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

共 30780 条记录，以下是1491-1500 订阅

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Enhanced Cyberattack Detection in Wireless sensor Networks Using Gradient Boosting Decision Tree 4

Enhanced Cyberattack Detection in Wireless Sensor Networks U...

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4th ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Pavithra, A. Vinothkumar, E.S. Amarnath Prabhakaran, A. Gupta, Sumit Kumar Malik, Neeru Chennai Institute of Technology Department of Electronics & Communication Engineering Chennai India Department of Information Technology Trichy India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Thiruvallur India Nandha Engineering College Department of Biomedical Engineering Erode India St. Wilfred's P.G College Department of Physics Jaipur India Pimpri Chinchwad University Department of Engineering and Technology Pune India

ISBN: (纸本)9798350352931

Wireless sensor Networks (WSNs) are essential to the Internet of Things despite their power constraints. As sensor deployment has increased, so have security risks, requiring effective attack detection and prevention solutions. Machine Learning (ML) might build strong systems that detect cyber threats in real time. Data preparation, feature extraction, and model training are used to improve WSN security in this research. Preprocessing included data purification, conversion, standardisation, and splitting. Principal Component Analysis (PCA) reduced dimensionality, preserved variance, and improved computing speed. Model training uses Gradient Boosting Decision Trees (GBDT), notable for its classification accuracy in complex datasets. Averaging 93.11 % accuracy, the GBDT model outperformed the BiLSTM and DCNN models. This high accuracy shows GBDT's capacity to identify and mitigate WSN-based IoT security threats. The findings show that machine learning, particularly GBDT, can efficiently identify WSN intrusions to improve IoT network security and resilience. This study establishes complete cybersecurity safeguards for the IoT ecosystem, enabling more secure and robust applications. © 2024 ieee.

关键词： Cyber attacks

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Tradespace Analysis of Cross-Calibration in Missions Observing Ocean Color 16

Tradespace Analysis of Cross-Calibration in Missions Observi...

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16th Annual ieee international systems conference (SysCon)

作者： Tapia-Tamayo, I. Josue Grogan, Paul T. Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

ISBN: (纸本)9781665439923

distributed spacecraft missions (DSMs) can provide high temporal resolution of transient Earth phenomena such as algae blooms. However, their miniaturized instruments may be susceptible to sensor drift or degradation over time. This paper investigates cross-calibration methods suitable for DSMs and presents a methodology to explicitly consider cross-calibration during conceptual design phase tradespace analysis. A simulation analysis tool based on the Skyfield Python library evaluates constellation cross-calibration performance and estimates mission cost. An application case considers a hypothetical ocean color observing mission with cross-calibration between DSM constellation members and a monolithic reference satellite. A tradespace analysis study generatesWalker delta constellation configurations with 10 to 19 satellites distributed in 3 to 5 orbital planes. Ocean color observation target regions distribute sample points over the East and West coasts of the United States. Results show constellations with four satellites per plane minimize the average revisit time for cross-calibration opportunity per million dollars. However, other relevant variables that affect the average revisit time for cross-calibration opportunities were also identified for future investigation.

关键词： Cross-calibration distributed Spacecraft Missions Tradespace

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Revolutionizing Cloud computing: Evaluating the Influence of Blockchain and Consensus Algorithms 3

Revolutionizing Cloud Computing: Evaluating the Influence of...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Saravanan, T.R. Rathinam, Anantha Raman Lenin, J. Komathi, A. Bharathi, B. Murugan, S. Srm Institute of Science and Technology Department of Computational Intelligence Chennai Tamil Nadu Kattankulathur India Kl University Department of Computer Science and Engineering Guntur Andhra Pradesh Vijayawada India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

Combining Proof of Space (PoSpace) and Proof of Replication (PoRep) with interoperability protocols and distributed file systems in cloud computing may change the game. This combination has the potential to usher in a paradigm change in cloud infrastructures, bringing with it new heights of safety, scalability, and efficacy. Both PoSpace and PoRep provide new ways of storing information on blockchain networks. The energy consumption of PoSpace is much lower than that of conventional Proof of Work (PoW) algorithms because it uses unused storage space as a resource for consensus. In contrast, PoRep strengthens the robustness of the cloud system by evaluating the storage replication between nodes to guarantee data integrity. Protocols for interoperability across blockchains are crucial to their capacity to communicate with one another. They make it easier to trade information and assets, leading to a more cohesive ecosystem that goes beyond the boundaries of different blockchains. Increased connectivity and productivity in the cloud are both benefits of interoperability. By distributing data storage over a network, distributed file systems usher in a new era of administration efficiency. © 2023 ieee.

关键词： Cloud computing Data Storage distributed File systems Efficiency Interoperability Protocols Proof of Replication (PoRep) Proof of Space (PoSpace) Resilience Scalability Security

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Exploiting Hybrid Medium Access Control and Relaying Strategies to Overcome Duty-Cycle Limitations in LoRa-Based sensor Networks

Exploiting Hybrid Medium Access Control and Relaying Strateg...

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ieee international Instrumentation and Measurement Technology conference (I2MTC) - Rising Above Covid-19

作者： Fedullo, Tommaso Mahmood, Aamir Tramarin, Federico Morato, Alberto Gidlund, Mikael Rovati, Luigi Univ Padua Dept Management & Engn Padua Italy Mid Sweden Univ Dept Comp & Elect Engn S-85170 Sundsvall Sweden Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari Modena Italy CNR IEIIT Natl Res Council Italy Via Gradenigo 6-B Padua Italy

ISBN: (纸本)9781665453837

The industrial Internet-of-things (IIoT) paradigm is reshaping the way industrial measurement systems are designed. Industrial systems require collecting accurate and timely measurements from the field using smart sensor networks distributed in wide production areas. In this context, wireless connectivity of sensors acquires undeniable importance, and in turn, opens significant research challenges. Therefore, the research community is actively analyzing the suitability of different wireless technologies, for instance, Wi-Fi, 5G-and-beyond, and low-power wide area networks (LPWANs), toward their possible industrial applications and optimizing them to realize high-performance and accurate smart measurement systems. In this paper, we focus on long range (LoRa)-based LPWANs (i.e., LoRaWAN), especially to overcome the duty cycle (DC) limitations of the adopted ALOHA-based medium access control (MAC) strategy in the industrial, scientific, and medical (ISM) bands. The ISM bands are subjected to an hourly constraint on the number of packet transmissions or inter-message delay, where the devices using higher spreading factors (SFs) can quickly consume the available transmission time. In this paper, we propose and assess the hybrid MAC designs in a LoRa network by combining carrier sense multiple access (CSMA) with ALOHA in two different ways i) exploiting different channel plans for the access mechanisms, ii) relay-assisted access, with devices using small SFs assisting neighboring higher-SF devices with listen-before-talk (LBT) mechanism. Our simulation results reveal that the proposed access strategies lead to a higher packet delivery rate (PDR) as well as lower mean and standard deviation of the communication delay;thus, increasing the overall measurement accuracy.

关键词： ALOHA CSMA Duty cycle limitations Factory Automation ISM bands LoRa LPWANs

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

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2nd international conference on Automation, computing and Renewable systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud computing (CC) is a massive breakthrough in Information Technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 ieee.

关键词： Big Data Cloud computing Deep learning distributed Denial of Service attacks Machine Learning

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IoT-Enabled Environmental Monitoring System Using AI 3

IoT-Enabled Environmental Monitoring System Using AI

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international conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Arabelli, Rajeshwarrao Boddepalli, Eswararao Buradkar, Mrunalini Goriparti, N. V. Sarathbabu Chakravarthi, M. Kalyan SR Univ Dept ECE Sch Engn Warangal Telangana India Virginia Tech Bradely Dept ECE Blacksburg VA USA St Vincent Pallotti Coll Engn & Technol Elect & Telecommun Engn Nagpur Maharashtra India Ramachandra Coll Engn Dept Elect & Elect Engn Eluru 534007 India Symbiosis Int Symbiosis Inst Technol CSE Pune Maharashtra India VIT AP Univ Sch Elect Engn Amaravathi 522237 India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

In light of the expansion of Internet of Things (IoT) bias, there is a great opportunity to improve environmental monitoring skills across a variety of fields, including but not limited to husbandry, civic planning, and artificial safety. The incorporation of artificial intelligence methods, such as machine literacy algorithms and deep literacy models, makes it possible to do real-time analysis and interpretation of environmental data that is gathered by Internet of Things cameras. This paper examines the approaches and technologies that are currently being utilized in Internet of Things (IoT)-based environmental monitoring systems. The focus is placed on the role that artificial intelligence plays in the process of perfecting data quality, predictive analytics, and decision-making procedures. Case studies and experimental findings illustrate the efficiency of the system in detecting and forecasting changes in the environment, which makes it easier to implement imaginative actions for environmental conservation and operation. It is important to note that the findings highlight the potentially disruptive outcome of merging Internet of Things (IoT) and artificial intelligence (AI) technologies to address urgent environmental concerns and pave the way for sustainable development enterprise.

关键词： IoT environmental monitoring AI Internet of Things machine learning sensor networks

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IoT-based Office Buildings Energy Management with distributed Edge computing Capability 8

IoT-based Office Buildings Energy Management with Distribute...

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8th international conference on Technology and Energy Management, ICTEM 2023

作者： Moghadam, Meisam Nesary Abapour, Mehdi Greater Tehran Electricity Distribution Company Tehran Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9781665452854

Building Energy Management systems (BEMS) are becoming very popular, for providing net Zero Energy Buildings (nZEBs). Attachment of these systems through IoT technologies, enables the building owners as well as utility companies to control energy's usage, generation, and storage in a more smart and real time manner, which is the foundational concept for Internet of Energy (IoE). High number of buildings especially in metropolitan areas imposes high amount of computation resources and increased communication and computation delays to the central cloud servers. In this paper, we first describe the details of a real IoT-based large scale deployment of office buildings energy management system in Greater Tehran Electricity Distribution Company (GTEDC), and then we propose an edge computing solution to reduce the complexity and delay of data dissemination in the system expansion phase. © 2023 ieee.

关键词： Internet of Energy

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DOSP: distributed computing-Based Operating systems Labs Platform 15

DOSP: Distributed Computing-Based Operating Systems Labs Pla...

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15th international conference on Computer Research and Development, ICCRD 2023

作者： Huang, Haoming Song, Lihua North China University of Technology Department of Computer Science Beijing China

ISBN: (纸本)9781665487504

Operating systems (OS), as a core course in Computer Science, is given much attention by all universities all over the world. Labs are the most important part of the course, and universities apply different solutions for it. Mainstream OS labs can be divided into two categories: Algorithm simulation-based labs are not beneficial for students to understand the actual mechanism of OS systematically;Kernel development-based labs have a higher barrier to entry and require more hardware resource, which is difficult for low-level universities to carry out. To solve the drawbacks of current labs solutions, this paper proposes a distributed computing-based OS labs platform, DOSP. In DOSP, students can perform labs in the virtualization-based pre-built environment, and get real-Time feedback from the judging system;Teachers can check students' score overview and real-Time teaching progress via the scoring system. The judging system can utilize idle resources in the PC room to perform distributed judging efficiently, to reduce the consumption of computing resources on the central server. Evaluation results show that our solution outperforms others in load capacity and scalability. As the result, it can solve many pain points in current OS lab teaching, which has good application prospects. © 2023 ieee.

关键词： Students

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Realizing Digital Product Passports with Crowdsourcing Principles: The Case of Sustainable Smart Grids 19

Realizing Digital Product Passports with Crowdsourcing Princ...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Voulgaridis, Konstantinos Lagkas, Thomas Karampatzakis, Dimitris Argyriou, Vasileios Sarigiannidis, Panagiotis International Hellenic University Department of Computer Science Kavala Campus Greece Kingston University Department of Networks and Digital Media Kingston upon Thames United Kingdom University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350346497

Due to constant technological advancements, Smart Grids have the potential to significantly reduce energy consumption, while providing efficient energy management. However, their implementation requires a thorough collection of data related to procedures of energy generation and distribution, as well as the inclusion of Circular Economy (CE) principles to ensure sustainability. Considering the impact of Industry 5.0, Digital Product Passports (DPPs) are a sufficient solution for the recording, presentation, and circularity of the respective data, combined with suitable crowdsourcing strategies for the integration of real-time data provided by consumers. This paper explores the potential implementation of DPPs, infused with crowdsourcing principles, for smart grid integration, through the design of a fundamental CE framework, which is further supported by a systematic literature review and a reference use- case scenario of its operational flow. © 2023 ieee.

关键词： Smart power grids

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A distributed Deep Learning Approach for A Team of Unmanned Aerial Vehicles for Wildfire Tracking and Coverage 6

A Distributed Deep Learning Approach for A Team of Unmanned ...

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6th ieee international conference on Robotic computing, (IRC)

作者： Shrestha, Kripash La, Hung Manh Yoon, Hyung-Jin Amazon Robot Res Reno NV USA Univ Nevada Dept Comp Sci & Engn Adv Robot & Automat ARA Lab Reno NV USA Univ Nevada Dept Mech Engn Reno NV USA

ISBN: (纸本)9781665472609

Recent large wildfires in the United States and the subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and therefore, there need to be alternatives to monitoring wildfires. Unmanned Aerial Vehicles (UAVs) have been previously used in this problem domain to track and monitor wildfires with approaches such as artificial potential fields and reinforcement learning. Our work aims to look at a team of UAVs, in a distributed approach, over an area to maximize the sensor coverage in dynamic wildfire environments. We proposed and implemented the Deep Q-Network (DQN) with a state estimator (auto-encoder), then compared it to existing methods including a Q-learning, a Q-learning with experience replay, and a DQN. The proposed DQN with a state estimator outperformed existing deep learning methods in terms of reward maximization and convergence.

关键词： Deep learning Deep Q Network state estimator UAV wildfires

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