Search Results (3)

The advent of sixth-generation (6G) networks brings unmatched speed, reliability, and capacity for massive connections, making it a cornerstone for revolutionary applications. One such application is in vehicular networks, which have their unique demands and complexities. Specifically, they face the complex issue of packet reordering due to the high-speed movement of vehicles and frequent switching of network connections. This paper examines the impact and causes of packet reordering, its threats to network efficiency, and potential countermeasures, particularly in the context of 6G-enabled vehicular networks. We introduce end-to-end methods and metrics to address packet reordering in 6G, discussing the development trends and application prospects. Our findings highlight the emergence of sophisticated strategies, such as prediction and avoidance, to manage packet reordering. They also reveal potential applications to boost network reliability, emulate traffic distributions, and enhance data security. Furthermore, we anticipate a growing integration of machine learning and data-driven optimization in tackling packet reordering. The insights provided aim to influence the future design and optimization of 6G networks, particularly concerning packet management and performance. This paper aims to assist researchers and practitioners in effectively leveraging packet reordering to promote efficient and secure operations of future 6G networks. Full article

In vehicle-to-everything (V2X) networks, security and safety are inherently difficult tasks due to the distinct characteristics of such networks, such as their highly dynamic topology and frequent connectivity disruptions. Jellyfish attacks are a sort of denial of service attack that are challenging to deal with, since they conform to protocol norms while impairing network performance, particularly in terms of communication overhead and reliability. Numerous existing approaches have developed new techniques with which to identify and prevent these attacks; however, no approach has been capable of facing all three types of Jellyfish attacks, which include reordering attacks, delay variance attacks, and periodic drop attacks. In this work, we design a new protocol that analyzes the behavior of every node in a network and selects the trusted routes for data transmission to their intended destination by calculating different Honesty metrics. The OMNET++ simulator was used to evaluate the overall performance of the proposed protocol. Various evaluation metrics, such as the packet delivery ratio, end-to-end delay, and throughput, are considered and compared with other existing approaches. Full article

Because of the specific characteristics of Unmanned Aerial Vehicle (UAV) networks and real-time applications, the trade-off between delay and reliability imposes problems for streaming video. Buffer management and drop packets policies play a critical role in the final quality of the video received by the end station. In this paper, we present a reactive buffer management algorithm, called Multi-Source Application Layer Automatic Repeat Request (MS-AL-ARQ), for a real-time non-interactive video streaming system installed on a standalone UAV network. This algorithm implements a selective-repeat ARQ model for a multi-source download scenario using a shared buffer for packet reordering, packet recovery, and measurement of Quality of Service (QoS) metrics (packet loss rate, delay and, delay jitter). The proposed algorithm MS-AL-ARQ will be injected on the application layer to alleviate packet loss due to wireless interference and collision while the destination node (base station) receives video data in real-time from different transmitters at the same time. Moreover, it will identify and detect packet loss events for each data flow and send Negative-Acknowledgments (NACKs) if packets were lost. Additionally, the one-way packet delay, jitter, and packet loss ratio will be calculated for each data flow to investigate the performances of the algorithm for different numbers of nodes under different network conditions. We show that the presented algorithm improves the QoS of the video data received under the worst network connection conditions. Furthermore, some congestion issues during deep analyses of the algorithm’s performances have been identified and explained. Full article