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A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

computers, Materials & Continua 2023年第6期75卷 5697-5715页

作者： Seema Rani Anju Anupma Sangwan Krishna Kumar Kashif Nisar Tariq Rahim Soomro Ag.Asri Ag.Ibrahim Manoj Gupta Laxmi Chandand Sadiq Ali Khan Department of CSE Guru Jambheshwar University of Science&TechnologyHisar125001India Department of Hydro and Renewable Energy Indian Institute of Technology RoorkeeIndia College of Computer Science and Information Systems Institute of Business ManagementKorangi Creek RoadKarachi75190SindhPakistan Faculty of Computing and Informatics Universiti Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale *** of the major issues in such a network is the localization of underwater *** is required for tracking objects and detecting the *** is also considered tagging of data where sensed contents are not found of any use without *** is useless for application until the position of sensed content is *** article’s major goal is to review and analyze underwater node localization to solve the localization issues in *** present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes ***,a detailed subdivision of these localization schemes is ***,these localization schemes are compared from different *** detailed analysis of these schemes in terms of certain performance metrics has been discussed in this *** the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

关键词： Underwater wireless sensor networks localization schemes node localization ranging algorithms estimation based prediction based

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Investigation of Machine Learning-Based Algorithms for Identification of Diabetes Disease

Investigation of Machine Learning-Based Algorithms for Ident...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Ali Shafique Arslan Saleem Abdul Sami Muhammad Hamza Ishaq Hazrat Bilal Yeliz Yesilada Computer Engineering Middle East Technical University Northern Cyprus Campus Kalkanli Turkiye Electrical and Electronics Engineering Middle East Technical University Northern Cyprus Campus Kalkanli Turkiye School of Automation Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science University of Galway Galway Ireland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Diabetes, a condition characterized by disrupted insulin production or insulin resistance, results in elevated blood glucose levels. It can lead to severe issues such as heart disease and kidney failure. Early prediction of diabetes is crucial for mitigating risks and managing the severity of the condition. The paper presents effective algorithms of machine learning (ML) for diabetes prediction, including Decision Trees (DT), K-Nearest Neighbors (KNN), Light Gradient-Boosting Machine (LGBM), Support Vector Machine (SVM), and Random Forest (RF), all based on ML algorithms. Various ML models’ performance was assessed using the PID Indian Diabetes (PID) dataset, encompassing parameters like age, glucose levels, insulin levels, and skin thickness, among others. The examination unveiled that among all models, the LGBM algorithm showcased superior performance, attaining an impressive accuracy of 92%. Consequently, LGBM emerges as the most efficient algorithm for accurately distinguishing between diabetic and non-diabetic individuals.

关键词： Support vector machines Measurement Machine learning algorithms Accuracy Nearest neighbor methods Prediction algorithms Diabetes Insulin Medical diagnostic imaging Diseases

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Integrating Deep Learning and Ant Colony Optimization for Improved Shortest Route Problem Solving 5

Integrating Deep Learning and Ant Colony Optimization for Im...

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5th International Conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Turky, Ayad Hussain, Abir Bettayeb, Anissa M. Serhani, Mohamed Adel Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Information Technology Center University of Sharjah Sharjah United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400708923

Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynamic factors such as weather, time, and day. This paper introduces a novel two-stage approach that integrates deep learning and ant colony optimization to address this limitation. Firstly, deep learning is employed to predict travel times, incorporating weather conditions as input for improved accuracy. Secondly, an ant colony optimization algorithm is utilized to identify the shortest route based on the predicted travel times. We evaluated the proposed approach using the New York City taxi benchmark dataset and observed superior performance compared to current state-of-the-art algorithms. © 2023 ACM.

关键词： Travel time

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Systematic Literature Review Langchain Proposed 25

Systematic Literature Review Langchain Proposed

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25th International Electronics Symposium, IES 2023

作者： Asyrofi, Rakha Dewi, Mutia Rahmi Lutfhi, Muhammad Irfan Wibowo, Prasetyo Faculty of Marine Science and Engineering Universitas Hangtuah Surabaya Indonesia Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Technology Politeknik Negeri Padang Padang Indonesia Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Graduate Institute of Network Learning Technology National Central University Taiwan Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350314731

While systematic literature reviews are frequently carried out within software engineering research, performing them in a rigorous and reproducible manner can be difficult. This paper proposes some new methods for evaluating and validating systematic literature reviews. Our approach consists of several steps, such as: Selecting a set of relevant scientific papers to analyze, Developing a list of questions and criteria to evaluate each literature review, and Determining what types of functionality and performance should be evaluated. We tested our method by having multiple experts evaluate the literature reviews based on our questions and criteria. We measured the similarity in scores between each expert to determine the reliability of the evaluations. The average similarity index between experts was 0.58 to 0.83, indicating a reasonable level of agreement in their assessments. This shows our evaluation method can produce fairly consistent results, even when different experts are involved. The relatively high level of agreement is notable considering each expert brings their perspectives and opinions in analyzing literature reviews. By providing concrete questions, criteria, and evaluation methods, we aimed to guide the experts toward more uniform evaluations. In summary, we developed and tested a new approach for evaluating and validating systematic literature reviews in software engineering. By assessing reliability via interrater agreement, we showed that consistent and reproducible results are possible using our evaluation framework and methodology. Our methods could help researchers gain more insight into what makes for an effective and high-quality literature review. © 2023 IEEE.

关键词： Software testing

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Forgive and forget: Self-stabilizing swarms in spite of Byzantine robots

Forgive and forget: Self-stabilizing swarms in spite of Byza...

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作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Graduate School of Science and Technology Nara Institute of Science and Technology Nara Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Tokyo Japan

In this article, we consider the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the program/policy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to forgive and forget, otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. We demonstrate the need to periodically forgive and forget to realize swarm several tasks including patrolling/cleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrol/clean the plane. © 2020 John Wiley & Sons, Ltd.

关键词： Robots

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Shuffle-F-ZFNet: ShuffleNet Fuzzy Zeiler and Fergus network for data aggregation in WSN data communication

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Wireless Networks 2025年 1-24页

作者： Dhanabal, S. Goswami, Chandrashekhar Praveen, Rani Venkata Satya Vetriselvi, T. Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Thottiam Trichy India Faculty of Computing and Informatics Sir Padampat Singhania University Udaipur India Digital Engineering & Assurance Hitech City Hyderabad India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Wireless sensor networks (WSN) have acquired great importance from industries owing to their wide range of applications. In system security, intrusion detection systems (IDS) play a significant role. In WSN, data aggregation protocols are utilized to maximize the network’s life span and minimize every sensor node’s communication weight as well as energy consumption. However, WSN faces energy and resource conservation conflicts. Furthermore, assuming uniform initial energy levels among sensors in existing algorithms is impractical for real-world applications. This variation in initial energy levels has a significant effect on data aggregation within sensor networks. To mitigate these issues, an innovative approach named ShuffleNet Fuzzy Zeiler and Fergus network (Shuffle-F-ZFNet) for data aggregation in WSN is proposed. Primarily, the system model for dynamic cluster WSN is simulated, by considering energy, mobility, link lifetime (LLT) and trust model. Thereafter, energy prediction is conducted utilizing the Deep Neuro-Fuzzy Network (DNFN). Then, dynamic cluster (DC) computation is carried out utilizing the Adaptive Genetic Fuzzy System (AGFS) by considering objectives like distance, throughput, trust factors, LLT, delay, residual and predicted energy. Here, AGFS is trained using Fractional Flamingo Jellyfish Search Optimization (FFJSO), which combines Fractional Calculus (FC) with Flamingo Jellyfish Search Optimization (FJSO). FJSO integrates Flamingo Search Algorithm (FSA) and Jellyfish Search Optimization (JSO). Next, the routing process is executed utilizing FFJSO with the above-mentioned objectives as fitness parameters. At Base Station (BS), feature selection is performed utilizing Angular Separation Distance (ASD) by getting input log data. Then, intrusion attack detection is carried out utilizing a Convolutional Deep Q Learning Network (CDQ-LN), which is designed by integrating a Deep Q-learning Network (DQN) and Convolutional Neural Network (CNN), where lay

关键词： Attack detection Data aggregation Deep Q-learning network (DQN) Jellyfish search optimization (JSO) Wireless sensor network (WSN)

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Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning

arXiv

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arXiv 2022年

作者： Kalais, Konstantinos Chatzis, Sotirios Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus

This work addresses meta-learning (ML) by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units results in sparse representations from each model layer, as the units are organized into blocks where only one unit generates a non-zero output. The main operating principle of the introduced units rely on stochastic principles, as the network performs posterior sampling over competing units to select the winner. Therefore, the proposed networks are explicitly designed to extract input data representations of sparse stochastic nature, as opposed to the currently standard deterministic representation paradigm. Our approach produces state-of-the-art predictive accuracy on few-shot image classification and regression experiments, as well as reduced predictive error on an active learning setting;these improvements come with an immensely reduced computational cost. Code is available at: https://***/Kkalais/StochLWTA-ML. © 2022, CC BY.

关键词： Stochastic systems

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Wirelessly Enabled Beam Steering MSF Antenna for Biomedical Imaging Applications

Wirelessly Enabled Beam Steering MSF Antenna for Biomedical ...

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URSI International Symposium on Electromagnetic Theory (EMTS)

作者： Asif Bilal Abdul Quddious Amin Darvazehban Sasan Ahdi Rezaeieh Stavros Iezekiel Marco A. Antoniades EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Research Center for Excellence University of Cyprus Nicosia Cyprus Chair of RF and Photonics Engineering TU Dresden Dresden Germany School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350399288

ISBN: (纸本)9798350399295

In this work, a wirelessly enabled beam steering metasurface (MSF) antenna for bio-imaging is proposed. The design consists of multiple layers, the top layer consists of an MSF layer, while the bottom layer consists of two layers, one that contains the feeding antennas and the second consists of the feeding network. In the feeding network of the MSF antenna, an RF energy harvesting (EH) system has been incorporated that allows dynamic switching to achieve the desired beam steering required for torso scanning. In the designed EH system, a receiving antenna and rectifying circuit harvests the low power received RF signal at three different frequencies in the 2.4 GHz ISM band. In order to avoid coupling between the EH system and MSF antenna, the former operates in the 2.4 GHz ISM band, while the latter operates in the 900 MHz ISM band. The proposed approach has potential to be used in biomedical imaging applications and to provide an alternative to complex biasing networks required to reconfigure devices.

关键词： Microwave antennas Torso Beam steering Antenna theory Switches Wireless power transfer Antenna feeds Metasurfaces Antennas Biomedical imaging

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Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

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computers, Materials & Continua 2022年第8期72卷 3481-3492页

作者： Harsh Goud Prakash Chandra Sharma Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Narendra Singh Yadav Pankaj Swarnkar Manoj Gupta Laxmi Chand Indian Institute of Information Technology NagpurIndia School of Computing&Information Technology Manipal UniversityJaipurRajasthanIndia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Maulana Azad National Institute of Technology BhopalIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Department of Computer Science and Engineering Hanyang UniversitySeongdong-guSeoul04763Korea

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging *** is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical *** this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain *** controller is used to control Mobile Robot System(MRS)at the required set *** FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system *** FF is touted as providing an easy,reliable,and efficient tuning technique for PID *** most suitable ideal performance is accomplished with FF-PID,according to the display in the time ***,the observed response is compared to those received by applying GA and conventional off-line tuning *** comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.

关键词： Metaheuristic algorithm genetic algorithm PID controller mobile robot system firefly algorithm

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A federated learning attack method based on edge collaboration via cloud

A federated learning attack method based on edge collaborati...

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作者： Yang, Jie Baker, Thar Gill, Sukhpal Singh Yang, Xiaochuan Han, Weifeng Li, Yuanzhang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computer Science Beijing Institute of Technology Beijing China School of Mechanical Manufacturing and Automation Engineer of China Railway Tunnel Bureau Group Co. Ltd Henan Zhengzhou China

Federated learning (FL) is widely used in edge-cloud collaborative training due to its distributed architecture and privacy-preserving properties without sharing local data. FLTrust, the most state-of-the-art FL defense method, is a federated learning defense system with trust guidance. However, we found that FLTrust is not very robust. Therefore, in the edge collaboration scenario, we mainly study the poisoning attack on the FLTrust defense system. Due to the aggregation rule, FLTrust, with trust guidance, the model updates of participants with a significant deviation from the root gradient direction will be eliminated, which makes the poisoning effect on the global model not obvious. To solve this problem, under the premise of not being deleted by the FLTrust aggregation rules, we construct malicious model updates that deviate from the trust gradient to the greatest extent to achieve model poisoning attacks. First, we utilize the rotation of high-dimensional vectors around axes to construct malicious vectors with fixed orientations. Second, the malicious vector is constructed by the gradient inversion method to achieve an efficient and fast attack. Finally, a method of optimizing random noise is used to construct a malicious vector with a fixed direction. Experimental results show that our attack method reduces the model accuracy by 20%, severely undermining the usability of the model. Attacks are also successful hundreds of times faster than the FLTrust adaptive attack method. © 2022 John Wiley & Sons Ltd.

关键词： Vectors

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