Journal ArticleDOI
Directional routing and scheduling for green vehicular delay tolerant networks
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TLDR
A directional routing and scheduling scheme (DRSS) for green vehicle DTNs is presented by using Nash Q-learning approach that can optimize the energy efficiency with the considerations of congestion, buffer and delay.Abstract:
The vehicle delay tolerant networks (DTNs) make opportunistic communications by utilizing the mobility of vehicles, where the node makes delay-tolerant based "carry and forward" mechanism to deliver the packets. The routing schemes for vehicle networks are challenging for varied network environment. Most of the existing DTN routing including routing for vehicular DTNs mainly focus on metrics such as delay, hop count and bandwidth, etc. A new focus in green communications is with the goal of saving energy by optimizing network performance and ultimately protecting the natural climate. The energy---efficient communication schemes designed for vehicular networks are imminent because of the pollution, energy consumption and heat dissipation. In this paper, we present a directional routing and scheduling scheme (DRSS) for green vehicle DTNs by using Nash Q-learning approach that can optimize the energy efficiency with the considerations of congestion, buffer and delay. Our scheme solves the routing and scheduling problem as a learning process by geographic routing and flow control toward the optimal direction. To speed up the learning process, our scheme uses a hybrid method with forwarding and replication according to traffic pattern. The DRSS algorithm explores the possible strategies, and then exploits the knowledge obtained to adapt its strategy and achieve the desired overall objective when considering the stochastic non-cooperative game in on-line multi-commodity routing situations. The simulation results of a vehicular DTN with predetermined mobility model show DRSS achieves good energy efficiency with learning ability, which can guarantee the delivery ratio within the delay bound.read more
Citations
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Journal ArticleDOI
Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey.
Ida Nurcahyani,Jeong Woo Lee +1 more
TL;DR: In this article, the authors presented how machine learning is leveraged in the vehicular network resource allocation strategy and provided an analysis of how authors designed their scenarios to orchestrate the resource allocation mechanism.
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CADMA: collision-avoidance directional medium access for vehicular ad hoc networks
TL;DR: A collision-avoidance directional medium access (CADMA) protocol and infrastructure-utilized clustering method for VANET to support reliable data transfer and simulation results indicate that the CADMA can reduce transmission delays and the collision rate of the broadcasting signal, and have shown that it can be effectively utilized for the VANet systems.
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Quality of service management for IPTV services support in VANETs: a performance evaluation study
TL;DR: The implementation assures a priority for handling IPTV traffic, such that maximise the usage of VANETs resources, and opens the possibility that loss and delay can be minimised to a degree that could guarantee quality IPTV service delivery among vehicle in a vehicular network system.
Journal ArticleDOI
Analysis of performance parameters for wireless network using switching multiple access control method
TL;DR: In this paper, a switching-based multiple access control model is proposed to improve the data transmission performance of wireless asynchronous transfer mode (ATM) in wireless networks, where three control access is processed; polling, token passing, and reservation algorithms for collision avoidance.
Journal ArticleDOI
Uniform description of interference and load based routing metric for wireless mesh networks
TL;DR: An isotonic routing metric is proposed, called MIL (metric based on uniform description of interference and load), which uniformly describes factors including physical interference, logical intra-flow and inter-flow interference, and node load.
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