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Design, Implementation and Analysis of HIBRI Cipher on IoT Platforms

Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1-13页

作者： Nagesh, H.R. Poojari, Asmita Kumar, V. G. Kiran Canara Engineering College Benjanapadavu Bantwal Taluk Karnataka Mangaluru574219 India Department of Computer Science and Engineering NMAM Institute of Technology Karnataka Nitte 574110 India Department of Electronics and Communication Engineering A J Institute Engineering and Technology Karnataka Mangaluru575006 India

With the exponential growth of the Internet of Things through time there has been an enormous increase in the usage of tiny devices and information being exchanged, between low resource devices like sensors, PDA’s and the high-end devices like servers or clouds. This opens up several challenges in securing the information being exchanged. Consequently, there is a greater demand for designing encryption algorithms, particularly for devices with limited resources. This paper introduces the HIBRI cipher, a combination of the HIGHT and the BRIGHT cipher implemented in software as well as hardware. The proposed cipher is evaluated for randomness test, strict avalanche criteria, key sensitivity tests to assess the cipher’s security while memory utilization, energy consumption, execution time and throughput are analyzed by implementing in Arduino Nano based on the Atmega328 microcontroller. The NIST tests for key generation yielded P ≥ 0.01 which proves that the proposed key generation scheme generated truly random numbers while the hardware implementation shows that the comparison results for code size, RAM and execution size yielded better results than the implementations that are cutting-edge. © The Institution of Engineers (India) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Random access storage

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Design of Clustering Techniques in Cognitive Radio Sensor Networks

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computer Systems Science & engineering 2023年第1期44卷 441-456页

作者： R.Ganesh Babu D.Hemanand V.Amudha S.Sugumaran Department of Electronics and Communication Engineering SRM TRP Engineering College621105Tamil NaduIndia Department of Computer Science and Engineering Sriram Engineering College602024Tamil NaduIndia Department of Electronics and Communication Engineering Saveetha School of Engineering602105Tamil NaduIndia Department of Electronics and Communication Engineering Vishnu Institute of TechnologyBhimavaram534202Andhra PradeshIndia

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)*** Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence *** this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their *** this research,multiple random Sec-ondary Users(SUs),and PUs are considered for ***,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization *** results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing ***,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing *** of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB *** proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary *** results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

关键词： Adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization

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Enhanced perceptual wavelet packet features for spontaneous Kannada sentence recognition under uncontrolled conditions

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International Journal of Speech technology 2025年第1期28卷 153-174页

作者： Shanthamallappa, Mahadevaswamy Pradeep Kumar, B.P. Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysuru Visvesvaraya Technological University Karnataka India Department of Computer Science and Design Atria Institute Of Technology Bengaluru Visvesvaraya Technological University Karnataka India

A Robust Automatic Speech Recognition (ASR) system is proposed through a hybrid combination of Perceptual Wavelet Packet features, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic models, and n-gram language models for recognizing Spontaneously spoken Kannada sentences. Two Experiments are performed on own Kannada speech corpus and the widely recognized TIMIT corpus. First one is the experiment on the clean speech datasets and the second one is on the speech degraded by noise data chosen from Noise-US dataset. The proposed system performance is evaluated through the metric Word Error Rate (WER). A comparative study on the performance analysis is presented. The results of the experimental investigations achieved an average reduction in Word Error Rate (WER) of 1.8% over Mel Frequency Cepstral Coefficients (MFCCs) and 2.1% over Perceptual Linear Prediction (PLP) features. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Hidden Markov models

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Eco-friendly Smart Sensor Adjustment using Deep Reinforcement Learning Approach 2

Eco-friendly Smart Sensor Adjustment using Deep Reinforcemen...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Reethika, A. Lavanya, S. Sheetal, Attar Mahay Vanitha, C.N. Priyadharsini, S. Jenolin rex, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

ISBN: (纸本)9798331541217

Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well. © 2024 IEEE.

关键词： Telecommunication industry

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Weapon Detection System For Security And Surveillance Using YOLOv8 2

Weapon Detection System For Security And Surveillance Using ...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Arivalagan, Divya Rubankumar, D. Thushiyanth, K. Suresh Kumar, S. Priyadharsini, V. Vignesh, O. Madras Institute of Technology Anna University Department of Electronics Engineering Chennai India QIS College of Engineering and Technology Department of Electronics and Communication Engineering Ongole India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

The study focuses on developing and refining an advanced object detection model for integration into security systems. It begins with an exploration of challenges during early training epochs, aiming to enhance precision, recall, and overall performance. The training process involves meticulous data preprocessing, augmentation, and iterative optimization of hyperparameters to foster model convergence. Significantly, this project holds implications for security applications, offering an innovative approach to threat detection through advanced computer vision technologies. Notably, the study incorporates the usage of YOLOv3 and YOLOv8 models. The YOLOv8 model showed a mAP50 of 0.936 compared to YOLOv3, which had an mAP50 of 0.719, demonstrating superior performance with YOLOv8. Considerations for real-time processing, integration of temporal information, and collaboration with security professionals are vital for further advancements. © 2024 IEEE.

关键词： Object detection

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Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

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computer Systems Science & engineering 2023年第6期45卷 2795-2810页

作者： R.Sabitha C.Gokul Prasad S.Karthik Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatoreTamilnaduIndia

In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,***,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to *** recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and *** traditional models,the solution has been made for defending the networks,only to specific ***,in real-time applications,the kind of attack that is launched by the adversary is not ***,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be *** that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various ***,for efficient model design,the work includes the part of feature evaluation of some general attacks of *** IDRM also includes determination of optimal secure paths and Node security for secure routing *** performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN *** is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.

关键词： Enhanced security model wireless sensor networks improved defensive routing mechanism secure paths node security

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MotionGuard: ESP32 CAM-Powered Smart Flooring System for Enhanced Security

MotionGuard: ESP32 CAM-Powered Smart Flooring System for Enh...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Marathe, Abha Ashutosh Sharma, Laksh Sailo, Lalropuia Limdiyawala, Mustafa Mate, Akhil Vishwakarma Institute of Technology Department Of Electronics and Telecommunication Engineering Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331541170

Traditional home security systems often face limitations in effectively addressing evolving security threats, such as theft and intrusion. These systems typically rely on conventional sensors and surveillance techniques, which may overlook crucial aspects of security, such as real-time monitoring and comprehensive threat detection. For instance, some systems may have limited coverage of entry points or may rely solely on single-factor authentication methods, rendering them vulnerable to sophisticated intrusions. In response to these shortcomings, our proposed solution introduces a novel anti-theft security system leveraging modern IoT (Internet of Things) technologies. One key feature of our system is the integration of ESP32 CAM, a versatile microcontroller for real-time monitoring of the premises. This allows homeowners to monitor their property remotely and receive instant alerts in case of any suspicious activity. Additionally, we incorporate a Telegram bot interface, enabling seamless communication between the system and the homeowner for immediate response to security threats. By addressing the limitations of previous systems, our proposed solution aims to enhance home security by providing multi-modal threat detection and proactive surveillance capabilities. Through a systematic approach to security system design and implementation, we seek to establish a robust and adaptable framework capable of safeguarding residential premises against unauthorized access and intrusion. © 2024 IEEE.

关键词： Cams

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Low-power test pattern generator using modified LFSR

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Aerospace Systems 2024年第1期7卷 67-74页

作者： Govindaraj, V. Dhanasekar, S. Martinsagayam, K. Pandey, Digvijay Pandey, Binay Kumar Nassa, Vinay Kumar Department of Electronics and Communication Engineering Dr NGP Institute of Technology Coimbatore641048 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore641202 India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Technical Education Uttar Pradesh Kanpur India Department of Information Technology College of Technology Govind Ballabh Pant University of Agriculture and Technology Pantnagar Udham Singh Nagar Uttarakhand India Computer Science Engineering Department Rajarambapu Institute of Technology Rajaramnagar Sakharale Tel Walwa Dist Sangli Maharashtra Islampur415414 India

Low-power designs are getting increased significance in numerous applications like high-performance computing and wireless communication due to the rise in power dissipation. Power dissipation of VLSI (very large scale integration) circuits in test mode is much higher than in the normal operation mode due to the high frequency of applied test patterns. Product lifetime, yield, reduced performance, and circuit damage will result from this additional power consumption in testing. Therefore, the main objective of today’s test applications is to minimize power dissipation by increasing the correlation of applied successive test vectors. Low-power test pattern generator (TPG) using LFSR (linear feedback shift register) and binary to the excess-4 converter and binary ripple counter is proposed. The test vectors generated by the TPG have a high correlation between successive test vectors, which leads to minimum switching. During the testing of benchmark circuits, the proposed method shows a significant reduction in dynamic power consumption concerning its peer works. © Shanghai Jiao Tong University 2023.

关键词： Electric power utilization

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Deep Fish: An Approach to Fish Species Identification Through Deep Learning Techniques 3rd

Deep Fish: An Approach to Fish Species Identification Throug...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Reddy, Penumalli Jithya Malathi, M. Julaiha, AG. Noorul Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9789819739905

This analysis introduces a system for automated identification of fish species and classification based on deep learning. This system can provide valuable insights to marine biologists, with better understanding on fish species habitats. The developed system relies on deep learning neural networks (DNNs) to extract features from underwater images and classify fish into different species. Classifying underwater images of fish species is crucial for conducting fish surveys, maintaining ecological balance, monitoring populations, and conserving endangered species. Yet, it proves challenging due to optical obstacles like smattering and raptness in ocean water, leading to dark and low images. This research delves into the application of deep learning in diverse aquatic tasks, encompassing fish identification, classification, feeding decisions and behavior analysis, estimation of its size, and quality of water prediction. Using a sizable labeled dataset from Kaggle, the proposed system employs a hierarchical CNN classification approach, achieving notable recognition rates and accuracy. The study highlights the efficacy of deep learning in fish species classification, emphasizing its automatic feature extraction capability, and removing the necessity for labor-intensive manual work when engineering features. Furthermore, the advantage of deep learning models lies in their capacity to attain high accuracy with extensive datasets, exemplified by the successful training on the Kaggle dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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YOLO-O2E:A Variant YOLO Model for Anomalous Rail Fastening Detection

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computers, Materials & Continua 2024年第7期80卷 1143-1161页

作者： Zhuhong Chu Jianxun Zhang Chengdong Wang Changhui Yang Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Institute of Flexible Electronics(Future Technologies) Xiamen UniversityXiamen361000China College of Mechanical Engineering Chongqing University of TechnologyChongqing400054China

Rail fasteners are a crucial component of the railway transportation safety *** fasteners,distinguished by their high length-to-width ratio,frequently encounter elevated failure rates,necessitating manual inspection and *** inspection not only consumes time but also poses the risk of potential *** the advancement of deep learning technology in rail fasteners,challenges such as the complex background of rail fasteners and the similarity in their states are *** have proposed an efficient and high-precision rail fastener detection algorithm,named YOLO-O2E(you only look once-O2E).Firstly,we propose the EFOV(Enhanced Field of View)structure,aiming to adjust the effective receptive field size of convolutional kernels to enhance insensitivity to small spatial ***,The OD_MP(ODConv and MP_2)and EMA(EfficientMulti-Scale Attention)modules mentioned in the algorithm can acquire a wider spectrum of contextual information,enhancing the model’s ability to recognize and locate ***,we collected and prepared the GKA dataset,sourced from real train *** testing on the GKA dataset and the publicly available NUE-DET dataset,our method outperforms general-purpose object detection *** the GKA dataset,our model achieved a mAP 0.5 value of 97.6%and a mAP 0.5:0.95 value of 83.9%,demonstrating excellent inference ***-O2E is an algorithm for detecting anomalies in railway fasteners that is applicable in practical industrial settings,addressing the industry gap in rail fastener detection.

关键词： Rail fastening detection deep learning anomalous rail fastening variant YOLO feature reinforcement

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