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2021 | Buch

Localization in Underwater Sensor Networks

verfasst von: Prof. Jing Yan, Haiyan Zhao, Yuan Meng, Prof. Xinping Guan

Verlag: Springer Singapore

Buchreihe : Wireless Networks

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SUCHEN

Über dieses Buch

Ocean covers 70.8% of the Earth’s surface, and it plays an important role in supporting all life on Earth. Nonetheless, more than 80% of the ocean’s volume remains unmapped, unobserved and unexplored. In this regard, Underwater Sensor Networks (USNs), which offer ubiquitous computation, efficient communication and reliable control, are emerging as a promising solution to understand and explore the ocean. In order to support the application of USNs, accurate position information from sensor nodes is required to correctly analyze and interpret the data sampled. However, the openness and weak communication characteristics of USNs make underwater localization much more challenging in comparison to terrestrial sensor networks.
In this book, we focus on the localization problem in USNs, taking into account the unique characteristics of the underwater environment. This problem is of considerable importance, since fundamental guidance on the design and analysis of USN localization is very limited at present. To this end, we first introduce the network architecture of USNs and briefly review previous approaches to the localization of USNs. Then, the asynchronous clock, node mobility, stratification effect, privacy preserving and attack detection are considered respectively and corresponding localization schemes are developed. Lastly, the book’s rich implications provide guidance on the design of future USN localization schemes.
The results in this book reveal from a system perspective that underwater localization accuracy is closely related to the communication protocol and optimization estimator. Researchers, scientists and engineers in the field of USNs can benefit greatly from this book, which provides a wealth of information, useful methods and practical algorithms to help understand and explore the ocean.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
This chapter presents the network architecture of underwater sensor networks (USNs). According to the different measurement ways, the localization schemes for wireless sensor networks are briefly reviewed. Based on this, the weak communication characteristics of USNs are summarized, through which the problems studied in this book are provided.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Chapter 2. Asynchronous Localization of Underwater Sensor Networks with Mobility Prediction
Abstract
This chapter considers the clock asynchronization and node mobility, and then an asynchronous localization solution with mobility prediction is developed for USNs. Particularly, an asynchronous localization scheme with mobility prediction is designed to eliminate the effect of asynchronous clocks and compensate the mobility. Based on this, iterative least squares estimators are conducted to estimate the positions of sensor nodes. Besides that, the Cramé r-Rao lower bound and convergence analysis are also presented. Finally, simulation results represent that the developed localization solution in this chapter can reduce the localization time as compared with the exhaustive search-based localization method. Meanwhile, it can effectively eliminate the influences of clock asynchronization and node mobility.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Chapter 3. Async-Localization of USNs with Consensus-Based Unscented Kalman Filtering
Abstract
With consideration of the asynchronous clock, the stratification effect and the strong-noise measurement, an asynchronous localization issue for underwater targets is studied. Specifically, the relationship between the propagation delay and the position can be constructed to eliminate the impacts of asynchronous clocks. Afterwards, a localization optimization problem is built to minimize the sum of all measurement errors, and then a consensus-based unscented Kalman filtering (UKF) localization algorithm is developed to solve this localization optimization problem. In addition, the Cramér-Rao lower bounds and convergence conditions are also analyzed. Finally, simulation results show that the proposed localization algorithm can reduce the localization time as compared with the exhaustive search method. Meanwhile, the proposed localization algorithm can improve localization accuracy by comparing with other works.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Chapter 4. Reinforcement Learning-Based Asynchronous Localization of USNs
Abstract
In this chapter, an autonomous underwater vehicle (AUV) aided localization issue is studied under the constraints of asynchronous time clock, stratification effect and node mobility. Particularly, an asynchronous localization protocol is constructed and then the localization problem is built to minimize the sum of all measurement errors. To solve this localization problem, we propose a reinforcement learning (RL) based localization algorithm to locate the positions of AUVs, active and passive sensor nodes. It is noted that, the proposed localization algorithm employs two neural networks to approximate the increment policy and value function, and more importantly, it is much preferable for nonsmooth and nonconvex underwater localization problem due to its insensitivity to the local optimal. Besides that, the performance analyses of proposed algorithm are given. Finally, simulation and experimental results show that the localization performance in this chapter can be significantly improved as compared with the other works.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Chapter 5. Privacy Preserving Asynchronous Localization of USNs
Abstract
Under the constraint of the asynchronous clock, security attack and node mobility, a privacy-preserving asynchronous localization issue is studied. To be specific, an asynchronous localization protocol is developed, and then two privacy-preserving localization algorithms are proposed to locate the position of active and ordinary sensor nodes. Note that the proposed localization algorithms reveal disguised positions to the network, while they do not adopt any homomorphic encryption technique. Besides that, the performance analyses of the proposed algorithms are also given. Finally, simulation and experiment results show that the proposed localization algorithms can avoid the leakage of location information, while the localization accuracy can be significantly enhanced by comparing with the other works.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Chapter 6. Privacy Preserving Asynchronous Localization with Attack Detection and Ray Compensation
Abstract
In this chapter, we are concerned with a privacy-preserving localization solution for USNs, and then asynchronous clock, stratification effect and forging attack are considered in cyber channels. In particular, a privacy-preserving asynchronous transmission protocol is developed to eliminate the influence of asynchronous clock and protect the private position information, where a RSS-based detection strategy is designed to detect the malicious anchor nodes. Based on the above, the position information of target is estimated by a least squares estimator. Finally, experiment and simulation results are represented to reveal that the developed localization solution outperforms the other existing works in terms of localization accuracy and effectiveness.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Chapter 7. Deep Reinforcement Learning Based Privacy Preserving Localization of USNs
Abstract
This chapter is concerned with a privacy-preserving localization solution for USNs in inhomogeneous underwater medium. Besides, an honest-but-curious model is proposed to develop a privacy-preserving localization protocol. Based on the above, a localization problem is developed for sensor nodes to minimize the sum of all measurement errors, where a ray compensation strategy is designed to remove the localization bias. In order to make the above problem tractable, we represent the unsupervised, supervised and semisupervised scenarios, through which deep reinforcement learning (DRL) based localization estimators are used to estimate the positions of sensor nodes. Of note, the proposed localization issue in this chapter can hide the private position information of USNs, and besides that, it is robust to local optimum for nonconvex and nonsmooth localization solution in inhomogeneous underwater medium. Finally, simulations are given to show the position privacy can be protected, while the localization accuracy can be enhanced as compared with the other existing works.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Chapter 8. Future Research Directions
Abstract
We have addressed several new canonical localization schemes for USNs, including asynchronous localization with mobility prediction, consensus-based UKF localization, RL-based localization in weak communication channel, privacy preserving asynchronous localization, privacy preserving asynchronous localization with malicious attacks, and DRL-based privacy preserving localization. In this chapter, we present several research directions that depict future investigation on underwater localization, including the network architecture, communication protocol and optimization estimator.
Jing Yan, Haiyan Zhao, Yuan Meng, Xinping Guan
Metadaten
Titel
Localization in Underwater Sensor Networks
verfasst von
Prof. Jing Yan
Haiyan Zhao
Yuan Meng
Prof. Xinping Guan
Copyright-Jahr
2021
Verlag
Springer Singapore
Electronic ISBN
978-981-16-4831-1
Print ISBN
978-981-16-4830-4
DOI
https://doi.org/10.1007/978-981-16-4831-1