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检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

共 5105 条记录，以下是21-30 订阅

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A Comparative Study of Datasets for Cyber-attacks Detection in Wireless sensor Networks 3

A Comparative Study of Datasets for Cyber-attacks Detection ...

引用

3rd international conference on computing and Machine Intelligence (ICMI)

作者： Ismail, Shereen Dawoud, Diana W. Reza, Hassan Univ N Dakota Sch Elect Engn & Comp Sci Grand Forks ND 58202 USA Univ Dubai Coll Engn & Informat Technol Dubai U Arab Emirates

ISBN: (纸本)9798350372977;9798350372984

Machine Learning (ML) is one of the effective security approaches to build cyber-attacks detection systems in Wireless sensor Networks (WSNs). ML leverages the power of data analysis and pattern recognition to detect and classify various types of cyber-attacks to enhance the security of WSNs. A well-constructed dataset is one of the key factors that significantly impact the performance and generalization capabilities of any ML classifier trained on it. In this paper, we evaluate the effectiveness of two datasets: WSN-DS and WSN-BFSF which are specialized for Denial-of-Service (DoS) attacks targeting WSNs. We compare the two datasets in terms of their key characteristics, dataset quality, and ML classification performance. Mutual Information (MI) and Recursive Feature Elimination (RFE) are used for feature selection. The dataset quality is measured using statistical information calculation. The ML classification performance is investigated for three supervised ensemble techniques: LightGBM, bagging, and stacking using evaluation metrics including probability of detection, probability of false alarm, probability of misdetection, classification accuracy, model size, and processing time.

关键词： Machine Learning Security cyber-attacks Detection Dataset Quality Classification Performance

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Improving Human Activity Recognition Through Multisensor Data Fusion Techniques in Wireless sensor Networks 3

Improving Human Activity Recognition Through Multisensor Dat...

引用

3rd international conference on computing and Machine Intelligence (ICMI)

作者： Gargees, Rasha S. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Wireless sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity Recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector Machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Wireless sensor network(WSN) human activity recognition (HAR) multi-sensor datasets data fusion machine learning classification Covariance Intersection (CI) Kalman Filter (KF)

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Hybrid Adam Coati Optimization with DLSTM for Biomedical Parameter Prediction Using sensor 3

Hybrid Adam Coati Optimization with DLSTM for Biomedical Par...

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3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Rajesh, L. Mohan, H.S. Bindiya, M.K. Visvesvaraya Technological University Dayananda Sagar Academy Of Technology And Management Department Of Information Science And Engineering Karnataka Bengaluru India Karnataka Bengaluru India Sjb Institute Of Technology Department Of Computer Science And Engineering Karnataka Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

A real-time patient monitoring system procesfses a wide range of biomedical parameters and can store and safely capture biomedical parameters. It lowers the diagnosis time of the several diseases that are prevalent in healthcare services. Accurate prediction of biomedical parameters can significantly reduce healthcare costs and assist in effective decision-making. This research proposes a deep learning approach with hybrid optimization for biomedical parameter prediction by using sensors. At first, a healthcare centre with many patients having various disorders is considered. Several biomedical sensors are placed on the patients and these sensors include a respiratory Rate (RR) sensor, Heart rate (HR) sensor, Pulse Rate (PR) sensor, Ambulatory Blood Pressure (ABP)-sensor, and Saturation of Peripheral Oxygen (SpO2). Based on these sensed parameters, biomedical parameter prediction by using sensor based Deep Long Short-Term Memory (DLSTM)is executed. The DLSTM is trained using the proposed Hybrid Adam Coati Optimization(HAdCO). Here, the HAdCO is developed by incorporating of Coati Optimization Algorithm (COA) with Adam Optimizer. Furthermore, the HAdCO-DLSTM is evaluated by utilising metrics, like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). The HAdCO-DLSTM had a minimal value of MAE at 0.0063, MSE at 0.0062, and RMSE at 0.0078. © 2024 ieee.

关键词： Long short-term memory

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Open Architecture for distributed and Cooperative Media Algorithms 3

Open Architecture for Distributed and Cooperative Media Algo...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Bhambu, Pawan Manjunatha, R. Srivastava, Pratima Vivekananda Global University Department of Computer Science and Engineering Jaipur India School of Sciences Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India Arka Jain University Department of Biotechnology Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

The Open structure for allotted and Cooperative Media Algorithms (OADCMA) is an open-deliver framework imparting a plug-in platform that lets customers, without problem, develop distributed and cooperative media algorithms to utilize the computational assets of diverse heterogeneous structures. It's miles designed for operations along with video capture, transmission, and machine operations. OADCMA affords unified APIs for allotted and cooperative media algorithms, which simplifies the implementation of distributed and cooperative media algorithms. Additionally, OADCMA permits green parallel computing while averting scalability problems. OADCMA helps a widespread fashion of verbal exchange networks, including allotted finite discipline computing. It is appropriate for an extensive kind of allotted and cooperative media programs together with video surveillance and broadcasting. © 2024 ieee.

关键词： Security systems

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A Secure distributed Estimation Based on Affinity 3

A Secure Distributed Estimation Based on Affinity

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Ren, Xiaoping Yi, Hao Yi, Honghua He, Yilin Southwest University College of Artificial Intelligence Chongqing China

ISBN: (纸本)9798331530280

This paper explores the issue of secure distributed estimation in the presence of attacks. Due to the presence of attacks, compromised nodes send erroneous information to their neighboring nodes, leading to a decline in network performance. Here, a distributed diffusion least-mean-square (DLMS) algorithm with affinity is proposed. The proposed algorithm uses affinity for secure distributed estimation. Specifically, we propose a distributed discrimination mechanism based on affinity to distinguish between normal nodes and malicious nodes. Simulations are conducted to demonstrate the superiority of the proposed DLMS-Affinity algorithm in comparison to other algorithms. © 2024 ieee.

关键词： Diffusion least-mean-square (DLMS) distributed estimation Relationship mapping attributes secure estimation

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Enhancing distributed Energy Efficiency through Optimized Cluster Head Re-Election in WSNs 3

Enhancing Distributed Energy Efficiency through Optimized Cl...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Kumar, R Vinod Kannan, M Ajay Arunkumar, S. Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350375190

The optimal cluster head re-election method proposed in this study offers a unique way to improve distributed energy efficiency in Wireless sensor Networks (WSNs). Smart energy management plays a crucial role in extending the lifespan of WSNs due to their limited resources. Cluster-based architecture groups nodes into clusters and assigns a cluster head to handle data aggregation and communication with the base station. This architecture is widely used to minimize energy usage. Ineffective cluster head election and selection, however, may cause early energy depletion and network deterioration. Here, the proposal is to choose cluster heads by considering energy levels, communication distance, and data forwarding capabilities in order to tackle this difficulty. By using simulation studies, shows that our suggested strategy performs better than current methods in terms of network lifespan extension and energy efficiency. The results of the study demonstrate how optimal cluster head re-election may enhance WSN performance and promote the adoption of energy-efficient Internet of Things devices. © 2024 ieee.

关键词： Wireless sensor networks

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2024 ieee 3rd international conference on Electrical Power and Energy systems, ICEPES 2024

2024 IEEE 3rd International Conference on Electrical Power a...

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3rd ieee international conference on Electrical Power and Energy systems, ICEPES 2024

ISBN: (纸本)9798350390728

The proceedings contain 154 papers. The topics discussed include: energy efficient routing technique in wireless sensor network using hybrid approach;simulation based actuator model creation and commissioning on virtual test bed;differential protection of active distribution network in the presence of inverter interfaced distributed generation;confirmatory check of 1 kW BLDC motor for use in electric vehicle application;design and performance analysis of utility scale grid connected hybrid wind-solar plant in an existing operational wind farm in India;enhancing home energy use with solar panels and battery technology;WPT system for bidirectional on-board electric vehicle charging - a review;detection and study of HIF with the help of discrete wavelet transform in distribution lines;and enhancing DC microgrid performance: a comprehensive review of control, operation, and protection strategies.

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A Novel Simulated Approach to Secure Multi-Tenant distributed systems using Anomaly Score based Virtual Machine Live Migration 3

A Novel Simulated Approach to Secure Multi-Tenant Distribute...

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3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Patil, Pravin Kale, Geetanjali Karmarkar, Tanmay Pune Institute of Computer Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350354348

Now a days, cloud computing services are being used exponentially which in turn increases in multi-tenant distributed service(MTDS). Targeted attacks in MTDS propagated through side channel, shared memories, network access, and configuration. These attacks could be discovered after analyzing side channel data. This paper proposes a novel anomaly approach for virtual machine placement (VM) considering anomaly scores of VM and the CPU utilization of the servers. The result reveals that the proposed approach ensures secure VM migrations among the servers during VM placement, and also ensures that the MTDS environment remains secure. © 2024 ieee.

关键词： Virtual machine

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Transmission Line Fault Detection using Flame sensor 3

Transmission Line Fault Detection using Flame Sensor

引用

3rd international conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Chincholkar, Manasvi Ashok Chormale, Aboli Suryakant Shah, Jeenali Udawant, Mansi Patil, Rajas Deshpande, Pallavi Dept. of Electronics and Telecommunication Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798331532420

This paper presents the design and implementation of a transmission line fault detection system aimed at improving electrical safety and reliability. The system uses a flame sensor to detect fire or sparks and an Arduino to interpret sensor data and manage a relay module that, in the occurrence of a problem, turns off the power and turns on visual indicators. An AC to DC converter is used to ensure reliable power supply. The system also offers flexibility by supporting optional temperature monitoring. Considering potential uses in a number of utility and industrial sectors, this technology offers a practical and affordable means of improving transmission line safety. © 2024 ieee.

关键词： Premixed flames

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On Reconstructing the Patient Zero from sensor Measurements 43

On Reconstructing the Patient Zero from Sensor Measurements

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Hahn-Klimroth, Max Kaaser, Dominik TU Dortmund Univ Dortmund Germany TU Hamburg Hamburg Germany

ISBN: (纸本)9798350339864

Epidemic spreading processes have been widely studied over the last years, with an additional boost due to the ongoing COVID-19 pandemic. However, epidemic spreading is not limited to infectious diseases;it forms the basis of understanding opinion formation processes in social networks, or models the spread of computer viruses in network security. In all of these application domains, the forward processes are typically well understood, both from a theoretical and a practical point of view. Interestingly, much less is known about the converse direction: suppose we are given "sensors" that report on the infection status, can we recover the source of the epidemic? This problem is known under the name of patient zero, rumor source detection, or finding the point of entrance in the context of intrusion detection systems. In this work we assume that the epidemic process spreads according to the classical Independent Cascade Model, and we are given sensors on edges of the communication network. We rigorously analyze under which sensor placement one can recover the source of the epidemic process. Our main contribution is an impossibility result: we formally prove a lower bound on the number of sensors required to recover the source of the epidemic process. Furthermore, we introduce a monitoring strategy that succeeds in recovering the patient zero with the minimum number of sensors possible for acyclic networks. Finally, we discuss unreliable sensor measurements and provide extensive simulations of according heuristics on realistic communication networks.

关键词： Epidemic Spreading Patient Zero Rumor Source Detection Independent Cascade Model SIR Inference

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