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Harmonia: Accurate Federated Learning with All-Inclusive Dataset 17

Harmonia: Accurate Federated Learning with All-Inclusive Dat...

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IEEE 17th international conference on Cloud computing (IEEE CLOUD)

作者： Choi, Wonmi Ahn, Juyoung Yoo, Yeonho Yoo, Chuck Yang, Gyeongsik Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350368543;9798350368536

Federated learning (FL) is an appealing model training technique that utilizes heterogeneous datasets and user devices, ensuring user data privacy. Existing FL research proposed device selection schemes to balance the computing speeds of devices. However, we observe that these schemes compromise prediction accuracy by similar to 57.7%. To solve this problem, we present Harmonia that enhances prediction accuracy, while also balancing the diverse computing speeds of devices. Our evaluation shows that Harmonia improves prediction accuracy by similar to 1.7x over existing schemes.

关键词： Federated Learning Data Privacy distributed Machine Learning Collaborative Learning Client Selection

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An intelligent cloud computing architecture supporting e-Governance

An intelligent cloud computing architecture supporting e-Gov...

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2011 17th international conference on Automation and computing, ICAC 2011

作者： Sharma, Rajkumar Kanungo, Priyesh Vikram University Ujjain India Patel College of Sc. and Technology Indore India

ISBN: (纸本)9781467300001

the development of high speed internet access, Web 2.0 applications and Virtualization techniques have made Cloud computing a leading edge technology. A user in 'Cloud' runs web based application over internet via browser with a look and feel of desktop program. Cloud computing provides dynamically scalable and virtualized resources as a service over the network at a nominal initial investment. Data-center works as backbone in Cloud computing where a large number of servers are networked to host computing & storage needs of the users. the area which needs more attention is Latency Optimization for cloud architecture to work as ubiquitous as expected. Many data intensive applications produce enormous amounts of data which travel on cloud network. As the cloud users grow, cloud architecture should accommodate movement of voluminous data to avoid data congestion in the network. In this paper, an intelligent & energy efficient Cloud computing architecture is proposed based on distributed data-centers to support application and data access from local data-center with minimum latencies. It was found that the proposed architecture is efficient for business entrepreneurs, suitable to apply for e-Governance and provides a green eco-friendly environment for Cloud computing. © 2011 CACSUK.

关键词： Cloud computing architecture

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Integration of Blockchain and Cloud computing: A Review 10

Integration of Blockchain and Cloud Computing: A Review

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17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Tarannum, Wajda Abidin, Shafiqul Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9789380544472

Blockchain technology is becoming more popular as it is utilized in various fields, including finance, healthcare, etc., due to its distributed nature, transparency, immutability, security, and auditability. these characteristics make blockchain technology advantageous because it can completely change how data is stored and operations are performed. Cloud computing is a system that stores, manages, and processes data over the internet and on distant computers. It is a form of computing that enables consumers to access data and applications from anywhere in the world with just an internet connection. Cloud computing is a popular centralized system, which is used by companies and individuals to keep and access data online. However, this system is vulnerable to cyber-attacks, data breaches, and other security threats because data are controlled by a single organization and stored centrally. therefore, research is needed to know the security issues of cloud computing that may be resolved using Blockchain and how Blockchain can be utilized to boost security and integrity. In this review paper, authors have examined how blockchain technology can be leveraged in cloud computing, discussed the advantages of using cloud-based blockchain services, and explored the current trends in this field, paying close attention to its development, advantages, and applications. Several blockchain applications in the supply chain, finance, healthcare, etc, have been explored. then it comes to some valuable conclusions with few unanswered questions that require more inquiry. this review article offers a broad foundation for researchers interested in cloud data security and data management using blockchain technology. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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Multi-objective Optimisation of Wireless Sensor Networks Deployment: Application to fire surveillance in smart car parks 17

Multi-objective Optimisation of Wireless Sensor Networks Dep...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Benghelima, Slimane Charafeddine Ould-Khaoua, Mohamed Benzerbadj, Ali Baala, Oumaya Univ Blida 1 Dept Comp Sci LRDSI Blida Algeria Ain Temouchent Univ Belhadj Bouchaib Dept Math & Comp Sci Ain Temouchent Algeria Res Lab Ind Comp & Networks RIIR Oran Algeria Univ Bourgogne Franche Comte FEMTO ST Inst CNRS UTBM Belfort France

ISBN: (纸本)9781728186160

the exponential growth of the internet-of-things (IoT) technology paradigm has resulted in new applications and on-line services. Smart car park is one interesting example among others that can take advantage of applications based on wireless sensor networks (WSNs) Which constitute the core of IoT. this paper focuses on the deployment optimization problem of WSNs dedicated to the fire detection in a smart car park. In such networks, the nodes are classified into two categories: Sensor Nodes (SNs) deployed within the smart car park for targets coverage and Relay Nodes (RNs) whose task is to relay alert messages generated by the sensor nodes up to the sink node. In this study, we propose a Multi-Objective Binary Integer Linear Programming (MOBILP) which minimizes simultaneously the number of sensor nodes, relay nodes and the maximum distance from sensor nodes to the sink node, while ensuring coverage and connectivity. We have conducted extensive tests in order to evaluate the performance of our proposal. the results demonstrate that the MOBILP outperforms the existing approaches in terms of quality of solutions compared to a sequential deployment method, which consists to deploy SNs then RNs, and in terms of the ability to find other efficient solutions compared to a simultaneous deployment method using a mono-objective function, which consists to deploy SNs and RNs simultaneously.

关键词： internet of things Wireless Sensor Networks Deterministic Deployment Multi-objective Combinatorial Optimization Smart Car Parks Fire Surveillance

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Optimizing Resource Allocation in Edge-distributed Stream Processing 17

Optimizing Resource Allocation in Edge-distributed Stream Pr...

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17th international conference on Web Information Systems and Technologies (WEBIST)

作者： Rocha Neto, Aluizio Silva, thiago P. Batista, thais, V Lopes, Frederico Delicato, Flavia C. Pires, Paulo E. Fed Univ RN UFRN Dept Informat & Appl Math BR-59078970 Natal RN Brazil Fluminense Fed Univ UFF Comp Sci Dept BR-24220900 Niteroi RJ Brazil

ISBN: (纸本)9789897585364

Emerging Web applications based on distributed IoT sensor systems and machine intelligence, such as in smart city scenarios, have posed many challenges to network and processing infrastructures. For example, environment monitoring cameras generate massive data streams to event-based applications that require fast processing for immediate actions. Finding a missing person in public spaces is an example of these applications, since his/her location is a piece of perishable information. Recently, the integration of edge computing with machine intelligence has been explored as a promising strategy to interpret such massive data near the sensor and reduce the end-to-end latency of processing events. However, due to the limited capacity and heterogeneity of edge resources, the placement of task processing is not trivial, especially when applications have different quality of service (QoS) requirements. In this paper, we develop an algorithm to solve the optimization problem of allocating a set of nodes with sufficient processing capacity to execute a pipeline of tasks while minimizing the operational cost related to latency and energy and maximizing availability. We compare our algorithm with the resource allocation algorithms (first-fit, best-fit, and worst-fit), achieving a lower cost in scenarios with different nodes' heterogeneity. We also demonstrate that distributing processing across multiple edge nodes reduces latency and energy consumption and still improves availability compared to processing only in the cloud.

关键词： internet of things Technical Infrastructure for Services New Trends in internet technology

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Data reduction through decision-making based on event-sensitivity in iot-enabled event monitoring 21

Data reduction through decision-making based on event-sensit...

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21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Bhuiyan, Md Zakirul Alam Wang, Tian Zaman, Aliuz Wang, Guojun School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China Department of Computer Science and Technology Huaqiao University Xiamen Fujian361021 China Department of Computer and Information Sciences Fordham University New YorkNY10458 United States

ISBN: (纸本)9781728120584

the internet of things (IoT) are being adopted to improve the efficiency and reliability of diverse monitoring applications, including critical events (such as fire, structural damage, crack) in the smart city's public safety application. In this paper, we present BigReduce, an IoT-enabled scheme to reduce a big quantity of data through decision-making when acquiring data and transferring the acquired data throughout the IoT network, while providing event sensitivity in industrial environments. At first, BigReduce executes decision-making briefly on the ongoing collected data in a discrete-time based on the event sensitivity so that data acquisition regarding the event-insensitivity is reduced. then, BigReduce executes decisionmaking locally at the IoT devices to decide whether or not transmitting only a part of the gathered data that have a high event-sensitivity so that data with event-insensitivity is reduced. At an upstream IoT device (say cluster head), BigReduce runs a distributed decision-making to verify whether or not the collected data have to be transferred. Outcomes achieved through a proof-of-concept implementation shows that BigReduce is capable of reducing data quantity by 87% and energy cost by at least 82% compared to related schemes. © 2019 IEEE.

关键词： internet of things

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CCOS: A Coded Computation Offloading Strategy for Satellite-Terrestrial Integrated Networks 17

CCOS: A Coded Computation Offloading Strategy for Satellite-...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Pang, Bo Gu, Shushi Zhang, Qinyu Zhang, Ning Xiang, Wei Harbin Inst Technol Shenzhen Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518052 Peoples R China Univ Windsor Windsor ON N9B 3P4 Canada La Trobe Univ Melbourne Vic 3086 Australia

ISBN: (纸本)9781728186160

Ultra-dense computation services are widely distributed in various application scenarios with the rapid development of artificial intelligence and machine learning. Relying on the existing ground cellular networks, it is challenging to satisfy the 6G vision of full coverage and massive machine connectivity. Satellite-terrestrial integrated network (STIN) has abundant computation resources and seamless coverage ability, which can be served as an effective supplementary for the task allocating in cellular networks. Nevertheless, STIN has the characteristic of architecture complexity, unavoidable stragglers and high economic costs. the rational computation resource allocation among distributed on-orbit satellites becomes an urge problem, synthesizing these drawbacks in STINs. In this paper, to address these issues, we attempt to design a coded computation offloading strategy (CCOS) to migrate ground ultra-dense computing tasks to distributed satellite constellations in space. Considering the effect of unpredictable computation resource occupation on satellites, we investigate two coded computation methods, i.e., maximum distance separable (MDS) code and rateless code, to resist the random stragglers occurring on satellite nodes. then, we formulate the optimization problem about minimizing the delay-energy tradeoff cost with different CCOSs under the tolerant time constraints, and obtain the optimal task offloading decisions (i.e., executing locations and coding parameters) using a proposed low-cost offloading decision searching algorithm (LODSA). Numerical simulation results show that, our coded computation strategies can significantly eliminate the effect of stragglers, and improve the cost performance obviously compared with the un-coded strategies in typical application cases.

关键词： satellite-terrestrial integrated network distributed computation offloading coded computation delay-energy cost optimization

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Multimedia mobile motion: Augmented handheld mobile device for motion control 17

Multimedia mobile motion: Augmented handheld mobile device f...

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17th international conference on distributed Multimedia Systems, DMS 2011

作者： Benatan, Matt Symonds, Ian Ng, Kia ICSRiM – University of Leeds School of Computing School of Electronic and Electrical Engineering and School of Music LeedsLS2 9JT United Kingdom

ISBN: (纸本)1891706306

As the popularity and capabilities of mobile devices increases, there are many more opportunities arising for innovative applications on the mobile platform. Over recent years a trend has emerged with the majority of handsets utilizing direct user interfaces such as touch screens. However, more current mobile platform developments have also resulted in the incorporation of a number of on-board sensor systems, such as accelerometers. this project looks to augment these existing onboard sensor technologies to facilitate user-centric motion detection, and from this develop a comprehensive gesture-based motion control system for the mobile platform. this paper discusses position detection techniques for the mobile platform utilizing additional sensor augmentation, and presenting a comparative study with a computer vision-based motion tracking technology to analyze the motion data. © 2011 by Knowledge Systems Institute Graduate School.

关键词： Accelerometers

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distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning 17

Distributed Task Offloading based on Multi-Agent Deep Reinfo...

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17th IEEE international conference on Mobility, Sensing and Networking (MSN)

作者： Hu, Shucheng Ren, Tao Niu, Jianwei Hu, Zheyuan Xing, Guoliang Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou 450001 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781665406680

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the 'curse of dimensionality' in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

关键词： task offloading mobile edge computing multi-agent deep reinforcement learning

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Predictive Placement of Geo-distributed Blockchain Nodes for Performance Guarantee 17

Predictive Placement of Geo-distributed Blockchain Nodes for...

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IEEE 17th international conference on Cloud computing (IEEE CLOUD)

作者： Lee, Junseok Yoo, Yeonho Yoo, Chuck Yang, Gyeongsik Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350368543;9798350368536

Blockchain-as-a-service (BaaS) in cloud datacenters is gaining widespread attention due to its high performance and privacy. However, existing BaaS solutions lack a method for deciding the proper placement of blockchain nodes across virtual machines in worldwide datacenters to achieve desired performance. Our motivating experiments show that transaction processing performance (TPS) varies similar to 31.6% depending on the placements. To provide an automatic placement solution for BaaS, we propose Cyan that predicts the TPS for blockchain node placements. Our evaluations on Google Cloud Platform demonstrate that Cyan improves the TPS guarantee similar to 2.39x compared to existing techniques.

关键词： Blockchain-as-a-service Hyperledger Fabric Node Placement Performance Guarantee

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