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The recent trends in optical communications are changing very quickly. It is quite amusing to see that the core of every large communication network carries huge traffic every now and then, which was very much unrealistic 20 years ago. This would not have been possible without optical fibers. With the changes in the demand and availability of the new technologies, new frontiers are being added to the main fiber-optic technologies. Now, there are so many emerging technologies in this list, such as the visible-light communication, wireless-optical communications, all-optical computing, intelligent and automated-optical networking, and software-defined optical networking. Furthermore, there are several new initiatives in the optical field outside of telecommunication. In the future, it will be more advanced and diversified with new applications and trends. One day, it may be possible that the whole static communication network will be purely optical.

Smart grids are the new generation power grids which replace the traditional power grids across the world. Traditional power grids have several limitations and they are not compatible with the modern load demands. Smart grids are normally hybrid networks comprising of power grids and a supporting information and communication technology (ICT) network. However, in terms of the functions and capability the smart grids are versatile networks and can provide a lot of services. For smart grids several ICT based services are used to assist in their functions. One of the popular among them is the Internet of thing (IoT). It is found to be exemplary in these service provision operations of smart grids. For large scale deployment of IoT for smart grids a lot of resources are needed. Thus an energy efficient and low resource consuming version of IoT is suitable for smart grids. Narrowband IoT (NBIoT) is one such resource efficient versions of IoT. It is found to be suitable for large scale deployment in projects like smart grids and smart cities. In this article, some of the supporting functions of NBIoT, which can assist in the smart grids are illustrated.

We analyze the shortest path lengths between node pairs of real optical transport networks. From the analysis, we find that Johnson SB distribution is suitable for the shortest path length modeling. The validity of the distributions is evaluated in terms of the Kolmogorov-Smirnov (KS) statistic. Johnson SB distribution provides an average KS statistic of 0.0423, which indicates its good accuracy. We also show that the key parameters of the shortest path lengths, such as the mean, the median, and the standard deviation, can be estimated from the convex area of the network. We develop the proposed Johnson SB distribution model for the shortest path lengths using the basic information of the networks. The developed model is able to estimate path-length dependent system parameters, such as the appropriate modulation formats in transparent optical networks with an average error of only 6.4%. It is noteworthy that these estimations can be made without full knowledge of the network. Only the node locations are required.

Internet of things (IoT) is an integral part of modern digital ecosystem. It is available in different forms. Narrowband IoT (NBIoT) is one of the special forms of the IoTs available for deployment. It is popular due to its low power wide area (LPWA) characteristics. For new initiatives such as smart grids and smart cities, a large number of sensors will be deployed and the demand for power is expected to be high for such IoT deployments. NBIoT has the potential to reduce the power and bandwidth required for large IoT projects. In this chapter, different practical aspects of NBIoT deployment have been addressed. The LPWA features of NBIoT can be realized effectively if and only if its deployment is done properly. Due to its large demand, it has been standardized in a very short span of time. However, the 5G deployment of NBIoT will have some new provisions.

Autonomous driving is no more a topic of science fiction. Advancement of autonomous driving technologies are now reliable. Effectively harnessing the information is essential for enhancing the safety, reliability, and efficiency of autonomous vehicles. In this article, we explore the pivotal role of visualization and visual analytics (VA) techniques used in autonomous driving. By employing sophisticated data visualization methods, VA, researchers and practitioners transform intricate datasets into intuitive visual representations, providing valuable insights for decision-making processes. This article delves into various visualization approaches, including spatial-temporal mapping, interactive dashboards, and machine learning-driven analytics, tailored specifically for autonomous driving scenarios. Furthermore, it investigates the integration of real-time sensor data, sensor coordination with VA, and machine learning algorithms to create comprehensive visualizations. This research advocates for the pivotal role of visualization and VA in shaping the future of autonomous driving systems, fostering innovation, and ensuring the safe integration of self-driving vehicles.