SeScR: SDN-Enabled Spectral Clustering-Based Optimized Routing Using Deep Learning in VANET Environment
24 Nov 2020-pp 1-9
TL;DR: 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.
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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).
Abstract: In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.
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TL;DR: This paper explores the design choices made in the development of clustering algorithms targeted at VANETs and presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms.
Abstract: A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles—most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming—the lack of realistic vehicular channel modeling—is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated.
273 citations