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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dorsala, Mallikarjun Reddy Nishitha, Charugundla Lakshmi Sathwik, Pendem Srilekha, Gattu Morampudi, Mahesh Kumar Indian Institute of Information Technology Sri City Department of Computer Science and Engineering Chittoor India Department of Computer Science and Engineering AP India

ISBN: (纸本)9798350391725

Many applications widely use broadcast communications (BC) due to their efficiency in simultaneously distributing data to many receivers. More specifically, BC is essential in resource-constrained devices (RCDs) for conserving energy and bandwidth, which are vital for increasing the longevity and performance of RCDs. The three major challenges in BC are source authentication, data integrity and continuous authentication (CA). The first two challenges are effectively addressed in TESLA and its variants. However, achieving CA without incurring overheads on the sender and receiver sides remains an unresolved challenge. In this paper, we propose Infinite (Inf) TESLA++, assisted by Blockchain and smart contracts, to address the CA challenge with minimal overhead at sender and receiver. Our theoretical and simulation analysis demonstrates that Inf-TESLA++ incurs minimal overhead compared to existing protocols while eliminating the need for direct communication between the sender and receiver for synchronization. © 2024 IEEE.

关键词： Authentication Protocol

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WOCP: Controller Placement using Whale Optimization in SDN-WAN 4

WOCP: Controller Placement using Whale Optimization in SDN-W...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Thalapala, Vidya Sagar Guravaiah, Koppala Department of Computer Science & Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

The established SDN multi-controller architecture faces the challenge of locating controllers to boost the scalability, reliability, and security of the network. Reliability is one of the important metric to handle when a failure event occurs in a network. In this paper, a Whale Optimization technique is used for controller placement and proposed Whale Optimization based Controller Placement (WOCP), that places the controllers in an optimized and efficient manner. The adequate placement of controllers minimizes the overall average latency with a high reliability index. The WOCP method resemblances with Minimizing Latency for Controller Placement (MLCP), resulting in an 18% reduction in latency in the Geant network topology. © 2025 The Authors. Published by Elsevier B.V.

关键词： Software reliability

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Designing a Resilient SDN Framework with IoT Integration for Enhanced Network Reliability 7

Designing a Resilient SDN Framework with IoT Integration for...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jegadeesan, S. Aishwarya, S.S. Harshini, R.G. Diraviam, A. Justin Sujatha, S. Krishnan, R. Santhana Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

The ever-expanding Internet of Things (IoT) landscape poses significant challenges for network reliability and resilience. This research investigates the design and implementation of a resilient Software-Defined Networking (SDN) framework with Internet of Things (IoT) integration, aiming to enhance network reliability. The proposed model, referred to as HybridSDN-IoT Resilience (HyRes), introduces a hybrid SDN approach by amalgamating traditional and distributed SDN strategies. This hybridization seeks to balance the advantages of centralized control and distributed resilience within the network architecture. To estimate the efficacy of the HyRes model implementation, comprehensive simulation analyses are conducted, comparing its performance against established algorithms in the SDN domain. Metrics such as network latency, packet loss, and throughput are employed to assess the reliability of the network under varying conditions. The simulation results offer significant performance results for the resilience and adaptability of HyRes in comparison to existing algorithms, showcasing its potential to mitigate network failures and enhance overall reliability. The proposed HyRes model not only establishes a foundation for resilient SDN architectures but also discourses the precise challenges in the integration of diverse IoT devices. By emphasizing adaptability and responsiveness, the research advances the discourse on the practical realization of reliable SDN frameworks in the context of emerging IoT technologies. © 2024 IEEE.

关键词： Software defined networking

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Agriconnect: Revolutionizing Farm Machinery Booking 2

Agriconnect: Revolutionizing Farm Machinery Booking

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Subrahmanya Pradhan, Trilochan Shaikh, Sauda Shabbir Shetty, Ruchitha N. Shreekumar, T. Department of Information Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India Department of Computer Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India

ISBN: (纸本)9798350383867

Crop assessment plays an important role in ensuring food safety, and recent technological advances such as machine learning and deep learning have revolutionized assessment, and crop and culture management. Agriconnect is the largest network combining image-based crop analysis with rental equipment to optimize agriculture. Agriconnect uses the latest image processing technology to identify crops from field images, allowing farmers to make informed decisions about their crops. In addition, the platform supports easy-to-assemble equipment, allowing farmers to adapt machines to their specific needs, thus reducing waste and improving resource allocation. Agriconnect simplifies the farming process and increases overall profitability by providing a user-friendly interface for product identification and equipment booking. By combining forward-thinking technology with powerful management tools, Agriconnect not only improves the accuracy of farming but also improves efficiency. He envisions a future in which technology will play a key role in making farming easier and more efficient for farmers around the world. Agriconnect is a prime example of how technology is changing agriculture, enabling farmers to make informed decisions, improving resource use, and ultimately increasing production to implement permaculture practices. © 2024 IEEE.

关键词： Algae control

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Next-k Items Recommendation with Item Novelty 14th

Next-k Items Recommendation with Item Novelty

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14th International Conference on Frontier Computing, FC 2024

作者： Ma, Hao-Shang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Recommendation engines leverage past user preferences to forecast their future interests. Many deep learning-based recommendation systems aim to explore the intricate dynamics between users and particular items. Typically, the most recommendation systems have a tendency to suggest items based on their popularity or relevance to the user’s previous interactions, which will lead to a narrow range of recommendations. We would like to tackle this issue by evaluating potential items based on the novelty concept, effectively broadening the spectrum of recommendations. Furthermore, we detail a framework that allows for the seamless integration of this novel feature into next-k item recommendation architectures. Through rigorous experiments, the proposed method demonstrated superior performance compared with existing methods, confirming that integrating a novelty score can significantly refine the quality of recommendations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Distance Matrix and Markov Chain Based Sensor Localization in WSN

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computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a Markov Chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the Markova chain to sustain higher energy ***,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix Markov chain

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Acceleration Object Detection using FPGA platform based on Improved CNN 10

Acceleration Object Detection using FPGA platform based on I...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Salehzadeh, Mohammad Hossein Monfared, Mohammad Hamidi Rostami, Vahid Salamati, Mahdi Department of Computer and Information Technology Engineering Science and Research Tehran Branch Islamic Azad University Tehran Iran Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran

ISBN: (纸本)9798350348873

Deep neural networks have emerged as a leading approach for object detection in recent years. However, their intensive computational requirements often make them unsuitable for embedded systems. The TinyYolo network is a widely-used deep neural network is designed to be simpler and smaller compared to its counterparts. Yet, it still presents challenges in FPGA implementations due to its large number of nodes. In this research, we focus on adapting the TinyYolo network for FPGA implementation. Our modified network comprises four deep neural sub-networks. One serves as the input, two as intermediate networks that extract both local and global features and the final one aggregates the extracted features. Based on these aggregated features, the target objects are detected. Our adaptation offers several advantages including Hardware-based implementation leading to increased computing power, Significant power savings compared to software implementations and Optimal parallel processing, power efficiency and memory usage making it suitable for embedded applications across various domains Our network implemented on the ZU3EG A484 chip achieved a processing speed of 58 FPS and consumed 4.105 W making it competitive with similar systems. ©2024 IEEE.

关键词： Object recognition

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

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computer Modeling in engineering & Sciences 2024年第10期141卷 847-869页

作者： Zengxiang Li Yongchong Wu Alanoud Al Mazroa Donghua Jiang Jianhua Wu Xishun Zhu Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang332020China School of Information Engineering Gongqing Institute of Science and TechnologyJiujiang332020China Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou511400China School of Information Engineering Nanchang UniversityNanchang330031China School of Mathematics and Statistics Hainan Normal UniversityHaikou571158China

Hidden capacity,concealment,security,and robustness are essential indicators of hiding ***,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the *** practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret *** image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding *** this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet *** wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding *** the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret *** lowresolution secret images are combined using densemodules to obtain a high-quality secret *** experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various *** proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical *** conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing *** ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various *** research and experimentation can explore the algorith

关键词： Image hiding robustness wavelet transform dynamic region attention

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