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IoT-Based Security Detection for Cloud Web applications: Leveraging Internet of Things Approaches 1

3rd International conference on Mechanical and Energy Technologies, ICMET 2023

作者： Sudhakar, A.V.V. Shweta, Shashwati Kumar, K. Suresh Dube, Anand Prakash Lakshmikanth, R. Sravan, Padala Department of EEE School of Engineering SR University Telangana Warangal India Tilak Maharashtra Vidyapeeth Vidyapeeth Bhavan Gultekdi Pune India MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai India Department of Computer Science School of Management Sciences Uttar Pradesh Varanasi India Department of Management Studies Adithya Institute of Technology Coimbatore Kovilpalayam India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

The surge of Internet of Things (IoT) gadgets and cloud-based applications has been increasing the pressure to manage data reliability and security in digital space. This abstract presents examining new techniques for the detection of security among cloud web applications with the capabilities of IoT. Through the equipment of IoT devices, there will be the enhancement of real-time monitoring and detection of safety threats in cloud-based applications that are Internet-based. This synergy enables banks to have access to a variety of data sources, that is, both internal and external data. It makes the collection of large volumes of data possible, and so the banks have a good picture of the environment in which the application runs. With the use of machine learning and AI algorithms in the background, the existing anomalies and known potential vulnerabilities can be found fast, hence paving the way for proactive remediation of the present and future threats. Besides, the sensor through IoT can be utilised to measure physical security areas, which include environmental conditions and access control device integrity, and they make sure provision of security to cloud web applications. A single security architecture that utilises IoT and cloud technologies will allow the organisations to have robust defences, enhanced data protection, and a diminishing risk level, thus keeping the digital services in a trusted and dependable state in an always advanced threat environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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3-Axis Force Estimation of a Soft Skin sensor using Permanent Magnetic Elastomer (PME) Sheet with Strong Remanence

3-Axis Force Estimation of a Soft Skin Sensor using Permanen...

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IEEE/ASME International conference on advanced Intelligent Mechatronics (AIM)

作者： Wang, Yushi Kamezaki, Mitsuhiro Wang, Qichen Sakamoto, Hiroyuki Sugano, Shigeki Waseda Univ 1 Chome 104 Totsukamachi Shinjuku City Tokyo 1698050 Japan Nippon Paint Holdings Corp Ltd Tokyo Japan

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

This paper describes a prototype of a novel Permanent Magnetic Elastomer (PME) sheet based skin sensor for robotic applications. Its working principle is to use a Hall effect transducer to measure the change of magnetic field. PME is a polymer that has Neodymium particles distributed inside it, after strong magnetization for anisotropy, the PME acquires strong remanent magnetization that can be comparable to that of a permanent magnet, in this work, we made improvement of the strength of the magnetic field of PME, so it achieved magnetic strength as high as 25 mT when there is no deformation. When external forces apply on the sensor, the deformation of PME causes a change in the magnetic field due to the change in the alignment of the magnetic particles. Compared with other soft magnetic sensors that employ similar technology, we implemented linear regression method to simplify the calibration, so we focus on the point right above the magnetometer. An MLX90393 chip is installed at the bottom of the PME as the magnetometer. Experimental results show that it can measure forces from 0.01-10 N. Calibration is confirmed effective even for shear directions when the surface of PME is less than 15 x 15 mm.

关键词： Magnetic field measurement Magnetometers Magnetization Robot sensing systems Particle measurements Skin Soft magnetic materials

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AI-Powered Business Intelligence for Smarter Decision-Making and Growth

AI-Powered Business Intelligence for Smarter Decision-Making...

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2024 International conference on Artificial Intelligence and Quantum Computation-Based sensor applications, ICAIQSA 2024

作者： Alijoyo, Franciskus Antonius Pradhan, Rahul Vats, Satvik Rani, V.Kanpur Kholmukhamedov, Timur Karthik, M. School of Business and Information Technology STMIK LIKMI Center for Risk Management and Sustainability Indonesia Bandung Indonesia GLA University Department of Computer Engineering and Applications Mathura India Graphic Era Hill University Department of CSE Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India Hindustan Institute of Technology and Science Department of EEE Chennai India Turin Polytechnic University in Tashkent Management Economics and Humanitarian Disciplines Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Department of AI & DS Trichy India

ISBN: (纸本)9798331517953

Business intelligence (BI) has become a cornerstone for data-driven decision-making in modern enterprises. With the increasing complexity of data, traditional methods often fall short in providing actionable insights. Machine learning techniques, such as Gradient Boosting Machines (GBMs), offer advanced predictive capabilities that can significantly enhance BI outcomes. This paper explores the application of Gradient Boosting Machines (GBMs) in enhancing BI for better decision-making and operational efficiency. GBMs, a powerful ensemble learning technique, are utilized to predict customer churn based on a comprehensive retail dataset encompassing customer demographics, purchase history, engagement scores, and monetary values. The Proposed Method is implemented using Python Software and demonstrates that GBMs achieve exceptional predictive accuracy of 99.2%, significantly outperforming traditional methods like Decision Tree, SVM, BDMS. Feature importance analysis reveals that 'Recency' (days since last purchase) is the most influential predictor of churn, followed by 'Engagement Score' and 'Monetary Value'. The findings emphasize the model's capability to enhance customer retention strategies, optimize resource allocation, and improve overall business outcomes. Utilizing GBMs, high-risk customers (top 10% predicted probabilities of churn) were targeted with a specialized retention campaign, which led to a 15% reduction in churn rates among this group. The effectiveness of the campaign validates the model's predictions and highlights the practical benefits of integrating GBMs into BI systems. This research underscores the transformative potential of AI-driven techniques in business intelligence, offering actionable insights for businesses seeking to leverage predictive analytics for competitive advantage. © 2024 IEEE.

关键词： Predictive analytics

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The Green Revolution from Space: A Survey of Satellite-Based Agricultural Monitoring systems 17

The Green Revolution from Space: A Survey of Satellite-Based...

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17th IEEE International conference on advanced Networks and Telecommunications systems, ANTS 2023

作者： Ghosh, Debjani Swarnkar, Jatin Gupta, Gunesh Rawat, Priyanka Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350307672

The utilization of satellite technology has brought forth a new era of possibilities in the field of agriculture, facilitating data-driven decisions that cater to the global need for improved agricultural productivity. This research paper extensively examines the crucial role played by satellite imagery in revolutionizing agricultural practices. We delve into the technical foundations of satellite sensors, which include optical, thermal infrared, radar, hyperspectral, and lidar sensors, establishing a comprehensive understanding of the diverse data sources. Moving into the realm of satellite image processing, we provide explanations of radiometric calibration, geometric correction, atmospheric correction, noise reduction, and image enhancement techniques, ensuring the accuracy and reliability of the data. Additionally, we shed light on the multifaceted applications of satellite imagery in agriculture, encompassing crop health monitoring, precision agriculture, crop growth monitoring, irrigation management, soil mapping, and pest management. While acknowledging the challenges and limitations, we address the technical complexities, economic considerations, social implications, and environmental impacts crucial for developing sustainable satellite-based agricultural solutions. Finally, we explore the promising trends and research directions, ranging from advanced sensor technologies to climate-resilient strategies, with the aim of advancing efficient and sustainable agricultural systems. This paper offers a comprehensive understanding of the mutually beneficial relationship between satellite technology and agriculture, contributing to the creation of resilient and sustainable agricultural systems for the future. © 2023 IEEE.

关键词： Image enhancement

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Edge Intelligence-Based E-Health Wireless sensor Network systems 23

Edge Intelligence-Based E-Health Wireless Sensor Network Sys...

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23rd IEEE/ACIS International conference on Computer and Information Science, ICIS 2023

作者： Hu, Bintao Isaac, Matilda Abdul Majeed, Anwar P.P. Liu, Hengyan School of Internet of Things Xi'an Jiaotong-Liverpool University Suzhou China School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China School of Ai and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798350345841

In order to support delay-sensitive applications of healthcare internet of things (HIoT) network systems, it is necessary to allow e-health wearable devices to process their computational applications at an artificial intelligence (AI) based mobile edge computing (MEC) server. Existing works mainly focused on optimising the offloading decisions of all users while minimising their corresponding energy consumption and/or transmission/processing delays. However, the huge data volumes offloading and/or privacy-preserving data processing have been largely ignored. In this paper, we consider an edge intelligence-based e-health wireless sensor network system, where the queues at the MEC server follow M/M/1 queueing model. To minimise energy consumption among all e-health wearable devices, we propose to jointly optimise the FL model task offloading decisions of all e-health wearable devices while guaranteeing delay constraints (which include the transmission delay, queueing delay and processing delay) and global/local FL training accuracy. This is achieved by devising an FL-based edge intelligence queueing offloading decision optimisation algorithm. Simulation results demonstrate that our proposed algorithm achieves a much lower maximum service delay than the benchmarks. © 2023 IEEE.

关键词： Energy utilization

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A Compact Microwave Rectifier for Wireless Power Transfer and Energy Harvesting applications 18

A Compact Microwave Rectifier for Wireless Power Transfer an...

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18th European conference on Antennas and Propagation (EuCAP)

作者： Moloudain, Gholamhosein Kumar, Sanjeev O'Flynn, Brendan Buckley, John L. Univ Coll Cork Tyndall Natl Inst Cork T12R5CP Ireland

ISBN: (纸本)9788831299091;9798350394436

This paper presents a microwave rectifier with compact size and high efficiency for radio frequency (RF) energy harvesting and low-power internet of things (IoT) applications. The proposed rectifier consists of a matching network, a doubler rectifier, a lowpass (LPF) structure and a low-power electronic circuit. An LPF structure is employed to pass a direct current (DC) rectified signal and to control harmonics which increases the output voltage (VOUT-DC) as well as power conversion efficiency (PCE). The proposed rectifier is designed to operate at f = 1.8 GHz which shows a maximum PCE of 62% for an RF input power (P-IN) level of 8 dBm and optimum load (R-L) of 820 Omega. A DC-DC boost converter and an energy storage capacitor (C-OUT = 370 mu F) are employed to deliver sufficient DC voltage to power a low-power electronic circuit. The proposed rectifier is demonstrated to be capable of powering a low-power circuit at varying P-IN levels. The developed rectifier can be used in RF energy harvesting and wireless power transmission systems to power sensor nodes and IoT devices.

关键词： Energy Harvesting Lowpass (LPF) harmonic structures Radio Frequency (RF) Rectifier

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Curving Motion Control of a Traction Rail Mover Using a Dual BLAC Motor Drive

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IEEE TRANSACTIONS ON INDUSTRY applications 2023年第1期59卷 1175-1183页

作者： Ok, Sangjae Bae, Jongnam Lee, Dong-Hee Kyungsung Univ Dept Mechatron Engn Busan 48434 South Korea

This article presents an improved position control method of an overhang-type rail system considering a curved path. The designed system is driven by two BLAC (brushless ac) motors, and the moving position is controlled based on the signal from a simple Hall position sensor mounted on the motor without any absolute position sensors. The rail is composed of both linear and curved paths. For the linear rail, the two motors are controlled to rotate in the same speed. However, in a curved path, the two motors must have different moving distance according to the curvature. Generally, in order to determine the proper motion commands for both traction motors, the actual curvature state and position have to be detected by a potentiometer or an encoder equipped with linear position sensor, but this method significantly increases complexity and cost of the system. This article aims to solve this problem by using a low-cost semiconductor IMU (inertia measurement unit) to estimate the actual rail condition. The IMU will then estimate the heading angle variation to compensate the motion commands for the two BLAC motors without any additional external absolute sensors. As a result, a balanced and improved motion can be achieved for an arbitrary rail path. Practical experiments using an overhang-type rail traction system were performed to verify the effectiveness of the proposed control scheme. The results show the advanced control performance in both linear and curved path, and also a smooth transition between them both.

关键词： Rails Traction motors Brushless motors AC motors Rotors Permanent magnet motors Synchronous motors Curving motion dual BLAC (brushless ac) motor Hall sensor heading angle estimation turning

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Real-time Heart Rate Estimation Using PVDF Flexible Piezoelectric sensor Based on Machine Learning 2

Real-time Heart Rate Estimation Using PVDF Flexible Piezoele...

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2nd International conference on advanced Manufacturing Technology and Manufacturing systems, ICAMTMS 2023

作者： Luo, Shijian Fang, Ting Dong, Chong Han, Jiaming School of Electrical and Information Engineering Anhui University of Technology Anhui Ma’anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China

ISBN: (纸本)9781510667327

To achieve non-invasive, non-contact, and real-time heart rate monitoring, proposed a pulse signal acquisition system using PVDF (Polyvinylidene Fluoride) piezoelectric film. In order to address the issue of errors in heart rate extraction caused by differences in the morphology of pulse signals across individuals or in different states, the K-means clustering algorithm was innovatively used to locate the peak of pulse waveforms in different states and constructed a heart rate data set. Real-time heart rate monitoring by training a large number of pulse signal samples with the proposed CNN-LSTM network model. Experimental results demonstrated that the performance metrics of this model, including the MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R2(Coefficient of Determination), are 0.2517, 0.3395, and 0.9863, respectively. the maximum error between the proposed system and the standard instrument within 3 minutes was only 1.55 beats/minute, indicating that the system exhibits high accuracy and reliability, and holds great potential for applications in heart rate detection. © 2023 SPIE.

关键词： Mean square error

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A Machine Learning (ML)-Inspired Method for Intrusion Detection in IoT Devices Networks 5th

A Machine Learning (ML)-Inspired Method for Intrusion Detect...

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5th International conference on Computing Science, Communication and Security

作者： Venkatesh, Veeramuthu Raj, Pethuru Nedium, Roshitha Edara, Jahnavi Reddy, Kalluru Amarnath Anushiadevi, R. SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamilnadu India Reliance Jio Cloud Serv JCS Bangalore 560025 Karnataka India

ISBN: (纸本)9783031751691;9783031751707

The Internet of Things (IoT) and Machine Learning (ML) represent dynamic research fields with significant growth. IoT applications have gained popularity among technology researchers and developers, and the increasing deployment of IoT devices in critical infrastructures improves efficiency and reliability and raises concerns about cyber-attacks. Enterprises have effectively implemented IoT services, enabling automated production through remote and intelligent control. However, this adoption has concurrently introduced novel security vulnerabilities. Addressing the security and privacy challenges in IoT, especially considering energy limitations and scalability issues, remains a critical focus in computer security. This paper aims to prevent multiple attacks targeting sensor nodes' data manipulation. It encompasses a range of threats, such as sensing layer attacks, malfunctions, tamper attacks, false data injection, base node and clone attacks. The proposed approach involves a threat model and a pairing algorithm that utilizes machine learning to associate each sensor node with its corresponding node. To accomplish the goal, the performance of the proposed solution is compared with various machine learning models, including Decision Tree, Linear Regression, k-nearest neighbors (KNN), Random Forest, and AdaBoost. The evaluation utilized two openly accessible real-world datasets, with metrics such as accuracy in attack detection, training time, and testing time being considered. By incorporating machine learning algorithms to improve attack detection in IoT environments, the proposed approach represents a significant advancement in enhancing security and privacy. The results highlight the importance of adopting advanced techniques to safeguard IoT systems from potential threats. The proposed method achieved an impressive 96.75% accuracy rate in detecting attacks, surpassing existing solutions with nearly twice the speed in training and testing times. As the IoT landsc

关键词： sensor nodes Security attacks Intrusion Detection System Critical infrastructure Machine learning Internet of Things (IoT)

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Multi-sensor Ensemble-guided Attention Network for Aerial Vehicle Perception Beyond Visible Spectrum

Multi-sensor Ensemble-guided Attention Network for Aerial Ve...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kwasniewska, Alicja MacAllister, Anastacia Nicolas, Rey Garza, Javier SiMa Technol 226 Airport Pkwy San Jose CA 95110 USA Gen Atom 14200 Kirkham Way Poway CA 92064 USA Lockheed Martin 1 Lockheed Blvd Ft Worth TX 76108 USA

ISBN: (纸本)9798350302493

Researchers from different market domains have made significant developments in Artificial Intelligence (AI) enabling more advanced automated sensing systems and, thus, eliminating the need for the time-consuming manual analysis of data, which is prone to human errors. However, successful deployment of such systems in real world applications requires careful design and analysis of the proposed models. This work focuses on perception done on Unmanned Aerial Vehicles (UAV) using multi-task learning. There are multiple challenges when considering such platforms. First of all, they often operate in difficult and dynamic conditions affected by various factors, such as background noises, ego-noise of the motors and occluded views. At the same time, they require high performance local compute, co-designed with optimized software solutions that meet small size, weight, and power (SWaP) requirements. Therefore, the AI models designed for such systems should not introduce computational and memory overheads to allow for real time processing at the embedded edge. Taking this into account, this work proposes a novel neural network-based system that utilizes ensemble-guided modulations of audio path fused with the infrared (IR) visual embedding using the attention mechanism. The ensemble mechanism doesn't require spawning new ensemble members, but instead operates on FiLM (Feature-wise Linear Modulation) activation, making it suitable for resource-constraints embedded edge platforms. The performed experiments show that the proposed network outperforms a single FiLM network by 15% and is more robust to noise.

关键词： Real time systems

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