Distributed Fog Computing Based on Batched Sparse Codes for Industrial Control

Yue, Jing; Xiao, Ming; Pang, Zhibo

doi:10.1109/tii.2018.2857203

Cited by 22 publications

(19 citation statements)

References 19 publications

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“…Different file system copy placement strategies are different. For example, HDFS file systems in IoT clusters use rack-aware data copy placement strategies [42], [43]. When the client uploads a file, the file system first places the data block on the node where the client is located, and then uses Pipline to store the second copy on other nodes in the same rack as the node where the client is located.…”

Section: Data Block Placement Methods a Rack-aware Based Date Plmentioning

confidence: 99%

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

Sun

et al. 2020

IEEE Access

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In order to addressing the issues of data matching deviation and load imbalance during the data scheduling process of the Internet of Things, Sensing Cloud Computing in IoT: A Novel Data Scheduling Optimization (SCC-DSO) Algorithm is proposed in this paper. First, according to the processing capacity of the IoT working node, a data placement algorithm is designed to reasonably place the input data of the job. Second, the data scheduling queue is optimized based on the data block storage location information to reduce non-local execution of data scheduling. Furthermore, a data prefetching method is designed to pull the data required for non-local data scheduling in advance, and shorten the waiting time of the task for input data. Finally, simulation experiment evaluated by the task localization execution rate and response time is performed. The effectiveness and stability of the algorithm is verified compared with other algorithms. INDEX TERMS Sensing-cloud computing, Internet of Things, data scheduling, optimization algorithm.

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Section: Data Block Placement Methods a Rack-aware Based Date Plmentioning

confidence: 99%

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

Sun

et al. 2020

IEEE Access

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“…In WSNs, the coverage areas of sensor nodes can overlap to achieve the requirement of covering the monitored area without vulnerabilities [55]. From the perspective of saving node energy and extending the network lifetime, it is necessary to cover the monitoring area with the least number of nodes.…”

Section: A Assumptions and Basic Definitionsmentioning

confidence: 99%

CoC-SCS: Cooperative-Optimization Coverage Algorithm Based on Sensor Cloud Systems in Intelligent Computing

Sun

2020

IEEE Access

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the coverage of traditional Wireless Sensor Networks (WSNs) is limited by node energy and data redundancy, which forces WSNs to be interrupted abnormally. Therefore, Cooperative-optimization Coverage Algorithm based on Sensor Cloud Systems in Intelligent Computing (CoC-SCS) is proposed. First, the algorithm determines the location information of the Focus Target Nodes (FTNs), and uses the Genetic Algorithm (GA) to give the node path planning. Second, the mutation parameters and the controllable threshold parameters are used in order to control the event domain nodes. Optimizing clustering makes the clustering of the nodes to be more uniform, so that it can improve the search ability of the global target nodes and even reduce the nodes energy consumption. Third, the adaptation function covers the continuity of the covered target positions and the monitoring range of the nodes. Optimization is also performed to achieve the goals of increasing network coverage and extending the network lifetime. Finally, simulation results show that the proposed CoC-SCS algorithm compared with other three algorithms in this paper has improved 11.27% and 16.35% on average in terms of network coverage, network lifetime, etc., and 13.95% in terms of network energy overhead, thereby we can further verify that CoC-SCS algorithm has strong stability and effectiveness.

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“…Coding has been applied to distributed fog computing and machine learning for dealing with the problem of stragglers [4] and reducing the usage of computation and communication resources [5]. For coded distributed machine learning [2], matrix multiplication [6], [7] and gradient descent [8], [9] have attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Coded Decentralized Learning With Gradient Descent for Big Data Analytics

Yue¹,

Xiao²

2020

IEEE Commun. Lett.

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Machine learning is an effective technique for big data analytics. We focus on the study of big data analytics with decentralized learning in large-scale networks. Fountain codes are applied to the decentralized learning process to reduce communication load for exchanging intermediate learning parameters among fog nodes. Two scenarios, i.e., disjoint datasets and overlapping datasets, are analyzed. Comparison results show that communication load can be reduced significantly by the Fountain-based scheme for large-scale networks, especially when the quality of communication links is relatively bad and/or the number of fog nodes is large.

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Distributed Fog Computing Based on Batched Sparse Codes for Industrial Control

Cited by 22 publications

References 19 publications

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

CoC-SCS: Cooperative-Optimization Coverage Algorithm Based on Sensor Cloud Systems in Intelligent Computing

Coded Decentralized Learning With Gradient Descent for Big Data Analytics

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