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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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4th international conference on Advanced computing and Intelligent Engineering, ICACIE 2019

4th International Conference on Advanced Computing and Intel...

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4th international conference on Advanced computing and Intelligent Engineering, ICACIE 2019

ISBN: (纸本)9789811563522

The proceedings contain 45 papers. The special focus in this conference is on Advanced computing and Intelligent Engineering. The topics include: Motif Discovery and Anomaly Detection in an ECG Using Matrix Profile;Efficient Watermarking in Color Video Using DWT-DFT and SVD Technique;gait-Based Person Identification, Gender Classification, and Age Estimation: A Review;developing a Framework for Acquisition and Analysis of Speeches;nucleus Segmentation from Microscopic Bone Marrow Image;Evaluation of Digital Forensic Tools in MongoDB Database Forensics;A High-Precision Pixel Mapping Method for Image-Sensitive Areas Based on SVR;fuzzy Cognitive Map-Based Genetic Algorithm for Community Detection;Human Action Recognition Using STIP Evaluation Techniques;adaptive Applications of Maximum Entropy Principle;BOSCA—A Hybrid Butterfly Optimization Algorithm Modified with Sine Cosine Algorithm;an Effective Hybrid Approach for Solving Prioritized Cube Selection Problem Using Particle Swarm Optimization and Tabu Search;exploration of Cognition Impact: An Experiment with Cover Song Retrieval Through Indexing;Benchmarking Performance of Erasure Codes for Linux Filesystem EXT4, XFS and BTRFS;performance Evaluation of Composite Fading Channels Using q-Weibull Distribution;preserving Privacy of Data in distributed systems Using Homomorphic Encryption;vehicle Number Plate Detection: An Edge Image Based Approach;a Survey on Handover Algorithms in Heterogeneous Wireless Network;A Novel Authentication Scheme for VANET with Anonymity;generic Framework for Privacy Preservation in Cyber-Physical systems;a Realistic Sensing Model for Event Area Estimation in Wireless sensor Networks;evaluation of the Applicability and Advantages of Application of Artificial Neural Network Based Scanning System for Grid Networks;Load Cell and FSR-Based Hand-Assistive Device.

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A Bayesian Approach for Fault Diagnosis in an Irrigation Canal 24

A Bayesian Approach for Fault Diagnosis in an Irrigation Can...

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24th international conference on System Theory, Control and computing (ICSTCC)

作者： Lami, Youness Lefevre, Laurent Lagreze, Andre Genon-Catalot, Denis Univ Grenoble Alpes Grenoble INP LCIS F-26000 Valence France

ISBN: (纸本)9781728198095

In this paper, we present a fault diagnosis approach based on a probabilistic model, more specifically a Bayesian one, while relying on a distributed Multi-Agent systems paradigm. First, a networked control water system benchmark is introduced, then a Bayesian approach for fault diagnosis is provided where inference and time dependencies between operating modes of systems are tackled. It is suggested that a Multi-Agent System, where entities interact and cooperate in a distributed architecture while expressing subjective knowledge of the system, is relevant regarding fault handling and offers more flexibility in the implementation and data collection.

关键词： Fault Detection and Diagnosis Water systems Probabilistic Models Bayesian Methods distributed Approaches Multi-Agent systems

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distributed Cyber-infrastructures and Artificial Intelligence in Hybrid Post-Quantum Era

Distributed Cyber-infrastructures and Artificial Intelligenc...

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ieee international conference on Trust, Privacy and Security in Intelligent systems and Applications (TPS-ISA)

作者： Attila A. Yavuz Saif E. Nouma Thang Hoang Duncan Earl Scott Packard University of South Florida Virginia Tech Qubitekk Inc.

distributed cyber-infrastructures and Artificial Intelligence (AI) are transformative technologies that will play a pivotal role in the future of society and the scientific community. Internet of Things (IoT) applications harbor vast quantities of connected devices that collect a massive amount of sensitive information (e.g., medical, financial), which is usually analyzed either at the edge or federated cloud systems via AI/Machine Learning (ML) algorithms to make critical decisions (e.g., diagnosis). It is of paramount importance to ensure the security, privacy, and trustworthiness of data collection, analysis, and decision-making processes. However, system complexity and increased attack surfaces make these applications vulnerable to system breaches, single-point of failures, and various cyber-attacks. Moreover, the advances in quantum computing exacerbate the security and privacy challenges. That is, emerging quantum computers can break conventional cryptographic systems that offer cyber-security services, public key infrastructures, and privacy-enhancing technologies. Therefore, there is a vital need for new cyber-security paradigms that can address the resiliency, long-term security, and efficiency requirements of distributed cyber *** this work, we propose a vision of distributed architecture and cyber-security framework that uniquely synergizes secure computation, Physical Quantum Key Distribution (PQKD), NIST Post- Quantum Cryptography (PQC) efforts, and AI/ML algorithms to achieve breach-resilient, functional and efficient cyber-security services. At the heart of our proposal lies a new Multi-Party Computation Quantum Network Core (MPC-QNC) that enables fast and yet quantum-safe execution of distributed computation protocols via integration of PQKD infrastructure and hardware- acceleration elements. We showcase the capabilities of MPC- QNC by instantiating it for Public Key Infrastructures (PKI) and federated ML in our HDQPKI and TPQ-ML, fram

关键词： Computers Privacy Protocols Public key NIST Multi-party computation Quantum networks

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Computation and Communication Co-Design for Real-Time Monitoring and Control in Multi-Agent systems 19

Computation and Communication Co-Design for Real-Time Monito...

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19th international Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt)

作者： Tripathi, Vishrant Ballotta, Luca Carlone, Luca Modiano, Eytan MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9783903176379

We investigate the problem of co-designing computation and communication in a multi-agent system (e.g., a sensor network or a multi-robot team). We consider the realistic setting where each agent acquires sensor data and is capable of local processing before sending updates to a base station, which is in charge of making decisions or monitoring phenomena of interest in real time. Longer processing at an agent leads to more informative updates but also larger delays, giving rise to a delay-accuracy trade-off in choosing the right amount of local processing at each agent. We assume that the available communication resources are limited due to interference, bandwidth, and power constraints. Thus, a scheduling policy needs to be designed to suitably share the communication channel among the agents. To that end, we develop a general formulation to jointly optimize the local processing at the agents and the scheduling of transmissions. Our novel formulation leverages the notion of Age of Information to quantify the freshness of data and capture the delays caused by computation and communication. We develop efficient resource allocation algorithms using the Whittle index approach and demonstrate our proposed algorithms in two practical applications: multi-agent occupancy grid mapping in time-varying environments, and ride sharing in autonomous vehicle networks. Our experiments show that the proposed co-design approach leads to a substantial performance improvement (18 - 82% in our tests).

关键词： wireless networks Age of Information distributed computing robotics networked control systems

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An Elliptic Curve-based Protocol for Privacy Preserving Frequency Computation in 2-Part Fully distributed Setting 12

An Elliptic Curve-based Protocol for Privacy Preserving Freq...

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12th international conference on Knowledge and systems Engineering (KSE)

作者： Thi Van Vu The Dung Luong Van Quan Hoang Acad Cryptog Tech Fac Informat Secur Hanoi Vietnam Acad Cryptog Tech Director Board Hanoi Vietnam

ISBN: (数字)9781728145105

ISBN: (纸本)9781728145105

Privacy-preserving frequency computation is critical to privacy-preserving data mining in 2-Part Fully distributed Setting (such as association rule analysis, clustering, and classification analysis) and has been investigated in many researches. However, these solutions are based on the Elgamal Cryptosystem, making computation and communication efficiency low. Therefore, this paper proposes an improved protocol using an Elliptic Curve Cryptosystem. The theoretical and experimental analysis shows that the proposed method is effective in both computing and communication compared to other methods.

关键词： Privacy preserving frequency computation Privacy-preserving data mining Elliptic curve cryptosystem 2part fully distributed setting Secure multi-party computation

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An Adaptive Multi-objective Salp Swarm Algorithm for Efficient Demand Side Management 17

An Adaptive Multi-objective Salp Swarm Algorithm for Efficie...

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17th ieee international conference on Mobile Ad Hoc and Smart systems (ieee MASS)

作者： Zhao, Zezheng Xia, Chunqiu Chi, Lian Chang, Xiaomin Li, Wei Yang, Ting Zomaya, Albert Y. Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Sydney NSW Australia Nanjing Univ Informat Sci & Technol Business Sch Nanjing Peoples R China Tianjin Univ Minist Educ Sch Elect & Informat Engn Key Lab Smart Grid Tianjin Peoples R China

ISBN: (纸本)9781728198668

With the continuous growth in population and energy demands more attention has been paid to energy consumption issues in residential environments. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers. However, it is a challenge to design a cost-effective scheduling strategies for HEMS which takes many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA) based on traditional multi-objective salp swarm algorithm (MSSA) to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%), compared with the case without a scheduling scheme.

关键词： intelligent swarm algorithm smart home edge computing demand response multi-objective salp swarm algorithm

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Enhancing Driver Safety through Convolutional Neural Networks for Drowsiness Detection

Enhancing Driver Safety through Convolutional Neural Network...

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Innovations in High Speed Communication and Signal Processing (IHCSP), international conference on

作者： Varun Mishra Siddharth Singh Chouhan Jay Vadula Devansh Mittal Rajneesh Kumar Patel Harshlata Viswakarma School of Computing Science and Engineering VIT Bhopal University Kothrikalan Sehore Madhya Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Driver drowsiness is a foremost reason for road accidents globally. This manuscript presents a driver drowsiness detection system using CNN, utilizing in-car camera video input to assess drowsiness levels. The system includes three main stages: face detection, landmark extraction, and classification. In the first stage, the face recognition algorithm detects the face of the driver from the video feed. In the second stage, landmark extraction is performed to detect and locate the facial landmarks. In the final step, CNN is used to classify facial features. This approach provides a unique and innovative solution that overcomes the limitations of existing methods that rely only on visual data. Using physiological signals such as EEG and EOG provides a more reliable and accurate measure of drowsiness levels, as these signals are directly related to the driver's brain activity and eye movement. Furthermore, the proposed methodology includes a real-time implementation that can provide drivers with alerts and warnings when drowsiness levels reach a dangerous threshold. This feature enhances road safety by preventing accidents resulting from driver drowsiness. The proposed system has been trained and tested on a dataset of drivers with different levels of drowsiness. The results show promising outcomes in detecting drowsiness with an accuracy of 92.25%. The proposed system can be unified into the existing vehicle structures to alert drivers when they become drowsy, reducing the risk of accidents on the road.

关键词： Training Image sensors Accuracy Feature extraction Road safety Real-time systems sensor systems Safety Convolutional neural networks Vehicles

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FNotify: A Low-Latency and Scalable Publish/Subscribe System using RDMA

FNotify: A Low-Latency and Scalable Publish/Subscribe System...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Yusen Xu Sijie Shen Mingcong Han Rong Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Shanghai China

Publish/subscribe systems are widely used as messaging middleware in various scenarios, such as online business, financial trading, and social media. However, the network overhead intro-duced by the pub/sub system can severely impact application performance. Especially in the latency-sensitive applications of the data center, optimizing the latency of publishing or delivering messages of the pub/sub system is particularly important. RDMA network has gained popularity in relevant applications due to its low latency, high bandwidth and ability to bypass the CPU. Never-theless, it is still challenging to design and optimize the pub/sub system using RDMA. Through an in-depth analysis of RDMA features, we design and implement FNotify, a high-performance RDMA-accelerated pub/sub system. FNotify optimizes latency and CPU load using in-memory message management and one-sided RDMA primitives for message collection. FNotify also proposes a topic-based partitioning scheme to achieve good scal-ability. We conducted a comprehensive evaluation and analysis on an 8-node RDMA cluster to verify the validity of our design by comparing the performance of FNotify with Apache Kafka (a state-of-the-art pub/sub system) in a typical pub/sub application scenario and various micro-benchmarks. Experimental results show that FNotify improves overall performance by $1.5\times$ to $3.9\times$ and reduces end-to-end latency by more than $20\times$ compared to Apache Kafka.

关键词： Data centers Publishing Social networking (online) Scalability Semantics systems architecture Low latency communication

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FedMax: Enabling a Highly-Efficient Federated Learning Framework 13

FedMax: Enabling a Highly-Efficient Federated Learning Frame...

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ieee 13th international conference on Cloud computing (CLOUD)

作者： Xu, Haohang Li, Jin Xiong, Hongkai Lu, Hui Shanghai Jiao Tong Univ Shanghai Peoples R China SUNY Binghamton Dept Comp Sci Binghamton NY USA

ISBN: (纸本)9781728187808

IoT devices produce a wealth of data desired for learning models to empower more intelligent applications. However, such data is often privacy sensitive making data owners reluctant upload their data to a central server for learning purposes. Federated learning provides a promising privacy-preserving learning approach, which decouples the model training from the need of accessing to the sensitive data. However, realizing a deployed, dependable federated learning system faces critical challenges, such as frequent dropouts of learning workers, heterogeneity of workers computation, and limited communication. In this paper, we focus on the systems aspects to advance federated learning and contribute a highly efficient and reliable distributed federated learning framework, FedMax, aiming to tackle these challenges. In designing FedMax, we contribute new techniques in light of the properties of a real federated learning setting, including a relaxed synchronization communication scheme and a similarity-based worker selection approach. We have implemented a prototype of FedMax and evaluated FedMax upon multiple popular machine learning models and datasets, showing that FedMax significantly increases the robustness of a federated learning system, speeds up the convergence rate by 25%, and increases the system efficiency by 50%, in comparison with state-of-the-art approaches.

关键词： Federated learning distributed systems machine learning privacy-preserving

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Ubiquitous Sensing the Convergence of WSNs and IoT in Contemporary Scenarios

Ubiquitous Sensing the Convergence of WSNs and IoT in Contem...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Manjula M M. P. Karnik P. N. Ramesh Sahil Suri P. Vijayaraghavan Jagmeet Sohal School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Institute of Technology Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mech Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Ubiquitous sensing, which combines WSNs and the IoT, makes settings smarter and more efficient for numerous procedures. For "Ubiquitous Sensing: The Convergence of WSNs and IoT in Contemporary Scenarios," this research examines a strategy to increase dependability, security, energy efficiency, and data accuracy. Recommended features include adaptive power management, data fusion methods, edge computing nodes, blockchain technology, and robust identification systems. These pieces improve the system and address common issues. The major research findings suggest that the strategy performs effectively in numerous success categories. Power management systems optimise energy consumption, reducing costs and environmental impact. Advanced data fusion and fault-tolerant designs improve data accuracy and reliability, allowing you to integrate data appropriately and keep the system functioning. Latency-sensitive applications may move and react to data in real time with reduced network delay and greater performance. Strong authentication and blockchain-based encryption maintain data accuracy, user privacy, and legal compliance while reducing hacking threats. The study describes the method's comprehensive approach to ubiquitous sensing, establishing a standard for IoT advances. Making systems more efficient and dependable, while prioritising data privacy and security, improves IoT infrastructure dependability and resilience.

关键词： Wireless sensor networks Data privacy Accuracy Power system management Data integration Real-time systems sensors Internet of Things Security Convergence

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