Recently, there has been increasing interest in the field of underwater wireless sensor networks (UWSNs), which is a basic source for the exploration of the ocean environment. A range of military and civilian applications is anticipated to assist UWSN. The UWSN is being developed by the extensive wireless sensor network (WSN) applications and wireless technologies. Therefore, in this paper, a review has been presented which unveils the existing challenges in the underwater environment. In this review, firstly, an introduction to UWSN is presented. After that, underwater localizations and the basics are presented. Secondly, the paper focuses on the architecture of UWSN and technologies used for underwater acoustic sensor network (UASN) localization. Various localization techniques are discussed in the paper classified by centralized and distributed localizations. They are further classified into estimated and prediction-based localizations. Also, various underwater localization algorithms are discussed, which are grouped by the algorithms based on range and range-free schemes. Finally, the paper focuses on the challenges existing in underwater localizations, underwater acoustic communications with conclusions.

Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization.

Localization is the basic feature of wireless sensor networks (WSN) and estimating the time difference of arrival (TDoA), and angle of arrival (AoA) is the most used schemes for localization. Underwater wireless sensor network (UWSN) is extensively used for data collection in an underwater environment for both military and civilian applications. Nevertheless, efficient and accurate localization algorithms are essential for the UWSN because of the dynamical property of the underwater surrounding. Also, data management, collection, and processing for WSNs have become a more active topic nowadays in computer science, such as database system and data mining. The objective of deploying the WSNs applications is to collect the real-time data, which has very challenging due to the capacity of communication and high data generated by WSNs. For this purpose, the time and location are the basic aspects when a sensor collects data, especially for the case of location-aware data. Many researchers have studied the underwater sensor nodes localization and they have considered the sensor location where the data is collected and most of them focused on the fixed sensor nodes. In this research work, energy efficient and accurate localization schemes are presented named as distance-based and angle-based schemes for the underwater environment with relatively less energy consumption and mean estimation errors (MEEs). The proposed schemes mainly focus on the localization of underwater nodes and especially on the MEEs in localization. The extensive simulation is performed to compare the proposed schemes with other counterpart schemes. The results show that the proposed schemes outperform other counterpart schemes in terms of MEEs of localization and energy consumption.

Several industries in many different domains are looking at deep learning as a way to take advantage of the insights in their data, to improve their competitiveness, to open up novel business possibilities, or to resolve the problem that thought to be impossible to tackle. The large scale of the systems where deep learning is applied and the need of preserving the privacy of the used data have imposed a shift from the traditional centralized deployment to a more collaborative one. However, this has opened up several vulnerabilities caused by compromised nodes and inputs, with traditional crypto primitives and access control models exploited to offer protection means. Providing security can be costly in terms of higher energy consumption, calling for a wise use of these protection means. This paper exploits game theory to model interactions among collaborative deep learning nodes and to decide when using actions to support security enhancements.

Maritime networks (MNs) typically cover tens to hundreds of kilometers, which can perform global oceanic observations via the support of maritime low-latency applications. However, current research on MNs with 5G related robust network optimization technologies has not been progressed in line with the rapid development of terrestrial communication networks. To explore the oceanic informatics resources in the 5G or upcoming 6G eras, this work provides a method for intelligent maritime networking by conducting analysis and investigation on maritime communication scenarios, probabilistic ship density, and MN connectivity. The edge-based MN services and the edge computation capability based on a cognitive big data platform are thoroughly analyzed. The developed theory is extensively simulated, the real-world-trace based results further confirm the validity of the proposed method.