Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks

Yang, Guang; Dai, Lie; Wei, Zhiqiang

doi:10.3390/s18113907

Cited by 100 publications

(50 citation statements)

References 85 publications

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“…For instance, in a WSN, a sink node requires more defensive techniques when compared to sensor nodes, since the sink node is responsible for collecting, aggregating and processing data coming from sensor nodes as well as managing the whole network [28]. To this point, the authors in [29] have stated that the malicious nodes in WSN that persistently emit unwanted signals towards the sink node or a base station could stop or paralyze the whole network.…”

Section: Sc7mentioning

confidence: 99%

A Novel Methodology for Securing IoT Objects Based on their Security Level Certificates

Abdulghani¹,

Konstantas²,

Nijdam³

2020

Preprint

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Internet of Things (IoT) provides a huge business value for customers, organizations, and governments due to the developments of so many applications in different sectors like energy and healthcare. Nevertheless, as a new emerging technology, IoT faces several security concerns that are more challenging than conventional Internet because of its limited resources as well as its complex ecosystem. Toward this end, we first highlight IoT security challenges and briefly discuss its security goals like confidentiality and integrity. Second, we discuss the most common attacks against IoT, along with their violated security goals. We also review the existing frameworks of security and privacy guidelines for IoT and illustrate their shortcomings. Third, we propose a novel framework for securing IoT objects, the key objective of which is to assign different Security Level Certificates (SLCs) for IoT objects based on their hardware capabilities and protection measures. Objects with SLCs, therefore, will be able to communicate with each other or with the Internet in a secure manner. The proposed framework is composed of five main phases. In phase 1, we classify IoT assets into four components: (i) physical objects, (ii) protocols, (iii) data at rest, and (iv) software, which includes operating systems, middlewares, and applications. We also classify IoT objects into five categories based on their hardware capabilities. In phase 2, we propose security and privacy guidelines for previously mentioned IoT assets, along with their protection measures. In phase 3, we classify protection measures into five SLCs, and then we assign different SLCs for IoT objects. In phase 4: we develop a communication plan between objects based on their SLCs. In phase 5, we propose a four-step method to seamlessly integrate our objects with legacy objects ( objects are not developed according to our framework). Fourth, the feasibility and application of this framework are illustrated using smart homes as a case study. Finally, we investigate how our framework would lessen several attacks and threats against IoT like routing attacks and physical damage. We also provide qualitative arguments to show that this framework could be utilized to solve some of IoT security challenges such as tight resource constrains. Moreover, we discuss the shortcomings of our suggested framework.

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Section: Sc7mentioning

confidence: 99%

A Novel Methodology for Securing IoT Objects Based on their Security Level Certificates

Abdulghani¹,

Konstantas²,

Nijdam³

2020

Preprint

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“…The acoustic waves propagation in underwater is nearly 1.5 × 10 3 m/s, which is five orders lesser than the radio speed in the air which is 3 × 10 8 m/s. Also, in underwater the wave speed is altered by various factors such as brininess, temperature, ocean deepness, which is calculated below [24]:…”

Section: Related Workmentioning

confidence: 99%

Efficient and Accurate Target Localization in Underwater Environment

Ullah

Liu

et al. 2019

IEEE Access

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Localization is the basic feature of wireless sensor networks (WSN) and estimating the time difference of arrival (TDoA), and angle of arrival (AoA) is the most used schemes for localization. Underwater wireless sensor network (UWSN) is extensively used for data collection in an underwater environment for both military and civilian applications. Nevertheless, efficient and accurate localization algorithms are essential for the UWSN because of the dynamical property of the underwater surrounding. Also, data management, collection, and processing for WSNs have become a more active topic nowadays in computer science, such as database system and data mining. The objective of deploying the WSNs applications is to collect the real-time data, which has very challenging due to the capacity of communication and high data generated by WSNs. For this purpose, the time and location are the basic aspects when a sensor collects data, especially for the case of location-aware data. Many researchers have studied the underwater sensor nodes localization and they have considered the sensor location where the data is collected and most of them focused on the fixed sensor nodes. In this research work, energy efficient and accurate localization schemes are presented named as distance-based and angle-based schemes for the underwater environment with relatively less energy consumption and mean estimation errors (MEEs). The proposed schemes mainly focus on the localization of underwater nodes and especially on the MEEs in localization. The extensive simulation is performed to compare the proposed schemes with other counterpart schemes. The results show that the proposed schemes outperform other counterpart schemes in terms of MEEs of localization and energy consumption. INDEX TERMS Underwater wireless sensor networks (UWSNs), underwater localization, underwater acoustic sensor networks (UASNs). I. INTRODUCTION Water has covered 70 % of the land surface; basically, water is an infrequent substance that characterizes just 0.05 % of the land entire mass. However, water always plays an important role in the appearance of life on earth and especially for the living organism. The earth would be considered as a dead planet without the existence of water. The Underwater environment is still not well explored and researched for the betterment of human being life. Nowadays underwater communication technology has become an important part of our daily life and attracted more attention due to its broad applications in underwater. Those applications The associate editor coordinating the review of this manuscript and approving it for publication was Chun-Wei Tsai. include control systems, environmental monitoring, navigation and many more. Underwater Acoustic Sensor Networks (UASN) knowledge delivers novel openings to discover the underwater environment and as a consequence it expands our understanding of the environmental problems that we face in daily life, such as climate changes, animals life in oceans and the continuous changes in the inhabitants...

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“…Monitoring of an underwater environment and communication is essential for many applications. The scope of these applications ranges from observing climate, underwater environment, marine ecology, natural calamities, navigation, and transmission of images to remote side on land, offshore investigation, surveillance and other military applications [ 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Node Clustering for Underwater Sensor Networks

Khan

Bibi

Aadil

et al. 2021

Sensors

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Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.

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Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks

Cited by 100 publications

References 85 publications

A Novel Methodology for Securing IoT Objects Based on their Security Level Certificates

A Novel Methodology for Securing IoT Objects Based on their Security Level Certificates

Efficient and Accurate Target Localization in Underwater Environment

Adaptive Node Clustering for Underwater Sensor Networks

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