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International Conference on ent Computing and Control Systems (ICICCS)

作者： Dinesh Besiahgari Manjunath R Kounte Shivananda S University of Cincinnati Electronics and Computer Engineering School of Electronics and Communication Engineering REVA University Bengaluru India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This paper outlines an IoT based smart system to improve road safety in developing countries by solving the problem of poorly maintained roads. The proposed solution uses ultrasonic sensors and Radio Frequency (RF) transmission system to detect potholes and humps and sends alarm signals to the drivers in real time through Blynk application. A GPS module is used to pin down the exact coordinates of these irregularities and the data is saved in a database which can be used by other drivers and road authority. Although this system presents a feasible and economical design, aspects such as the cost of implementation, robustness towards environmental factors and long term sustainability are analyzed in detail to determine the feasibility of the system. This work is intended to minimize vehicle break down and accidents resulting from road hazards and therefore provides a solution that can be scaled up for enhanced road safety.

关键词： Radio frequency Smart systems Sensor systems and applications Road safety Robustness Real-time systems Pins Internet of Things Sustainable development Vehicles

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Secure Cloud-WSNs with Advanced Intrusion Detection System using Kepler Optimization and Bi-ConGRU Network Approaches 9

Secure Cloud-WSNs with Advanced Intrusion Detection System u...

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9th International Conference on communication and electronics Systems, ICCES 2024

作者： Pitta, Sheela Gopalakrishnan, S. Chand, S.Ravi Hindustan Institute of Technology and Sciences Department of Computer Science and Engineering Kelambakkam Tamil Nadu Chennai603103 India Hindustan Institute of Technology and Sciences Department of Information Technology Kelambakkam Tamil Nadu Chennai603103 India Dnr College of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram534202 India

ISBN: (纸本)9798350377972

As WSNs are popularly used in the contemporary world in various fields such as environmental monitoring, smart cities, monitoring and controlling of infrastructure and several essential activities, a detailed cloud security of the WSNs is vital. However, the establishment of these networks has come with new enormous security concerns such as peoples' unauthorized access, data leakage, and cyberattacks to these networks and all that leads to a compromise and poor performance of these networks. To solve these problems, it is necessary to apply high-quality and effective approaches to improve intrusion detection and meliorate the network's robustness. Based on the identified issues, this paper aims to discuss the critical problems and challenges in WSN cloud security with advances in intrusion detection systems that can address the emerging threats. Conventional approaches are not very effective in generalizing the threats or accurately detecting and preventing them as they cannot analyse the high dimensional data and learn from the new attack types. To eliminate these issues, the following approach has been developed including the Kepler Optimization Algorithm, KePO, and the Bi-Directional Convolutional Gated Recurrent Unit Network, BiConGRUNet. Thus, the KePO algorithm is used for feature selection resulting in the dataset improvement to boost up the intrusion detection system. The BiConGRUNet model takes advantage of state of the art deep learning methods, thus allowing for the two-dimensional analysis of network traffic. The current strategy focuses on considerable assessment with the help of benchmark datasets, including CICIDS-17 and UNSW-NB15, to prove the efficiency of the presented model. © 2024 IEEE.

关键词： Cloud security

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Performance Comparison of MANETs under Congestion and Packet Loss using Distance based Geographic Routing Protocols 18

Performance Comparison of MANETs under Congestion and Packet...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ganesan, T. Murali, N. Manojkumar, K. Priya, D. Mohana Alagarsamy, Manjunathan Manikandan, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India Government College of Engineering Department of Computer Science and Engineering Tamil Nadu Thanjavur India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9789380544519

The performance of Mobile Ad-Hoc Networks (MANETs) can be verified based on number of input packets and life time of protocol requirements. Distance based geographic routing protocol are used to provide secure routing under congestion control and identifies packet loss. This paper presents performance evaluation and comparison of MANETs by using congestion on limited buffer requirements and malicious packet loss route discovery. We proposed distance based geographic routing protocol to monitor the routing requests, polling response and reduce the routing delays. In this paper analyze path control, available resources, and conditional delays and simulate the networks. This paper uses to reveal both buffer size and network performance characteristics. Analyze the exact frame work of throughput capacity, packet loss ratio and end-to-end delay in MANETs. In this mechanism reduce the overhead of routing strategies and calculate estimated distance over propagation of each node. Simulation experiments are verified by various expected values based on changes in number of hops and routing paths. In the protocol, every node evaluates the link quality and eliminates weak node or malicious node in MANET. Based on ready queue random early detection methods are used to reduce the network traffic delays and propagation delays. So this paper clearly indicates the MANETs performance under congestion control, packet loss and gives clear details about the life time of the networks and packet delay. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Congestion Control Distance Based Geographic Routing Protocol MANET Packet Loss Ready queue based Random early detection

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Smart Environment and Plant Disease Monitoring with Analysis System in Agricultural Field 3

Smart Environment and Plant Disease Monitoring with Analysis...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kavitha, M. Senthilkumar, S. Kalaivani, K. Saranya, E. Arunkumar, P. Malu, V. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamilnadu Nagapattinam611002 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamilnadu Nagapattinam611002 India Sir Issac Newton College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Nagapattinam611102 India INurture Nagindas Khandwala College Department of Information Technology Malad West Mumbai400064 India

ISBN: (纸本)9798350343274

Agriculture is deemed to be the most significant segment in India. The main objective of this research work is to monitor plant diseases and analyze the system using a smartphone. The remote monitoring framework, including temperature, stickiness and soil humidity sensors, a microcontroller, an ultrasonic sensor, a camera, and a GSM module is validated for effective environment observation. The technology could offer a fresh method for farming to obtain information related to agriculture. In this monitoring system of agricultural diseases, the main aim is to achieve disease monitoring information and collections of the results are detected by Android application through GSM. ARM processor is used for analog to digital converter (ADC) and sends the gathered information to portable through GSM. An IP web camera is occasionally empowered to catch the plant leaf picture for disease checking and an ultrasonic sensor is utilized with the end goal of plant development checking. It uses a soil battery to drive the temperature sensor to foster plant development checking framework and plant infection observation, likewise adaptive android application to screen such circumstances by utilizing this innovation to accomplish a green hardware framework. © 2023 IEEE.

关键词： Wireless sensor networks

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Investigating the Practicality of DNS-Based Authentication for Securing Wireless Networks 3

Investigating the Practicality of DNS-Based Authentication f...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Kulkarni, Omkaresh S. Khurana, Jatin Mamatha, G.N. Naval, Preeti Kaushik, Harshita Renuka, G. Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

The security of Wi-Fi networks is a crucial situation for community directors. DNS-based total authentication is frequently mentioned as an opportunity for traditional username and password authentication since it gives appropriate scalability, stepped-forward security, and reduced risk. This paper strives to analyze the practicality of using the area name structures (DNS) for authentication of Wi-Fi networks, which integrates the users' authentication, roaming, and different operations along with encryption key distribution into the structure's core. The point of interest of the paper is to assess the ease of implementation, scalability, security, and overall performance of the DNS-based authentication technique. This study examines the practicality of using domain name system (DNS)-based authentication as a security measure for wireless networks. DNS-based authentication is a promising approach that leverages the inherent security of DNS and its widespread use. Through a literature review and analysis of existing protocols, this study explores the strengths and limitations of DNS-based authentication, and evaluates its effectiveness in mitigating common security threats in wireless networks. The findings suggest that while DNS-based authentication offers certain advantages, it also faces challenges such as scalability and single point of failure. Further research and development are needed to fully harness its potential and integrate it into existing wireless network security practices. In the end, the paper offers pointers for future studies. Universal, the paper suggests that DNS-primarily based authentication can be visible as a powerful method for securing wireless networks. © 2023 IEEE.

关键词： authentication comparison networks recommendations technologies

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Real-Time Monitoring and Control Systems Using IoT-Integrated Wireless Sensor Networks-Leveraging Machine Learning Algorithms for Enhanced Performance and Efficiency

Real-Time Monitoring and Control Systems Using IoT-Integrate...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Swaminathan, Subbiah Poongavanam, N. Arun, M. Pushparani, S. Muthukrishnan, A. Johri, Prashant Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai602105 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Avadi Chennai India School of Computer Application and Technology Galgotias University India

ISBN: (纸本)9798331542344

This paper introduces a developed real-Time monitoring and control system using Internet of Things (IoT) incorporated Wireless Sensor Networks (WSNs) supported by machines learning algorithms. The system focuses at meeting core issues and questions in terms of productivity, reliability and timeliness within evolving settings. The current paper contributes to the ongoing research and development in the area of IoT and WSN to enable, always-on real-Time data gathering from a variety of discrete sensors which is invaluable for various applications including industrial, environmental and smart city applications. The heart of the system is based on the idea of utilising machine learning methods to acquire and elaborate sensor signals. More specifically, the deep learning models that are used include Long Short-Term Memory (LSTM) networks as well as Reinforcement Learning (RL) algorithms for predictive analytics, as well as anomaly detection. The work also entails a comparison of the system performance based on energy consumption and other factors such as latency, packet delivery ratio and even the accuracy of the predictions made by the system. The findings suggest improved utilisation within substantial ranges;decreased energy use by 30 per cent or more. 7%, latency decrease by 33. Although achieving this was accompanied by an increased data packet loss rate to 3%, the overall packet delivery ratio was boosted to 98%. Additionally, the system established overall accuracy rate of 95% with negligible error percentage. In conclusion, this research proves the applicability of incorporating the IoT and the advanced machine learning methods in the real-Time monitoring and control systems development to overcome cases of low efficiency and reduced performance in numerous real-world applications. © 2024 IEEE.

关键词： Predictive analytics

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Deep Learning Approach for Optimal Segmentation and Classification of Multi-Class Skin Cancer 11

Deep Learning Approach for Optimal Segmentation and Classifi...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Shekhawat, Jyoti Kumar, Ritesh Jebaraj, N.R Solomon Brar, Khushmeen Kaur Al-Jawahry, Hassan M. Thakur, Gaurav Vivekananda Global University Computer Science and Application Jaipur India Maharishi University of Information and Technology Uttar Pradesh India Department of Cse and Information Technology Bangalore India Chandigarh University Department of Electronics and Communication Engineering Punjab Mohali India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798350350357

This study proposed an efficient image fusion method for skin cancer diagnosis which combines two methods: image segmentation and image enhancement. A new image is created by fusing the k-means clustering and gray scale pseudo-color transformation methods for images. To extract both fine and coarse information from an image, the basic DWT is first used. With DWT, the pictures are broken down into wavelet co-efficients. The approximation coefficient, horizontal detailed coefficient, vertical detailed coefficients, and diagonal detailed coefficients are the four coefficient matrices that are created following decomposition. A fused coefficient matrix is produced by fusing the wavelet coefficients of two images together. The finished image is then recreated using the Inverse Wavelet Transform. The last step is to use the Random Forest Classifier to classify the test features. A number of performance metrics have been employed for evaluating the classification of benign and malignant skin cancer types: accuracy, mean absolute error (MAE), root-mean squared error (RMS), relative absolute error (RAE), root relative squared error (RRS), TP rate, FP rate, precision, recall, F-Measure, MCC, ROC area, and PRC area. With a kappa value of 0.73, MAE of 0.17, RMS value of 0.27, RAE value of 55.60, and RRS value of 69.08, the suggested methodology achieved an accuracy of 92.5%, according to the data. Additionally, the results for the benign class are 0.98, 0.325, 0.92, 0.98, 0.95, 0.75, 0.92, and 0.97 for TP, FP, Precision, Recall, F, MCC, ROC, and PRC, respectively. In contrast, the malignant class yielded results for TP, FP, Precision, Recall, F, MCC, ROC, and PRC of 0.67, 013, 0.893, 0.67, 0.78, 0.75, 0.92, and 0.83, respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Adiabatic Physical Unclonable Function Using Cross-Coupled Pair 8

Adiabatic Physical Unclonable Function Using Cross-Coupled P...

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8th IEEE International Symposium on Smart Electronic Systems, iSES 2022

作者： Bajjuri, Vishnu Nalesh, S. Rajendran, Sree Ranjani Kala, S. Jyothishmathi Institute of Technology and Science Department of Electronics and Communication Engineering Telangana India Cochin University of Science and Technology Department of Electronics Kerala India University of Florida Department of Electrical and Computer Science Engineering Gainesville United States Indian Institute of Information Technology Department of Electronics and Communication Engineering Kottayam India

ISBN: (纸本)9798350399226

Physical Unclonable Function (PUF) is a hardware circuit used to safeguard computing devices by capitalizing on process variations introduced during integrated circuit (IC) manufacturing. Adiabatic-based PUFs are promising solutions for low-power and secure portable embedded applications. This paper proposes a new adiabatic PUF using a cross-coupled pair (XCP). The proposed PUF requires fewer transistors than the quasi-adiabatic tristate PUF. The proposed PUF design is implemented in LTspice® XVII using the 45nm technology from Arizona State University Predictive Technology Model (PTM). The results are validated and compared with state-of-the-art implementations. © 2022 IEEE.

关键词： Hardware security

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Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

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computer Systems Science & engineering 2022年第8期42卷 545-560页

作者： R.Saravana Ram M.Vinoth Kumar N.Krishnamoorthy A.Baseera D.Mansoor Hussain N.Susila Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing *** play a vital role in emerging technologies of the 20th *** of sensors in needed locations is a very serious *** environment is home to every living being in the *** growth of industries after the industrial revolution increased pollution across the *** to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are *** interesting and challenging task is choosing the place to fit the *** meta-heuristic techniques have been introduced in node *** intelligent algorithms have proven their efficiency in many studies on localization *** this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor ***,our work aims at selecting industrial areas in the sensed *** use random forest regression methodology to select the polluted ***,the elephant herding algorithm is used in sensor node *** two algorithms are combined to produce the best standard result in localizing the sensor *** check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with *** results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer *** can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the *** proposed method achieves a lower root mean square value with 0.06 to 0.08 for lo

关键词： Wireless sensor networks node localization industrial-centric approach random forest regression elephant herding optimization swarm intelligence pollution

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Efficient Cell Segmentation in Microscopy Images using Sub-Sampling Integrated SVM 2

Efficient Cell Segmentation in Microscopy Images using Sub-S...

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

作者： Ramprasath, J. Kumar, M. Arun Krishnaraj, N. Vignesh, C. Chandru Saranya, N. Ramprakash, P. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of AI&DS Coimbatore India

ISBN: (纸本)9798331534950

Support Vector Machine (SVM) integrated with subsampling methods provides the latest approach to cell segmentation of microscopic images. This approach aims to increase computing efficiency and also maintain segmentation accuracy. To begin with, the processing load associated with large-scale microscopic images is reduced by employing subsampling techniques. On applying Support Vector Machine (SVM), classification of the picture patches into cell and non-cell portions based on the gathered attributes. The produced method is highly valuable for high-throughput analysis in medical and biological diagnostics as it provides competitive segmentation performance while drastically cutting down time for its process. Through a detailed analysis from rigorous testing and comparison with existing methods, efficiency and efficacy are demonstrated in accurately differentiating cells from microscopic images and extending solutions for vast biomedical applications. © 2024 IEEE.

关键词： Image segmentation

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