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IoT cryptography with privacy-preserving ElGamal public key cryptosystem using oppositional artificial flora optimization algorithm

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Multimedia Tools and Applications 2025年第11期84卷 9287-9308页

作者： Paramasivam, Devi Sivaji, Sathyalakshmi Dayanandan, Venkata Subramanian Alqahtani, Abdullah Saleh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of CSE Hindustan Institute of Technology and Science Kelambakam Tamil Nadu Chennai603103 India Department of CSE Velammal Institute of Technology Chennai India Department of Self-Development Skills - Common First Year Deanship King Saud University Riyadh12373 Saudi Arabia

Privacy and security have received significant interest among the research communities Due to the increased exploitation of the Internet of Things (IoT). Since IoT devices gather most of the public data like identity, location, contact numbers, and energy utilization, security, and privacy become crucial. The lack of well-defined IoT-enabled security and privacy schemes will prevent the applicability of the IoT technologies from the client side. Therefore, this paper presents an ElGamal public key cryptosystem (EGPKC) for generating optimal keys utilizing an Oppositional Artificial Flora Optimization (EGPKC-OAFA) technique intended for preserving privacy in IoT. Additionally, the EGPKC approach with an optimum generation of authentication keys is applied on the medium access control (MAC) header for secure authentication. Besides, the optimal key selecting process for EGPKC occurs by employing the OAFA approach which inherits the oppositional-based learning (OBL) concept and classical artificial flora approach. To evaluate the effective analysis of the EGPKC-OAFA algorithm, huge simulations such as Known plaintext attack (KPA), chosen-plaintext attack (CPA), key sensitivity, and correlation coefficient are employed and the result is determined by different feature intervals. Accordingly, KPA and CPA reached the minimum value of 0.0987 and -0.0499 respectively. As a result, the proposed EGPKC-OAFA algorithm obtained superior performance compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Garbage Detection System Using Robot and Advanced YOLO Algorithm 5th

Garbage Detection System Using Robot and Advanced YOLO Algo...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Maurya, Saransh Prachi Tiwari, Ayush Singh, Aryan Dwivedi, Shashank Computer Science and Engineering United Institute of Technology Uttar Pradesh Naini Prayagraj 211010 India

ISBN: (纸本)9789819763177

Escalating urbanization has exacerbated challenges in waste management, demanding innovative solutions. This study introduces a smart waste management system integrating the YOLOv5-OCDS (Object Counting Detection System) with TensorFlow and IoT technologies. The YOLOv5-OCDS algorithm, trained on a diverse dataset, efficiently categorizes paper, cardboard, glass, metal, and plastic waste, ensuring rapid and precise results with minimal background noise. The system utilizes a camera module and servomotor on a Raspberry Pi 4 for efficient waste sorting. An ultrasonic sensor monitors bin fill levels, and GPS technology provides real-time location data. The LoRa(Long Range) module transmits bin status at 915 MHz, enhancing remote monitoring. Electronic work is secured by Radio Frequency Identification(RFID)-dependent locker accessible only with authorized badges for maintenance. The system, framed around robotics, neural networks, IoT, and deep learning, demonstrates superior speed and accuracy in garbage detection compared to existing methods. YOLOv5-OCDS’s real-time prediction capability streamlines detection, offering precise results with minimal background noise and exceptional learning capabilities. This research contributes to efficient waste management in urban areas, addressing the escalating challenges of increasing garbage output. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ultrasonic sensors

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IoT-based Food Quality Monitoring System Using Machine Learning 2

IoT-based Food Quality Monitoring System Using Machine Learn...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vasanthi, R. Ashika, K. Archana, D. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

One of the main considerations in preventing food waste is food safety and cleanliness. Foods should not be allowed to deteriorate or rot due to environmental elements including humidity, temperature, and darkness. The quality of the food should also be regularly checked. Deploying quality monitoring systems is beneficial due to the following reasons:. These food quality monitoring systems keep an eye on the external elements influencing food quality. Factors such as vacuum storage, refrigeration, and others later kept the items from spoiling. More specifically, the sensors in this system - which measure temperature, pH, humidity, gas, and DHT - provide the vital data required to assess the condition of the product that is being packed or kept. Leveraging Internet of Things (IoT) technology involves transmitting data to a computer system. Subsequently, this system displays a user interface that tracks the product's evolution over time. The goal of the project is to enhance food quality assessment by employing machine learning algorithms. © 2024 IEEE.

关键词： Internet of things

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VANET Handoff from IEEE 80.11p to Cellular Network Based on Discharging with Handover Pronouncement Based on Software Defined Network (DHP-SDN) 22nd

VANET Handoff from IEEE 80.11p to Cellular Network Based on ...

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22nd International Conference on Hybrid Intelligent systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Sarvavnan, M. Narayanan, R. Lakshmi Kavitha, K. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (纸本)9783031274084

In Vehicular Adhoc Network (VANET) is an emerging domain with high dynamic mobility and disrupted network in Discharging with Handover Pronouncement based on Software Defined Network (DHP-SDN) with advance predictive management tool described in this work for discharging of vehicle-to-infrastructure communication with Software Defined Network (SDN) architecture. Various parameters were addressed in research articles like maintaining connectivity, identifying appropriate intermediate node for carrying signal and transferring of data but in the proposed model SDN monitors the discharging signal based on the speediness, terrestrial location, nearby vehicle RSU from the vehicle which has cellular and IEEE 802.11p network boundary. SDN regulator computes when the due time is to settle on a choice, chooses whether it is appropriate or not have the vehicle to handoff from cell organization to the ahead IEEE 802.11p organization. The simulation results that using DHP-SDN method maintains the networking quality by reducing the load and traffic congestion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicles

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Gestures to Movements Crafting A Robotic Arm With OPENCV 2

Gestures to Movements Crafting A Robotic Arm With OPENCV

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Janagiraman, S. Mithun, P. Hemashri, S. Susan, M. Bersheba St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

In modern time, the field of robotics is growing fast. A robot is a machine that can do physical jobs, with people overseeing and guiding it. Many robots have been developed to perform hazardous tasks that people cannot carry out by themselves. One of the popular types of robots is the robot arm. This paper suggests that control of robot arms should be by how fingers and hands move. The study aims to explain basic robot arms that need to be guided by the motions of human fingers and hands. This paper develops a gesture-controlled robotic arm based on OpenCV and Mediapipe. It discusses how advanced image processing techniques shall be used innovatively to bring a seamless integration of human-to-robot interaction. The system captures hand gestures through a webcam;in real time, it processes signals and transforms them into control commands for the robotic arm, thus providing the user with a friendly solution at low cost yet with high accuracy in the control of robotic systems without the necessity of complex hardware and specialized training. It responds to the increasing demand of frictionless interaction in most industries, directed toward industrial automation, healthcare, and service robotics. Continuing from real-time performance, precision counts, the proposed system not only brings an efficiency of functionality but enables better user experience, allowing it to reach an even larger population. The paper covers the system architecture, the methodology of gesture recognition, and experimental results, drawing attention to advantages associated with using open-source tools for gesture- controlled robotics. Yet another important fact that follows from these results is that such systems can indeed be incorporated in real applications, thus further holding the potential for improvement in human-computer interaction through gesture control © 2024 IEEE.

关键词： Robotic arms

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Energy-Efficient Clustering in Wireless Sensor Networks: A Multi-Objective Approach Using PSO and Fuzzy Logic 4

Energy-Efficient Clustering in Wireless Sensor Networks: A M...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Sabaresan, V. Thanigai Selvan, M. Rajkumar, S. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

Energy efficiency is one of the biggest challenges in WSNs;typically, these networks are plagued with several problems such as uneven distribution of energy amongst nodes, hotspots, and clustering with big disparities. This work tries to some extent solve some of these problems through a prototype: Efficient Wireless Sensor Network with Multi-Objective Clustering, or EEMOC, which is a new framework that exploits the power of Particle Swarm Optimization to realize optimal cluster head selection. In addition, it uses the FIS in the dynamic selection of a cluster radius to maximize its effectiveness in networking. EEMOC exploits the fuzzy logic benefits in governing uncertainty in data and optimizes the sizing of clusters, thereby controlling hotspots and optimizing the energy distribution scheme. Simulation results show EEMOC to be far superior to traditional clustering algorithms and may significantly extend the network lifetime with the utmost saving in energy under various operating conditions. The technique which integrates PSO with FIS provides a robust platform for improving WSN performance. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Network Intrusion Detection with Feature Elimination and Selection Using Deep Learning

Network Intrusion Detection with Feature Elimination and Sel...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Mann, Aryam Kiran, M Vamsi Nithya, B. Das, Rishav National Institute of Technology Trichy Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350372502

In the contemporary digital landscape, marked by an ever-increasing number of individuals seeking to exploit vulnerabilities in systems and cause immense damage to users, it becomes imperative for users to safeguard their systems, detect threats correctly, and carry out an appropriate response. Implementing Intrusion Detection systems (IDS) is vital for monitoring network traffic, spotting threats, and swiftly alerting users to suspicious activities. It is categorized into two parts: Network-based (NIDS) and Host-based (HIDS). NIDS is strategically placed and monitors data flow across devices. A major NIDS challenge is the prevalence of false positives and, more critically, false negatives, potentially allowing undetected threats to cause significant harm. To improve intrusion detection, a new approach using Feature Selection and Deep Learning framework is used for Network Intrusion Detection System. The study analyzes metrics like accuracy, precision, F1 score, and confusion matrix. © 2024 IEEE.

关键词： Intrusion detection

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Enhancing Athletic Performance:2D Human Pose Estimation Using Deep Neural Networks for Movement Analysis 7

Enhancing Athletic Performance:2D Human Pose Estimation Usin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Devi, Pothreddypally Jhansi Sumani, Aleshwaram Chandra, Chenna Mukesh Thrisha, Battu Bhanu Vamshi, Arkatala Sai B V Raju Institute of Technology Dept. of Computer Science Engineering Telangana India

ISBN: (纸本)9798350372816

computer vision is a field that is used to build many neural network-based prototypes efficiently. It analyses images and videos effectively and processes them digitally. Human Pose estimation (HPE) is one of the computer vision applications which additionally uses neural networks for prediction. Human pose estimation is used widely in many fields which includes action recognition, fitness, gaming, and motion capture. HPE identifies and locates the key points on the body joints of a person's body. These body points are connected to form a Skeletal Structure. This skeletal representation helps to easily visualize the image in 2-D. Focusing on Sports domain, HPE plays major role in estimating athlete movements. It helps in analyzing the feedback on the posture which allows to make adjustments during training. It also helps viewers to analyse the insights of athlete performance. To achieve the model that contains all these features, requires involvement of few frameworks. OpenPose is utilised for fast processing of videos and can detect the multiple people in one frame. OpenPose has the capability of handling the occulsion. On the Other hand, the model also import the capabilities of HRNet, which is used for analyzing athlete movements and tracks the gestures. After detecting the keypoints on the body joints, these points must be integrated to form 2-D images. To achieve the spatial relationship between the bodypoints Joint Graph Convolutional Neural Networks (JGCN) framework is used. The proposed model is estimated to generate approximately (4- 5)percent more efficiency and accuracy in its output 2-D image compared with the previous models. © 2024 IEEE.

关键词： Convolutional neural networks

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Robotics Process Automation Technologies for Data Collection and Analysis 15

Robotics Process Automation Technologies for Data Collection...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Usman, K. Hayath, T.M. Shafiulla, Mohammed Dadapeer Ballari Institute of Technology and Management Dept. of Computer Science and Engineering Karnataka Ballari India

ISBN: (纸本)9798350370249

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies. © 2024 IEEE.

关键词： Automation Data collection Process Robotics RPA

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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