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Abstract-This paper provides an introductory overview of Vehicular Delay-Tolerant Networks. First, an introduction to Delay-Tolerant Networks and Vehicular Delay-Tolerant Networks is given. Delay-Tolerant schemes and protocols can help in situations where network connectivity is sparse or with large variations in density, or even when there is no end-to-end connectivity by providing a communications solution for non realtime applications. Some special issues like routing are addressed in the paper and an introductory description of applications and the most important projects is given. Finally, some research challenges are discussed and conclusions are detailed.

Abstract-The software-defined network (SDN) advocates a centralized network control, where a controller manages a network from a global view of the network. Large SDN networks may consist of multiple controllers or controller domains that distribute the network management between them, where each controller has a logically centralized but physically distributed vision of the network. In this context, a key challenge faced by providers is to define a scalable control network that exploits the benefits of SDN when used in conjunction with efficient management strategies. Most of the control layer models proposed are not concerned with controller scalability, because they assume that commercial controllers are scalable in terms of capacity (quantity of flows processed per second). However, it has been demonstrated that overloads and long propagation delays among controllers and controllers-switches can lead to a long response time of the controllers, affecting their ability to respond to network events in a very short time and reducing the reliability of communication.In this work we define the principles for designing a scalable control layer for SDN, and show the desired control layer characteristics that optimize the management of the network. We address these principles from the perspective of the controller placement problem. For this purpose we improve and evaluate our previous approach, the algorithm called k-Critical. K-Critical discovers the minimum number of controllers and their location to create a robust control topology that deals robustly with failures and balances the load among the selected controllers. The results demonstrate the effectiveness of our solution by comparing it with other controller placement solutions.Index Terms-Software-defined network, controller scalability, control layer, controller placement problem.

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Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.