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Social-Aware DT-Assisted Service Provisioning in Serverless Edge Computing

Social-Aware DT-Assisted Service Provisioning in Serverless ...

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International conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jing Li Jianping Wang Weifa Liang Jie Wu Quan Chen Zichuan Xu Department of Computer Science City University of Hong Kong Hong Kong P. R. China Department of Computer and Information Sciences Temple University Philadelphia USA Guangdong University of Technology Guangzhou P. R. China School of Software Dalian University of Technology Dalian P. R. China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

The Internet of Things (IoT) is gathering paces in the new era of Industry 4.0, and the Digital Twin (DT) technology bridges the gap between the bursting amounts of data generated by IoT devices and the user requirements for real-time data processing. DT services maintain living digital models of physical objects, and a DT network enables comprehensive service provisioning with the global knowledge of a group of DTs. On the other hand, exposing serverless computing in network edges, the recent advances in Serverless Edge Computing (SEC) introduce new inspirations to the DT landscape that ensure fine-grained resource management and low network-wide delay of DT services. However, social relationships among IoT devices and DT data privacy impact the orchestration of DTs. In this paper, we design a differential privacy-based federated learning framework to build a DT network for DT services in response to user DT service requests in SEC, thereby enhancing the Quality of Services (QoS). To this end, we first formulate a novel social-aware problem for placing DTs in an SEC network, and show its NP-hardness. We then provide an Integer Linear Program (ILP) solution to the problem when the problem size is small; otherwise, we design an approximation algorithm with a provable approximation ratio. We finally evaluate the algorithm performance through simulations. Simulation results demonstrate the proposed algorithm is promising, which improves by no less than 21.1 % of the performance of benchmarks.

关键词： Federated learning Computational modeling Simulation Serverless computing Quality of service Approximation algorithms Real-time systems Digital twins Sensors Internet of Things

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Efficient Multilevel Federated Compressed Reinforcement Learning of Smart Homes Using Deep Learning Methods

Efficient Multilevel Federated Compressed Reinforcement Lear...

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2021 IEEE International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2021

作者： Ravichandran, P. Saravanakumar, C. Rose, J. Dafni Vijayakumar, M. Lakshmi, V. Muthu St. Joseph's Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9781665435215

Internet of Things connects all real time devices using the wireless nature for collecting, sharing and processing of data. These data are analyzed using machine learning models based on the structure of data. Reinforcement learning is a type of learning method which performs with past experience of data. Traditional algorithms use data with a specific environment with a learning process for prediction. Federated Learning (FL) is achieved through the integration of the various learning models for achieving accuracy. The proposed learning algorithm uses multilevel FL over the smart homes with two house data for analysis of the user behavior. Various kinds of sensors are used for analyzing the behavior, namely local and global. The data is shared with agents and servers with the use of communication networks. It suffers the bandwidth issues because of heterogeneity in the data, so this is overcome by using FL compression method. Multilevel FL compression method achieves reduced latency with efficient interaction. The proposed technique achieves better accuracy when compared to existing RL method with maximum performance and reliability. © 2021 IEEE.

关键词： Reinforcement learning

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A Multi-spot Murmur Sound Detection Algorithm and Its Application to a Pediatric and Neonate Population 14

A Multi-spot Murmur Sound Detection Algorithm and Its Applic...

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14th International Joint conference on Biomedical Engineering systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired systems and Signal processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and devices (BIOdevices)

作者： Oliveira, Marisa Oliveira, Jorge Camacho, Rui Ferreira, Carlos Univ Porto Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Inst Super Engn Porto Rua Dr Antonio Bernardino de Almeida 431 P-4249015 Porto Portugal Knowledge Engn & Decis Support Res Grp GECAD Rua Dr Antonio Bernardino de Almeida 431 P-4249015 Porto Portugal LIADD INESC TEC Inst Engn Sistemas & Comp Tecnol Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (纸本)9789897584909

Cardiovascular diseases are one of the leading causes of death in the world. In low income countries, heart auscultation is of capital importance since it is an efficient and low cost method to monitor the heart. In this paper, we propose a multi-spot system that aims to detect cardiac anomalies and to support a diagnosis in remote areas with limited heath care response. Our proposed solutions exploits data collected from the four main auscultation spots: Mitral, Pulmonary, Tricuspid and Aorta in a asynchronous way. From the several multi-spot systems implemented, the best results were obtained using a bi-modal system that only processes the Mitral and the Pulmonary spot simultaneously. Using these two spots we have achieved an accuracy between 85.7% (smallest value, using ANN) and the best value of 91.4% (obtained with a logistic regression algorithm). Taking into a account the pediatric population and the incident cardiac pathologies, it happens to be the spots where the observed murmurs were most audible. We have also find out that when using four auscultation spots, the choice of the algorithm is of secondary priority, which does not seem to be the case for a single auscultation spot system. With one single auscultation we have an average of 4% of difference between the results obtained with the algorithms and with four auscultation spots we have a smaller average of 2.1%.

关键词： Heart Sounds Data processing Heart Auscultation Cardiovascular Data Machine Learning Data Mining

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CROMA: remote sensing representations with contrastive radar-optical masked autoencoders 23

CROMA: remote sensing representations with contrastive radar...

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Proceedings of the 37th International conference on Neural information processing systems

作者： Anthony Fuller Koreen Millard James R. Green Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Geography and Environmental Studies Carleton University Ottawa Canada

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled, spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable. We present CROMA: a framework that combines contrastive and reconstruction self-supervised objectives to learn rich unimodal and multimodal representations. Our method separately encodes masked-out multispectral optical and synthetic aperture radar samples—aligned in space and time—and performs cross-modal contrastive learning. Another encoder fuses these sensors, producing joint multimodal encodings that are used to predict the masked patches via a lightweight decoder. We show that these objectives are complementary when leveraged on spatially aligned multimodal data. We also introduce X- and 2D-ALiBi, which spatially biases our cross- and self-attention matrices. These strategies improve representations and allow our models to effectively extrapolate to images up to 17.6 × larger at test-time. CROMA outperforms the current SoTA multispectral model, evaluated on: four classification benchmarks— finetuning (avg.↑ 1.8%), linear (avg.↑ 2.4%) and nonlinear (avg.↑ 1.4%) probing, kNN classification (avg.↑ 3.5%), and K-means clustering (avg.↑ 8.4%); and three segmentation benchmarks (avg.↑ 6.4%). CROMA's rich, optionally multimodal representations can be widely leveraged across remote sensing applications.

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Research on A Distributed Cooperative Transmission Scheme with Edge Computing

Research on A Distributed Cooperative Transmission Scheme wi...

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Networks, Communications and Intelligent Computing (NCIC), International conference on

作者： Hua-Min Chen Ruijie Fang Tao Chen Hui Li Fan Li Shaofu Lin Faculty of Information Technology Beijing University of Technology Beijing China MediaTek (Beijing) Beijing China Network Optimization Center China Unicom Beijing Branch Beijing China

ISBN: (数字)9798350371949

ISBN: (纸本)9798350371956

As wireless communication services continue to advance, the demand for reliability and latency has become increasingly stringent. This is especially crucial in areas like personal consumption and the Industrial Internet of Things (IIoT). To address the need for Hyper Reliable and Low-Latency Communication (HRLLC) and cater to diverse user equipment (UE) capabilities, this paper presents a novel approach with a focus on optimizing both latency and energy efficiency. It introduces a distributed geo-location aware UE aggregation transmission scheme based on edge computing for HRLLC services. This scheme includes a communication procedure designed to enhance reliability and reduce latency, wherein some UEs or particular higher end devices play as coordination nodes for packet duplication through multi-path simultaneously. Considering the system signaling overhead and signal processing capability of each cooperation node (CN), a multiple-user scheduling algorithm with geo-location awareness under different coordination nodes is performed at each cooperation node, with a restriction of latency requirement and energy efficiency, i.e., a lower Energy-Delay Product (EDP) performance. The research presented in this paper offers valuable insights into the development of distributed cooperation systems for reliability and delay sensitive services.

关键词： Performance evaluation Wireless communication Scheduling algorithms Signal processing Energy efficiency Delays Reliability

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The Impact of Feature Selection on Malware Classification Using Chi-Square and Machine Learning

The Impact of Feature Selection on Malware Classification Us...

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International conference on Computer and Communication Engineering, ICCCE

作者： Areeg Fahad Rasheed M. Zarkoosh Sana Sabah Al-Azzawi College of Information Engineering Al-Nahrain University Baghdad Iraq Software Engineering Computiq Baghdad Iraq EISLAB Machine Learning Luleå University of Technology Luleå Sweden

The Internet of Things (IoT) is a network of physical objects, automobiles, household appliances, and other items that are integrated with sensors, software, and connections to gather and share data via the Internet. The rapid proliferation of the Internet of Things devices has ushered in a wave of new security challenges, specifically in the realm of malware detection and these challenges necessitate innovative solutions. Consequently, the primary objective of this study is to develop an advanced malware detection system leveraging machine learning algorithms in tandem with natural language processing techniques like tokenization and vectorization, in addition to a feature selection method named Chi-Square. The proposed method is tested using the IoTPot dataset and compared with recent research in the field, where it outperformed the current work with respect to the accuracy, F1-score, recall, and precision. Furthermore, the proposed method was compared with time-based consulting and demonstrated superior performance with NLP and Chi-square than without, making it more suitable for resources constrained by such IoT systems. We also provide the code for the proposed method to foster transparency. 1 . 1 https://***/AREEG94FAHAD/chisqaure

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Realtime Object Recognition Method Inspired by Multimodal information processing in the Brain for Distributed Digital Twin systems

Realtime Object Recognition Method Inspired by Multimodal In...

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International Wireless Communications and Mobile Computing conference, IWCMC

作者： Ryoga Seki Daichi Kominami Hideyuki Shimonishi Masayuki Murata Masaya Fujiwaka Graduate School of Information Science and Technology Osaka University Osaka Japan NEC Corp.

Recently, digital twins have been paid much attention as a major application towards Beyond 5G/6G network, and real-time object recognition methods are key technology to digitize the real world as a digital twin. However, it is challenging to make a fast and accurate decision on what the object is from real-time streaming information such as video because accurate object recognition algorithms require a huge computation. To satisfy delay requirement of digital twin applications, such computations have to be moved from cloud to edges or even small terminal devices, where computing capacity is very limited. Thus, recognition mechanisms have to be simplified for small devices but they would result in degraded accuracy. In this paper, we focus on the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information, to improve accuracy of simplified recognition mechanisms. We first propose a unimodal object recognition mechanism based on the Bayesian attractor model, which continuously recognizes objects from noisy streaming media data. Then, we extend the mechanism with Bayesian causal inference to fuse the results of unimodal media recognition. Through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.

关键词： Computer simulation Computational modeling information processing Streaming media Real-time systems Mathematical models Digital twins

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Revolutionizing waste management: harnessing the power of IoT

Revolutionizing waste management: harnessing the power of Io...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Ankita Karale Anand Singh Rajawat S. B. Goyal Computer Engineering Sandip Foundation's Sandip Institute of Research Center Nashik India School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia

Waste management has become a pressing problem in a world that is becoming more and more urbanised. Traditional ways of dealing with trash are usually inefficient and bad, leading to pollution, health problems, and wasted resources. As an answer to these problems, the Internet of Things (IoT) has changed the way trash is handled in a big way. This abstract looks at how smart waste management based on the Internet of Things could totally change the business of garbage disposal. Through a network of sensors and devices, the Internet of Things (IoT) connects trash cans, garbage trucks, and waste processing facilities so that they can be monitored and improved in real time. A smart waste management system that is built on IoT has many benefits. With the real-time information it gives about how much trash is being made, routes for collecting trash can be made more efficient in terms of time and fuel use. This cuts down on the number of times trash needs to be picked up, reduces the amount of greenhouse gases released, and saves money. Sensors in trash cans can also find dangerous or flammable items, so they can be dealt with right away and any problems can be avoided. Also, the Internet of Things makes it possible for predictive analytics, which help waste management officials predict how much trash will be made and use their resources well. Using smart waste sorting methods like optical sensors and machine learning algorithms, recyclable and non- recyclable trash can be correctly found and sorted. This helps people recycle more and puts less pressure on landfills. Smart trash management systems that are based on the Internet of Things (IoT) get more people involved and help them understand. Mobile apps and smart devices can tell a person when their trash will be picked up, where the nearest recycling sites are, and how much trash they make. People are encouraged to act responsibly and throw away trash in ways that are good for the environment. Smart waste management bas

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Predicting Software Energy Consumption Using Time Series-Based Recurrent Neural Network with Natural Language processing on Stack Overflow Data

Predicting Software Energy Consumption Using Time Series-Bas...

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Communication and Networks (ASIANComNet), Asian conference on

作者： S Deepajothi Kalyankumar Dasari N Krishnaveni R Juliana Neeraj Shrivastava Kireet Muppavaram Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India Department of CSE-CS Chalapathi Institute of Technology Mothadaka Guntur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Loyola ICAM College of Engineering and Technology Loyola Campus Nungambakkam Chennai India Medicaps University Indore India Department of CSE Gitam Deemed To be University Hyderabad India

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

In recent years, there has been an increasing number of software solutions presented to tackle the issue of energy usage at the application level. Nevertheless, there is little knowledge about the level of concern among software developers over energy use, the specific areas of energy consumption that they deem significant, and the potential solutions they propose for enhancing energy efficiency. Especially, the increasing amount of data and IoT devices require more storage space and computational power, which results in higher energy consumption. In order to address this problem, academics and professionals have been investigating several strategies to enhance energy efficiency in computer systems. It may be an interesting project to use deep learning algorithms, especially those that make use of natural language processing (NLP) methods, to estimate software energy usage based on Stack Overflow data. This NLP techniques can analyze the text of questions and answers. This involves tokenization, lemmatization, and named entity recognition to identify terms and phrases related to energy consumption. This study examines the concerns of practitioners about energy consumption on Stack Overflow via the utilization of lexicon-based sentiment analysis, a concept in NLP, combined with RNNs. The objective is to improve energy efficiency by forecasting time series data. The results of this study indicate that the practitioners’ desire to start conversations in the field of energy is closely linked to the utilization of ideas. This analysis of software energy consumption issues may assist academics in identifying the most significant concerns for software developers and end users.

关键词： Energy consumption Recurrent neural networks Terminology Computational modeling Time series analysis Software Natural language processing Energy efficiency Tokenization Long short term memory

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Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System

Distributed Quantum Learning with co-Management in a Multi-t...

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IEEE International conference on Big Data

作者： Anthony D’Onofrio Amir Hossain Lesther Santana Naseem Machlovi Samuel Stein Jinwei Liu Ang Li Ying Mao Computer and Information Science Department Fordham University Pacific Northwest National Laboratory (PNNL) Florida A&M University

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting.

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