Localization and Its Effects an Data Delivery in Underwater Sensor Networks
Localization and Its Effects an Data Delivery in Underwater Sensor Networks

Author: Melike Erol

Publisher:

Published: 2009

Total Pages: 108

ISBN-13:

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Underwater Sensor Networks (USN) are used for tough oceanographic missions where human operation is dangerous or impossible. USNs consist of tethered (stationary) or untethered (mobile) sensor nodes. When the nodes are untethered, they move passively with the currents. A realistic mobility model is required to capture this mobility pattern. In our studies we collaborate in an oceanographic mobility model. We use this model to evaluate the performance of the proposed protocols under a mobile network. Localization and data delivery are among the fundamental tasks in USNs. In this study, we propose two localization algorithms. The proposed techniques are evaluated by simulation. We propose Dive and Rise Localization (DNRL) and Proxy Localization (PL). We compare their performance with a recognized technique from the literature. The simulation results show that DNRL has high localization success, high accuracy, low energy consumption and low overhead. Hence, it outperforms the other techniques for the mobile USN. DNRL and PL are shown to be energy efficient and suitable for long-term underwater missions. In our studies, we also analyze data delivery in stationary USNs. We employ a greedy, location-based routing algorithm and investigate its performance under location inaccuracies. We analyze the delivery ratio, overhead and average end-to-end delay of the location-based routing protocol when localization techniques are employed for location estimation. Our simulations show that location estimates with low mean error do not have significant affect. However, when location estimates with higher location inaccuracy are used, the delivery ratio of the routing protocol decreases significantly.

The Underwater World for Digital Data Transmission
The Underwater World for Digital Data Transmission

Author: Parikshit N. Mahalle

Publisher: Springer Nature

Published: 2021-05-06

Total Pages: 67

ISBN-13: 9811613079

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This book covers all small details about Underwater Sensor Networks (UWSN). Researchers can use this book as a prerequisite before starting any research on underwater networks or underwater applications. This book covers the introduction, challenges, different architectural models for UWSN, various attacks on UWSN, underwater applications, and networking layers. The target audience includes professors and students in engineering, and researchers and engineers working on marine applications. In academic level, the book is helpful for students having Networking and Information Security as elective subject and doing projects in Wireless Networks. It is also helpful for spostgraduates and Ph.D. researchers to learn basics of Underwater Sensor Networks.

Localization in Wireless Networks
Localization in Wireless Networks

Author: Jessica Feng Sanford

Publisher: Springer Science & Business Media

Published: 2012-05-03

Total Pages: 207

ISBN-13: 1461418399

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In a computational tour-de-force, this volume wipes away a host of problems related to location discovery in wireless ad-hoc sensor networks. WASNs have recognized potential in many applications that are location-dependent, yet are heavily constrained by factors such as cost and energy consumption. Their “ad-hoc” nature, with direct rather than mediated connections between a network of wireless devices, adds another layer of difficulty. Basing this work entirely on data-driven, coordinated algorithms, the author’s aim is to present location discovery techniques that are highly accurate—and which fit user criteria. The research deploys nonparametric statistical methods and relies on the concept of joint probability to construct error (including location error) models and environmental field models. It also addresses system issues such as the broadcast and scheduling of the beacon. Reporting an impressive accuracy gain of almost 17 percent, and organized in a clear, sequential manner, this book represents a stride forward in wireless localization.

Autonomous Underwater Vehicles
Autonomous Underwater Vehicles

Author: Jing Yan

Publisher: Springer Nature

Published: 2021-11-01

Total Pages: 222

ISBN-13: 9811660964

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Autonomous underwater vehicles (AUVs) are emerging as a promising solution to help us explore and understand the ocean. The global market for AUVs is predicted to grow from 638 million dollars in 2020 to 1,638 million dollars by 2025 – a compound annual growth rate of 20.8 percent. To make AUVs suitable for a wider range of application-specific missions, it is necessary to deploy multiple AUVs to cooperatively perform the localization, tracking and formation tasks. However, weak underwater acoustic communication and the model uncertainty of AUVs make achieving this challenging. This book presents cutting-edge results regarding localization, tracking and formation for AUVs, highlighting the latest research on commonly encountered AUV systems. It also showcases several joint localization and tracking solutions for AUVs. Lastly, it discusses future research directions and provides guidance on the design of future localization, tracking and formation schemes for AUVs. Representing a substantial contribution to nonlinear system theory, robotic control theory, and underwater acoustic communication system, this book will appeal to university researchers, scientists, engineers, and graduate students in control theory and control engineering who wish to learn about the core principles, methods, algorithms, and applications of AUVs. Moreover, the practical localization, tracking and formation schemes presented provide guidance on exploring the ocean. The book is intended for those with an understanding of nonlinear system theory, robotic control theory, and underwater acoustic communication systems.

Underwater Information Perception and Processing Via Underwater Sensor Networks
Underwater Information Perception and Processing Via Underwater Sensor Networks

Author: Meiqin Liu

Publisher: Springer

Published: 2024-12-10

Total Pages: 0

ISBN-13: 9789819746682

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This book highlights the latest advances and trends in information perception and processing of underwater sensor networks (USNs). Underwater sensor networks are networks of autonomous sensor nodes distributed over a given region of water to collaboratively perform a given task. Nearly 70% of the Earth's surface is covered by water, mainly oceans, and more than 80% of the ocean remains unexplored. The emergence of USNs is a new direction in ocean exploration and data collection. USNs offer many applications in both civilian and non-civilian fields. However, due to the characteristics of underwater environments, USNs face challenges such as limited bandwidth, high propagation delay, media access control, routing, resource utilization, power limitation, etc. Researchers have studied and provided many techniques over the past decades to address these issues. This book systematically summarizes the development and covers a wide range of applications of USNs, including mobile node localization, target detection, target recognition, target tracking, sensor scheduling, and environmental monitoring. It also focuses on the introduction of new technologies, including deep reinforcement learning, into underwater information perception and processing. This book is suitable for university lecturers, graduate students, and industry professionals working in the field of USNs.

Decentralized Sensor Placement and Mobile Localization on an Underwater Sensor Network with Depth Adjustment Capabilities
Decentralized Sensor Placement and Mobile Localization on an Underwater Sensor Network with Depth Adjustment Capabilities

Author: Carrick James Detweiler

Publisher:

Published: 2010

Total Pages: 214

ISBN-13:

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Over 70% of our planet is covered by water. It is widely believed that the underwater world holds ideas and resources that will fuel much of the next generation of science and business. Unfortunately, underwater operations are fraught with difficulty due to the absence of an easy way to collect and monitor data. In this thesis we propose a novel underwater sensor network designed to mitigate the problems of underwater sensing and communication. A key feature of this system is the ability of individual nodes to control their depth in water. This single degree of freedom allows the network to cooperatively optimize placement for communication and data collection while minimizing time and energy use. The sensor network also enables a GPS-like system for localizing underwater robots to aid in data retrieval and sensing. We develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for modeling 3D properties of the water. We prove that the controller converges to a local minimum, and implement the controller on our underwater sensor network, where each node is capable of adjusting its depth. We verify the algorithm through simulations and in-water experiments. Most applications require that we associate a location with the sensed data. We have developed an underwater mobile robot localization algorithm that allows underwater robots to act as mobile sensors in the sensor network by using ranging information. The algorithm is a minimalist, geometric-based algorithm that only relies on knowing an upper bound on the robot speed and known static node locations. We prove that the algorithm finds the optimal location of the robot and analyze the algorithm in simulation and in water with our underwater sensor network.

Machine Learning Modeling for IoUT Networks
Machine Learning Modeling for IoUT Networks

Author: Ahmad A. Aziz El-Banna

Publisher: Springer Nature

Published: 2021-05-29

Total Pages: 71

ISBN-13: 3030685675

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This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT). The authors first present seawater’s key physical variables and go on to discuss opportunistic transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition, the authors present applications of machine learning techniques for opportunistic communication and underwater localization. They also discuss the current challenges of machine learning modeling of underwater communication from two communication engineering and data science perspectives.