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Novel phase adjournment data capturing technique for a mobile object in wireless sensor network

International Journal of Information technology (Singapore) 2024年第2期16卷 993-1004页

作者： Vaidya, Anand Joshi, Shrihari M. Department of Computer Science and Engineering SDM College of Engineering and Technology Dharwad Visvesvaraya Technological University Karnataka Belagavi 590018 India

In the modern era, the wireless sensor networks play a vital role in scientific and industrial applications. The enduring energy is a crucial metric parameter to be considered in the process of calculating the life span of the wireless sensor networks. Because each sensor node having non-renewable battery supply. In the first generation of wireless sensor applications, data has been transferred from each sensor node to the sink. Further evolution suggests a cluster head approach for transferring data from sensor field to sink. Different strategies of cluster head selection have provided better energy utilization. In the context of effective energy utilization, this work proposes an algorithmic technique called Phase Adjournment Data Capture Technique for a mobile object. In this technique, data will be captured by the mobile object based on its present traveling layer count. When a mobile object goes beyond the receiving limit, it will initiate the sleep mode flag to indicate the master node to stop transferring the data. This technique extends the improvement in energy consumption and also provides effective control of data flooding. It extends and eradicates the need for an erudite antenna essential for setting the direction of the data. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Data flooding Duty cycle Energy consumption Erudite antenna Master node Mobile object Non-renewable battery supply Sensor networks

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Examining IoT Concerns with the Network P2P technology Blockchain 5th

Examining IoT Concerns with the Network P2P Technology Block...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Raj Pal Verma, Rajat Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9789819771776

Integration between the Internet of Things (IoT) and equipment across sectors has resulted in formerly unthinkable efficiency and convenience. The critical issues, like data privacy, security flaws, and the lack of a reliable framework for controlling interactions among innumerable networked devices have been raised because of this rapid development. To address these issues, the research study examines how blockchain and IoT meet. The proposed method makes use of the decentralized and immutable characteristics of blockchain to create a safe and transparent basis for IoT devices. The fundamental ideas of blockchain technology are explored in this article along with potential applications to the IoT, with a focus on how blockchain might enhance access control, data integrity, and authentication. The blockchain successfully reduces the risk of IoT devices. IoT network works and use of several cases to examined and implementation of network devices which are smooth functionality of network devices. The ability to substantiality changes the environment of the IoT devices in the future references. It is making more security, reliability, and privacy focused on the network devices of IoT. In the real world, the study of IoT devices is widely used in household infrastructure. The blockchain technology can be used to conquer the preventing mainstream adoption of IoT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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SeBROP:blind ROP attacks without returns

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Frontiers of computer science 2022年第4期16卷 185-202页

作者： Tianning Zhang Miao Cai Diming Zhang Hao Huang Department of Computer Science and Technology Nanjing UniversityNanjing210023China School of Computer and Information Hohai UniversityNanjing211106China College of Computer Engineering Jiangsu University of Science and TechnologyZhenjiang212008China

Currently,security-critical server programs are well protected by various defense techniques,such as Address Space Layout Randomization(ASLR),eXecute Only Memory(XOM),and Data Execution Prevention(DEP),against modern code-reuse attacks like Return-oriented Programming(ROP)***,in these victim programs,most syscall instructions lack the following ret instructions,which prevents attacks to stitch multiple system calls to implement advanced behaviors like launching a remote *** this kind of gadget greatly constrains the capability of code-reuse *** paper proposes a novel code-reuse attack method called Signal Enhanced Blind Return Oriented Programming(SeBROP)to address these *** SeBROP can initiate a successful exploit to server-side programs using only a stack overflow *** leveraging a side-channel that exists in the victim program,we show how to find a variety of gadgets blindly without any pre-knowledges or reading/disassembling the code ***,we propose a technique that exploits the current vulnerable signal checking mechanism to realize the execution flow control even when ret instructions are *** technique can stitch a number of system calls without returns,which is more superior to conventional ROP ***,the SeBROP attack precisely identifies many useful gadgets to constitute a Turing-complete *** attack can defeat almost all state-of-the-art defense *** SeBROP attack is compatible with both modern 64-bit and 32-bit *** validate its effectiveness,We craft three exploits of the SeBROP attack for three real-world applications,i.e.,32-bit Apache 1.3.49,32-bit ProFTPD 1.3.0,and 64-bit Nginx *** results demonstrate that the SeBROP attack can successfully spawn a remote shell on Nginx,ProFTPD,and Apache with less than 8500/4300/2100 requests,respectively.

关键词： code-reuse atack ROP signal

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Transfer learning based SSD model for helmet and multiple rider detection

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International Journal of Information technology (Singapore) 2023年第2期15卷 565-576页

作者： Nandhini, C. Brindha, M. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli 620015 India

Deep learning advancements have enabled the development of practical applications for smart surveillance. In order to improve road safety, a systematic effort is required to identify and punish violators. The present study is one of several deep surveillance applications used for traffic rule violation monitoring of helmetless motorcyclists and multiple pillion riders on motorbikes. Transfer learning is used to extract the multiscale features, and a single-shot multi-box detector (SSD) has been employed to handle crowded traffic scenarios. The proposed motorcycle violation detection (MVD) model combines aspect ratio aware training in the subsequent fine-tuning stage to improve the detection performance. The proposed MVD model is evaluated on the real-world MVD dataset, collected from surveillance cameras, with busy and sparse roads under different views and weather conditions. The proposed method achieves a mean average precision of 78.1% with four classes and a detection speed of 57 frames per second in the challenging image and video dataset. Experimental results show that the MVD model outperforms the state-of-the-art approaches in terms of speed and accuracy. As a result, the proposed system can be used in complex traffic scenarios with an existing traffic surveillance infrastructure. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Deep learning Motorcycle violation detection (MVD) Object detection Single shot multibox detector (SSD) Traffic rule enforcement Traffic surveillance Two-wheeler safety

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Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

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computers, Materials & Continua 2025年第4期83卷 1023-1039页

作者： Faten S.Alamri Tariq Sadad Ahmed S.Almasoud Raja Atif Aurangzeb Amjad Khan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan AIDA Lab Department of Information SystemsCollege of Computer&Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science International Islamic UniversityIslamabad44000Pakistan

Mango farming significantly contributes to the economy,particularly in developing ***,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial *** development of deep learning models for detecting crop diseases is an active area of research in smart *** study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)*** datasets were *** first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery *** second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and *** datasets were obtained from publicly available *** proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS *** results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more *** system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual ***,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

关键词： ConvNeXt model fusion mango disease smart agriculture vision transformer

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Advanced Course Equivalence Certification Using NLP technology through Word Mover's Distance in Global Academic Credit Transfers 8

Advanced Course Equivalence Certification Using NLP Technolo...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Krishnan, Sumathy Surendran, R. Jayaraman, Poornima Sathish Kumar, P.J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331520762

As the competition for higher education and professional employment intensifies globally, the proper evaluation and translation of academic credits and qualification systems has taken on a key significance. Most classic techniques for evaluating the contents of equivalence course certificates, such as Jaccard Index or Cosine, are too simplistic and do not reveal differences in educational credentials or course materials that are found at a semantically deeper structure. These techniques are primarily based on the surface-level characteristics of the texts, ignoring their inherent pictures. To overcome these challenges, this paper considers the possibility of using methods of Natural Language Processing (NLP), especially Word Mover's Distance, in the context of higher education and professional qualification, academic credit and course equivalency evaluation. WMD, or Word Mover's Distance, is a more sophisticated method of NLP, which describes the food transport distance problem and searches for the optimal distance between two different documents. The advantages of this methodology lie in comparing the course description and qualifications from a different perspective without losing the true meaning of these words in relation to course equivalence certificate comparison. On the basis of our study, we can state that WMD implements better than other conventional approaches and is an appealing solution for credit system transfer, the degree equivalence process evaluation in higher education, and employment clearance services. In this way, WMD applications for NLP purposes will allow for the sustainable recognition of the international qualifications of professionals and students, thus enhancing their movement across borders. © 2025 IEEE.

关键词： Equivalence classes

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Attention-Based Robust Video Identification in Social Networks 6

Attention-Based Robust Video Identification in Social Networ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Konyak, Chenlep Yakha Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima797103 India

ISBN: (纸本)9798331522667

In online social networks, video identification plays a critical role in various applications such as video retrieval, personalized content recommendation, copyright protection, and video forensics. This work proposes a novel dual attention framework that integrates spatial and temporal attention mechanisms for robust feature extraction, coupled with SHA-256 hashing for unique video identity generation in social network. The proposed model achieves a identical video detection with accuracy of 78.3% and a zero hash collision rate on the UCF101 dataset, with computational efficiency, for processing videos at an average speed of 0.02 seconds per video, making it suitable for large-scale applications compare to traditional techniques which relying on handcrafted features. © 2025 IEEE.

关键词： Feature extraction

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Faster R-CNN based Framework For Bone Fracture Detection in Telesurgery Systems in Healthcare 4.0 5

Faster R-CNN based Framework For Bone Fracture Detection in ...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Patel, Yogi Patel, Dron Patel, Het Gupta, Rajesh Tanwar, Sudeep Nirma University Institute of Technology Department of Computer Science and Engineering India Nirma University Institute of Technology Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350376685

This paper introduces an ideal structure for the detection and treatment of bone fractures utilizing advanced deep learning and telesurgery techniques. The approach begins with the collection of medical imaging data from the X-Ray Layer, which is subsequently divided into training, validation, and testing datasets. The proposed model, ResNet50, is trained on training dataset and validated with the validation dataset to ensure robust performance. Once the model is trained, it accurately detects bone fractures in X-ray images and identifies the region of the fracture using an array of four points representing bounding box defined by four corners. Upon detecting a fracture, telesurgery is employed to treat the patient through a remote arm controlled by a doctor, providing minimally invasive procedures that reduce surgical risks. In case of no fracture, the patient is advised on precautionary measures and follow-up care. This comprehensive framework demonstrates a method for improving the diagnosis and treatment of bone fractures, offering efficient, precise, error-free, and patient-centric solutions. The combination of deep learning with telesurgery shows advancement in medical technology, potentially reducing need for in-person consultations also improving specialized care. Future advancement can include model improvement by using more diverse datasets, integrating additional imaging modalities such as CT and MRI, and expanding real-time capabilities in emergency situations. © 2024 IEEE.

关键词： Medical imaging

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