An improved multicast based energy efficient opportunistic data scheduling algorithm for VANET

Shrivastava, Anurag; Bansod, Prashant; Gupta, Kamlesh; Merchant, S. N.

doi:10.1016/j.aeue.2017.10.011

Cited by 19 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chang et al [31] proposed an energy-efficient geographic routing algorithm that exploits the direction, density, and distance between nodes in a cross-roads routing strategy to improve link stability. An improved multicast-based energyefficient opportunity data scheduling algorithm has also been proposed to serve selected groups of users by multicasting services at optimal data rates [32]. All these schemes are good at enhancing the connectivity of the network, but they do not pay much attention to the energy consumption aspect.…”

Section: Related Workmentioning

confidence: 99%

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Feng

Huang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

With the development of smart cities and smart electric vehicles (EVs), the problem of improving the performance of Vehicular Ad-hoc Networks (VANETs) is gradually being emphasized. To improve the network performance of VANETs, some scholars have considered parked vehicles as roadside units, but have not paid attention to the energy consumption characteristics of vehicles, especially electric vehicles. Therefore, in this paper, we propose a QL-mRSU series artificial intelligence energy saving method to optimize the energy consumption of parked electric vehicles during communication. The method is based on electric vehicle self-organizing networks (E-VANETs), which dynamically cluster electric vehicles parked in parking lots by parameters such as traffic flow, number of service demands, and charging index in reinforcement learning, select the most suitable vehicles as mobile roadside units (mRSUs), and adjust the working mode according to environmental changes such as the number of service demands to achieve the effects of self-learning and energy saving. The simulation experimental results show that compared with other energy-based routing algorithms, the method is able to make optimal choices through self-learning with guaranteed communication quality and is more adaptable to traffic flow changes on the road, thus ensuring the stability of energy-saving efficiency. In addition, the method significantly improves the energy structure of electric vehicle parking clusters.

show abstract

Section: Related Workmentioning

confidence: 99%

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Feng

Huang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Therefore, by estimating the optimum data rate and optimum number of users with good channel conditions and serving the users by multicasting the service at the optimal data rate, the power efficiency and throughput of RSU broadcasting can be improved. The authors in [50] consider a scheduling scenario where the RSU is installed to provide infotainment services to the vehicles within the communication range. Each broadcast time slot is equivalent to the coherence time during which the channel state remains unchanged.…”

Section: V2i Communicationsmentioning

confidence: 99%

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

View full text Add to dashboard Cite

As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

show abstract

“…Similarly, the work of [24] selected rebroadcast vehicle by considering link available time which is acquired by the relative speed, radio transmission range, and the inter-vehicle distance between vehicles moving in the same direction and on the same road. Whereas, the work of [25], [26] considered channel conditions for broadcast/multicast. Combining duty-cycle with context-awareness, the work of [25] studied efficient context-aware multi-hop broadcasting protocol, which allows nodes to adapt their behavior according to the changing network dynamics and context.…”

Section: Related Work and Paper Contributionsmentioning

confidence: 99%

“…Combining duty-cycle with context-awareness, the work of [25] studied efficient context-aware multi-hop broadcasting protocol, which allows nodes to adapt their behavior according to the changing network dynamics and context. The authors [26] considered two state channel conditions, either good or bad, and investigated an opportunistic multicast scheduling algorithm. The algorithm estimates optimum data rate and optimum number of users having appropriate channel conditions to select user group for multicast.…”

Section: Related Work and Paper Contributionsmentioning

confidence: 99%

Energy Efficient Resource Allocation and Scheduling for Delay-Constrained Multi-Cell Broadcast Networks

2020

View full text Add to dashboard Cite

Resource allocation in wireless cellular networks has been an important and challenging issue. With the rapid development of wireless communications, more and more mobile multimedia and innovative applications (e.g. augmented reality and video conferences) have emerged, which are suitable for wireless broadcast and have requirements on transmission delay. In this paper, we focus on delayconstrained multi-cell broadcast networks, taking user mobility into consideration for future channel estimation. The promising network coding technique is also adopted to improve transmission efficiency. The objective is to minimize total transmission energy consumption via joint resource allocation and scheduling optimization. Both optimal and heuristic solutions are proposed. Simulation results show that the proposed schemes perform better than other benchmark schemes, in terms of transmission energy consumption and success rate.

show abstract

An improved multicast based energy efficient opportunistic data scheduling algorithm for VANET

Cited by 19 publications

References 11 publications

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Energy Efficient Resource Allocation and Scheduling for Delay-Constrained Multi-Cell Broadcast Networks

Contact Info

Product

Resources

About