Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

International audienceNowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal

Information-Centric Design and Implementation for Underwater Acoustic Networks

Over the past decade, Underwater Acoustic Networks (UANs) have received extensive attention due to their vast benefits in academia and industry alike. However, due to the overall magnitude and harsh characteristics of underwater environments, standard wireless network techniques will fail because current technology and energy restrictions limit underwater devices due to delayed acoustic communications. To help manage these limitations we utilize Information-Centric Networking (ICN). More importantly, we look at ICN\u27s paradigm shift from traditional TCP/IP architecture to improve data handling and enhance network efficiency. By utilizing some of ICN\u27s techniques, such as data naming hierarchy, we can reevaluate each component of the network\u27s protocol stack given current underwater limitations to study the vast solutions and perspectives Information-Centric architectures can provide to UANs. First, we propose a routing strategy used to manage and route large data files in a network prone to high mobility. Therefore, due to UANs limited transmitting capability, we passively store sensed data and adaptively find the best path. Furthermore, we introduce adapted Named Data Networking (NDN) components to improve upon routing robustness and adaptiveness. Beyond naming data, we use tracers to assist in tracking stored data locations without using other excess means such as flooding. By collaborating tracer consistency with routing path awareness our protocol can adaptively manage faulty or high mobility nodes. Through this incorporation of varied NDN techniques, we are able to see notable improvements in routing efficiency. Second, we analyze the effects of Denial of Service (DoS) attacks on upper layer protocols. Since UANs are typically resource restrained, malicious users can advantageously create fake traffic to burden the already constrained network. While ICN techniques only provide basic DoS restriction we must expand our detection and restriction technique to meet the unique demands of UANs. To provide enhanced security against DoS we construct an algorithm to detect and restrict against these types of attacks while adapting to meet acoustic characteristics. To better extend this work we incorporate three node behavior techniques using probabilistic, adaptive, and predictive approaches for detecting malicious traits. Thirdly, to depict and test protocols in UANs, simulators are commonly used due to their accessibility and controlled testing aspects. For this section, we review Aqua-Sim, a discrete event-driven open-source underwater simulator. To enhance the core aspect of this simulator we first rewrite the current architecture and transition Aqua-Sim to the newest core simulator, NS-3. Following this, we clean up redundant features spread out between the various underwater layers. Additionally, we fully integrate the diverse NS-3 API within our simulator. By revamping previous code layout we are able to improve architecture modularity and child class expandability. New features are also introduced including localization and synchronization support, busy terminal problem support, multi-channel support, transmission range uncertainty modules, external noise generators, channel trace-driven support, security module, and an adapted NDN module. Additionally, we provide extended documentation to assist in user development. Simulation testing shows improved memory management and continuous validity in comparison to other underwater simulators and past iterations of Aqua-Sim

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes