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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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Evaluation of Centralized, distributed and Federated Learning for IoT Intrusion Detection systems 11

Evaluation of Centralized, Distributed and Federated Learnin...

引用

11th ieee international conference on Intelligent computing and Information systems, ICICIS 2023

作者： Elnakib, Omar Shaaban, Eman Mahmoud, Mohamed Emara, Karim Ain Shams University Faculty of Computer and Information Sciences Department of Computer Systems Cairo Egypt Tennessee Technological University Electrical and Computer Engineering CookevilleTN United States

ISBN: (纸本)9798350322101

Introducing smart intrusion detection systems (IDSs) in the IoT system was a crucial requirement in the last decades to secure these systems due to the high sensitivity of the sensed data. Smart IDS has often been built based on a machine/deep learning model trained at a centralized node in the cloud. However, the centralized learning approach violates data privacy since IoT nodes are required to share their data to the cloud to train the model. distributed and federated learning approaches were introduced to solve this issue by keeping data within the local network and letting the edge devices train the model. In this paper, a comparative study among centralized, distributed, and federated learning approaches is presented. First, a deep learning model is proposed to identify traffic behavior using CICIDS2017 dataset. The three learning approaches are then applied with the proposed model to evaluate the effect of each approach on the model accuracy. In the centralized learning approach, an F-Score of 98% can be achieved. Moreover, the dataset is split over ten simulated nodes with various data distribution and entropies to evaluate the distributed and federated learning approaches. In distributed learning, the F-Score ranges between 63% to 93% with an average of 78% over the ten nodes. In the federated learning, the F-Score ranges between 73% to 98% with an average of 89% over the ten nodes. This result confirms that IDSs based on federated learning are a promising solution for both accuracy and preserving data privacy. © 2023 ieee.

关键词： Intrusion detection

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Design and Characterization of a Novel, Low-Cost Resistive Stretch sensor Band for Interactions in HCI Applications 15

Design and Characterization of a Novel, Low-Cost Resistive S...

引用

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

作者： Sankar, B. Dhuraisamy, Arun Kumar Sindhu, S. Rajath, S. Dept. of Mechanical Engineering Bangalore560012 India PSG College of Technology Dept. of Robotics & Automation Engineering Coimbatore641004 India Dept. of Electronics Engineering Chennai600127 India Dept. of Design and Manufacturing Bangalore560012 India

ISBN: (纸本)9798350370249

In recent years, Human-Computer Interaction (HCI) technology has progressed significantly toward creating immersive experiences. Yet, the physical interaction within these environments often relies on hand controllers or hand tracking systems using gestures that lack the naturalistic feeling of grasping objects and are computationally expensive, respectively. This study introduces the Resistive Stretch sensor (RSS) Band, a novel approach to bridging this gap by enabling interactions in HCI through the use of conductive rubber. The RSS is designed as a wearable band that conforms to the user's forearm, responding to muscle expansion during a full power grasp and release motion. As the RSS band stretches, its resistance value changes, which can be used as input for interactions within a virtual environment. The research characterises the conductive rubber material to determine the optimal RSS band design. The design process of the RSS band is detailed, considering ergonomic factors and the need for reliable and repeatable sensor output. A calibration method is developed to translate resistance changes into accurate input for HCI systems. The study concludes by examining the RSS's potential applications, such as in human-computer interaction. © 2024 ieee.

关键词： Conductive Rubber Human-Computer Interaction Resistive Stretch sensor

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Q-Learning Energy Management System (Q-EMS) in Wireless sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

引用

2024 international conference on Smart computing, IoT and Machine Learning, SIML 2024

作者： Ardiansyah, Deden Mustafid Mantoro, Teddy Universitas Diponegoro Doctoral Program of Information Systems School of Postgraduate Central Java Semarang Indonesia Universitas Pakuan Vocational School Computer Engineering Department West Java Bogor Indonesia Sampoerna University Media-Tech-Lab Computer Science Department DKI Jakarta Indonesia

ISBN: (纸本)9798350364101

The energy control of a Wireless sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm. © 2024 ieee.

关键词： Energy management systems

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distributed Robust State Estimation of Uncertain Complex Networked systems

Distributed Robust State Estimation of Uncertain Complex Net...

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2023 international conference on Neuromorphic computing, ICNC 2023

作者： Zhao, Yufan Lv, Yuezu Yu, Hao Beijing Institute of Technology School of Automation Beijing China Advanced Research Institute for Multidisciplinary Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350316889

This paper studies the consensus problem of multi-agent systems with unknown inputs under directed communication topologies. The structure of a specific-time observer is designed which takes a pulsative form at some time instants, and then the distributed attack-free protocol is presented, which only utilizes relative output measurement. To test the validity of the algorithm, the proposed algorithm is compared with existing Kalman filtering algorithm through simulation analysis, thereby drawing a conclusion on the advantages of reducing calculation costs, improving estimation accuracy, and convergence speed. © 2023 ieee.

关键词： Multi agent systems

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Enhanced Tree-Seed Continuous Optimization Algorithm for Optimal Multipath Routing in Wireless sensor Networks 4

Enhanced Tree-Seed Continuous Optimization Algorithm for Opt...

引用

4th international conference on Ubiquitous computing and Intelligent Information systems, ICUIS 2024

作者： Manoranjani, M. Sukumaran, S. Erode Arts & Science College Department of Computer Science Tamilnadu Erode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

In Wireless sensor Networks, efficient and reliable data routing is critical, especially in environments characterized by dynamic topology changes and resource constraints. Multipath routing techniques are essential for reducing energy consumption, improving scalability and facilitating load balancing in WSNs. This paper presents a novel routing framework the Enhanced Tree-Seed-Optimal Path Routing Protocol which operates in three main phases: Network Clustering, Optimized multipath routing and Route maintenance. High quality clustering is done by Fuzzy Gaussian Adaptive Clustering with the prediction of residual energy, number of neighbor node and distance to BS ensuring the selection of CL and formation of cluster. In the second phase, the Enhanced Tree-Seed Algorithm is applied to discover and optimize multiple routing paths between sensor nodes, providing robust and reliable data transmission. Lastly, the routing paths are maintained to prevent the path breakages which lead to regenerate route discovery. The proposed work enhances the adaptability of the network to dynamic condition but also prolongs the network's operational lifetime. The protocol has been simulated across various WSN configurations and the results are compared with existing routing protocols. Simulation outcomes shows that the Enhanced Tree-Seed Optimal Path Routing Protocol is superior to other routing protocols in terms of End-to-End delay, Energy Consumption, Packet Delivery Ratio, Throughput and Network lifetime. © 2024 ieee.

关键词： sensor nodes

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Blockchain based Secure Data Management for Healthcare Applications 3

Blockchain based Secure Data Management for Healthcare Appli...

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3rd international conference on Communication, computing and Digital systems, C-CODE 2023

作者： Khan, Muhammad Zalkifal Nadeem, Maham Kaleem, Mohammad Nazir, Sajid Comsats University Department of Electrical and Computer Engineering Islamabad Pakistan Glasgow Caledonian University Department of Computing Glasgow United Kingdom

ISBN: (纸本)9798350332391

Blockchain technology is poised to transform the data storage and data interchange models. A blockchain is a distributed ledger of transactions stored across a network of nodes. This decentralized model provides immutability and traceability of the records and is known as distributed Ledger Technology (DLT). In the implemented healthcare system, we used a decentralized blockchain based peer to peer architecture ensuring data security, availability, and reliability. We leverage the Substrate framework, part of Polkadot ecosystem for building blockchains. Once the patient logs into their profile, they are able to view their medical history along with any doctor's prescriptions and laboratory reports. When the doctor accesses a patient's profile, they can make new entries to the medical records and prescriptions. In addition, we show that a tamper-proof record of the healthcare assets can be securely maintained, and accessible to the authorized entities. © 2023 ieee.

关键词： distributed ledger

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ADRIN: An Adaptive and distributed Routing Mechanism for Challenged IoT Networks 16

ADRIN: An Adaptive and Distributed Routing Mechanism for Cha...

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16th international conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Halder, Shayantan Roy, Satyaki Ghosh, Preetam Ghosh, Nirnay Iiest Department of Cst Shibpur India Uah Department of Math. Sc. AL United States Vcu Department of Cs VA United States

ISBN: (纸本)9798350383119

Data and communication networks in disaster-hit areas are often crippled due to partial or complete outages of the networking infrastructures. In such challenging post-disaster scenarios, message sharing is indispensable for a timely recovery. Typically, it is done by building mobile ad-hoc networks by devices in possession of users. However, challenged networks are often constrained by intermittent connectivity, delay, and energy constraints, necessitating routing strategies that offer seamless communication in the face of node failure and mobility. In this paper, we present an adaptive and distributed routing mechanism termed ADRIN. It employs backtracking-based mobility tracing, inspired by the backward algorithm of Hidden Markov Models, to infer periodicity in mobility to determine stable routes and then constructs a spatiotemporal ad-hoc network to relay data in a multi-hop fashion to the base station. Preliminary experiments show that ADRIN approximates the underlying mobility distribution and performs data forwarding despite failures. © 2024 ieee.

关键词： Challenged networks Disaster response distributed routing Internet of Things Mobile computing Periodicity

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WiDeo: One Shot Domain Adaptation for Cross-Environment WiFi-Based Activity Classification Through RF-Visual Joint Embedding 17

WiDeo: One Shot Domain Adaptation for Cross-Environment WiFi...

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17th Annual ieee international conference on distributed computing in sensor systems (ieee DCOSS)

作者： Islam, Md Tamzeed Nirjon, Shahriar Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA

ISBN: (纸本)9781665439299

The effect of environmental change in WiFi signal is a major obstacle for generalized WiFi-based activity recognition. In this paper, we propose a novel method for adapting a WiFi-based activity classifier, that is trained on a large-scale dataset for an environment, to a new environment with only one labeled sample per activity. To this end, we propose a novel representation extraction algorithm using the supervision of visual data during the training phase. Our proposed feature extraction explicitly learns the corresponding relation between WiFi signal and movement of human body parts. For environment adaptation, we propose a framework that relies on only one labeled sample per activity class in comparison with current state-of-the-art solutions which are not suitable for few-shot adaptation. We collect data from four volunteers from five different environments and show that our proposed solution is able to achieve 28% higher accuracy than state-of-the-art solutions for environmental adaptation.

关键词： Training Visualization Adaptation models Correlation distributed databases Activity recognition Feature extraction

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Gray Wolf Optimization Algorithm to Enhance Data Aggregation in Wireless sensor Network 5

Gray Wolf Optimization Algorithm to Enhance Data Aggregation...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Loganathan, Sathyapriya Victoria Jancee, B. Senkamalavalli, R. Subbu Lakshmi, T.C. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India East Point College of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Francis Xavier Engineering College Department of Information Technology Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350321425

Wireless sensor networks (WSNs) have become increasingly popular as distributed systems using small, lightweight nodes with limited resources for monitoring environmental conditions or measuring physical parameters. To minimize energy utilization, data aggregation can be employed to reduce the data quantity forward to the base station (BS). To solve these issues, a Gray wolf optimization algorithm (GWOA) is proposed to enhance data aggregation in WSN. Increasing the network lifetime, node reliability, and throughput with the selection of best cluster heads (CHs) utilizing the Gray Wolf Optimization (GWO) algorithm is the major objective of this research. This approach selects the CH by applying the GWO algorithm established on the node remaining energy, available bandwidth, and distance. The GWO-based clustering protocol minimizes dead nodes and energy utilization and raises the clustering rounds and throughput. The simulation results demonstrate that the increases the node reliability and network throughput. © 2023 ieee.

关键词： Energy efficiency

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Proceedings - international conference on computing, Power, and Communication Technologies, IC2PCT 2024

Proceedings - International Conference on Computing, Power, ...

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

ISBN: (纸本)9798350383522

The proceedings contain 359 papers. The topics discussed include: classification and structuring of DL-based method for finding Alzheimer's disease through MRI images;impact of social media on the professional development of health professionals;use of sensor networks with self-organizing algorithm to increase the agricultural productivity;cyberbullying : research challenges and opportunities;improving brain tumor detection and classification using intelligent methods;rotten and fresh fruits classification using deep learning;structured implementation of ML algorithms for cardiovascular disease detection;enhanced retail shopper behavioral analysis using human machine interaction and model validation;java-powered digital healthcare management: innovating medical administration systems;and applicability of machine learning in waste water quality detection.

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