Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks

Las redes de sensores acústicas submarinas (UASN) han ganado mucha importancia en los últimos años: el 71% de la superficie de la Tierra está cubierta por océanos. La mayoría de ellos, aún no han sido explorados. Aplicaciones como prospección de yacimientos, prevención de desastres o recopilación de datos para estudios de biología marina se han convertido en el campo de interés para muchos investigadores. Sin embargo, las redes UASN tienen dos limitaciones: un medio muy agresivo (marino) y el uso de señales acústicas. Ello hace que las técnicas para redes de sensores inalámbricas (WSN) terrestres no sean aplicables. Tras realizar un recorrido por el estado del arte en protocolos para redes UASN, se propone en este TFM un protocolo de enrutamiento denominado "SOSRP", descentralizado y basado en tablas en cada nodo. Se usa como criterio para crear rutas una combinación del valor de saltos hasta el nodo recolector y la distancia. Las funciones previstas del protocolo abarcan: autoorganización de las rutas, tolerancia a fallos y detección de nodos aislados. Mediante la implementación en MATLAB de SOSRP así como de un modelo de propagación y energía apropiados para entorno marino, se obtienen resultados de rendimiento en distintos escenarios (variando nºextremo de paquetes, consumo de energía o longitud de rutas creadas (con y sin fallo). Los resultados obtenidos muestran una operación estable, fiable y adecuada para el despliegue y operación de los nodos en redes UASN

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

Classification of Routing Algorithms in Volatile Environment of Underwater Wireless Sensor Networks

The planet earth is basically a planet of water with less than 30% land mass available for humans to live on. However, the areas covered with water are important to mankind for the various resources which have been proven to be valuable. Such resources are gas, oil, marine products which can be used as food, and other minerals. In view of the vast area in which these resources can be found, a network of sensors is necessary so that they can be explored. However, sensor networks may not be helpful in the exploration of these resources if they do not have a sufficiently good routing mechanism. Over the past few decades, several methods for routing have been suggested to address the volatile environment in underwater communications. These continue researches; have enhanced the performance along with time. Meanwhile, there are still challenges to deal with for a better and efficient routing of data packets. Large end-to-end delays, high error channel rates, limited bandwidth, and the consumption of energy in sensor network are some such challenges. A comprehensive survey of the various routing methods for the partially connected underwater communication environment are presented in this paper

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

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

A Survey of Routing Issues and Associated Protocols in Underwater Wireless Sensor Networks

Underwater Wireless Sensor Network is newly emerging wireless technology in which small size sensors with limited energy, limited memory and bandwidth are deployed in deep sea water and various monitoring operation like tactical surveillance, environmental monitoring and data collection are performed through these tiny sensor. Underwater Wireless Sensor Network is used for exploration of underwater resources, oceanographic data collection, flood or disaster prevention, tactical surveillance system and unmanned underwater vehicles. Sensor node consist of small memory, central processing unit and antenna. Underwater network is much different from terrestrial sensor network as radio waves cannot be used in Underwater Wireless Sensor Network. Acoustic channels are used for communication in deep sea water. Acoustic Signals carries with itself many limitation. Such as Limited bandwidth, higher end to end delay, network path loss, higher propagation delay and dynamic topology. Usually these limitation results in higher energy consumption with less number of packets delivered. The main aim now a days is to operate sensor node having smaller battery for a longer time in network. This survey has discussed the state of the art Localization based and Localization free routing protocols. Routing associated issues in the area of Underwater Wireless Sensor Network has also been discussed

EFFICIENT DYNAMIC ADDRESSING BASED ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS

This thesis presents a study about the problem of data gathering in the inhospitable underwater environment. Besides long propagation delays and high error probability, continuous node movement also makes it difficult to manage the routing information during the process of data forwarding. In order to overcome the problem of large propagation delays and unreliable link quality, many algorithms have been proposed and some of them provide good solutions for these issues, yet continuous node movements still need attention. Considering the node mobility as a challenging task, a distributed routing scheme called Hop-by-Hop Dynamic Addressing Based (H2- DAB) routing protocol is proposed where every node in the network will be assigned a routable address quickly and efficiently without any explicit configuration or any dimensional location information. According to our best knowledge, H2-DAB is first addressing based routing approach for underwater wireless sensor networks (UWSNs) and not only has it helped to choose the routing path faster but also efficiently enables a recovery procedure in case of smooth forwarding failure. The proposed scheme provides an option where nodes is able to communicate without any centralized infrastructure, and a mechanism furthermore is available where nodes can come and leave the network without having any serious effect on the rest of the network. Moreover, another serious issue in UWSNs is that acoustic links are subject to high transmission power with high channel impairments that result in higher error rates and temporary path losses, which accordingly restrict the efficiency of these networks. The limited resources have made it difficult to design a protocol which is capable of maximizing the reliability of these networks. For this purpose, a Two-Hop Acknowledgement (2H-ACK) reliability model where two copies of the same data packet are maintained in the network without extra burden on the available resources is proposed. Simulation results show that H2-DAB can easily manage during the quick routing changes where node movements are very frequent yet it requires little or no overhead to efficiently complete its tasks

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

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