A Hierarchical Failure Detector Based on Architecture in VANETs

Liu, Jiaxi; Ding, Fei; Zhang, Dengyin

doi:10.1109/access.2019.2948599

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…Multi-objective optimization, and the implementation of fuzzy logic to make appropriate gateway decisions based on the QoS scheme. This standard relates to the signal strength, load, and vehicle link connection time received by the cluster head and candidate gateway on the vehicle [9].…”

Section: Related Workmentioning

confidence: 99%

QoS Aware Data Congestion Control Routing in Mobile Ad Hoc Networks for Intelligent Transportations Systems

2024

Teh. vjesn.

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The cooperative QoS Aware Based Data Congestion Control (QoS-ADCC) algorithm is proposed to achieve this goal. It leverages QoS indicators such as connection probability, packet arrival rate, and delay to determine the best route from the source to the destination. These indicators are evaluated for each road segment and combined with estimated relay quality. The QoS-ADCC algorithm combines passive and preventive elements to establish and maintain the routing of data packets. If the source route to the target path is unavailable, the algorithm employs forward and backward reactions to search for candidate routes. The selected route is implemented in the wireless network, and pheromone levels are updated as packets pass through intersections. This approach reduces overhead and transmission delay, utilizing dedicated lanes on urban buses to establish a mobile backbone network and enhance vehicle connectivity. The proposed QoS-ADCC algorithm selects relay data packets between intersections based on QoS dynamic road segments, aiming for a straightforward and efficient routing mechanism. The research involves evaluating different routing protocols and congestion control mechanisms, considering factors such as route stability, end-to-end delay, throughput, and packet loss rate. Simulation experiments using network simulators like NS-3 and OMNET++ as well as real-world testbed experiments will be conducted to assess the performance of the proposed system. The findings of this research will contribute to the development of efficient and reliable data routing mechanisms in MANETs for intelligent transportation systems, ensuring high QoS and improved network performance. Overall, the proposed QoS-ADCC algorithm demonstrates promising results with a throughput value of 31000 Mbps, a Link Stability Speed of 40 meters, a Packet Delivery Ratio of 1.2%, and a routing overhead of 1. These outcomes highlight the potential of the algorithm to enhance data delivery in MANETs within ITS.

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Section: Related Workmentioning

confidence: 99%

QoS Aware Data Congestion Control Routing in Mobile Ad Hoc Networks for Intelligent Transportations Systems

2024

Teh. vjesn.

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“…This algorithm is complex and, therefore, the latency and energy consumption are quite high. A hierarchical failure detection mechanism for data communications in VANET is introduced in [51]. This failure detection system adapts to the network configuration while maintaining high-quality of network performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Gradient Monitored Reinforcement Learning for Jamming Attack Detection in FANETs

Ghelani,

Gharia,

El-Ocla

2024

IEEE Access

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Unmanned Aerial Vehicles (UAVs) have several military and civilian applications to perform tasks that do not require a central processing unit or human involvement. There are several vulnerable characteristics, alternatively limitations, in UAV systems such as data loss, signals interference, disabling sensors, misleading weapons, cyber attacks, disrupting services, etc. Jamming attack is one of the cyber threats that likely lead to denial of service that often occurs in wireless communication systems like Flying ad hoc networks (FANETs) and Internet of Drones (IoD). Over years, there are several approaches proposed by researchers to detect jamming attacks such as rule-based jamming attack detection mechanism, Bayesian game-theoretic mechanism, IoD-based protection mechanism, communication channel techniques (channel hopping, spectrum spreading, MIMO-based jamming mitigation, coding, etc), delay tolerant networking technique, and cryptographic algorithms, however, these methods were not suitable for jamming detection in UAV environment. The major challenges are on the delivery efficiency, processing time, accuracy, energy consumption, flight distance, and flight autonomy. In this paper, we introduce a method to detect the Jamming Attack using Reinforcement Learning-based Gradient Monitored (RLGM) mechanism. RLGM maintains safe regions and reduces gradient variance for intended training and this provides a better accuracy of the learning goal. In addition, RLGM achieves prompt training progress and selects preciously the series of parameters required by the network during the training phase. RLGM produces spontaneous derivation of the essential deep network scale over the training process drawing on automatically unvarying trained weights. Our proposed approach outperforms other reinforcement learning methods such as Federated RL, Deep Q Learning (DQL), and GA-AOMDV.

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“…In [15], a hierarchical failure detection mechanism based on the architecture of VANETs was suggested. The failure detector can adapt to the dynamic network conditions and meet the different Quality of Service (QoS) requirements of multiple applications in VANETs.…”

Section: Topological Approachmentioning

confidence: 99%

Intelligent Transport System Using Time Delay-Based Multipath Routing Protocol for Vehicular Ad Hoc Networks

Ghaemi

El-Ocla

Yadav

et al. 2021

Sensors

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During the last decade, the research on Intelligent Transportation System (ITS) has improved exponentially in real-life scenarios to provide optimized transport network performance. It is a matter of importance that alert messages are delivered promptly to prevent vehicular traffic problems. The fact is an ITS system per se could be a part of a vehicular ad hoc network (VANET) which is an extension of a wireless network. In all sorts of wireless ad hoc networks, the network topology is subjected to change due to the mobility of network nodes; therefore, an existing explored route between two nodes could be demolished in a minor fraction of time. When it comes to the VANETs, the topology likely changes due to the high velocity of nodes. On the other hand, time is a crucial factor playing an important role in message handling between the network’s nodes. In this paper, we propose Time delay-based Multipath Routing (TMR) protocol that effectively identifies an optimized path for packet delivery to the destination vehicle with a minimal time delay. Our algorithm gives a higher priority to alert messages compared to normal messages. It also selects the routes with the short round-trip time (RTT) within the RTT threshold. As a result, our algorithm would realize two goals. Firstly, it would speed up the data transmission rate and deliver data packets, particularly warning messages, to the destination vehicle promptly and therefore avoid vehicular problems such as car accidents. Secondly, the TMR algorithm reduces the data traffic load, particularly of the normal messages, to alleviate the pressure on the network and therefore avoids network congestion and data collisions. This, in turn, lessens the packets’ retransmissions. To demonstrate the effectiveness of the proposed protocol, the TMR has been compared with the other protocols such as AOMDV, FF-AOMDV, EGSR, QMR, and ISR. Simulation results demonstrate that our proposed protocol proves its excellent performance compared to other protocols.

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A Hierarchical Failure Detector Based on Architecture in VANETs

Cited by 6 publications

References 23 publications

QoS Aware Data Congestion Control Routing in Mobile Ad Hoc Networks for Intelligent Transportations Systems

QoS Aware Data Congestion Control Routing in Mobile Ad Hoc Networks for Intelligent Transportations Systems

Gradient Monitored Reinforcement Learning for Jamming Attack Detection in FANETs

Intelligent Transport System Using Time Delay-Based Multipath Routing Protocol for Vehicular Ad Hoc Networks

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