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Sensor data fusion for optimal robotic navigation using regression based on an IOT system

Measurement: Sensors 2022年 24卷

作者： Aroulanandam, Vijay Vasanth Satyam Sherubha, P. Lalitha, K. Hymavathi, J. Thiagarajan, R. Networking and Communications Department School of Computing SRM Institute of Science & Technology Kattankulathur India Sree Vidyanikethan Engineering College Tirupati India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Department of Computer Science and Engineering K L University India Department of IT Prathyusha Engineering College Chennai India

Engineers can now build compact gadgets with potent processing power and network capabilities thanks to the semiconductor industry's rapid growth. The Internet of Things is one of the greatest subjects in the communication world right now, both professionally and academically. The technical idea behind the Internet of Things is to provide these various physical items the ability to use sensors to perceive information and transfer that data to a server. The goal of the industrial Internet of Things (IIoT) is to integrate multiple technologies to enhance business services across various industries. It alludes to how machine-to-machine connections operate. Every industrial Internet of Things (IIoT) service domain has unique communication needs that are assessed differently in terms of efficiency, dependability, and quality of service (QoS). For example, there are many surveillance applications for an Internet of Things-based robot with AI. To get around the problems with current robotic navigation, we suggest an Internet of Things (IoT) platform with an intelligent navigational robot that uses machine learning. It was designed with its Internet of Things (IoT) server, and it can do line tracing, gather contextual data from the sensors, and identify the aberrant condition in an environment before navigating appropriately. To allow contextually aware IoT platform services and to validate the learning accuracy of the provided CNN model using a dataset of images collected from the robot, we developed the Support-Vector Regression with Improved Adaptive Lion Optimization (IALOSVR) Algorithm model. According to experimental findings, learning accuracy exceeded 0.98, indicating that we had improved learning in picture context identification. The real-time evaluation of this paper's method demonstrates its ability to estimate navigation accurately, robustly, and with the estimation error, high estimation accuracy is steady within two percentage. © 2022 The Authors

关键词： Internet of things

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Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks

Optimizing Wireless Sensor Networks in E-Commerce with Messa...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： M. Jeyaselvi Saravanan B M. Boobalan Ganesha M Anil Kumar Lamba D. Suganthi Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India Department of CSE Sri Eshwar College of Engineering Coimbatore Tamilnadu India Vivekanandha College of Engineering for Women Namakkal India Department of CSE AJIET Mangaluru Karnataka India School of Computer Science and Engineering Geeta University Haryana India Department of Computational Intelligence Saveetha College of Liberal Arts and Sciences SIMATS Chennai India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

In today's e-commerce landscape, successful logistics management is essential for achieving success. In this post, we will go over how wireless sensor networks (WSNs) can help online stores streamline their logistics. Data collection and real-time monitoring made possible by wireless sensor networks (WSNs) increase supply chain visibility and control. The proposed approach includes both preprocessing and training the model. Part of the preprocessing phase is principal component analysis (PCA), which entails cleaning and transforming raw data into an appropriate format for analysis or model training. Throughout the training, CNN-BiGRU-BiLSTM was continuously utilized. With a 93.46% average accuracy, this innovative method surpasses the BiGRU and BiLSTM.

关键词： Training Wireless sensor networks Analytical models Accuracy Supply chains Bidirectional long short term memory Turning Data models Electronic commerce Principal component analysis

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A Symmetric TDMA Mechanism to Optimize the Performance of the Body Sensor Network for Sports Application

A Symmetric TDMA Mechanism to Optimize the Performance of th...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Mohammed M. Mustafa Ahmed A. Khalifa Korhan Cengiz Department of Information Technology Sri Krishna College of Engineering & Technology Coimbatore India Department of Networking and Security College of Information Technology University of Fujairah Fujairah UAE Switching and Traffic Department National Telecommunication Institute Cairo Egypt Department of Computer Engineering Istinye University Zeytinburnu Istanbul Turkey

The body sensor network plays a vital role in the analysis of the gait analysis of human, sensing the various events happening in the human body. The communication that takes place between the sensed device and the processing system is very important in which the nodes worn on the human body communicate with the sink node placed in the center of the human body. A wireless communication mechanism TDMA was used and the results gave around 60 percent reliability among the nodes. The dynamic TDMA gave a reliability of 90 percent and retransmission mechanism of 95 percent. Our research work focused on to improve the reliability between the nodes and to develop an application for the users to retrieve the data from the sink node. A Symmetric TDMA algorithm was used in which the reliability was increased up to 2 percent resulted in 97 percent. Also, a Bayesian model was developed to identify the probability of nodes initiating to transmit at the same time. The model proved that the chances of nodes transmitting at the same time are 8 percent when comparing to all other techniques. The reliability of the network was also increased. Further, the work will be developed to bring the security concepts into the transmission of data packets, so that the loss can be minimized.

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Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data

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Measurement: Sensors 2023年 29卷

作者： Syed Masood, Jafar Ali Ibrahim Jeyaselvi, M. Senthamarai, N. Koteswari, S. Sathya, M. Chakravarthy, N.S. Kalyan Department of Internet of Things School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India Dept of Electronics and Communication Engineering Pragati Engineering College Surampalem Andhra Pradesh Kakinada India Department of Information Technology Nadar Saraswathi College of Engineering and Technology Tamilnadu Theni India Centre for Data Science School of Computer Science Engineering QIS College of Engineering and Technology Andhra Pradesh Ongole523272 India

Every day, WSNs (Wireless Sensor Networks) based healthcare systems is witnessing improvement in providing advice to the health condition and living habits of people. Healthcare applications based on WSNs face crucial problems of security and are vulnerable to assaults. In this study, a privacy-preserving method using the multipath routing and principles of secret keys are suggested for healthcare applications based on WSNs. Multiple paths to the sink will be computed using ECSO (Enhanced Cuckoo Search Optimization). The proposed algorithm utilizes distance and also the traffic rate of paths in the form of a parameter for discovering the optimum paths utilizing ECSO. Among these chosen paths, just one ideal path is chosen based on node energy using BO (Butterfly Optimization) that helps in decreasing the energy demands of the network. Encrypted data will be sent to the sink using the selected path. It is found from the results that the secret key merged with multipath routing is helpful in achieving security of privacy in healthcare systems based on WSNs. The results of simulation performance metrics are throughput, End to End delay, PDR, Energy Consumption, encryption time. © 2023

关键词： Wireless sensor networks

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Grid Search based Optimum Feature Selection by Tuning hyperparameters for Heart Disease Diagnosis in Machine learning

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Open Biomedical engineering Journal 2023年第Special Issue 2期17卷

作者： Saranya, G. Pravin, A. Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu India

Background: Heart disease prediction model helps physicians to identify patients who are at high risk of developing heart disease and target prevention strategies accordingly. These models use patient demographics, medical history, lifecycle factors, and clinical measurements to calculate the risk of heart disease within a certain time frame. In identifying important features of heart disease, a popular approach is using Machine learning (ML) models. ML models can analyse a large amount of data and find patterns that are difficult for humans to detect. Methods: In this proposed work, Random Forest classifier is used to identify the most important features that contribute to heart disease and increase the prediction accuracy of the model by tuning the hyperparameters using grid search approach. Results: The proposed system was evaluated and compared in terms of accuracy, error rate and recall with the traditional system. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 84.22% and 96.53%. Conclusion: These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease by finding optimum features. © 2023 Saranya and Pravin.

关键词： Feature selection (FS) Grid search optimization (GSO) Heart disease Hyperparameter tuning Machine learning(ML) Random forest (RF)

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A deep learning approach for unsupervised failure detection in smart industry 29

A deep learning approach for unsupervised failure detection ...

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29th Italian Symposium on Advanced Database Systems, SEBD 2021

作者： Liguori, Angelica Manco, Giuseppe Ritacco, Ettore Ruffolo, Massimilano Iiritano, Salvatore Department of Computer Engineering Modeling Electronics and Systems University of Calabria Italy Institute for High Performance Computing and Networking of Italian National Research Council Revelis S.r.l.

We propose an unsupervised anomaly detection model that is able to identify abnormal behavior by analysing streaming data coming from IoT sensors installed on critical devices. The proposed model is based on a Siamese neural network which embeds time series windows in a latent space, thus generating distance-based clusters of normal behavior. We experiment the proposed model on a case study aimed at the predictive maintenance of elevators where specific sensors measure the oscillations of the lift during its daily use. The experiments show that the proposed model successfully isolates anomalous oscillations thus correlating them to prospective malfunctions and thus preventing possible faults. © 2021 Copyright for this paper by its authors.

关键词： Time series analysis

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Parameter Obfuscation and Restoration for Secure Federated Learning Aggregation

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International Journal of Network Security 2024年第1期26卷 116-124页

作者： Ma, Xiangxiang Gong, Linming Chen, Jian Wang, Daoshun The Shaanxi Key Laboratory of Clothing Intelligence School of Computer Science Xi’an Polytechnic University Xi’an710048 China State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University Xi’an710048 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Federated Learning (FL) is a privacy-preserving solution that addresses the issue of data security. It achieves model aggregation by uploading only the locally trained parameters of each party to a central server, thus preserving the original privacy of each entity. However, recent studies indicate that the global parameters aggregated by the server and the local parameters submitted by users may inadvertently reveal private data. In this paper, we propose a secure FL aggregation protocol based on parameter confusion and restoration. Our protocol ensures the protection of local parameters uploaded by a single client by uploading only blurred parameters to the server. The global model aggregated by the server remains unaffected by the interference data, ensuring the high accuracy of the final model. Furthermore, the aggregation server remains unaware of any parameter information during training. We demonstrate the security of our protocol against a semihonest adversary environment and prove that even if n-2 participants collude with the server, it is impossible to learn the private information of the honest participants. Our tests on the MNIST and cifar10 datasets show that our system achieves accuracy comparable to the plaintext baseline. © (2023) All Rights Reserved.

关键词： Restoration

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IoT Assisted Fisherman Aid to Detect Borders and Alert System by using Intelligent GPS Technology

IoT Assisted Fisherman Aid to Detect Borders and Alert Syste...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： A P. Ramesh R. Nithya Paranthaman M. Porkodi V. Aruna S. Rohini Department of ECS Sri Ramakrishna College of Arts and Science Coimbatore Department of Networking and Communication School of Computing College of Engineering & Technology SRM Institute of Science and Technology Chennai Department of EEE Sona College of Technology Salem Department of ECE Panimalar Engineering College Chennai Department of Computer Science R.M.K. Engineering College Chennai

The safety of fisherman will be improved with the use of technology such as the Global Positioning System (GPS) and Wireless Sensor Networks (WSN), which will be employed in this project. The GPS may be used to track exactly where the boat is at any given time. In the event that the fisherman ventures outside of the permitted region, the alarm will sound. The user or owner of the vessel is able to monitor the location of the vessel in reference to known dimensions (latitude and longitude) by making use of the vessel's own electrical equipment and software that has been specifically created for this purpose. If a person has a GPS receiver, they will be able to pinpoint their exact location on a digital map. The relay circuit on the boat will give it the ability to turn around. Technology based on wireless sensor networks might be used to transmit communication between the fisherman and the Internet of Things device. The fishermen are alerted when they get close to the first two borders by a loudspeaker (which acts as a siren) and an indicator that has an LCD. In the event that the warning system is unsuccessful, there is a backup option. The boat's engine will automatically turn off when it gets closer to the third border as it travels. Now, both the government of India and the government of Sri Lanka would have access to identical data on the fishermen.

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction

Impurity explicit optimal Machine learning and statistical p...

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networking and Communications (ICNWC), International Conference on

作者： Nareshkumar R R. Shalinikumari S. Sri Nandhini Kowsalya Vijay Anand P Ajmal Mohamed S. Gayathri Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Architecture Bharath Institute of Higher Education and Research Selaiyur Chennai Tamilnadu India Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy.

关键词： Measurement Machine learning algorithms Urban areas Sociology Machine learning Predictive models Air pollution

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Enabling Support for TLS and TLS Fingerprinting in Network Stack Tester

Enabling Support for TLS and TLS Fingerprinting in Network S...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Nitin Dabi Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Transport Layer Security (TLS) is the standard protocol widely used for securing client-server communications over the Internet. It ensures the confidentiality and integrity of data transmitted between systems. With the rising deployment of TLS 1.3, which is the latest version of the protocol, stronger encryption and improved security features are introduced. The adoption of TLS 1.3 brings about the need for techniques such as TLS fingerprinting. TLS fingerprinting involves analyzing the unique characteristics of TLS handshakes to identify and classify different implementations or configurations of TLS. However, researchers currently lack a dedicated platform to test TLS and TLS fingerprinting techniques in a controlled environment. To address this gap, we aim to enhance NeST (Network Stack Tester) by adding support for TLS and TLS fingerprinting. NeST is a Python tool for network emulation that internally uses Linux network namespaces and Linux utilities to create virtual testbeds, perform experiments using various network protocols and collect statistics. By integrating TLS and TLS fingerprinting into NeST, we provide an easy-to-use tool that enables researchers to experiment with TLS, analyze TLS traffic, and evaluate the effectiveness of TLS fingerprinting techniques.

关键词： Protocols Linux Emulation Fingerprint recognition Communications technology Internet Encryption

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