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Research on Big Data Processing Technology Based on Machine and Deep Learning and System Framework Construction

Research on Big Data Processing Technology Based on Machine ...

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sensors, Electronics and Computer Engineering (ICSECE), IEEE International conference on

作者： Ran Fang State Grid E-Learning Service Centre State Grid Technology Institute Jinan China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In this paper, we propose and build a deep learning-based big data analytics system, which aims to overcome the limitations of traditional analytics methods in handling large-scale and multi-type data. The system integrates advanced data processing techniques and deep learning models, aiming to achieve efficient data import, automated preprocessing, and accurate recognition and prediction of complex data patterns. The system utilizes the Spark architecture to achieve a flexible multi-type data import mechanism, supporting data from Hbase, NoSQL and various cloud storage services. It integrates a variety of data preprocessing methods, including data cleaning, and provides feature engineering methods, including cluster analysis and statistical analysis, for regular types of data. The system has a built-in RNN network model for processing and fitting time series data, as well as built-in algorithmic models such as SVM for conventional data types. Through real case applications, the system shows excellent performance in the field of financial risk prediction and natural speech recognition.

关键词： Deep learning Support vector machines Statistical analysis Time series analysis Speech recognition Big Data Data models sensor systems sensors Sparks

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Nature-Inspired Algorithms to perform Localization and Security in Wireless sensor Networks

Nature-Inspired Algorithms to perform Localization and Secur...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International conference on

作者： R. Vivek Krishna G. Balachandran Venkata Siva Prasad Ch S Ranjith School of Engineering and Technology Jeppiaar University Chennai Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai

This research article embarks on a dual-pronged journey, ardently addressing the challenges of both localization enhancement and security fortification in the context of wireless sensor networks (WSNs). The study introduces a holistic approach, employing nature-inspired computing algorithms - namely Particle Swarm Optimization (PSO) and Firefly Optimization (FF) - to diligently tackle the intricate issue of localization errors. To amplify the potency of these algorithms, hybrid variations are ingeniously crafted, ushering in heightened effectiveness. Furthermore, the research article undertakes a visionary stride by unveiling a hybridized system that harmoniously merges two stalwart cryptographic forces - Elliptic Curve Cryptography (ECC) and the advanced Encryption Standard (AES). This fortified system stands poised to bolster data security within WSNs, aligning seamlessly with the overarching goals of the research. Through meticulous simulated analyses, the article meticulously unveils the tangible outcomes of these dual approaches. The research validates the efficacy of these innovative methods in real-world scenarios, painting a vivid picture of their collective impact. The findings resoundingly underscore the potential of these approaches to revolutionize the realms of accuracy, efficiency, and security across an array of WSN applications. As the modern landscape continues to demand both enhanced precision and robust security measures, this research article emerges as a beacon of innovation. By synergizing localization enhancements and security fortifications through nature-inspired algorithms and hybrid cryptographic methodologies, the study unfolds a comprehensive strategy that holds profound implications for the trajectory of WSNs.

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SUSTAINA-OP2TM: Customizable Kid-sized Open Hardware Platform Humanoid Robot for Research and Competition

SUSTAINA-OP2TM: Customizable Kid-sized Open Hardware Platfor...

引用

IEEE-RAS International conference on Humanoid Robots

作者： Masato Kubotera Yasuo Hayashibara Department of Advanced Robotics Chiba Institute of Technology Chiba JAPAN

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

This research focuses on developing a humanoid robot hardware platform that improves accessibility by allowing most mechanical and electronic components to be purchased from e-commerce sites and obtained as custom-made parts through external services. The SUSTAINA-OP ${ }^{\text {TM }}$ series, developed through research and competition activities in the RoboCup Humanoid League is designed as a robust robot using metal and FRP and has influenced the development of various robots in RoboCup and other international competitions. The public availability of design data allows researchers to efficiently build and customize robots for diverse applications. The successor model, SUSTAINA-OP2 ${ }^{\text {TM }}$, adopts official developer kits for its computer boards and sensor modules to reduce the burden of software development and features new actuators that further enhance the robot’s robustness. All design data is available at ***/SUSTAINA-OP2. The SUSTAINAOP2 ${ }^{\text {M }}$ was evaluated at Robocup 2024, held in Eindhoven in July 2024, where it secured first place in the Humanoid KidSize class soccer competition.

关键词： Computational modeling Humanoid robots Metals Robot sensing systems Robustness Hardware Manufacturing Research and development Software development management Sports

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A Robust Framework to Secure IoT sensor Data using Elliptic Curve Cryptography based CP-ABE and Secret Sharing Scheme

A Robust Framework to Secure IoT Sensor Data using Elliptic ...

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Information and Communication Technology (ICICT), International conference on

作者： Anika Bushra Mahit Kumar Paul Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

The growth of the Internet of Things (IoT) sector has ushered in an unprecedented era of connectedness, enabling a profusion of applications ranging from smart homes to industrial automation. Researchers predict that by 2030, there will be about 50 billion IoT devices and gadgets globally, completely changing how we interact with our environment. However, because these interconnected systems are vulnerable to many cyber attacks, the rise of IoT devices has also raised worries about data security and privacy. This paper investigates the potential of cryptographic approaches to mitigate IoT data security issues. It proposes an all-encompassing framework to protect IoT data from various threats and vulnerabilities. The framework leverages the robustness of the advanced Encryption Standard (AES) as the primary data encryption technology. Additionally, it integrates ciphertext-policy attribute-based encryption (CP-ABE) in conjunction with Elliptic Curve Cryptography to enable fine-grained access control, ensuring that only authorized entities can access specific IoT data. Furthermore, to fortify the encryption process, the framework implements a secret sharing method for the encryption key, distributing the key across multiple entities, reducing the risk of a single point of failure. The suggested framework seeks to improve the security posture of IoT systems by integrating these cryptographic algorithms and guaranteeing the confidentiality, integrity, and accessibility of data in IoT contexts.

关键词： Data privacy Smart homes Interconnected systems Elliptic curve cryptography Robustness Distance measurement Encryption Information and communication technology Internet of Things Standards

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WASP: A Wearable Super-Computing Platform for Distributed Intelligence in Multi-Agent systems

WASP: A Wearable Super-Computing Platform for Distributed In...

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IEEE High Performance Extreme Computing conference (HPEC)

作者： Patnayak, Chinmaya McClure, James E. Williams, Ryan K. Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA Virginia Tech Adv Res Comp Blacksburg VA USA

ISBN: (纸本)9781665423694

Autonomous unmanned aerial vehicle (UAV) systems have broad applications in surveillance, disaster management, and search and rescue (SAR) operations. Field deployments of intelligent multi-UAV systems are heavily constrained by available power and networking capabilities, and limited computational processing resources which are needed to reduce large volumes of on-board sensor data in real-time. In this work, we design a WearAble Super-Computing Platform (WASP) to address such challenges associated with multi-UAV deployments in remote field environments based on a human-in-the-loop (HITL) design. The WASP system is an advanced edge computing instrument designed from commodity embedded processing devices interconnected through an on-board Ethernet network. Networking is further extended through wireless networking capabilities to communicate with UAVs. Computational workloads and storage are orchestrated as discrete containers across WASP and the UAVs, which accounts for processor heterogeneity and time-varying workloads that must adapt dynamically to account for unpredictable failures of wireless networking in the field. We use our prototype to demonstrate advantages in terms of power management, redundancy, robustness, and human-robot collaboration in challenging field environments.

关键词： Artificial Intelligence Inference Distributed systems Cyber physical systems Edge Computing Wearable Computing

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Power Grid Surveillance and Control Based on Wireless sensor Network Technologies: Review and Future Directions 5

Power Grid Surveillance and Control Based on Wireless Sensor...

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5th International Scientific conference on advanced Engineering Technologies, ISC-AET 2020

作者： Abdulwahid, Ali Hadi Department of Automation and Control Engineering Techniques Southern Technical University Basra Iraq

In this paper, the concept of the smart grid and the emerging challenges of wireless communication technology for use on the smart grid (SG)is reviewed. One modern-day technology of great significance is wireless sensor networks (WSNs), which has the potential to ensure the smooth operation of smart grid management and increase its energy efficiency, reliability and cost-effectiveness. The advanced electric power grid infrastructure known as smart grid takes advantage of automated control and cutting-edge communication strategies to achieve greater efficiency, reliability and safety, whilst seamlessly combining renewable and alternative sources of energy. Unlike conventional communication technologies, WSNs are more beneficial to modern electric power systems because they are affordable and operate on a cooperative basis. For this reason, WSNs are considered to have good potential for improving the way electric power is produced, distributed and used. Therefore, they are a key element of the smart grid. Nevertheless, the implementation of WSNs in smart grid settings may be inadequately reliable due to the complexity and unfavourable aspects of electric power system environments. The purpose of the current work is to review WSN uses in electric power systems and the opportunities and difficulties offered by this technology, as well as to delineate possible facets of smart grid applications and wireless technologies that future research could explore. Therefore, this work is significant. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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An advanced Smart Biotechnological Algal Plate Air Purification System for Use in Conjunction with Real-Time Air Quality Monitoring

An Advanced Smart Biotechnological Algal Plate Air Purificat...

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International conference on Computer and Communications Technologies (ICCCT)

作者： Bharathy S Srivatsaav Murali Jaswant Singh dept. Computer Science Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Air pollution has become a health and environmental hazard as its effects are worsened by the increase inor pollution levels. Conventional air purification methods, although effective, are not environmentally friendly and can be expensive. We propose an advanced Smart Biotechnological Algal Plate Air Purification System that combines the real-time monitoring of air quality with biological purification processes. An ATmega328 microcontroller, which synchronizes different parts of the system to function well together, powers the entire system. The gas sensor senses air contaminants and draws polluted air with a fan to the algal bioreactor chamber, onto which microalgae consume carbon dioxide and contaminants and output clean air. The biological system is controlled by a microcontroller where a DC fan is used to switch the flow of air in the system allowing the pollutants to be optimized to expose more and expose it to algae purification. It also handles the control of GPIO-based sensors and actuators to allow air quality metrics tracking and adaptive management. It contains an adjustable alarm and relay for turning on the fan in response to dangerous levels of pollution as well an LCD for easy reading of system status. The GPIO interface provides the ease of connecting other sensors and actuators making scalability possible for various applications. This simple and low-cost environmental friendly approach combines microcontroller-assisted technology with biological cleaning to give environmentally friendly air to urban, industrial and household consumers as the cost-effective alternative to existing conventional air purifiers.

关键词： Air cleaners Technological innovation Fans Purification Microcontrollers Algae Air pollution Real-time systems Liquid crystal displays Monitoring

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An Efficient Localization Approach in Wireless sensor Networks Using Chicken Swarm Optimization 11

An Efficient Localization Approach in Wireless Sensor Networ...

引用

11th International conference on Information systems and advanced Technologies, ICISAT 2021

作者： Sandeli, Mohamed Bouanaka, Mohamed Ali Kitouni, Ilham University Constantine 2-Abdelhamid Mehri LISIA Laboratory Faculty of NTIC Constantine Algeria

ISBN: (纸本)9781665478243

Wireless sensor networks (WSN) have piqued the interest of many academics in recent years, and this technology has potential applications in a variety of sectors. A WSN is made up of nodes that are placed in a target environment and interact with it to detect data. Node deployment, clustering, data aggregation, energy consumption, and localization are all issues that WSNs confront. Localization is one of the most pressing concerns confronting the WSN. To solve the challenge of localization, many measuring methods and algorithms have been proposed. In order to use executing metaheuristics, several scholars have phrased the localization issue as an optimization problem. In terms of performance and accuracy, nature-inspired metaheuristics are viable alternatives to traditional approaches. One of the swarm-based metaheuristics we're interested in is Chicken Swarm Optimization (CSO). Because of its simplicity and effectiveness, CSO has shown to be quite effective in resolving localization challenges in WSNs. In this research, we present a method for reducing localization error in WSNs and improving CSO performance and efficiency. The suggested method's efficacy was evaluated and compared to traditional CSO and Particle Swarm Optimization algorithms. The collected findings suggest that the proposed technique lowers localization error in WSNs in a cost-effective way. They also demonstrate that the suggested technique compares with and even exceeds the alternatives. © 2021 IEEE.

关键词： Particle swarm optimization (PSO)

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An Overview of Adaptive Network Control in the Internet of Things: Machine Learning Methods for Optimizing Traffic and Dynamic Resource Management

An Overview of Adaptive Network Control in the Internet of T...

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Data Science and Business systems (ICDSBS), International conference on

作者： A Karthikeyan J Joel Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Department of Computer Science and Engineering RMK Engineering College Chennai

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The Internet of Things (IoT) ecosystem has witnessed exponential growth, making network settings complex and dynamic. These challenges in energy efficiency, traffic, and resource allocation have already made adaptive network control an indispensable instrument for Internet of Things (IoT) systems. The survey provides a comprehensive overview of adaptive network control techniques, particularly those based on ML algorithms to maximize traffic handling and dynamic resource allocation. Focusing on Internet of Things (IoT) use cases such as edge computing, sensor networks, and heterogeneous device management, the research examines a wide variety of ML algorithms, including supervised, unsupervised, and reinforcement learning. The study shows that network scaling using ML-based methods will be stable and load-balanced and reduce latency continuously. Here adaptive resource management is explored with reinforcement learning (RL) methods that can alter on-demand storage, processing and bandwidth to match the dynamic requirements. It says federated learning provides a way to optimize decentralized systems without compromising user data while making full use of distributed resources. Recent studies from the wide spectrum of ML applications provide case studies and experimental findings for increasing fault tolerance, reducing energy consumption, and improving Quality of Service (QoS) in IoT networks. Enabling flexible, efficient, and sustainable IoT networks, this study presents the transformative potential of machine learning. Other important research issues identified include scalable algorithms, real-time decision-making abilities, and high security. Suggestions for future work reveals the area of hybrid ML models, 6G AI convergence and development of light weight algorithms that can be run in low power Internet of Things devices. This work aims to provide a foundation for researchers and practitioners to use machine learning for advanced network control in the context of

关键词： Adaptive systems Machine learning algorithms Federated learning Scalability Heuristic algorithms Reinforcement learning Quality of service Dynamic scheduling Internet of Things Resource management

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Detailed characterization of a hyperspectral snapshot imager for full-field chromatic confocal microscopy

Detailed characterization of a hyperspectral snapshot imager...

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conference on Optics and Photonics for advanced Dimensional Metrology

作者： Hahn, Robin Haemmerling, Freya-Elin Haist, Tobias Fleischle, David Schwanke, Oliver Hauler, Otto Rebner, Karsten Brecht, Marc Osten, Wolfgang Univ Stuttgart Inst Tech Opt Pfaffenwaldring 9 D-70569 Stuttgart Germany Twip Opt Solut GmbH Stammheimer Str 10 D-70806 Kornwestheim Germany Reutlingen Univ PA&T Altenburgstr 150 Reutlingen Germany

ISBN: (数字)9781510634770

ISBN: (纸本)9781510634770

Hyperspectral imaging opens a wide field of applications. It is a well established technique in agriculture, medicine, mineralogy and many other fields. Most commercial hyperspectral sensors are able to record spectral information along one spatial dimension in a single acquisition. For the second spatial dimension a scan is required. Beside those systems there is a novel technique allowing to sense a two dimensional scene and its spectral information within one shot. This increases the speed of hyperspectral imaging, which is interesting for metrology tasks under rough environmental conditions. In this article we present a detailed characterization of such a snapshot sensor for later use in a snapshot full field chromatic confocal system. The sensor (Ximea MQ022HG-IM-SM5X5-NIR) is based on the so called snapshot mosaic technique, which offers 25 bands mapped to one so called macro pixel. The different bands are realized by a spatially repeating pattern of Fabry-Perot filters. Those filters are monolithically fabricated on the camera chip.

关键词： hyperspectral imaging hyperspectral sensor snapshot sensor chromatic confocal microscopy single-shot metrology

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