A Survey on Underwater Acoustic Sensor Network Routing Protocols

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research

Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective

Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Priority based energy efficient hybrid cluster routing protocol for underwater wireless sensor network

A little change in the environment that goes unnoticed in an underwater communication network might lead to calamity. A little alteration in the environment must also be adequately analyzed in order to deal with a potential crisis. A priority-based routing protocol is required to ensure that the vital data perceived by the sensor about the environment changes. The priority-based routing system guarantees that vital data packets are delivered at a quicker pace to the destination or base station for further processing. In this work, we present a priority-based routing protocol based on the energy efficient hybrid cluster routing protocol (EEHRCP) algorithm. The suggested approach keeps two distinct queues for lower and higher priority data packets. In order to ensure that these packets get at their destination without any information loss and at a quicker rate, all of the crucial sensed data is passed through a higher priority queue. Test findings show that the suggested technique increases throughput, delivery percentage, and reduces latency for the crucial data packets

PB-ACR: Node Payload Balanced Ant Colony Optimal Cooperative Routing for Multi-Hop Underwater Acoustic Sensor Networks

For a given source-destination pair in multi-hop underwater acoustic sensor networks (UASNs), an optimal route is the one with the lowest energy consumptions that usually consists of the same relay nodes even under different transmission tasks. However, this will lead to the unbalanced payload of the relay nodes in the multi-hop UASNs and accelerate the loss of the working ability for the entire system. In this paper, we propose a node payload balanced ant colony optimal cooperative routing (PB-ACR) protocol for multi-hop UASNs, through combining the ant colony algorithm and cooperative transmission. The proposed PB-ACR protocol is a relay node energy consumption balanced scheme, which considers both data priority and residual energy of each relay node, aiming to reduce the occurrence of energy holes and thereby prolong the lifetime of the entire UASNs. We compare the proposed PB-ACR protocol with the existing ant colony algorithm routing (ACAR) protocol to verify its performances in multi-hop UASNs, in terms of network throughput, energy consumption, and algorithm complexity. The simulation results show that the proposed PB-ACR protocol can effectively balance the energy consumption of underwater sensor nodes and hence prolong the network lifetime

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

Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields

Adapting Deep Learning for Underwater Acoustic Communication Channel Modeling

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work. Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and field experiments. The proposed test-bed also demonstrated the capabilities of developing and testing SDN protocols for a UWA network in both simulation and field experiments. Secondly, due to the lack of appropriate UWA CIR data sets for deep learning, a series of field UWA channel experiments have been conducted across a shallow freshwater river. Abundant UWA CIR data under various weather conditions have been collected and studied. The environmental factors that significantly affect the UWA channel state, including the solar radiation rate, the air temperature, the ice cover, the precipitation rate, etc., are analyzed in the case studies. The obtained UWA CIR data set with significant correlations to weather conditions can benefit future deep-learning research on UWA channels. Thirdly, a Wasserstein conditional generative adversarial network (WCGAN) is proposed to model the observed UWA CIR distribution. A power-weighted Jensen–Shannon divergence (JSD) is proposed to measure the similarity between the generated distribution and the experimental observations. The CIR samples generated by the WCGAN model show a lower power-weighted JSD than conventional estimated stochastic distributions. Finally, a modified conditional generative adversarial network (CGAN) model is proposed for predicting the UWA CIR distribution in the 15-minute range near future. This prediction model takes a sequence of historical and forecast weather information with a recent CIR observation as the conditional input. The generated CIR sample predictions also show a lower power-weighted JSD than conventional estimated stochastic distributions