Towards the internet of underwater things: a comprehensive survey.

The innovative concept of Internet of Underwater Things (IoUT) has a huge impact in different sectors including a small scientific laboratory, to a medium sized harbor, and to monitor vast undiscovered oceans. Internet of Underwater Things (IoUT) has become a powerful technology to support various applications such as collecting real-time aquatic information, naval military applications, maritime security, natural disaster prediction and control, archaeological expeditions, oil and gas exploration, shipwrecks discovery, water contamination, marine life observation and smart Ocean. IoUT is referred as smart intricately linked underwater objects to monitor these underwater operations. The IoUT framework incorporates several underwater communication technologies based on magnetic induction, optical signals, radio signals and acoustic waves. It is an emerging communication ecosystem which can reveal a new era of research, business and naval applications. It is a novel and vibrant paradigm for the Blue Economy sector bringing the ability to communicate autonomous underwater vehicles (AUVs), sensing, actuating and transferring this data to control centers using regular internet speeds through low cost technologies. It is anticipated to support future networking systems which can bring tremendous improvement in previous generations in terms of stable networking, high coverage, massive connectivity, low latency, high data rate and low power consumption. This study introduces the possible network framework of IoUT which is naturally heterogeneous and must be flexible enough to work under unpredicted ocean conditions. In this study, we examine channel models, routing protocols, networking topologies and simulation tools. Furthermore, we discussed recent advancements in IoUT in terms of smart devices, consumer electronics, communication and role of AUVs. In addition, edge computing, optical wireless communication (OWC), data analytics, blockchain, intelligent reflecting surfaces (IRS) and machine learning were viewed as promising techniques to support IoUT. We have dedicated a complete section to applications of IoUT. Finally, numerous open research challenges and future directions were presented. We believe this survey will be helpful to aggregate the research efforts and eliminate the technical uncertainties towards breakthrough novelties of IoUT. [ABSTRACT FROM AUTHOR]