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Internet of Underwater Things
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Internet of Underwater Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 23686

Special Issue Editors

Prof. Dr. Dongkyun Kim

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Interests: connected cars; vehicular ad hoc networks; the Internet of Things (machine-to-machine/device-to-device); Wi-Fi networks (including Wi-Fi Direct); wireless mesh networks; wireless sensor networks; future Internet
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan-Carlos Cano

E-Mail Website
Guest Editor
Department of Computer Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: mobile ad hoc networks; vehicular networks; mobile communication; computer networks; wireless networks; MANETs; VANETs
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Wang

E-Mail Website
Guest Editor
Department of Computer Science, College of Sciences, San Diego State University, San Diego, CA 92182-7720, USA
Interests: wireless networks; wireless multimedia communications; QoE–QoS issues; network economics; IoTs
Dr. Syed Hassan Ahmed

E-Mail Website
Guest Editor
Computer Science Department, California State University, Fullerton, CA 92831, USA
Interests: wireless communications; connected vehicles; named data; networks; information centric networks and ad hoc networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

From wired sensor nodes sensing, collecting, and forwarding data underwater, technology has evolved tremendously. In the past few decades, more feasible underwater communication solutions have been introduced, including wireless underwater sensor nodes. These sensor nodes have the capability to communicate underwater via high-frequency signals, acoustic signals, or light signals based on application-specific requirements. The introduction of autonomous robots with sensing and reporting capabilities that have the capability to dive deep within underwater environments has further enhanced the capacity for underwater environment monitoring. Small autonomous submarines equipped with underwater sensing capabilities, commonly known as autonomous underwater vehicles (AUVs), can communicate and cooperate within a group of AUVs to expand underwater operational capabilities.

Underwater sensing nodes communicate and forward data to the control station, forming the application-specific network of things. These networks of underwater things share resources and interact with other networks forming the Internet of Underwater Things (IoUT). The applications of IoUT include surveillance, oil and gas exploration, tectonic plate monitoring, and marine life and coral reef harvesting. The introduction of artificial intelligence into underwater communication further expands and improves upon the efficiency of underwater communication technologies due to its self-sustainable nature along with self-governance and independent decision making.

Several artificial intelligence (AI) problem-solving algorithms have been proposed, and most of them are still are at early stages, improving and evolving with time. These algorithms have the capability to process huge amounts of data and make intelligent decisions independently. In the absence of intelligence, IoUT systems will operate on a conventional communication system with preset rules of business. In future, the huge number of IoUT devices and massive traffic from these devices means only one thing—a massive amount of data arriving from stationary nodes as well as mobile AUVs. Numerical analysis techniques and state-of-the-art optimization algorithms can also result in intelligence of some level for the IoUT communication system as they can enhance the system performance.

Considering the importance of intelligent AUV in the internet of underwater things and the massive amounts of data generated from the communication system, this Special Issue seeks to collect relevant and original research and review articles to advance the field and encourage researchers. We welcome contributions from both the industry and academia in highlighting and introducing solutions to the challenges associated with the internet of underwater things systems.

Potential topics include but are not limited to the following:

  • Intelligent green underwater vehicular communication;
  • Security and privacy for underwater vehicular communication;
  • Intelligent internet of Autonomous Underwater Vehicles (AUVs);
  • Intelligent applications of the IoUT;
  • Underwater localization and tracking;
  • Big data/data mining for underwater vehicular communication;
  • Protocols and standards of intelligent underwater vehicular communication;
  • Intelligent IoUT solutions for smart eHealth of marine life monitoring.

Prof. Dr. Dongkyun Kim
Prof. Dr. Juan-Carlos Cano
Dr. Wei Wang
Dr. Syed Hassan Ahmed
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

Jump to: Review

34 pages, 1906 KiB  
Article
TANVEER: Tri-Angular Nearest Vector-Based Energy Efficient Routing for IoT-Enabled Acoustic Sensor and Actor Networks (I-ASANs)
by Umar Draz, Sana Yasin, Muhammad Irfan, Tariq Ali, Amjad Ali, Adam Glowacz, Frantisek Brumercik and Witold Glowacz
Sensors 2021, 21(11), 3578; https://doi.org/10.3390/s21113578 - 21 May 2021
Cited by 6 | Viewed by 2485
Abstract
The Internet of Things (IoT) is an emerging technology in underwater communication because of its potential to monitor underwater activities. IoT devices enable a variety of applications such as submarine and navy defense systems, pre-disaster prevention, and gas/oil exploration in deep and shallow [...] Read more.
The Internet of Things (IoT) is an emerging technology in underwater communication because of its potential to monitor underwater activities. IoT devices enable a variety of applications such as submarine and navy defense systems, pre-disaster prevention, and gas/oil exploration in deep and shallow water. The IoT devices have limited power due to their size. Many routing protocols have been proposed in applications, as mentioned above, in different aspects, but timely action and energy make these a challenging task for marine research. Therefore, this research presents a routing technique with three sub-sections, Tri-Angular Nearest Vector-Based Energy Efficient Routing (TANVEER): Layer-Based Adjustment (LBA-TANVEER), Data Packet Delivery (DPD-TANVEER), and Binary Inter Nodes (BIN-TANVEER). In TANVEER, the path is selected between the source node and sonobuoys by computing the angle three times with horizontal, vertical, and diagonal directions by using the nearest vector-based approach to avoid the empty nodes/region. In order to deploy the nodes, the LBA-TANVEER is used. Furthermore, for successful data delivery, the DPD-TANVEER is responsible for bypassing any empty nodes/region occurrence. BIN-TANVEER works with new watchman nodes that play an essential role in the path/data shifting mechanism. Moreover, achievable empty regions are also calculated by linear programming to minimize energy consumption and throughput maximization. Different evaluation parameters perform extensive simulation, and the coverage area of the proposed scheme is also presented. The simulated results show that the proposed technique outperforms the compared baseline scheme layer-by-layer angle-based flooding (L2-ABF) in terms of energy, throughput, Packet Delivery Ratio (PDR) and a fraction of empty regions. Full article
(This article belongs to the Special Issue Internet of Underwater Things)
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Figure 1

Figure 1
<p>Description of angle adjustment discussed in L2-ABF [<a href="#B7-sensors-21-03578" class="html-bibr">7</a>].</p> Full article ">Figure 2
<p>Proposed model of Tri-Angular Nearest Vector-Based Energy Efficient Routing (TANVEER).</p> Full article ">Figure 3
<p>Layers-Based Adjustment (LBA) to ordinary nodes.</p> Full article ">Figure 4
<p>Possibility of angle flooding zone when Node Uses <span class="html-italic">K</span> = 1, 2 and 3.</p> Full article ">Figure 5
<p>Tri-angular adjustment of horizontal <span class="html-italic">A(x)</span>, vertical <span class="html-italic">A(y)</span> and diagonal <span class="html-italic">A(z)</span> with reference to <a href="#sensors-21-03578-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 6
<p>Mechanism of Data Packet Delivery (DPD)-TANVEER.</p> Full article ">Figure 7
<p>Working mechanism of binary inter nodes (BIN)-TANVEER with watchman nodes.</p> Full article ">Figure 8
<p>Possible transmission adjustment of Watchman Node (WN) with Empty Node <span class="html-italic">(EN</span>).</p> Full article ">Figure 9
<p>Flowchart of the proposed scheme.</p> Full article ">Figure 10
<p>Possible feasible region of throughput maximization.</p> Full article ">Figure 11
<p>Possible feasible region of energy minimization.</p> Full article ">Figure 12
<p>Fraction of empty regions occurrence in TANVEER vs L2-ABF.</p> Full article ">Figure 13
<p>Packet delivery analysis of TANVEER vs. L2-ABF.</p> Full article ">Figure 14
<p>Energy consumption of TANVEER vs L2-ABF.</p> Full article ">Figure 15
<p>Throughput Analysis of TANVEER.</p> Full article ">Figure 16
<p>Analysis of coverage area.</p> Full article ">
29 pages, 536 KiB  
Article
Enhancements and Challenges in CoAP—A Survey
by Muhammad Ashar Tariq, Murad Khan, Muhammad Toaha Raza Khan and Dongkyun Kim
Sensors 2020, 20(21), 6391; https://doi.org/10.3390/s20216391 - 9 Nov 2020
Cited by 36 | Viewed by 8455
Abstract
The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, [...] Read more.
The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, etc. CoAP also provides reliable communication among nodes in wireless sensor networks in addition to features such as resource observation, resource discovery, congestion control, etc. These capabilities of CoAP have enabled the implementation of CoAP in various domains ranging from home automation to health management systems. The use of CoAP has highlighted its shortcomings over the time. To overcome shortcomings of CoAP, numerous enhancements have been made in basic CoAP architecture. This survey highlights the shortcomings of basic CoAP architecture and enhancements made in it throughout the time. Furthermore, existing challenges and issue in the current CoAP architecture are also discussed. Finally, some applications with CoAP implementation are mentioned in order to realize the viability of CoAP in real world use cases. Full article
(This article belongs to the Special Issue Internet of Underwater Things)
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Figure 1

Figure 1
<p>An overview of CoAP architecture.</p> Full article ">Figure 2
<p>CoAP Message Header.</p> Full article ">Figure 3
<p>Examples of Confirmable and Non-confirmable CoAP messages.</p> Full article ">Figure 4
<p>CoAP Default Congestion Control.</p> Full article ">Figure 5
<p>CoAP Resource Observation</p> Full article ">
20 pages, 3871 KiB  
Article
Underwater Localization via Wideband Direction-of-Arrival Estimation Using Acoustic Arrays of Arbitrary Shape
by Elizaveta Dubrovinskaya, Veronika Kebkal, Oleksiy Kebkal, Konstantin Kebkal and Paolo Casari
Sensors 2020, 20(14), 3862; https://doi.org/10.3390/s20143862 - 10 Jul 2020
Cited by 18 | Viewed by 3891
Abstract
Underwater sensing and remote telemetry tasks necessitate the accurate geo-location of sensor data series, which often requires underwater acoustic arrays. These are ensembles of hydrophones that can be jointly operated in order to, e.g., direct acoustic energy towards a given direction, or to [...] Read more.
Underwater sensing and remote telemetry tasks necessitate the accurate geo-location of sensor data series, which often requires underwater acoustic arrays. These are ensembles of hydrophones that can be jointly operated in order to, e.g., direct acoustic energy towards a given direction, or to estimate the direction of arrival of a desired signal. When the available equipment does not provide the required level of accuracy, it may be convenient to merge multiple transceivers into a larger acoustic array, in order to achieve better processing performance. In this paper, we name such a structure an “array of opportunity” to signify the often inevitable sub-optimality of the resulting array design, e.g., a distance between nearest array elements larger than half the shortest acoustic wavelength that the array would receive. The most immediate consequence is that arrays of opportunity may be affected by spatial ambiguity, and may require additional processing to avoid large errors in wideband direction of arrival (DoA) estimation, especially as opposed to narrowband processing. We consider the design of practical algorithms to achieve accurate detections, DoA estimates, and position estimates using wideband arrays of opportunity. For this purpose, we rely jointly on DoA and rough multilateration estimates to eliminate spatial ambiguities arising from the array layout. By means of emulations that realistically reproduce underwater noise and acoustic clutter, we show that our algorithm yields accurate DoA and location estimates, and in some cases it allows arrays of opportunity to outperform properly designed arrays. For example, at a signal-to-noise ratio of –20 dB, a 15-element array of opportunity achieves lower average and median localization error (27 m and 12 m, respectively) than a 30-element array with proper λ / 2 element spacing (33 m and 15 m, respectively). We confirm the good accuracy of our approach via emulation results, and through a proof-of-concept lake experiment, where our algorithm applied to a 10-element array of opportunity achieves a 90th-percentile DoA estimation error of 4 ? and a 90th-percentile total location error of 5 m when applied to a real 10-element array of opportunity. Full article
(This article belongs to the Special Issue Internet of Underwater Things)
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Figure 1

Figure 1
<p>Flow diagram of the DoA estimation and localization algorithm.</p> Full article ">Figure 2
<p>Example of successful DBSCAN clustering for peaks collected by a 10-element array. The light-blue time series is the output of the NMF for channel 1. Large blue circles represent peak detections for this NMF time series. Smaller dark-purple peaks represent peak detections from the remaining nine channels. DBSCAN correctly detects a cluster of target related arrivals around 0.16 s (vertical orange line). Data from a real lake experiment.</p> Full article ">Figure 3
<p>Intensity map at the output of the wideband delay-sum beamformer without (<b>left</b>) and with (<b>right</b>) TDoA multilateration-based masking. The latter mitigates the ambiguity and makes it possible to correctly estimate the location of the target (red star), while ruling out the strongest peak (red dot) which would correspond to a wrong target location. Yellow hues denote a stronger signal.</p> Full article ">Figure 4
<p>Array topologies considered in this paper.</p> Full article ">Figure 5
<p>Examples of ambiguity in the directivity pattern for arrays 1 and 2 at frequency <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <msub> <mi>f</mi> <mi>max</mi> </msub> </mrow> </semantics></math>, once steered towards the <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> direction.</p> Full article ">Figure 6
<p>(<b>a</b>) rendering of part of the upper portion of SYMBIOSIS platform, showing the acoustic array of opportunity employed in our experiment (two cylindrical SDM-USBL units, facing right); (<b>b</b>) internal configuration of an SDM-USBL unit. Each sphere denotes a receiving acoustic element (5 in total, arranged into a pentahedral, square-base pyramid). The unit includes a transducer (the large cylindrical element in the sagittal C-C section), not used in our setting.</p> Full article ">Figure 7
<p>Conceptual organization of the experiment.</p> Full article ">Figure 8
<p>Photos of the deployment: (<b>a</b>) acoustic array of the SYMBIOSIS platform on the jetty, before deployment; (<b>b</b>) ongoing experiment, showing a snapshot of a captured signal on the laptop’s screen.</p> Full article ">Figure 9
<p>Geographical map of the experiment site near the Werbellin lake marina, Germany. The red arrow on the jetty represents the location and the reference (i.e., 0<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>) direction of the acoustic array; the blue line and arrow represent the trajectory and movement direction of the target throughout the experiment.</p> Full article ">Figure 10
<p>Localization error results for arrays 1 to 5, at an SNR of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>20</mn> </mrow> </semantics></math> dB: azimuthal angle error (<b>left</b>); depth error (<b>center</b>); and total location error (<b>right</b>).</p> Full article ">Figure 11
<p>Localization error results for array 1 at different SNRs of 0, <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>20</mn> </mrow> </semantics></math> dB: azimuthal angle error (<b>left</b>); depth error (<b>center</b>); and total location error (<b>right</b>).</p> Full article ">Figure 12
<p>Localization error results for array 2 at different SNRs of 0, <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>20</mn> </mrow> </semantics></math> dB: azimuthal angle error (<b>left</b>); depth error (<b>center</b>); and total location error (<b>right</b>).</p> Full article ">Figure 13
<p>Results of the target localization experiment in the Werbellin lake using the SYMBIOSIS array of opportunity (cf. <a href="#sec4dot3dot1-sensors-20-03862" class="html-sec">Section 4.3.1</a>). While moving, the active target transmits every 2 s for 20 times. Each marker represents the azimuthal angle of arrival estimate (<span class="html-italic">x</span>-axis coordinate) for each transmission and for the corresponding array (light blue triangles: top sub-array; dark-grey triangles: bottom sub-array; purple diamonds: full array of opportunity). Grey “+” markers show the azimuth estimate yielded by multilateration. Our algorithm enables the opportunistic use of two pyramidal arrays, and makes it possible to improve the azimuth estimation accuracy with respect to using a single sub-array or multilateration per se.</p> Full article ">Figure 14
<p>CDF of the azimuthal angle estimation error (<b>left</b>) and of the total location error (<b>right</b>) achieved in the lake experiment, showing the performance of our method as applied to different portions of the array of opportunity, as well as multilateration.</p> Full article ">

Review

Jump to: Research

35 pages, 6490 KiB  
Review
A Systematic Review on Recent Trends, Challenges, Privacy and Security Issues of Underwater Internet of Things
by Delphin Raj Kesari Mary, Eunbi Ko, Seung-Geun Kim, Sun-Ho Yum, Soo-Young Shin and Soo-Hyun Park
Sensors 2021, 21(24), 8262; https://doi.org/10.3390/s21248262 - 10 Dec 2021
Cited by 39 | Viewed by 6916
Abstract
Owing to the hasty growth of communication technologies in the Underwater Internet of Things (UIoT), many researchers and industries focus on enhancing the existing technologies of UIoT systems for developing numerous applications such as oceanography, diver networks monitoring, deep-sea exploration and early warning [...] Read more.
Owing to the hasty growth of communication technologies in the Underwater Internet of Things (UIoT), many researchers and industries focus on enhancing the existing technologies of UIoT systems for developing numerous applications such as oceanography, diver networks monitoring, deep-sea exploration and early warning systems. In a constrained UIoT environment, communication media such as acoustic, infrared (IR), visible light, radiofrequency (RF) and magnet induction (MI) are generally used to transmit information via digitally linked underwater devices. However, each medium has its technical limitations: for example, the acoustic medium has challenges such as narrow-channel bandwidth, low data rate, high cost, etc., and optical medium has challenges such as high absorption, scattering, long-distance data transmission, etc. Moreover, the malicious node can steal the underwater data by employing blackhole attacks, routing attacks, Sybil attacks, etc. Furthermore, due to heavyweight, the existing privacy and security mechanism of the terrestrial internet of things (IoT) cannot be applied directly to UIoT environment. Hence, this paper aims to provide a systematic review of recent trends, applications, communication technologies, challenges, security threats and privacy issues of UIoT system. Additionally, this paper highlights the methods of preventing the technical challenges and security attacks of the UIoT environment. Finally, this systematic review contributes much to the profit of researchers to analyze and improve the performance of services in UIoT applications. Full article
(This article belongs to the Special Issue Internet of Underwater Things)
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Figure 1

Figure 1
<p>UIoT Architecture.</p> Full article ">Figure 2
<p>Existing applications of UIoT.</p> Full article ">Figure 3
<p>Dynamic topology formation.</p> Full article ">Figure 4
<p>Challenges in adapting FCAPSC functionality.</p> Full article ">Figure 5
<p>Goals and classification of security attacks in the UIoT environment.</p> Full article ">Figure 6
<p>Types of DoS attacks in the UIoT environment.</p> Full article ">Figure 7
<p>Jamming attack in the UIoT environment.</p> Full article ">Figure 8
<p>Battery-oriented attack.</p> Full article ">Figure 9
<p>Sybil attack in UIoT environment.</p> Full article ">Figure 10
<p>Wormhole attack in the UIoT environment.</p> Full article ">Figure 11
<p>Hello flooding attack in the UIoT environment.</p> Full article ">Figure 12
<p>(<b>a</b>) Selective forwarding attack, (<b>b</b>) black hole attack in the UIoT environment.</p> Full article ">Figure 13
<p>Results based on the systematic analysis of UIoT applications.</p> Full article ">Figure 14
<p>Results based on the systematic analysis of mitigation methods to overcome the technical challenges in UIoT.</p> Full article ">Figure 15
<p>Results based on the systematic analysis of security attacks and management.</p> Full article ">
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