paradigm overcomes these limitations allowing the development of distributed applications that can perform parallel computations using heterogeneous devices interconnected by different types of communication technologies. In this way, we can resort to a computing environment composed by fixed ormobile devices (e.g., smartphones, PDAs, laptops) interconnected through broadband wireless or wired networks where the devices are able to take part to a grid computing process. Suitable techniques for the pervasive grid computing should be able to discover and organize heterogeneous resources, to allow scaling an application according to the computing power, and to guarantee specific QoS profiles (Darby III & Tzeng, 2010; Roy & Das, 2009). In particular, aim of this chapter is to present the most important challenges for the communication point of view when forming a distributed network for performing parallel and distributed computing. The focus will be mainly on the resource discovery and computation scheduling on wireless not infrastructured networks by considering their capabilities in terms of reliability and adaptation when facing with heterogeneous computing requests
Network Coding Channel Virtualization Schemes for Satellite Multicast Communications
In this paper, we propose two novel schemes to solve the problem of finding a
quasi-optimal number of coded packets to multicast to a set of independent
wireless receivers suffering different channel conditions. In particular, we
propose two network channel virtualization schemes that allow for representing
the set of intended receivers in a multicast group to be virtualized as one
receiver. Such approach allows for a transmission scheme not only adapted to
per-receiver channel variation over time, but to the network-virtualized
channel representing all receivers in the multicast group. The first scheme
capitalizes on a maximum erasure criterion introduced via the creation of a
virtual worst per receiver per slot reference channel of the network. The
second scheme capitalizes on a maximum completion time criterion by the use of
the worst performing receiver channel as a virtual reference to the network. We
apply such schemes to a GEO satellite scenario. We demonstrate the benefits of
the proposed schemes comparing them to a per-receiver point-to-point adaptive
strategy
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision
The forthcoming 6G-enabled Intelligent Transportation System (ITS) is set to
redefine conventional transportation networks with advanced intelligent
services and applications. These technologies, including edge computing,
Machine Learning (ML), and network softwarization, pose stringent requirements
for latency, energy efficiency, and user data security. Distributed Learning
(DL), such as Federated Learning (FL), is essential to meet these demands by
distributing the learning process at the network edge. However, traditional FL
approaches often require substantial resources for satisfactory learning
performance. In contrast, Transfer Learning (TL) and Split Learning (SL) have
shown effectiveness in enhancing learning efficiency in resource-constrained
wireless scenarios like ITS. Non-terrestrial Networks (NTNs) have recently
acquired a central place in the 6G vision, especially for boosting the
coverage, capacity, and resilience of traditional terrestrial facilities.
Air-based NTN layers, such as High Altitude Platforms (HAPs), can have added
advantages in terms of reduced transmission distances and flexible deployments
and thus can be exploited to enable intelligent solutions for latency-critical
vehicular scenarios. With this motivation, in this work, we introduce the
concept of Federated Split Transfer Learning (FSTL) in joint air-ground
networks for resource-constrained vehicular scenarios. Simulations carried out
in vehicular scenarios validate the efficacy of FSTL on HAPs in NTN,
demonstrating significant improvements in addressing the demands of ITS
applications.Comment: 6 pages, 5 figure
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