Optimization of Dynamic Data Types in Embedded Systems using DEVS/SOA-based Modeling and Simulation

2008 ◽  
Author(s):  
José L. Risco-Martín ◽  
Saurabh Mittal ◽  
David Atienza ◽  
J. Ignacio Hidalgo ◽  
Juan Lanchares
Keyword(s):  
Embedded Systems ◽  
Modeling And Simulation ◽  
Data Types ◽  
Dynamic Data

scholarly journals A parallel evolutionary algorithm to optimize dynamic data types in embedded systems

2008 ◽  
Vol 12 (12) ◽  
pp. 1157-1167 ◽  
Author(s):  
José L. Risco-Martín ◽  
David Atienza ◽  
J. Ignacio Hidalgo ◽  
Juan Lanchares
Keyword(s):  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Data Types ◽  
Dynamic Data ◽  
Parallel Evolutionary Algorithm

scholarly journals Dynamic Data Driven Methodologies for Multiphysics System Modeling and Simulation

2005 ◽  
pp. 616-623 ◽  
Author(s):  
J. Michopoulos ◽  
C. Farhat ◽  
E. Houstis ◽  
P. Tsompanopoulou ◽  
H. Zhang ◽  
...  
Keyword(s):  
Modeling And Simulation ◽  
System Modeling ◽  
Data Driven ◽  
Dynamic Data

Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

2020 ◽  
Vol 10 (2) ◽  
pp. 21-39
Author(s):  
Archana Yashodip Chaudhari ◽  
Preeti Mulay
Keyword(s):  
Smart Grids ◽  
Clustering Algorithm ◽  
Incremental Clustering ◽  
Clustering Method ◽  
Data Types ◽  
Dynamic Data ◽  
Real Dataset ◽  
Processing Power ◽  
Microsoft Azure ◽  
Socioeconomic Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

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