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16th international conference on distributed computing and internet technology, icdcit 2020

16th International Conference on Distributed Computing and I...

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16th international conference on distributed computing and internet technology, icdcit 2020

ISBN: (纸本)9783030369866

the proceedings contain 29 papers. the special focus in this conference is on distributed computing and internet technology. the topics include: A Very Gentle Introduction to Multiparty Session Types;trust: Anthropomorphic Algorithmic;Prospero’s Books: A distributed Architecture for AI: Invited Extended Abstract;uniform Circle Formation by Swarm Robots Under Limited Visibility;Identifying Reduced Features Based on IG-threshold for DoS Attack Detection Using PART;transcript Management Using Blockchain Enabled Smart Contracts;healthChain: A Secure Scalable Health Care Data Management System Using Blockchain;distributed and Lazy Auditing of Outsourced Data;an Efficient and Novel Buyer and Seller’s distributed Ledger Based Protocol Using Smart Contracts;text Document Clustering Using Community Discovery Approach;word2vec’s distributed Word Representation for Hindi Word Sense Disambiguation;a distributed and Trusted Web of Formal Proofs;a Framework Towards Generalized Mid-term Energy Forecasting Model for Industrial Sector in Smart Grid;online Context-Adaptive Energy-Aware Security Allocation in Mobile Devices: A Tale of Two Algorithms;a Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embedding;multi-Winner Heterogeneous Spectrum Auction Mechanism for Channel Allocation in Cognitive Radio Networks;community Detection in Social Networks Using Deep Learning;bearing Fault Classification Using Wavelet Energy and Autoencoder;resource Scheduling for Tasks of a Workflow in Cloud Environment;design of a Scheduling Approach for Budget-Deadline Constrained Applications in Heterogeneous Clouds;secure Content-Based Image Retrieval Using Combined Features in Cloud;verifying Implicitly Quantified Modal Logic over Dynamic Networks of Processes;distributed Graph Analytics;preface.

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A lightweight, fully-distributed AI framework for energy-efficient resource allocation in LoRa networks 16

A lightweight, fully-distributed AI framework for energy-eff...

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16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Scarvaglieri, Antonio Palazzo, Sergio Busacca, Fabio Univ Catania Catania Italy

ISBN: (纸本)9798400702341

As the internet of things (IoT) continues to grow rapidly, efficient resource utilization is crucial for the sustainability and performance of IoT networks. In this context, LoRa technology, known for its low-power, long-range communication, has become popular for IoT applications. However, the limited energy and spectrum resources in Long Range (LoRa) networks present challenges in achieving optimal network performance in dense deployments. In particular, existing centralized approaches, such as LoRa Adaptive Data Rate, may fail to scale up to the large networks typical of IoT scenarios. To address all of these issues, we propose a smart, fully-distributed resource allocation scheme based on multi-agent cooperative Q-learning approach. Simulations results prove that our approach improves the Packet Delivery Ratio (PDR) and reduces the energy consumption of up to 43% as compared to fixed SF and random non-smart strategies, respectively, while also keeping the decision process at a device-level, with no centralized entities involved.

关键词： LoRa IoT Reinforcement Learning LPWANs Q-Learning distributed Decisioning

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Wireless Alert technology for Elderly Care at Home 1

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16th international conference on Ubiquitous computing and Ambient Intelligence

作者： Poulin, Sebastien Obradovic, Natasa Lacerte, Julie Provencher, Veronique Pigot, Helene Giroux, Sylvain Ngankam, Hubert Univ Sherbrooke Lab DOMUS Departement dinformat Sherbrooke PQ Canada Univ Sherbrooke Ecole Readaptat Fac Med Sherbrooke PQ Canada

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Ambient Assistance uses the internet of things (IoT) technologies to monitor elderly people at home, improving their quality of life through wearable sensors, audio-video technology, and ubiquitous computing. Social isolation, a critical issue for seniors, poses a greater risk to mortality than obesity or physical inactivity, threatening the health and independence of adults over 65. While commercial solutions exist, they are costly and often disregard user context and caregiver needs. this article presents a system that prioritizes user autonomy by notifying the elderly adult before caregivers. It supports various devices and provides a platform for exploring wearable sensors for the elderly, with the aim of reducing social isolation by reassuring loved ones when the user is in a safe area. the system offers caregivers contextual alerts, real-time visualization, and two-way communication when the elderly person leaves a designated safe zone. Our study consisted of two phases: laboratory tests to validate system components and interfaces, followed by a 2-year longitudinal study with an elderly participant and their caregiver, analyzing long-term usage patterns and real-world effectiveness. Our technological findings demonstrate the system's robustness and efficiency. We achieved an 80% uptime over the 2-year period, with an average notification delay of less than 2 s. the system successfully integrated with various IoT devices, including smartwatches, door sensors, and motion detectors. Key challenges included minimizing false alarms, which were reduced to less than 1% using MapReduce-based data processing algorithms, and ensuring seamless operation in diverse network conditions. Although promising, the limitations of the study require further research with a larger sample size. this project contributes to the field of ambient assistance by offering a distributed lambda architecture and a technologically advanced approach to combating social isolation among the elde

关键词： Ambient Assisted Living internet of things Social Isolation distributed Lambda Architecture

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DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-Clouds with Dynamic Power-Budgets 16

DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-...

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IEEE 16th international conference on Cloud computing (IEEE CLOUD)

作者： Hewage, tharindu B. Ilager, Shashikant Rodriguez, Maria A. Arroba, Patricia Buyya, Rajkumar Univ Melbourne Cloud Comp & Distributed Syst CLOUDS Lab Sch Comp & Informat Syst Melbourne Vic Australia Vienna Univ Technol TU Wien Vienna Austria Univ Politecn Madrid Lab Sistemas Integrados LSI CCS Ctr Computat Simulat Madrid Spain

ISBN: (纸本)9798350304817

the internet of things (IoT) driven latency-critical applications are deployed on lightweight Micro-Clouds at the network's edge. Renting physical space from geographically distributed colocation datacenters connected via a Wide Area Network (WAN) is a cost-effective way of deploying Micro-Clouds, despite WANs' dynamic communication latency from traffic congestion. However, this deployment approach can limit Micro-Clouds to operate within a soft power budget, as colocation datacenter providers utilize it to add more servers and lower capital costs through oversubscribing power infrastructure. As a result, Micro-Clouds use extreme energy reduction measures like power capping and task throttling to address power overdraw events, where power consumption exceeds soft power budget limits, which reduces the performance of latency-critical applications. We propose a solution where a dynamic power budget can be achieved by adding renewable energy sources to the existing soft power budget without upgrading power delivery systems. To take advantage of this, we propose a dynamic, decentralized task-scheduling algorithm called DEMOTS. DEMOTS effectively utilizes the available dynamic power budget in a WAN with varying degrees of network traffic congestion, thereby avoiding the need for extreme energy reduction measures. We implement DEMOTS on a simulation test-bed. Compared to state-of-the-art baseline using MCOP for decentralized task-scheduling in Micro-Clouds, DEMOTS reduces Power Overdraw Impact up to 19%, Task Latency Increase Impact up to 47%, and Task Schedule Time Impact up to 49%.

关键词： Decentralized Resource Management Micro-Clouds Task Scheduling Energy Efficient computing

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Evaluating Indicator Weights for Satellite internet Security Assessment: an Approach to Combining Rough Set with Fuzzy Hierarchical Analysis 21

Evaluating Indicator Weights for Satellite Internet Security...

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21st IEEE international Symposium on Parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Lu, Yuming Li, Fenghua Wen, Yitong Fang, Liang Guo, Shoukun Li, Zifu Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

In order to reduce the impact of increasingly sophisticated cyberattacks on the satellite internet security, it is necessary to assess the satellite internet security. Most of existing assessment schemes for network security risk mainly rely on expert knowledge to subjectively weight assessment indicator and quantify security risks, thus leading to results of assessment which are not objective. To address this problem, combining rough set with fuzzy hierarchy analysis, we propose a combination scheme to evaluate the indicator weights for assessing the satellite internet security. First, from the perspective of IPDR model, we propose IPDR-based evaluation indicator system, and fuzzy hierarchy analysis is designed to fuse the indicator scores of more than one experts into one final value and obtain the subjective weight. Second, rough set theory is adopted to reduce indicators and correct the subjective weight obtained in the first step, thus obtaining the comprehensive weight. Finally, a series of experiments are carried out to validate our scheme. © 2023 IEEE.

关键词： Rough set theory

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OCDE: Adaptive Differential Evolution Algorithm in distributed Intelligent Systems 16

OCDE: Adaptive Differential Evolution Algorithm in Distribut...

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16th international conference on Cyber-Enabled distributed computing and Knowledge Discover, CyberC 2024

作者： Wang, Minghao Du, Shaoyi Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China

ISBN: (纸本)9798331506896

In autonomous driving, the optimization theory and algorithms for distributed intelligent systems are essential for enhancing vehicle decision-making capabilities, path planning, and environmental perception. Evolutionary algorithms, as a global optimization method inspired by biological evolution, is widely used in these scenarios. Unfortunately, the performance of evolutionary algorithms relies on the properties of the solution space, the optimization strategy, and parameter settings, and their efficiency also requires more rigorous evaluation. this study focuses on differential evolution algorithm, examining the properties of the solution space for optimization problems, the preferences of strategies and parameters in different optimization scenarios, and setting new standards for efficiency evaluation. Based on the experimental conclusions, modifications are made to our algorithm to further verify the correctness of our findings. All experimental results are deduced based on the CEC2017 benchmark problem suite from the IEEE Congress on Evolutionary Computation (CEC). © 2024 IEEE.

关键词： Anthropogenic

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Partitioning-based Active Trust Mechanism in Cloud-Edge-Device Collaborative Data Collection 21

Partitioning-based Active Trust Mechanism in Cloud-Edge-Devi...

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作者： Xia, Zhuoqun Wang, Ziyu Liu, Xiao Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China Guangxi University School of Computer Electronics and Information Nanning China

ISBN: (纸本)9798350329223

Since the open character of the internet of things (IoT), IoT devices are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud. Furthermore, existing trust evaluation models struggle to balance data security needs across different regions. To address this issue, the paper proposes a partition-based active trust mechanism (PATM). this PATM scheme mainly includes the following parts: First, the region is divided into subregions, and evaluations are conducted for the indicators of each subregion. Mechanism modeling and fuzzy analytic hierarchy process are used to analyze the impact of each indicator on the sensitivity of subregions. Second, the direct estimation method is utilized to estimate the demand for active detection routing based on the sensitivity of subregions. then, the number of operations of malicious nodes is increased through the high-frequency transmission of detection routes required by each subregion. Additionally, a trust calculation method based on node operations is proposed to evaluate the reliability of the device. the experimental results show that the PATM scheme proposed in this article can quickly identify the trust degree of nodes while balancing the needs of different regions, ensuring the credibility of data collection. © 2023 IEEE.

关键词： internet of things

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in internet of things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. the main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. this encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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Application Research of Vertical Federated Learning technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

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作者： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

this study centers on the application of vertical federated learning technology in the context of internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. through empirical experiments on real internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. the average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. this study offers innovative solutions for data privacy protection and risk control in the internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. the potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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distributed computing and internet technology: 13th international conference, icdcit 2017 Bhubaneswar, India, January 13–16, 2017 proceedings 13th

Distributed Computing and Internet Technology: 13th Internat...

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13th international conference on distributed computing and internet technology, icdcit 2017

作者： Krishnan, Padmanabhan Krishna, P. Radha Parida, Laxmi Oracle Labs BrisbaneQLD Australia Infosys Limited Hyderabad India IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

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