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IOT-based Encrypted for Knowledge Safeguarding

IOT-Based Encrypted for Knowledge Safeguarding

引用

2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Krishna, Vijaya Manju Bargavi, S.K. Chaudhary, Chetan Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

A clandestine intelligence tactic emerged, involving the concealment of data within images reduced to a minuscule size, imperceptible to the human eye. In the modern era, characterized by the proliferation of internet technology and global internet use, the focus shifts towards fortifying the security of IoT services. The objective is to enhance the reliability and efficacy of IoT systems in managing customer interactions. This work offers a secret-key steganographic technique that enables four 128x128 -pixel grayscale cover images to contain four 128x128 -pixel hidden pictures. The used technique analyses the frequency components of the cover image using the wavelet transform. Combining steganography with encryption techniques increases the system's security and makes it more resistant to potential attackers. The primary purpose of steganography is accomplished when the generated stego-image is conveyed, successfully avoiding suspicion in both objective and subjective assessments. The recommended system is created in MATLAB R2018b on a Pentium-4 computer. The reliability of IoT services. In the internet of Things, where data encryption is in line with scientific development. © 2023 IEEE.

关键词： Covert communication Data security Encrypted Encryption techniques image concealment and PSNR (Peak signal to Noise Ratio) IoT-based Knowledge safeguarding Steganography Wavelet transform

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PLATERA: A Comprehensive Web-based System and Sensor Dataset for Activity Recognition in Households Environments 1

引用

16th international conference on Ubiquitous Computing and Ambient Intelligence

作者： Diaz-Jimenez, David Luis Lopez, Jose Gaitan-Guerrero, Juan F. Espinilla, Macarena Univ Jaen Jaen Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

In this work we present a web-based system, PLATERA, and an extensive dataset, Living Dataset PLATERA, designed to be used for activity recognition and monitoring in households environments. Each user of the platform can access different functions depending on their role, including activity monitoring, data management, sensor visualization, home creation, and sensor management. The dataset contains information gathered from a variety of sensors, including motion sensors that use passive infrared (PIR) technology to detect movement, anchor devices that measure Received signal Strength Indicator (RSSI) to determine location, and activity bands that record physical activity data. Temperature and humidity sensors provide accurate measurements of environmental conditions, while magnetic technology powers the opening and closing sensors that operate doors. Through this work we hope to encourage the development of sensor-based activity recognition systems.

关键词： Activity Recognition Smart Homes Sensor Data Web-based System internet of Things

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MACHINE-ATTENTION-based VIDEO CODING FOR MACHINES 30

MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES

引用

30th IEEE international conference on image Processing (ICIP)

作者： Lee, Yegi Kim, Shin Yoon, Kyoungro Lim, Hanshin Kwak, Sangwoon Choo, Hyon-Gon Konkuk Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9781728198354

Conventional video coding methods have been developed based on the human visual system (HVS). However, in recent years, video has occupied a huge portion of internet traffic, and the mount of video data for machine consumption has increased rapidly due to the progress of neural networks. This paper proposes a novel machine-attention-based video coding method for machines. Inspired by the saliency-driven research, we first extract attention regions, sensitively affecting the machine vision performance, from the object detection network. Subsequently, a maximum a posterior (MAP)-based bit allocation method is applied to assign more bits to the attention regions. Our proposed method helps to maintain high machine vision performance whereas reducing the bitrate. Experimental results show that our proposed method achieves up to 34.89% bjontegaard delta (BD)-rate reduction for the video dataset and up to 44.70% BD-rate reduction for the image dataset compared to state-of-the-art video coding technology.

关键词： Video Coding for Machines Video Compression image Compression Machine-Attention MPEG VCM

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Attack detection on multi-architecture internet of Vehicles honeypots and response mechanism research 24

Attack detection on multi-architecture Internet of Vehicles ...

引用

3rd international conference on signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Ning, Yuqiao Huo, Quanrui Mu, Shiyu Xue, Shihao China Automobile Data of Tianjin Co. Ltd. Tianjin China CATARC Intelligent and Connected Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798400710834

Intelligent connected vehicles (ICVs) refer to a new vehicle type that utilizes advanced information and communication technology to enable information exchange among vehicles, between vehicles and roads, and between vehicles and the internet. Malicious actors may exploit vulnerabilities in ICVs to gain malicious control, steal data, or launch denial-of-service attacks, resulting in significant economic losses and personal harm. Therefore, it is crucial to research attack detection and response mechanisms for ICVs and build effective network defense systems to ensure the safe operation of these vehicles. However, different manufacturers may adopt different architectures, posing significant challenges to network security defense for these vehicles. To address this issue, this paper proposes an attack detection and response mechanism based on Docker container technology for Multi-Architecture Vehicle Honeypots (MAV-honeypots). The aim is to simulate ICVs under various architectures using honeypot technology, attracting and inducing attackers to carry out attacks, thus achieving identification, analysis, and response to attackers. © 2024 Copyright held by the owner/author(s).

关键词： Network security

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Research on image Encryption Algorithm based on Chaotic System 4

Research on Image Encryption Algorithm Based on Chaotic Syst...

引用

4th IEEE international conference on Data Science and Computer Application, ICDSCA 2024

作者： Yihang, Wu Yanling, Sun Yingqi, Li School of Mechanical Engineering Shenyang Jianzhu University Shenyang China Department of Science Shenyang Jianzhu University Shenyang China Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350368239

With the development of the internet, social software and media have become ubiquitous, making images increasingly important in daily life. The dissemination of a large number of images on social networks has made information security issues increasingly prominent, and image encryption technology has become increasingly important. This article mainly studies image encryption algorithms based on chaotic systems. Firstly, the background and history of image encryption were introduced, followed by an explanation of the basic concepts based on chaotic systems. Then, several chaos based image encryption schemes were comprehensively compared, and their advantages and disadvantages were analyzed. Subsequently, several common measures for evaluating the effectiveness of encryption schemes were introduced. Finally, the future development of image encryption algorithms based on chaotic systems is discussed. ©2024 IEEE.

关键词： Encryption algorithms

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Leveraging Enhanced Manta-Ray Foraging Optimization with Deep Learning for Maize Diseases image Classification 17

Leveraging Enhanced Manta-Ray Foraging Optimization with Dee...

引用

17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Mostafa, Reham R. Marie, Attiea El-Bannany, Magdi Khedr, Ahmed M. University of Sharjah Research Inst. of Sci. and Eng. Sharjah27272 United Arab Emirates Umm Al Quwain University College of Business Administration Umm Al Quwain536 United Arab Emirates University of Sharjah Computer Science Dept. Sharjah27272 United Arab Emirates

ISBN: (纸本)9798350370911

The manual identification of maize diseases is labor-intensive, requiring expertise and incurring substantial costs. Consequently, there is a pressing need for computer-based automated systems. Maize diseases not only compromise quality but also quantity. Addressing this, early disease detection and subsequent remedies can be facilitated through computer-based techniques. Nevertheless, this approach encounters challenges, including suboptimal contrast, limited training datasets, and inadequacies in feature extraction for final classification. This paper introduces an automated framework for maize leaf disease detection, harnessing CNN pre-trained models in conjunction with an Enhanced Manta-Ray Foraging Optimization (EMRFO) algorithm. Initial data augmentation is executed to harmonize the chosen maize dataset, followed by the fine-tuning and training of two pre-trained deep models through transfer learning. A fusion technique is then applied to consolidate feature vectors, subsequently optimized using the EMRFO algorithm. The resultant selected features undergo classification via the K-Nearest Neighbors (KNN) algorithm. Experimental validation is conducted on the non-augmented Plant Village dataset, yielding an exceptional accuracy of 98.7%. Furthermore, the performance of the proposed framework is compared with other CNN models, emerging as the superior performer. © 2023 IEEE.

关键词： Classification (of information)

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Multi-Domain image Translation Adversarial Network based on Artificial Intelligence 3

Multi-Domain Image Translation Adversarial Network Based on ...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Zhang, Lili College of Digital Art and Media Shandong University of Art & Design Jinan China

ISBN: (纸本)9798331508456

Traditional image translation methods often face difficulties such as differences in cross domain data distribution and difficulty in maintaining semantic consistency. Therefore, this article cites a generative adversarial network (GAN) based approach to capture mapping relationships between different domains. Firstly, this article adopts the Conditional Generative Adversarial Network (cGAN) framework to generate multi domain images. Next, this article introduces a shared encoder and multiple decoder structure. Finally, this article improved the performance of image translation results through optimization. In the experimental results, our method performed well on multiple standard datasets. The SSIM (Structural Similarity Index) value for the ink painting to oil painting task remains stable at around 0.88, and the PSNR (Peak signal to Noise Ratio) reaches 32.5 dB. In terms of diversity generation, this method also significantly improves the diversity and visual naturalness of image translation. Finally, the method proposed in this article significantly improves the quality and diversity of style transfer. © 2025 IEEE.

关键词： Generative adversarial networks

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Power internet of Things Access System based on Low Power Communication technology 14

Power Internet of Things Access System Based on Low Power Co...

引用

14th IEEE international conference on Communication systems and Network Technologies, CSNT 2025

作者： Jiang, Zhigang Ren, Liming Zhu, Bing Jiang, Jiafeng School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China School of Department of Electronic Information and Electrical Engineering Hefei University Anhui Hefei230000 China School of Computer and Information Hefei University of Technology Anhui Hefei230000 China School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014017 China

ISBN: (纸本)9798331531935

In response to the issues of data transmission energy efficiency and network coverage in the power internet of Things system, this study introduces an access scheme based on low-power communication technology. By optimizing the network architecture and communication protocol, the aim is to improve the energy efficiency of sensor nodes in the power internet of Things and reduce communication latency. Firstly, an access scheme based on low power wide area network (LPWAN) technology is designed. LoRa (Long Range Radio) or NB-IoT (Narrow Band internet of Thing) is selected as the communication protocol. By optimizing the working mode of sensor nodes, periodic transmission and event-driven mechanism are adopted. Secondly, an adaptive data transmission mechanism is designed to dynamically adjust the transmission frequency and rate according to environmental changes to ensure reliability under different network conditions. Furthermore, deep sleep mode and power consumption scheduling algorithm are adopted, and the network architecture adopts star topology to ensure the efficiency of signal coverage and data forwarding, and the secure transmission of data information is guaranteed by encryption and authentication mechanism. Finally, through simulation and actual deployment, the performance of the scheme in the actual environment is verified, and its stability and data transmission efficiency under different network scales are evaluated. The battery life of periodic wake-up mode is significantly lower than that of deep sleep mode. Under low-frequency transmission (once an hour), the battery life is 320 days. In periodic wake-up mode, the average power consumption of sensor nodes is significantly higher than that of deep sleep mode. At low frequency transmission, average power consumption is 3.5 mW and battery life drops to 75 days. The power internet of Things access system based on low-power communication technology proposed in this work effectively solves the bottleneck pr

关键词： internet of things

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Numerical simulation and analysis of leakage signal characteristics of different leakage holes in gas pipelines 3

Numerical simulation and analysis of leakage signal characte...

引用

3rd international conference on Advanced Manufacturing technology and Manufacturing systems, ICAMTMS 2024

作者： Han, Yu Xiao, Junsheng Zuo, Hongfei Zhou, Xiaofeng School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China Department of Electrical Engineering Baotou Vocational and Technical College Inner Mongolia Baotou014030 China

ISBN: (纸本)9781510681804

In this study, numerical simulation and analysis of leakage signal characteristics of gas pipelines with different leakage holes were carried out based on Fluent software. It was found that the differences in leakage holes significantly affect the flow field distribution of the leaking pipeline, which in turn affects the leakage detection results. The leakage hole structures and dynamic characteristics of the model significantly affect the acoustic response, and the model with a ball valve produces a higher sound pressure level. The experimental results provide guidance for the design of piping systems and the detection of leaks, and are of great significance in analysing the differences in flow field and noise generation of different leakage hole models. They provide a scientific basis for the design of piping systems and the detection of leaks. © 2024 SPIE.

关键词： Gas pipelines

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Gait Feature Perception and Identity Recognition Under Visual-Tactile Dual Modality 9

Gait Feature Perception and Identity Recognition Under Visua...

引用

9th international conference on Communication, image and signal Processing, CCISP 2024

作者： Yang, Huiyu Song, Fengqian Zhang, Yangyang DataGrand Co. LTD. Shanghai China Internet Academy Anhui University Hefei China

ISBN: (纸本)9798350356656

Gait recognition technology, as a key branch in the field of identity recognition, has significant value but also faces challenges such as discomfort from wearable devices, the need for synchronized processing of gait feature time series data, and susceptibility to environmental influences in image-based methods. A fusion method combining tactile and visual information is proposed to improve recognition effects when sensors or visual information are limited. Not only is a fusion gait dataset constructed, but identity recognition experiments are also conducted on flexible strain sensor data through Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) CNN-LSTM networks. The effectiveness of the visual-tactile fusion method in enhancing recognition accuracy is verified through decision-level fusion strategies. © 2024 IEEE.

关键词： Convolutional neural networks

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