Scheduling Algorithm supporting V2X Communications based on NOMA access

Bouhamed, Emna; Marouane, Hend; Trabelsi, Ala Din; Zerai, Faouzi

doi:10.1016/j.ifacol.2020.12.1984

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [16], a new scheduling algorithm is introduced, based on a Block Resource Allocation policy in a Non-Orthogonal Multiple Access (NOMA) way, so that a well-defined set of users is selected to use the same RB. To identify such sets, the scheme considers metrics related to the distance and the equity between users.…”

Section: Proposed Schedulers For 5g and Beyond Including V2x Communic...mentioning

confidence: 99%

An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks

Kanavos

Barmpounakis

Kaloxylos

2023

Telecom

View full text Add to dashboard Cite

Automated driving requires the support of critical communication services with strict performance requirements. Existing fifth-generation (5G) schedulers residing at the base stations are not optimized to differentiate between critical and non-critical automated driving applications. Thus, when the traffic load increases, there is a significant decrease in their performance. Our paper introduces SOVANET, a beyond 5G scheduler that considers the Radio Access Network (RAN) load, as well as the requirements of critical, automated driving applications and optimizes the allocation of resources to them compared to non-critical services. The proposed scheduler is evaluated through extensive simulations and compared to the typical Proportional Fair scheduler. Results show that SOVANET’s performance for critical services presents clear benefits.

show abstract

Section: Proposed Schedulers For 5g and Beyond Including V2x Communic...mentioning

confidence: 99%

An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks

Kanavos

Barmpounakis

Kaloxylos

2023

Telecom

View full text Add to dashboard Cite

show abstract

“…A message in VANET can propagate roughly 300 m to 1000 m and is either an Event-based alert or a Basic Safety Message (BSM) [4]. Event-based alerts are sent out when criteria are met, like lane changes and collisions [5]. In contrast, BSMs are broadcast every 100 ms to share vehicle-related information, like location coordinates, velocity, acceleration, and heading [6].…”

Section: Introductionmentioning

confidence: 99%

Using the received signal strength indicator and machine learning to enhance the security of vehicular ad-hoc networks

Abdelkreem,

Hussein,

Tammam

2024

Preprint

View full text Add to dashboard Cite

A vehicular Ad-hoc Network (VANET) is a wireless network established between vehicles and surrounding infrastructure to enable information exchange between them. Consequently, many applications that can enhance passengers' security assurance and traffic flow are built upon this information. However, malicious nodes can manipulate the exchanged data to attack other nodes and disrupt the network’s normal behavior. For example, if an attacker broadcasts a falsified vehicle’s location, the functionality of applications that rely on accurate location sharing will be confused, and deadly accidents may occur. Thus, many studies have been conducted to implement Misbehavior Detection Schemes (MDSs) to identify position spoofing attacks in VANET. However, the performance results of these MDSs are limited in detecting some attack types, and most of them rely on application layer features that the attacker can alter before transmission to evade the detection model. So, this paper proposes an MDS for position falsification attack detection that employs a physical-layer-based feature called RSSIConf. The RSSIConf feature relies on the Received Signal Strength Indicator of received messages and its confidence intervals at different confidence levels and distances. The evaluations show that the proposed model outperforms existing approaches, where the improvements range between 0.76% to 13.26% in accuracy and 0.74% to 12.71% in F1-Score, depending on the detected attack type.

show abstract