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Number of packets received versus time for energy‐depth aware channel routing protocol (ED‐CARP) and energy aware channel routing protocol (ECARP) considering Internet of underwater things (IOUT).

Number of packets received versus time for energy‐depth aware channel routing protocol (ED‐CARP) and energy aware channel routing protocol (ECARP) considering Internet of underwater things (IOUT).

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Article
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Internet of underwater things (IoUT) for underwater monitoring is known worldwide for smart interlinked underwater things that exhibit the capacity to monitor the vast unexplored waters of the oceans. Concept of IoUT has been derived from Internet of Things (IoT) in order to acquire the exquisite benefits of networking in underwater environment. IO...

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... It protects sensitive data. This data is sent by IoT devices [4], [5], [6]. It prevents data theft. ...
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One of the major needs and challenges of this century is the use of cutting-edge technology considering the industry 4.0 revolution. The Internet of Things (IoT) falls in the category of a cutting-edge example of such innovation in the computing and information industry. In IoT compared to classical networking methods practically; every device we employ is accessible at any time from any location. Nevertheless, IoT continues to encounter several security challenges, and the magnitude of cyber physical security risks is escalating alongside the widespread use of IoT technologies considering Moore’s laws expected to be 30 billion devices by 2025. IoT will continue to face vulnerabilities and risks unless there is a comprehensive understanding and proactive approach towards tackling its security concerns. To ensure both the cyber and physical security of IoT devices during data gathering and sharing, it is imperative to evaluate security considerations, identify instances of cyber-attacks, and implement effective security protocols at multiple layers for making highly secured IoT. Conventional security measures like data classification, strict access controls, monitoring privileged account access, encrypting sensitive data, security awareness training, network segregation, segmentation cloud security, application security, patch management, and physical security employed in the realm of IoT are inadequate in light of the current security difficulties posed by the proliferation of sophisticated attacks and threats. Utilization of artificial intelligence (AI) techniques, especially machine and deep learning models is becoming a compelling and effective approach to enhance security of the IoT devices. This research article presents a comprehensive review of the key aspects of IoT security, including the challenges, potential opportunities, and AI-driven solutions. The primary goal of this article is to provide technical resources for cybersecurity experts and researchers working on IoT initiatives.
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Internet of underwater things (IoUT) is a technology enabling underwater sensor nodes to communicate with the IoT networks to support various applications. Efficient energy utilization and network lifetime are the two main critical issues related to the IoUT. Both performance factors are related to how efficiently the routing protocol is tailored especially for heterogeneous IoUT. In this context, efficient routing protocols that consider underwater nodes (UNs) energy heterogeneity can adjust the energy imbalance, especially in heterogeneous IoUT scenarios. In IoUT, both the UN's energies and their depths can greatly affect the energy efficiency and clustering-based routing protocol. This paper provided an energy and depth-based stable election routing protocol (SEP) to suit the requirements of underwater communications. The objective of the depth-based SEP protocol is to enhance energy-efficient routing while considering the varying energy levels of UNs in IoUT. The proposed protocol depends on the depth-based cluster head (CH) election approach by defining distinct functions tailored for UNs with different energy levels and depths, in addition to the distance between the selected CH and the surface base station. Experimental results indicate that depth-based SEP-IoUT protocol outperforms network lifetime more than conventional SEP and SEP-IoUT. It gives an average stability period of 19.6 % in addition to reducing energy consumption by 39 % and 15.7 % when compared to SEP and SEP-IoUT respectively.
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With the widespread use of the Internet of Things, underwater control and monitoring systems for purposes such as ocean data sampling, natural disaster prevention, underwater surveillance, submarine exploration, and the like have become a popular and challenging topic in computers. So far, various topology control and routing solutions have been proposed for these networks. However, as technology expands and applications grow, so does the need for a stable underwater communication platform. On the other hand, underwater communication is associated with challenges such as node mobility, long propagation delays, low bandwidth, limited resources, and high error rates. In this research, for the first time, a topology control platform based on the RPL tree is modelled by applying its structural changes underwater. The proposed RPLUW methods in the case of RPLUWM fixed nodes are introduced to support the mobility of nodes underwater. Flexible objective functions, utilisation of decision-making systems, and application of control schedules in these methods have increased network life, reduced overhead, and increased node efficiency. The simulation results of the proposed method, in comparison with recent methods in this field, show an increase in network efficiency.
Preprint
Full-text available
With the widespread use of the Internet of Things, underwater control and monitoring systems for purposes such as ocean data sampling, natural disaster prevention, underwater surveillance, submarine exploration, and the like have become a popular and challenging topic in computers. So far, various topology control and routing solutions have been proposed for these networks. However, as technology expands and applications grow, so does the need for a stable underwater communication platform. On the other hand, underwater communication is associated with challenges such as node mobility, long propagation delays, low bandwidth, limited resources, and high error rates. In this research, for the first time, a topology control platform based on the RPL tree is modelled by applying its structural changes underwater. The proposed RPLUW methods in the case of RPLUWM fixed nodes are introduced to support the mobility of nodes underwater. Flexible objective functions, utilisation of decision-making systems, and application of control schedules in these methods have increased network life, reduced overhead, and increased node efficiency. The simulation results of the proposed method, in comparison with recent methods in this field, show an increase in network efficiency.
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Water has covered a wide part of the earth’s surface. Oceans and other water bodies contain significant natural and environmental resources as well as aquatic life. Due to humans’ hazardous and unsuitable underwater (UW) settings, these are generally undiscovered and unknown. As a result of its widespread utility in fields as diverse as oceanography, ecology, seismology, and oceanography, underwater wireless sensor networks (UWSNs) have emerged as a cutting-edge area of study. Despite their usefulness, the performance of the network is hampered by factors including excessive propagation delay, a changing network architecture, a lack of bandwidth, and a battery life that is too short on sensor nodes. Developing effective routing protocols is the best way to overcome these challenges. An effective routing protocol can relay data from the network’s root node to its final destination. Therefore, the state of the art in underwater wireless acoustic sensor network (UWASN) routing protocols is assessed with an eye toward their potential for development. In real-world applications, sensor node positions are frequently used to locate relevant information. As a result, it is crucial to conduct research on routing protocols. Reinforcement learning (RL) algorithms have the ability to enhance routing under a variety of conditions because they are experience-based learning algorithms. Underwater routing methods for UWSN are reviewed in detail, including those that rely on machine learning (ML), energy, clustering and evolutionary approaches. Tables are incorporated for the suggested protocols by including the benefits, drawbacks, and performance assessments, which make the information easier to digest. Also, several applications of UWSN are discussed with security considerations. In addition to this, the analysis of node deployment and residual energy is discussed in this review. Furthermore, the domain review emphasizes UW routing protocol research difficulties and future directions, which can help researchers create more efficient routing protocols based on ML in the future.