<abstract xmlns="http://www.w3.org/1999/xhtml">

Due to their limited frequency range and fast fading channels, underwater sensor networks (USNs) are vulnerable to collisions of packets. In this paper, we propose a deep reinforcement learning-based relay selection scheme with shortest latency (DRL-SL) for USNs that enables to choose the relay based on the state that comprised of the bit error rate (BER) of the previous transmission, and the jamming power measured by the relay node. The DRL-SL-based relay selection scheme completed in two phases. In the first phase, a deep neural network based learning is performed and second phase is the real-time interaction with the underwater sensor network. Numerical results give the bound on how efficiently the system performs in terms of bit error rate, energy use, and node utility. According to the numerical results, the proposed DRL-SL based relay selection scheme can enhance relay performance in comparison to the benchmark underwater relay techniques.
</abstract>

A Deep Reinforcement-Learning-Based Relay Selection for Underwater Sensors Network

<abstract xmlns="http://www.w3.org/1999/xhtml">

Academics and businesses are very interested in using artificial intelligence (AI) and machine learning (ML) to solve the most challenging problems in a variety of fields. This white paper provides a brief overview of key applications of Artificial Intelligence and Machine Learning in SDN- and NFV-based networks. According to their application history, we categorize the most important developments in this field into different groups and identify the relevant AI methods used. It also covers key challenges and the domain’s future course. We highlight the critical role AI/ML can play in opening the door to self-configuring, self-adapting as well as self-managing networks, but research is inadequate due to the difficulties found in this domain.
</abstract>

Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences

Application of Artificial Intelligence and Machine Learning in Software Defined Networks

<abstract xmlns="http://www.w3.org/1999/xhtml">

Communication networks are constantly increasing in size and complexity. Hence, the traditional rule-based algorithms of these networks will probably not operate at their most effective efficiency. Machine learning (ML) is being used these days to solve tough problems in a variety of industries, including banking, healthcare, and enterprise. Communication network performance can be improved using computational models that can deliver ML algorithms. This paper investigates the use of ML models in communication networks for prediction, intruder detection, route and path allocation, quality of service enhancement, and resource management. A review of the current literature suggests that there is a wealth of potential for researchers to leverage ML to solve challenging network performance problems, especially in the development of software-based networks and 5G.
</abstract>

Applications of Machine Learning in Mobile Networking

<abstract xmlns="http://www.w3.org/1999/xhtml">

This entire system stands upon blockchain technology. The system is initially broken down in sections like different modules for patient, doctor, pharmacy and insurance agent. It is completed utilising a personal blockchain from Corda and Ethereum called Ganache as well as a private blockchain. This supports the development, testing, and general deployment of dApps. An Ethereum development framework is called Truffle Suite. Completely control the lifetime of modern contracts that includes bespoke installations, linking with different libraries and intricate applications on Ethereum. In dApps, Meta Mask is usefull for account creation, login and bitcoin transcations. The storage of all huge files and documents is done using the IPFS technology. Because IPFS is a P2P network circuit for decentralised data storage and the produced hash is simple to store on the blockchain, it is used. ReactJS was used to build the system’s front end. The Remix online compiler for Solidity is used to build and test smart and modern contracts that are written in the Solidity blockchain programming language.
</abstract>

Design and Development of Healthcare System Using Blockchain

<abstract xmlns="http://www.w3.org/1999/xhtml">

Two basic strategies have developed by network designers to deal including errors. by adding extra information to data before sending. such approach is to comprise sufficient extra information to make destination to infer what the sent data should be, or it possible to add only sufficient extra information to let destination to infer that errors have been occurred, however not which error then have it ask re-send defected data. Previous strategy utilizes errors correcting mechanism and latter uses errors detecting mechanism. Utilize of errors correcting mechanism is named as Forward Error Correction (FEC). In this thesis, sliding window has been implemented over Multi-Protocol Label Switching (MPLS). Therefore, MPLS send data from one node to other node on network depend on short path labels instead of logical addresses in network layer, in addition to that MPLS is connection oriented compared to IP network. Finally, we have study and analyze sliding technique performance over MPLS from delay, throughput and packet loss point of view. It is show that go back N has belter performance compared to selective repeat in term of throughput.
</abstract>

The Department of Electronics and Communication Engineering

Department of Electrical and Electronics Engineering

Effect of Sliding Windows Technique on the Performance of TCP/IP Networks

<abstract xmlns="http://www.w3.org/1999/xhtml">

Due to growing concerns regarding their use in fields including oceanography, commercial marine operations, and military surveillance, demand in the exploration of underwater sensor networks for marine studies has developed. Network channels for underwater sensor network (USN) rapidly change (spatially and temporally) depending on the surroundings. To increase system efficiency by adjusting transmission parameters to channel fluctuations, it is alluring to utilize adaptive modulation and coding (AMC) for USNs. In order to determine the best link adaptation method based on the channel quality, this article focuses on evaluating a measured sea trial dataset utilizing a rule-based approach (i.e., three-dimensional evaluation, modulation-wise analysis, and a fixed-SNR strategy). To determine the optimum AMC combinations in terms of channel adaptively, we draw a situation of the measured USN data rate versus Bit Error Rate (BER) and Signal to Noise Ratio (SNR). The work further extends to apply machine learning (ML) methods to identify the MCS levels by looking into the channel characteristics due to the non-reversibility limitation of the rule-based strategy. One of the ML methods we used for the investigation, gradient boosted regression tree (GBRT), exhibits impressive accuracy of 99.988% in classifying MCS levels. The MCS levels are related to channel statistics and signal characteristics, particularly those that are susceptible to SNR and BER limitations, using an ensemble of trees that learns from the buoy and base station’s uplink data.
</abstract>

Budapest University of Technology and Economic

Link Adaptation Strategy for Underwater Acoustic Sensor Networks: A Machine Learning Approach

AHEAD OF PRINT

Volume 2023 (2023): Issue 2 (December 2023)

Volume 2023 (2023): Issue 1 (June 2023)

Volume 2022 (2022): Issue 1 (December 2022)

Journal of Smart Internet of Things

Journal of Smart Internet of Things (JSIoT) is an international double-blind peer-reviewed open-access scientific journal published as a quarterly by Future Science and scientifically sponsored by Islamic University (Iraq).JSIoT is a multidisciplinary journal in engineering area, committed not only to a single approach, discipline, methodology or paradigm concerned with Artificial Intelligence in general and devoted to all concerned with Internet of Things and Engineering Systems.  The Journal embraces a wide scope of research areas while not being limited to the following domains:    Artificial Intelligence of Things:  Explainable Machine Learning for Internet of Things: Intelligent Edge: Ethical AI:  Heuristic Search:  Human Interfaces:  Intelligent Robotics:  Knowledge Representation: Machine Learning:  Multi-Agent Systems:  Natural Language Processing:  Planning and Action:  Decision Making and Reasoning under Uncertainty:   Moreover, the Journal covers diverse applications of IoT in various domains, including but not limited to:    Energy  Manufacturing and Industry  Smart Cities  Smart Home  Communication and Electronics Engineering  Smart Buildings  eLearning and Education  Medicine and Healthcare  Nano IoT  Personalised Precision Medicine  Biological IoT  Chemical IoT   Journal of Smart Internet of Things (JSIoT) aims to showcase cutting-edge research in the field by publishing original research papers and review papers. Manuscripts submitted to JSIoT should demonstrate significant advancements compared to the existing state of research.  Archiving Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections. Plagiarism Policy The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process.