Model-Driven Approach to Smart Grid Stability Prediction in Neo4j
Published in: International Conference on Science, Technology and Management in Energy (eNergetics)
Dec 08, 2021
Stability is of utmost importance when it comes to smart grid infrastructures. Dramatic parameter variations and fluctuations can lead to wrong decisions, which could lead to fatal consequences. In this paper, we propose a model-driven methodology for highly automated machine learning approach to smart grid stability prediction. Stability prediction is treated as binary classification problem and implemented relying on Neo4j graph database's Graph Data Science Library (GDS). The proposed framew...
A Utility to Transform CSV Data into EMF
Published in: International Conference on Software Defined Systems (SDS)
Dec 06, 2021
With the era of data evolution, enterprises increasingly depend on data utilization tools to import or export data from various data sources. Traditionally, enterprises archive such data into row formats, commonly in CSV files. The flat representation of these files has become an excessive burden to opt the right approach for developing and designing applications that structurally meet business needs. CASE (Computer-Aided Software Environment) tools have been praised by domain experts to build ...
A Model-Driven Approach for Solving the Software Component Allocation Problem
Published in: Algorithms
Dec 01, 2021
The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units coul...
Blockchain-Based Authentication in Internet of Vehicles: A Survey
Published in: Sensors
Nov 27, 2021
Model-driven multi-objective optimization approach to 6G network planning
Published in: International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS)
Oct 20, 2021
Ultra high-speed and reliable next-generation 6G mobile networks are recognized as key enablers for many innovative scenarios in smart cities – from vehicular use cases and surveillance to healthcare. However, deployment of such network requires tremendous amount of time and involves various costs. For that reason, optimal network planning is of utmost importance for development of 6G mobile networks in smart cities. In this paper, we explore the potential of multi-objective linear optimization...