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Designing an Energy-Efficient Wireless Sensor Network Model for Effective Intruder Detection and Minimizing Energy Consumption

Designing an Energy-Efficient Wireless Sensor Network Model ...

引用

Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： C. Mukuntharaj Komal Parashar D Little Femilin Jana Divya Sharma Kiran Lokesh Maney Shalini Y. Nigam Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

A new and exciting field of study known as WSNs has recently grown in prominence. The individual nodes that make up these networks are tiny, battery-operated gadgets that include sensors, CPUs, and mobile connectivity modules. When placed properly over a region, WSNs can detect and record changes in variables like moistness, illumination, tension, and vibration, among others. Wireless transmissions carry the gathered data to a centralized gateway or base station (BS), which then transmits it to a data center for more processing and analysis. In order for WSNs to be deployed and operated effectively, a number of obstacles must be overcome. The radius of coverage and the network’s capacity to gather data from faraway nodes are both impacted by communication range limits. Intruder detection requiring extra monitoring nodes is made possible by a network of wireless sensors protocol that is proposed in this section. The protocol identifies valid nodes and detects any intrusions by using acknowledgments rather than depending on specialized monitoring nodes. By using this route, we can guarantee efficient intrusion detection inside the network without wasting any additional resources. Hence, building a network with varying node density also boosts network lifetime and decreases energy use. Using different node densities improves intrusion detection, according to the simulation findings. In order to ensure the privacy and security of sensitive information, $\mathbf{C H}$ is crucial for identifying potential threats. When compared to SEP, LEACH, and TSEP, the proposed EERP model achieves $\mathbf{9 7 \%}$ in packet delivery rate and also better results in areas covered, energy efficiency, and intrusion detection.

关键词： Wireless communication Meters Vibrations Wireless sensor networks Base stations Surveillance Intrusion detection Sensor phenomena and characterization Energy efficiency communication system security

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Traffic-Aware Secure Routing Protocol with Reliable Authentication and Integrity Techniques for WSN

Traffic-Aware Secure Routing Protocol with Reliable Authenti...

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Innovations in High Speed communication and Signal Processing (IHCSP), International Conference on

作者： Suhas Ballal Shalini Y. Nigam G. Premananthan Girish Kalele M. Aishwarya Gunveen Ahluwalia School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Wireless Sensor Networks (WSNs) are very significant in several fields owing to their efficient data collection capabilities from the physical environment. Ensuring the security as well as accuracy of gathering and transmitting data in WSNs is a crucial topic. To elaborate, relying on the existing information, conventional routing protocols such as FAEM, NPBNS, NSSR have been employed by Wireless Sensor Network (WSN) for data transfer. The flaw with FAEM is that the throughput is unable to sustain high rates whenever the status of the network has been altered. undefined It has also been disclosed that while both routers have experienced an enhanced throughputs via NPBNS and NSSR, the latter boasted more effective routing algorithms, prompting an appreciably higher performance recognition for NSSR. As concerning IT services in the total, the differences were identified to compare NSSR with RITASR where NSSR IT services are 98% less than RITASR. achieves 7% accuracy in relation to sustaining consistent throughput, something NSSR itself only has a 97. undefined From this, it can be inferred that there has been a rise by 1. However, it is more important to mention here is that it is proportional to the increase of the throughput accuracy of 5%. For this reason, RITASR is a communication protocol that can be conveniently applied in WSNs. Same to this, the traffic management and security aspects of RITASR are also enhanced to provide an overall performance gain of 15 percent on the network.

关键词： Wireless sensor networks Accuracy Authentication Quality of service Data collection Throughput Routing Routing protocols Security Reliability

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Innovative System for Environmental Monitoring with Connected Sensors and Data Analysis for Chemical Pollutants

Innovative System for Environmental Monitoring with Connecte...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： A Suganya Mahesh A. Suryawanshi Manisha V. Bagal Ruchira Rawat S. Kaliappan G. Manikandan Department of Information Technology Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Chemical Engineering Bharati Vidyapeeth College of Engineering CBD Navi Mumbai India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The Chemical Pollutants system, which combines sophisticated data processing capabilities with networked sensors to identify and control chemical pollutants, transforms environmental monitoring. It collects real-time data on pollutants using extremely accurate sensors, which are then evaluated using cloud infrastructure and a reliable wireless connection. Predictive insights using machine learning algorithms provide notable improvements: 97% accuracy in sensor data, 3 seconds of data transmission latency, and $\mathbf{99.5\%}$ system uptime. There are now 1200 daily active users, and the prediction model’s accuracy is $\mathbf{92\%}$. Environmentally, it resulted in a 30% decrease in PM2.5 levels, a halving of high pollution alerts to 10 per month, and an 85% increase in public awareness. The number of policy changes tripled to four each year, while community engagement quadrupled to twelve. The results highlight the system’s contribution to improving the timeliness and quality of environmental data, influencing public health and policy, and highlighting its efficacy and potential for wider environmental management use.

关键词： Chemical sensors Accuracy Pollution Pollution control Predictive models Sensor systems Sensors Pollution measurement Environmental monitoring Chemicals

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A Surveillance System Utilizing mmWave-FMCW Radar for Restricted Control Area

A Surveillance System Utilizing mmWave-FMCW Radar for Restri...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we introduce a surveillance system utilizing mmWave-FMCW radar designed for restricted control areas. The experiment, conducted in our laboratory, defines the restricted control area parameters. Employing the TI-IWR6843AOPEVM model, operating at 60–64 GHz, and providing point cloud and Doppler information, we assumed walking paths for testing, partitioning scenarios into four distinct cases to simulate real-world scenarios. System performance evaluation, assessed through confusion matrices, demonstrates an achieved accuracy of up to 76.9%. Notably, errors are mainly caused by multipath effects due to static objects within the testing environment.

关键词： Point cloud compression Measurement Accuracy Surveillance System performance Radar tracking Doppler radar

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Leveraging CNN and CNN-LSTM Networks for Automated Diabetes Detection Using Heart Rate Signals

Leveraging CNN and CNN-LSTM Networks for Automated Diabetes ...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： M.S. Gowtham Sulabh Mahajan S.Sathiya Naveena Sourav Rampal R Roopashree L.A. Bewoor Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Computer Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Simple biological signals are unpredictable and hard to plan for. The function of different bodily systems may be revealed via biomedical signals. This makes it difficult to observe these signals and extract temporal data. Use of signal processing methods has allowed for the extraction of valuable information from these signals. A large variety of illnesses may be found and named. Innovative technology that is safe and useful Doctors may find it helpful in identifying patients and therapy due to its exceptional accuracy in spotting abnormalities. control several illnesses. Until recently, machine learning was the only method available for bioanalysis of process data. Methods that rely on machine learning are now in use. Here, we use a deep learning network grouping of CNNs and LSTMs to automate diabetes diagnosis. An analysis of variability in heart rate (HRV) may be used to find diabetes. Up until now, no one has attempted to differentiate between normal HRV and diabetes using deep learning approaches. Its basis as LSTM is a modified continuous neural network (RNN). LSTM customs recollection chunks to address the issue of the disappearing and growing gradient. Once equated to RNNs, LSTMs performs much better when dealing with persistent dependencies. In other words, long short-term memories (LSTMs) may be able to connect seemingly distant events with the here and now. The current record for automated diabetes diagnosis employing HRV is 95.1%, accomplished by means of a CNN-LSTM 3 system with five-fold cross-validation. Regular classifiers and active feature extraction are unnecessary.

关键词： Deep learning Accuracy Medical treatment Signal processing Feature extraction Diabetes Convolutional neural networks Data mining Heart rate variability Long short term memory

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Blockchain Technology for Enhanced Transparency, Efficiency, and Security in Supply Chain Management

Blockchain Technology for Enhanced Transparency, Efficiency,...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Santhosh Krishna B V M. Vanitha Lakshmi S Sathyanarayanan Balaji L Harshith C M S. Kaliappan Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of ECE School of Engineering Presidency University Bengaluru Sri Sathya Sai University for Human Excellence Gulbarga Karnataka Dept of ECE School of Electrical and Communication Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Division of Research and Development Lovely Professional University Punjab

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Though innovation in barcodes, GPS, and IoT sensors has enhanced the scope of modern supply chains to track assets to a larger extent, it raises a critical challenge for centralized systems in terms of data breaches, unauthorized access, and manipulation. Blockchain technology serves as an optimum decentralized and tamper-proof solution for further improvement of data integrity, security, and transparency. This paper discusses the integration of blockchain into supply chains its benefits and challenges and its potential to change global supply chain operations at its very core. In a nutshell, the system proposed outlines a blockchain-based framework using IoT, GPS, and smart contracts that guarantee traceability, security, and efficiency throughout the supply chain in real time. The results are showing high improvement in transparency, security, and operational efficiency, which will certainly lead to its usage cases in varied sectors.

关键词： Technological innovation Supply chain management Data integrity Supply chains Smart contracts Transforms Blockchains Security Stakeholders Global Positioning System

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A Novel Smartphone Recommendation System Using Ensemble Machine Learning

A Novel Smartphone Recommendation System Using Ensemble Mach...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Almadhor, Ahmad Abbas, Sidra Sampedro, Gabriel Avelino Abisado, Mideth Gadekallu, Thippa Reddy College of Computer and Information Sciences Jouf University Sakaka72388 Saudi Arabia Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of the Philippines Open University Faculty of Information and Communication Studies Los Baños4031 Philippines De la Salle University Center for Computational Imaging and Visual Innovations 2401 Taft Ave. Manila1004 Philippines College of Computing and Information Technologies National University Manila Philippines Zhongda Group Jiaxing City Zhejiang Province Haiyan County314312 China Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing314001 China Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798350341072

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences. © 2023 IEEE.

关键词： Machine learning

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FMCW Radar-Based Enhancing Moving Target Positioning Accuracy in Spectrogram Mapping

FMCW Radar-Based Enhancing Moving Target Positioning Accurac...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we propose a methodology aimed to enhancing the accuracy of moving target positioning using FMCW radar within spectrogram mapping. Utilizing a 10 GHz operating frequency, the FMCW radar effectively detects moving targets. However, the interpretation of intermediate frequency (IF) signals posed challenges due to multiple reflections and resulting ambiguous distances between objects. For solving this problem, we propose two steps of solution: employing an enveloping method and normalization of the IF signal. This process can extract maximum amplitude peaks specific to the target. Experimental validation involved detecting a drone moving away from the radar. Comparative analysis pre and post signal processing in spectrogram terms demonstrate the efficacy of our technique. Furthermore, verification of the detection range of radar was conducted using the GSM-015 GNSS speed meter. Results confirm the accuracy of the positioning capabilities of radar, emphasizing the effectiveness of the proposed approach.

关键词： Visualization Accuracy Refining Radar detection Radar Radar antennas Reflection

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Modeling Blockage in High Directional Wireless Systems

Modeling Blockage in High Directional Wireless Systems

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Virtual Conference on communications (VCC), IEEE

作者： Evangelos Koutsonas Alexandros-Apostolos A. Boulogeorgos Stylianos E. Trevlakis Tanweer Ali Theodoros A. Tsiftis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Research & Development Department InnoCube P.C. Thessaloniki Greece Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Informatics & Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China

ISBN: (数字)9798331530099

ISBN: (纸本)9798331530105

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker's shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage.

关键词： Wireless communication Closed-form solutions Receivers Probability Propagation losses Power system reliability Numerical models

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Deep Learning Approaches for Solving Challenges in computer Vision Applications and Exploring Evolving Patterns

Deep Learning Approaches for Solving Challenges in Computer ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sujay Gejji B. R. Supreeth Pathan Firoze Khan S. Venkat K Pradeepa S. Kaliappan Department of Electronics & Communication Engineering S. G. Balekundri Institute of Technology Belagavi Karnataka India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore Department of Computer Science & Technology Madanapalle institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. The results of the study show significant improvements: the accuracy of Enhanced CNNs was $81.6 \%$, the accuracy of GANs was $14.8 \%$, the accuracy of Hybrid CNN-RNN models was $86.2 \%$, the accuracy of ViTs was $79.2 \%$, and the accuracy of transfer for self-supervised learning techniques was $76.8 \%$. These findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. These advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality.

关键词： computer vision Visualization Accuracy Computational modeling Surveillance Self-supervised learning Transformers Generative adversarial networks Object recognition Image classification

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