Complexity

Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems


Publishing date
01 Mar 2019
Status
Published
Submission deadline
19 Oct 2018

Lead Editor

1University of Alicante, Alicante, Spain

2Magnus Johnsson AI Research AB, Lund, Sweden

3Gdansk University of Technology, Gdansk, Poland


Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems

Description

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, to be able to use it at a later stage. However, there are important limitations for a large-scale achievement in this revolution. Furthermore, IoT allows developing big data architectures based on services. IoT technologies are available and every day there are new ones that arrive. Some of them are sensors, RFID, GPS, and many other types of smart devices.

However, this potential opportunity is often not exploited. There are some reasons like the excessively big interval between the data collection and the capability to process and analyze it. Another reason is the need of new models of design for suitable big data architecture. To effectively synthesize big data and communicate among devices using IoT, machine learning techniques are employed. Machine learning extracts meaning from big data using various techniques which include regression analysis, clustering, Bayesian methods, decision trees and random forests, support vector machines, reinforcement learning, ensemble learning, and deep learning. Currently, the quantitative data description of complex huge systems is no longer exclusively experimental sample data but the full overview data for the entire state. In this scenario, data analysis should endorse complex scientific intelligent analysis method for modeling and simulating. It has also to utilize and constantly optimize big data for machine learning and analyze and study the self-organizing and evolving rules of complex systems.

The purpose of this special issue is to publish high-quality research papers as well as review articles addressing recent advances in modeling, formal methods, and complexity handling of architectures, big data, and machine learning techniques for complex Internet of Things systems. Theoretical studies and state-of-the-art practical applications are welcome for submission.

Potential topics include but are not limited to the following:

  • Complexity modeling and formalization of architectures for big data
  • Regression, classification, and clustering for complex big data analysis
  • Deep learning and artificial neural network for optimizing big data
  • Data integration in big data environments
  • Genetic algorithm based data integration and management for Hadoop ecosystems
  • Data virtualization, ELT, or ETL for complex data integration
  • Cellular automata model of data mining over the cloud
  • Data mining with big data: new machine learning algorithms
  • MapReduce algorithms for complex IoT
  • Big data for open data and privacy protection for complex IoT
  • Complex modeling and management in IoT domains
  • Chaotic approach for stream mining in IoT
  • Cloud computing based evolutionary game for Internet of Things systems
  • Genetic algorithms analysis for Mobile Cloud Computing systems
  • Complexity modeling and decision-making methods for complex IoT applications
  • Internet of Things and complexity handling
  • Chaos theory, information theory, genetic and biologically inspired algorithms, cellular automata, neural networks, intelligent search algorithms, and evolutionary game theory in complex manufacturing applications
  • Tools and techniques for solving complex machine learning problems
  • Actual versus perceived complexity in knowledge representation of big data
  • Handling complexities with big data as the new technology
  • Development of science of complexity

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 4184708
  • - Editorial

Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems

David Gil | Magnus Johnsson | ... | Julian Szymanski
  • Special Issue
  • - Volume 2019
  • - Article ID 2653512
  • - Research Article

Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization

Qinghua Gu | Xuexian Li | Song Jiang
  • Special Issue
  • - Volume 2019
  • - Article ID 4592902
  • - Review Article

Review of the Complexity of Managing Big Data of the Internet of Things

David Gil | Magnus Johnsson | ... | Julian Szymański
  • Special Issue
  • - Volume 2019
  • - Article ID 2686378
  • - Review Article

Recent Progress of Anomaly Detection

Xiaodan Xu | Huawen Liu | Minghai Yao
  • Special Issue
  • - Volume 2019
  • - Article ID 7206096
  • - Research Article

Secure UAV-Based System to Detect Small Boats Using Neural Networks

Moisés Lodeiro-Santiago | Pino Caballero-Gil | ... | Cándido Caballero-Gil
  • Special Issue
  • - Volume 2019
  • - Article ID 2694126
  • - Research Article

Fuzzy Linguistic Protoforms to Summarize Heart Rate Streams of Patients with Ischemic Heart Disease

María Dolores Peláez-Aguilera | Macarena Espinilla | ... | Javier Medina
  • Special Issue
  • - Volume 2018
  • - Article ID 8679579
  • - Research Article

Fully Flexible Parallel Merge Sort for Multicore Architectures

Zbigniew Marszałek | Marcin Woźniak | Dawid Połap
  • Special Issue
  • - Volume 2018
  • - Article ID 4867607
  • - Research Article

Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element

Francisco Maciá Pérez | Jose Vicente Berna Martienz | ... | Miguel Abreu Ortega
  • Special Issue
  • - Volume 2018
  • - Article ID 9280787
  • - Research Article

Case-Based Reasoning: The Search for Similar Solutions and Identification of Outliers

P. S. Szczepaniak | A. Duraj
Complexity
 Journal metrics
Acceptance rate37%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore3.200
Impact Factor2.462
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