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

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DEFT: distributed, Elastic, and Fault-tolerant State Management of Network Functions 19

DEFT: Distributed, Elastic, and Fault-tolerant State Managem...

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19th international conference on Network and Service Management (CNSM) - Network and Service Management in the Era of Generative AI and Digital Twins

作者： Shahriyar, Md Mahir Saha, Gourab Bhattacharjee, Bishwajit Reaz, Rezwana Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9783903176591

Network function virtualization is the key to developing elastically scalable and fault-tolerant network functions (e.g. load balancer, firewall etc.). By integrating NFV and SDN technologies, it is feasible to dynamically reroute traffic to new network function (NF) instances in the event of an NF failure or overload scenario. The fact that the majority of network functions are stateful makes the task more challenging. State migration and state replication are common approaches in achieving elasticity and fault tolerance. The majority of the studies in the literature either emphasize fault tolerance or elastic scalability while designing a state management system for network functions. In this paper, we have designed a complete state management system, called DEFT, that meets both elasticity and fault-tolerance goals. Our system also supports strong consistency on global state updates. While existing designs rely on a central controller or remote central storage to achieve strong consistency on state updates, DEFT utilizes distributed consensus mechanism to achieve the same. We have done a proof of concept implementation of DEFT and extensively tested DEFT under several model conditions to evaluate its scalability and performance. Our experimental results show that DEFT is scalable and maintains a considerably high throughput throughout. It incurs minimal performance overhead while achieving strong consistency on state updates.

关键词： Network function virtualization Software defined networking distributed computing Fault tolerant systems

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Wireless sensor Network Hop Count Theory Modeling and Deep Learning Prediction 4

Wireless Sensor Network Hop Count Theory Modeling and Deep L...

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4th ieee international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2024

作者： Sati, Salem Omar Abugharsa, Afif Misurata University Information Technology Faculty Libya

ISBN: (纸本)9798350372632

Wireless sensor Networks (WSNs) are utilized as distributed systems for wireless communication and data collection from sensors. A WSN can be represented as a graph consisting of sensors and wireless links. If this graph has a path connecting any source sensor to any destination sensor, it is considered a connected network graph. Otherwise, it is a disconnected graph. The connectivity of a network graph is crucial for its proper functioning and applications. Network connectivity can be affected if a certain number of sensor nodes are missing from the topology. This number of nodes can be used as a threshold for connectivity. In a WSN, sensors communicate with their direct neighbors based on the quality of their communication links. This paper proposes a mathematical model to address connectivity and link probability issues in WSNs. The model examines the impact of varying link probability and the number of deployed sensors on network connectivity. The model is evaluated using different radio models in the Cooja emulator with the Rime stack. A multi-hop WSN is implemented as a linear path with various radio models to analyze network connectivity probability and estimate the maximum hop count. Additionally, the success of flood route estimation based on link probability is investigated. Network performance metrics such as packet delivery ratio and packet loss rate are used to evaluate network connectivity and measure the maximum hop count for both radio models. Furthermore, the collected simulation dataset is employed in Deep Learning models to predict the hop count based on link quality parameters. © 2024 ieee.

关键词： sensor nodes

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Ensemble Regression Machine Learning Based Detection of Intrusion Packets in Healthcare Wireless sensor Networks 2

Ensemble Regression Machine Learning Based Detection of Intr...

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2nd international conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Ranathive, S. Vidhya, R. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350328202

Wireless sensor networks (WSNs) have arisen as a feasible technology in healthcare applications, due to recent developments in wireless networking, wireless sensors, and low-power systems with networks. Securing these networks is essential for practical deployment in healthcare applications since the data transferred in these wireless body area networks (WBAN) frequently consists of sensitive and important patient health and personal information. The objective of this study is to create and develop sophisticated intrusion detection methods that will increase protection in WBAN. In this study, we offer an intrusion detection system (IDS) that utilises Ens-learnHealth to offer the best identification of attacks for such environments. The suggested approach ensures that the intrusion detection process uses exactly the features required for identifying a particular attack, reducing the difficulty of computation. As a result, it is found that Ens-learnHealth achieves 99.7% of accuracy, 98.4% of precision, 95% of recall,96% of F1-score, and 21% of miss rate. © 2024 ieee.

关键词： Wireless sensor networks

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Research on distributed Multi-Task Learning System Based on Data Analysis Algorithm 1

Research on Distributed Multi-Task Learning System Based on ...

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1st ieee international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Liu, Xin Wang, Yanjun Wang, Qiaoli Ma, Jun Cao, Jinping State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Urumqi830000 China Information and Telecommunication Company State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Urumqi830000 China

ISBN: (纸本)9798350323795

This paper presents a research study on distributed multi-task learning systems based on data analysis algorithms. The paper starts by providing an introduction to distributed multi-task learning and data analysis algorithms, along with their importance in machine learning and artificial intelligence applications. The background section provides a more detailed discussion of these concepts, highlighting their specific applications and challenges. The paper then focuses on data analysis algorithms, discussing their types, features, and usage in different applications, including distributed multi-task learning. The paper also highlights some of the popular data analysis algorithms, such as clustering, classification, regression, and dimensionality reduction, along with their mathematical formulations and applications. The section on distributed multi-task learning provides an overview of its concepts, benefits, and challenges. The paper then discusses some of the popular algorithms used in distributed multi-task learning, such as Bayesian methods, transfer learning, and multi-task feature learning, along with their mathematical formulations and applications. The paper concludes by discussing some of the recent research trends in this field, including the use of deep learning and reinforcement learning in distributed multi-task learning systems. Overall, this paper provides a comprehensive overview of the distributed multi-task learning system based on data analysis algorithms, highlighting its importance and potential in various applications. © 2023 ieee.

关键词： Deep learning

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An Atom Quest Optimizer for CNN to Distinguish IDs in SDN and IoT Eco System 5

An Atom Quest Optimizer for CNN to Distinguish IDs in SDN an...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Devi, V.S. Anusuya Raj, Vijilius Helena Kavin, Balasubramanian Prabhu Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Loyola College Department of Computer Science Chennai India Delhi India Symbiosis Institute of Technology Aiml Department Pune India Pune India

ISBN: (纸本)9798350383522

Security has become crucial as Internet of Things (IoT) applications have grown in popularity. The prevalence of IoT vulnerabilities was recently exposed by a distributed denial-of-service (DDoS) assault, and many IoT strategies unintentionally assisted in the DDoS attack. The new software-defined anything (SDx) paradigm offers a method for managing IoT devices in a secure manner. We initially provide a broad agenda for the software-defined Internet of Things (SD-IoT) in this paper, which is based on the SDx paradigm. The implementation of an IDS based on Deep Learning (DL) procedures into an SDN construction is the focus of this paper, which also suggests a very real-world method to shorten the delay by using the consecutive implementation of prototypes with cumulative hardware difficulty. This method enables quick tests to identify the key issues, address them, and then implement the solution. © 2024 ieee.

关键词： Atom Search Optimization Algorithm Convolutional Neural Network Deep Learning distributed Network Internet of Things

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Topological Dependencies in Deep Learning for Mobile Edge: distributed and Collaborative High-Speed Inference 2

Topological Dependencies in Deep Learning for Mobile Edge: D...

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2nd international conference on Electronics and Renewable systems, ICEARS 2023

作者： Abd Algani, Yousef Methkal Kumar, A Suresh Ala Walid, Md. Abul Balu, S. Velayutham, Priya Sasi Kumar, A. The Arab Academic College for Education in Israel-Haifa Department of Mathematics Israel Rathinam Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore India Khulna9203 Bangladesh Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamil Nadu Namakkal India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Inurture Education Solutions Pvt Ltd Bangalore India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Karnataka State India

ISBN: (纸本)9798350346640

Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested

关键词： Edge computing

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Open Loop Resonator Loaded Self Complementary Antenna With T-Shaped Dipoles for sensor Applications 10

Open Loop Resonator Loaded Self Complementary Antenna With T...

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Rajendran, Jolly Minu, K.H. Keerthana Mohan, U. Ace College of Engineering Department of Electronics and Communication Engineering Thiruvananthapuram India

ISBN: (纸本)9798350384369

This paper proposes a novel antenna, which can be used for detecting electromagnetic field signatures associated with lightning strikes. The proposed printed antenna demonstrates sensitivity to electromagnetic emissions within the frequency range between 330 MHz to 960 MHz. To enhance the antenna's performance, OLRs (Open Loop Resonators) are integrated into a conventional self-complementary antenna (SCA) having T- shaped dipoles. The resulting antenna, the OLR- loaded self- complementary antenna with T-shaped dipoles (OSCTDA) is compact and exhibits a VSWR bandwidth spanning from 330 MHz to 960 MHz, maintaining a Voltage Standing Wave Ratio (VSWR) below 2 throughout the entire frequency spectrum. The antenna's performance is examined through simulations and experiments. The proposed antenna can be used to analyze electromagnetic emissions generated by lightning-induced surge currents. © 2024 ieee.

关键词： Loop antennas

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Wireless Network distributed and Cooperative Media Algorithms with QoS Awareness 15

Wireless Network Distributed and Cooperative Media Algorithm...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ranganathaswamy, M.K. Amin, Rahul Dheer, Meenakshi Department of Mechanical Engineering School of Engineering and Technology JAIN Karnataka Bangalore India Department of Journalisim & Mass Communication ARKA JAIN University Jharkhand Jamshedpur India Department of Agriculture Vivekananda Global University Jaipur India

ISBN: (纸本)9798350370249

—QoS-conscious dispensed and cooperative media algorithms for Wi-Fi networks leverage the particular features of a wireless communications platform to allow the transmission of digital media with the progressed pleasant of a carrier (quality of service). The disbursed nature of those algorithms allows the improved quality of service aid by permitting the distribution of media assets throughout a couple of nodes in a Wi-Fi network cooperatively. Through the sensor-based Wi-Fi community, statistics may be used to tell aid allocation decisions, taking into consideration an optimized utilization of media resources. Allotted media algorithms also permit advanced media shipping strategies inclusive of multiple descriptions coding (MDC) and network coding (NC), supplying advanced video streaming, voice, and other media applications under numerous network conditions. The disbursed, cooperative media algorithms can further be greater with quality of service awareness, leveraging community facts and temporal statistics to successfully allocate media resources to enable progressed quality of service performance. By leveraging the unique capabilities of wireless networks, an advanced QoS-conscious distributed and cooperative media gadget may be constructed to meet the needs of modern-day rising media applications. ©2024 ieee.

关键词： Wi-Fi

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Artificial Intelligence in American Agriculture: A Comprehensive Review of Spatial Analysis and Precision Farming for Sustainability

Artificial Intelligence in American Agriculture: A Comprehen...

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2024 ieee international conference on computing, Applications and systems, COMPAS 2024

作者： Akter, Jahanara Kamruzzaman, Md Hasan, Rakibul Khatoon, Rabeya Farabi, Syeda Farjana Ullah, Md Wali Westcliff University Department of Business Administration 17877 Von Karman Ave 4th Floor IrvineCA92614 United States International American University Department of Business Administration 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States

ISBN: (纸本)9798331529765

Artificial Intelligence (AI) is revolutionizing the agricultural sector by enhancing precision farming and spatial analysis, particularly within the diverse agro-ecological zones of USA. This study investigates the transformative potential of AI technologies, such as sensor-based monitoring and satellite imaging analysis, in improving crop yields, soil health, and climate resilience. Despite the opportunities presented by AI in agriculture, such as increased efficiency and sustainability, the African agricultural landscape also faces significant challenges, including infrastructural limitations and socio-economic disparities. The research delves into how AI can address these challenges by promoting economic growth and sustainable development. Moreover, it highlights the critical role of AI in precise crop monitoring, soil health assessment, and decision-making through advanced weather forecasting. By examining the socio-economic effects of AI adoption, this study underscores the necessity of supportive policies and best practices to harness AI's full potential in fostering resilient and sustainable farming methods in USA. Through comprehensive analysis and strategic recommendations, this research contributes to the broader understanding of AI's impact on American agriculture, paving the way for innovative and effective agricultural practices. © 2024 ieee.

关键词： Sustainable agriculture

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distributed Key Value Store for IoT Edge Devices 9

Distributed Key Value Store for IoT Edge Devices

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9th international conference on Computer Science and Engineering, UBMK 2024

作者： Aslantaş, Burak Pektaş, Elif Nurdan Baydere, Şebnem Yeditepe University Dept. of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

The proliferation of the Internet of Things (IoT) requires efficient and scalable data management solutions at the edge of the network. Edge devices play a critical role in processing data and making real-time decisions, thereby minimizing latency and reducing the load on central servers. However, these devices face challenges such as scalability, latency, and fault tolerance. This paper proposes a distributed key-value store specifically designed for IoT edge devices to address these challenges. By integrating distributed key-value stores with IoT edge devices, we aim to enhance data access and management efficiency. Our approach includes the development of optimal methodologies and architectural standards for this integration, addressing key issues such as data consistency, fault tolerance, and low latency. Through prototype creation and testing, we demonstrate significant improvements in data processing speed and system reliability. The ultimate objective of this research is to advance edge computing for IoT, fostering more robust, scalable, and efficient systems. © 2024 ieee.

关键词： Data consistency

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