518 research outputs found
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
On the Experimental Evaluation of Vehicular Networks: Issues, Requirements and Methodology Applied to a Real Use Case
One of the most challenging fields in vehicular communications has been the
experimental assessment of protocols and novel technologies. Researchers
usually tend to simulate vehicular scenarios and/or partially validate new
contributions in the area by using constrained testbeds and carrying out minor
tests. In this line, the present work reviews the issues that pioneers in the
area of vehicular communications and, in general, in telematics, have to deal
with if they want to perform a good evaluation campaign by real testing. The
key needs for a good experimental evaluation is the use of proper software
tools for gathering testing data, post-processing and generating relevant
figures of merit and, finally, properly showing the most important results. For
this reason, a key contribution of this paper is the presentation of an
evaluation environment called AnaVANET, which covers the previous needs. By
using this tool and presenting a reference case of study, a generic testing
methodology is described and applied. This way, the usage of the IPv6 protocol
over a vehicle-to-vehicle routing protocol, and supporting IETF-based network
mobility, is tested at the same time the main features of the AnaVANET system
are presented. This work contributes in laying the foundations for a proper
experimental evaluation of vehicular networks and will be useful for many
researchers in the area.Comment: in EAI Endorsed Transactions on Industrial Networks and Intelligent
Systems, 201
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A Q-Learning approach with collective contention estimation for bandwidth-efficient and fair access control in IEEE 802.11p vehicular networks
Vehicular Ad hoc Networks (VANETs) are wireless networks formed of moving vehicle-stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimising channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-Learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based Medium Access Control (MAC) protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that "learn" how to act optimally in a network in order to maximise their achieved packet delivery and minimise bandwidth wastage. Additionally, via a Collective Contention Estimation (CCE) mechanism which we embed on the Q-Learning agent, faster convergence, higher throughput and short-term fairness are achieved
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Intelligent and bandwidth-efficient medium access control protocols for IEEE 802.11p-based Vehicular Ad hoc Networks
Vehicle-to-Vehicle (V2V) technology aims to enable safer and more sophisticated transportation via the spontaneous formation of Vehicular Ad hoc Networks (VANETs). This type of wireless networks allows the exchange of kinematic and other data among vehicles, for the primary purpose of safer and more efficient driving, as well as efficient traffic management and other third-party services. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs.
This thesis focuses on optimising channel access strategies, which is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. To start with, the default channel access control method for V2V is evaluated hardware via modifying the appropriate wireless interface Linux driver to enable finer on-the-fly control of IEEE 802.11p access control layer parameters. More complex channel sharing scenarios are evaluated via simulations and findings on the behaviour of the access control mechanism are presented. A complete channel sharing efficiency assessment is conducted, including throughput, fairness and latency measurements. A new IEEE 802.11p-compatible Q-Learning-based access control approach that improves upon the studied protocol is presented. The stations feature algorithms that “learn” how to act optimally in VANETs in order to maximise their achieved packet delivery and minimise bandwidth wastage. The feasibility of Q-Learning to be used as the base of selflearning protocols for IEEE 802.11p-based V2V communication access control in dense environments is investigated in terms of parameter tuning, necessary time of exploration, achieving latency requirements, scaling, multi-hop and accommodation of simultaneous applications. Additionally, the novel Collection Contention Estimation (CCE) mechanism for Q-Learning-based access control is presented. By embedding it on the Q-Learning agents, faster convergence, higher throughput, better service separation and short-term fairness are achieved in simulated network deployments.
The acquired new insights on the network performance of the proposed algorithms can provide precise guidelines for efficient designs of practical, reliable, fair and ultra-low latency V2V communication systems for dense topologies. These results can potentially have an impact across a range of related areas, including various types of wireless networks and resource allocation for these, network protocol and transceiver design as well as QLearning applicability and considerations for correct use
Smart handoff technique for internet of vehicles communication using dynamic edge-backup node
© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/electronics9030524A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV); it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.Published onlin
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Contention-based learning MAC protocol for broadcast Vehicle-to-Vehicle Communication
Vehicle-to-Vehicle Communication (V2V) is an upcoming technology that can enable safer, more efficient transportation via wireless connectivity among moving cars. The key enabling technology, specifying the physical and medium access control (MAC) layers of the V2V stack is IEEE 802.11p, which belongs in the IEEE 802.11 family of protocols originally designed for use in WLANs. V2V networks are formed on an ad hoc basis from vehicular stations that rely on the delivery of broadcast transmissions for their envisioned services and applications. Broadcast is inherently more sensitive to channel contention than unicast due to the MAC protocol’s inability to adapt to increased network traffic and colliding packets never being detected or recovered. This paper addresses this inherent scalability problem of the IEEE 802.11p MAC protocol. The density of the network can range from being very sparse to hundreds of stations contenting for access to the channel. A suitable MAC needs to offer the capacity for V2V exchanges even in such dense topologies which will be common in urban networks. We present a modified version of the IEEE 802.11p MAC based on Reinforcement Learning (RL), aiming to reduce the packet collision probability and bandwidth wastage. Implementation details regarding both the learning algorithm tuning and the networking side are provided. We also present simulation results regarding achieved message packet delivery and possible delay overhead of this solution. Our solution shows up to 70% increase in throughput compared to the standard IEEE 802.11p as the network traffic increases, while maintaining the transmission latency within the acceptable levels
DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems
In a world where Artificial Intelligence revolutionizes inference, prediction
and decision-making tasks, Digital Twins emerge as game-changing tools. A case
in point is the development and optimization of Cooperative Intelligent
Transportation Systems (C-ITSs): a confluence of cyber-physical digital
infrastructure and (semi)automated mobility. Herein we introduce Digital Twin
for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles
shortcomings of traditional vehicular and network simulators. It provides a
flexible, modular, and scalable implementation to ensure large-scale, city-wide
experimentation with a moderate computational cost. The defining feature of our
Digital Twin is a unique architecture allowing for submission of sequential
queries, to which the Digital Twin provides instantaneous responses with the
"state of the world", and hence is an Oracle. With such bidirectional
interaction with external intelligent agents and realistic mobility traces,
DRIVE provides the environment for development, training and optimization of
Machine Learning based C-ITS solutions.Comment: Accepted for publication at IEEE ISCC 202
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