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Performance Impact of Wind Speed on MAC Protocols for Underwater Wireless Sensor Networks

Procedia computer science 2025年 257卷 190-197页

作者： Md Al Shayokh Anne-Lena Kampen Kjell Eivind Frøysa Lars Michael Kristensen Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Acoustic communication in underwater environments is challenging due to high propagation delay and severely limited bandwidth. Furthermore, the quality of the channel is dynamically changing due to wind, rain, temperature and other environmental factors. Efficient medium access control (MAC) protocols are therefore particularly important for acoustic sensor networks (UWSNs) to prevent a significant portion of the scarce bandwidth to be wasted on packet collision. Increased wind speed add surface roughness for underwater communication, which in turn can affect the overall performance of MAC protocols in UWSNs. The contribution of this paper is to investigate the performance of five MAC protocols for UWSNs in terms of packet delivery rate, energy consumption and delay under different wind speeds. The protocols investigated are: Aloha, Aloha with carrier sense (Aloha-CS), Carrier Sense Multiple Access (CSMA), T-Lohi, and Distance Aware Collision Avoidance Protocol (DACAP). Our simulation studies show that at low wind speeds of 10 m/s, CSMA performs better than other protocols in terms of Packet Delivery Ratio (PDR) while Aloha without acknowledgement has the lowest End-to-End (E2E) delay with a trade-off in packet delivery ratio (PDR). At higher wind speed of 20 m/s to 30 m/s, T-Lohi outperforms the other protocols both in terms of PDR, E2E delay, and energy consumption.

关键词： Emerging Networking Technologies Wireless Sensor Networks MAC Protocols Acoustic Underwater Communication

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Total Labelings of Graphs with Prescribed Weights

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Journal of Combinatorial mathematics and Combinatorial Computing 2024年 120卷 367-376页

作者： Bača, Martin Miller, Mirka Phanalasy, Oudone Ryan, Joe Semaničová-Feňovčíková, Andrea Sillasen, Anita A. Department of Applied Mathematics and Informatics Technical University Košice Slovakia School of Mathematical and Physical Sciences The University of Newcastle Australia School of Electrical Engineering and Computer Science The University of Newcastle Australia Department of Mathematics University of West Bohemia Pilsen Czech Republic Department of Informatics King's College London United Kingdom Department of Mathematics National University of Laos Vientiane Laos Department of Mathematical Sciences Aalborg University Aalborg Denmark

The total labeling of a graph G = (V, E) is a bijection from the union of the vertex set and the edge set of G to the set {1, 2, . . . , |V(G)| + |E(G)|}. The edge-weight of an edge under a total labeling is the sum of the label of the edge and the labels of the end vertices of that edge. The vertexweight of a vertex under a total labeling is the sum of the label of the vertex and the labels of all the edges incident with that vertex. A total labeling is called edge-magic or vertex-magic when all the edge-weights or all the vertex-weights are the same, respectively. When all the edge-weights or all the vertex-weights are different then a total labeling is called edge-antimagic or vertex-antimagic total, respectively. In this paper we deal with the problem of finding a total labeling of some classes of graphs that is simultaneously vertex-magic and edge-antimagic or simultaneously vertex-antimagic and edge-magic, respectively. We show several results for stars, paths and cycles. © 2024 Charles Babbage Research Centre. All rights reserved.

关键词： Edge-antimagic total labeling Edge-magic total labeling Vertex-antimagic total labeling Vertex-magic total labeling

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IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks 2

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Hasan, M D Ashfaqul Balasubadra, K. Vadivel, G. Arunfred, N. Ishwarya, M.V. Murugan, S. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India R.M.D. Engineering College Kavaraipettai Department of Information Technology Tamil Nadu Chennai India University of Technology and Applied Science Department of Information Technology Dhofar Salalah Oman Srm Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics' influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management. © 2024 IEEE.

关键词： Large datasets

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Development of Smart Tongue using Ionic Polymer Metal Composite for Robotic Applications 23

Development of Smart Tongue using Ionic Polymer Metal Compos...

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6th International Conference on Advances in Robotics, AIR 2023

作者： Das, Suman Bhadra, Subir Bhattacharya, Srijan Chattopadhyay, Subrata Department of Electrical Engineering Mckv Institute of Engineering Howrah India Department of Computer Science and Engineering Mckv Institute of Engineering India Applied Electronics and Instrumentation Engineering Department Rcc Institute of Information Technology Kolkata India Electrical Engineering Department National Institute of Technical Teachers Training and Research Kolkata India

ISBN: (纸本)9781450399807

This paper focus on the development Ionic Polymer Metal Composite (IPMC) based robotic tongue as artificial taste sensor to identify and discriminate between different taste senses. In this study, taste samples are used which denotes five taste senses- Salty, Sweetness, Spicy, Bitterness and Sourness. NafionTM-117 (DuPont) substrate based IPMC due to its hydrophilic nature allow cations like Li+, Na+, K+ etc. diffuse through its perfluorinated polymer membrane penetrating Platinum (Pt)-surface electrode layer on both side of the membrane. Cations of different concentration present in natural and artificial food products. Based on the amount of diffused cations inside the polymer matrix of IPMC, various taste samples are identified. While bending force applied on IPMC strip, mobile cations and fixed anions get polarized inside IPMC polymer membrane and create potential difference in millivolts (mV). The potential difference measured across the Pt-surface electrode of IPMC. Magnitude of the developed emf depends on cationic concentration for different taste samples. Considering taste sense as identification index, bending deformation of IPMC strip has clear correlation with magnitude of emf developed across the Pt-surface electrode. K-nearest neighbor (KNN) based Classification technique is applied in developing the Machine Learning (ML) Model for prediction of different taste senses. The model is trained with real-time dataset having information about output voltage developed across IPMC' vs. taste samples of variable concentration'. Training is done in two phases where the ratio of training data: test data was 70%: 30% and 80%: 20% respectively. It is observed that Prediction accuracy of the learned model increases if the volume of training dataset increases. High prediction accuracy of the developed ML model confirms the competence IPMC based e-tongue technology, applicable for robotic system as artificial taste sensor. © 2023 ACM.

关键词： Machine learning

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Climate change-driven coastal erosion in Sandbanks Provincial Park, Ontario, Canada 33rd

Climate change-driven coastal erosion in Sandbanks Provincia...

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33rd International Ocean and Polar engineering Conference, ISOPE 2023

作者： Kouhgardi, Esmaeil Hemati, Mohammadali Shakerdargah, Elaheh Mahdianpari, Masoud Shiri, Hodjat Civil Engineering Department Faculty of Engineering and Applied Sciences Memorial University of Newfoundland Canada Department of Electrical and Computer Engineering Memorial University of Newfoundland Canada Department of Environmental Sciences Faculty of Science Memorial University of Newfoundland Canada C-Core Facult y of Engineering and Applied Sciences Memorial University of Newfoundland Canada

ISBN: (纸本)9781880653807

Climate change is impacting coastal areas causing shoreline changes across the globe. Sandbanks Provincial Park in Ontario is becoming a more popular destination for seasonal residents and visitors. The inland water bodies of the area have suffered from water level fluctuations mainly due to climate events and local situations. Remotely sensed data were used to detect coastal changes of the West and East Lakes and the vicinal shore of Lake Ontario. Results revealed a water-to-land trend due to climate change-driven forces and growing demand for water in all sectors. Wetlands were introduced as the main host of the coastal changes. © 2023 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： Erosion

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Emotion Recognition of Humans using modern technology of AI: A Survey 7

Emotion Recognition of Humans using modern technology of AI:...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Alheeti, Ahmed Adil Mohammed Salih, Mustafa Muhanad M. Mohammed, Alaa Hamid Hamood, Mostafa A. Khudhair, Nemer Radhwan Shakir, Ahmed Thair Karabuk University Department of Computer Engineering Turkey AL Mansour University College Department of Computer Science and Information System Baghdad Iraq Al-Maarif University College Department of Medical Instruments Techniques Engineering Iraq University of Baghdad College of Engineering Department of Chemical Engineering Baghdad Iraq University of Anbar College of Applied Sciences Department of Biophysics Ramadi Iraq

ISBN: (纸本)9798350383065

This comprehensive investigation and evaluation of the subject matter of emotion recognition is presented, focusing on the broader implications for society at large. The study utilizes a thorough analysis of scholarly literature and practical observations to establish a theoretical framework that facilitates comprehension of the topic under investigation. The findings have considerable implications for future investigations and pragmatic applications. Emotion recognition holds immense importance in diverse domains such as human-computer interaction and healthcare. The analysis of techniques used for emotional recognition includes facial expression assessment, speech patterns analysis, physiological signal interpretation, music perception, and written expression evaluation. The study presents a comprehensive overview of research methodologies commonly used in emotion recognition, discussing datasets, feature extraction techniques, and classification algorithms. The analysis of challenges and limitations pertaining to emotion recognition systems, including privacy concerns, is also discussed. Performance evaluation is analyzed through various methods, including machine assessment and self-report. The significance of continued investigation within the domains encompassing data integration across diverse modalities, the creation of robust classification algorithms, and the exploration of the intricate connection between the brain and affective states is underscored. © 2023 IEEE.

关键词： Emotion Recognition

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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ZigBee Network for AGV Communication in Industrial Environments 10

ZigBee Network for AGV Communication in Industrial Environme...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Flak, Jaroslaw Skowron, Tomasz Cupek, Rafal Fojcik, Marcin Caban, Dariusz Domanski, Adam Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering Bergen Norway

ISBN: (纸本)9798350345032

Automated Guided Vehicles (AGVs) are a key component of many modern industrial systems. AGVs are supposed to communicate with each other in real time using wireless networks. In this article, the advantages and disadvantages of the ZigBee wireless network related to the control of AGVs are considered. We analyze the performance of the ZigBee network programmed with both C# and Python libraries to control ZigBee devices. The throughput and signal strength are presented and discussed depending on the transmission speed of the serial port, the payload size, and the presence and distance from the obstacles. The results of the experiments show the effective values of these parameters, the methods of using C# and Python, and the reliability of the throughput up to a certain point in network devices. © 2023 IEEE.

关键词： Python

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Quantized Wavelet Dilated Convolutional Neural Network Based Intelligent Surveillance System for Public Safety in IoT Security Networks 4

Quantized Wavelet Dilated Convolutional Neural Network Based...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sowmya, R. Rawat, Ruchira Mishra, Dinesh Megha, Mamtaben Kamleshbhai Patel, Monikaben Maheshbhai Kannan, Sathish Tamil Nadu Chennai600077 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Mangalayatan University-Jabalpur Department of Computer Science and Engineering Madhya Pradesh Jabalpur483001 India Natubhai V. Patel College of Pure and Applied Sciences A Constituent College of Cvm University Department of Computer Science Gujarat Anand388120 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Smart cities have produced vast amounts of surveillance video data that can be processed to identify anomalies since the introduction of visual sensors. A novel Quantized Wavelet Auxiliary Encoder Self-Attention Dilated Convolutional Neural Network with Gold Rush Optimizer (QWAESDCNN-GRO) designed to enhance security and reduce crime potential. Through the integration of the Quantized Discrete Haar Wavelet Transform (QDHWT), frame-level features have been extracted. Subsequently, the recovered features are fed to the Auxiliary Guided Knowledge Encoder-Decoder (AGED), which uses them to rank the features. These features are then sent to the Self-Attention Causal Dilated Convolutional Neural Network (SACDCNN) with Gold Rush Optimization (GRO) for anomaly detection. Performance indicators including Fl-score, recall, accuracy, precision, and AUC are compared with those of earlier techniques in order to assess the effectiveness of the recommended strategy. Using the UCF-Crime dataset, the model achieved an accuracy of 99.87% and an error rate of 0.021%. On the Hockey Fights dataset, it obtained an accuracy of 99.88% and an error rate of 0.019%. © 2024 IEEE.

关键词： Convolutional neural networks

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Visual Identification of Oysters Using Machine Learning

Visual Identification of Oysters Using Machine Learning

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Essandoh, Joshua Straus, Michael Vukov, Nikolai Jin, Yuanwei Lu, Enyue Virginia Tech Department of Computer Science BlacksburgVA United States Columbia University School of Engineering and Applied Sciences New YorkNY United States Salisbury University Department of Mathematical Sciences SalisburyMD United States University of Maryland Eastern Shore Department of Engineering and Aviation Sciences Princess AnneMD United States Salisbury University Department of Computer Science SalisburyMD United States

ISBN: (纸本)9798350362480

Agricultural technology has evolved significantly since the 1800s. However, with respect to monitoring and farming of aquaculture such as oysters, industry is still stuck in the past. This project aims to modernize oyster monitoring and identification with machine learning. We developed a program capable of accurately identifying and classifying oysters in three states. They include closed, meaning they are healthy, or open for long periods of time, indicating mortality, and indeterminate. We also increased the scope of existing software with a website for real-time identification that is accessible to the public. In the process, we added support for video annotation, which was previously difficult to perform for those with no prior coding experience.1 © 2024 IEEE.

关键词： Adversarial machine learning

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