Proceedings ArticleDOI
SeScR: SDN-Enabled Spectral Clustering-Based Optimized Routing Using Deep Learning in VANET Environment
Ankur Nahar,Debasis Das +1 more
- pp 1-9
TLDR
In this article, a spectral clustering technique along with the deep deterministic policy gradient (DDPG) algorithm using hybrid SDN architecture is proposed to enhance cluster stability and route selection method.Abstract:
In recent years, integration of clustering architecture with software-defined networking (SDN) has emerged as is the crucial enabler for next-generation intelligent transportation services (ITS). This paper proposes a spectral clustering technique along with the deep deterministic policy gradient (DDPG) algorithm using hybrid SDN architecture, called SeScR to enhance cluster stability and route selection method. The spectral clustering is used to overcome the arbitrary node distribution of vehicular ad-hoc networks (VANETs) and provide a flexible clustering using eigenvalues of graph laplacian. Moreover, the DDPG algorithm addresses the continuous address space of VANETs and provides an actor-critic architecture for optimal routing decisions. The experimental results demonstrate that the proposed scheme improves path selection and load balancing with better performance in terms of low average transmission delay up to 15%, throughput up to 18-22%, and low computation overhead 10% compared to the existing state-of-the-art protocols used in this research.read more
Citations
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Journal ArticleDOI
A Survey on Machine Learning Techniques for Routing Optimization in SDN
TL;DR: In this paper, the authors present a survey of machine learning techniques for routing optimization in SDN based on three core categories (i.e., supervised learning, unsupervised learning, and reinforcement learning).
Journal ArticleDOI
Topology-Aware Resilient Routing Protocol for FANETs: An Adaptive Q-Learning Approach
TL;DR: A topology-aware resilient routing strategy based on adaptive TARRAQ-learning to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way is proposed.
Journal ArticleDOI
Topology-Aware Resilient Routing Protocol for FANETs: An Adaptive <i>Q</i>-Learning Approach
TL;DR: In this paper , a topology-aware resilient routing strategy based on adaptive Q-learning (TARRAQ) is proposed to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way.
Journal ArticleDOI
Reinforcement Learning-Based Routing Protocols in Vehicular Ad Hoc Networks for Intelligent Transport System (ITS): A Survey
TL;DR: In this paper , the authors review reinforcement learning and its characteristics and study how to use this technique for creating routing protocols in VANETs and propose a categorization of RL-based routing schemes in these networks.
Journal ArticleDOI
Reinforcement Learning-Based Routing Protocols in Vehicular and Flying Ad Hoc Networks – A Literature Survey
TL;DR: In this paper , a comprehensive categorisation of RL-based routing protocols for both network types, having in mind their simultaneous use and the inclusion with other technologies is performed, based on different factors that influence the reward function in RL and the consequences they have on network performance.
References
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Novel self-adaptive routing service algorithm for application in VANET
TL;DR: A reliable self-adaptive routing algorithm (RSAR) based on this heuristic service algorithm is proposed and, by combining the reliability parameter and adjusting the heuristic function, RSAR achieves good performance with VANET.
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Journal ArticleDOI
SDN-based real-time urban traffic analysis in VANET environment
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Journal ArticleDOI
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