Intelligent energy efficient vehicle automation system with sensible edge processing protocol in Internet of Vehicles using hybrid optimization strategy

Deepa, J.; Ali, S. Ahamed; Hemamalini, S.

doi:10.1007/s11276-022-03204-5

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…In future work, we aim to compare the VILE mechanism with other proposals in the literature and evaluate it in more complex scenarios. We also intend to consider the perception and processing latency from the sensors, for example, by adding the processing model defined by activity estimation [52], as well as evaluating the transmission delay and a prediction model that chooses the CAV that will stay longer inside the hex region. The security of the provided proposal will also be examined in future work.…”

Section: Discussionmentioning

confidence: 99%

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

et al. 2023

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Due to poor local range of the perception and object recognition mechanisms used by autonomous vehicles, incorrect decisions can be made, which can jeopardize a fully autonomous operation. A connected and autonomous vehicle should be able to combine its local perception with the perceptions of other vehicles to improve its capability to detect and predict obstacles. Such a collective perception system aims to expand the field of view of autonomous vehicles, augmenting their decision-making process, and as a consequence, increasing driving safety. Regardless of the benefits of a collective perception system, autonomous vehicles must intelligently select which data should be shared with who and when in order to conserve network resources and maintain the overall perception accuracy and time usefulness. In this context, the operational impact and benefits of a redundancy reduction mechanism for collective perception among connected autonomous vehicles are analyzed in this article. Therefore, we propose a reliable redundancy mitigation mechanism for collective perception services to reduce the transmission of inefficient messages, which is called VILE. Knowledge, selection, and perception are the three phases of the cooperative perception process developed in VILE. The results have shown that VILE is able to reduce it the absolute number of redundant objects of 75% and generated packets by up to 55%. Finally, we discuss possible research challenges and trends.

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Section: Discussionmentioning

confidence: 99%

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

et al. 2023

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“…Another approach to resource allocation is based on evolutionary algorithms [13]. In [14], the authors proposed an improved differential evolution algorithm for resource allocation in ad hoc networks of quantum-enabled IoT devices. The proposed algorithm aims to optimize energy consumption and data transmission rate by adapting the algorithm parameters based on the optimization progress.…”

Section: Resource Allocation Techniques For Ad Hoc Network Of Quantum...mentioning

confidence: 99%

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

2024

Preprint

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Ad hoc networks of quantum-enabled IoT devices require efficient resource allocation techniques to optimize energy consumption and data transmission rates. This paper proposes an enhanced version of quantum-inspired hybrid particle swarm optimization called the Improved quantum-inspired hybrid particle swarm optimization (IQHPSO) algorithm for resource allocation in ad hoc networks of quantum-enabled IoT devices. The enhanced QHPSO algorithm incorporates a novel innovation technique based on adaptive parameter tuning and a global best selection strategy that further improves the optimization performance of the QHPSO approach. We evaluate the proposed approach through simulations and compare its performance with the original QHPSO algorithm and other state-of-the-art optimization techniques regarding objective functions such as energy consumption, data transmission rate, and network lifetime. Our results show that the IQHPSO algorithm outperforms the original QHPSO algorithm by 10-15% in energy consumption, 5-10% in data transmission rate, and 3-7% in network lifetime. Furthermore, our results demonstrate that the IQHPSO algorithm outperforms other state-of-the-art optimization techniques regarding these objective functions. We also discuss the practical implementation of the IQHPSO algorithm in a real-world ad hoc network of quantum-enabled IoT devices. This research contributes to the ongoing development of quantum-inspired optimization techniques for wireless networking applications. Furthermore, it demonstrates the benefits of incorporating innovative approaches, such as adaptive parameter tuning and global best selection strategy, to improve the performance of existing optimization algorithms.

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SIoV-FTFSA-CAOA: a fuzzy trust-based approach for enhancing security and energy efficiency in social internet of vehicles

Jegatheesan,

Arumugam

2024

Wireless Netw

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Intelligent energy efficient vehicle automation system with sensible edge processing protocol in Internet of Vehicles using hybrid optimization strategy

Cited by 6 publications

References 43 publications

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

SIoV-FTFSA-CAOA: a fuzzy trust-based approach for enhancing security and energy efficiency in social internet of vehicles

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