Mobile Information Systems

Machine Learning, Deep Learning and Optimization Techniques for Heterogeneous Sensor Information Integration


Publishing date
01 Feb 2022
Status
Closed
Submission deadline
01 Oct 2021

Lead Editor

1Fujian University of Technology, Fuzhou, China

2Chaoyang University of Technology, Taichung, Taiwan

3Guilin University of Electronic Technology, Guilin, China

4Swinburne University of Technology, Swinburne, Australia

This issue is now closed for submissions.
More articles will be published in the near future.

Machine Learning, Deep Learning and Optimization Techniques for Heterogeneous Sensor Information Integration

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Recently, various Internet of Things (IoT) based algorithms and applications have been developed by making use of a substantial amount of sensor data. For example, they have been used in mobile data reception for wireless sensor networks and have been widely applied in urban sustainable development. To optimize the utilization of data from multiple sources for decision-making, it is vital to properly interpret and reuse sensor data from different domains. Since most IoT devices operate in real-world environments, the quality of information and services in the IoT domain can vary over time. The heterogeneity of underlying devices and networks also makes it difficult to provide one-fits-all solutions to represent data and services that emerge from the IoT networks. Building sensor ontology and mapping sensor data to domain ontology is a feasible way to address these issues. Currently, several sensor ontologies have been developed to define the capabilities of the sensors and sensor networks (e.g., Commonwealth Scientific and Industrial Research Organisation (CSIRO) sensor ontology, OntoSensor, sensor webs for mission operations agent (SWAMO), marine metadata interoperability (MMI) device ontology, sensor model language (SensorML) processes, coastal environment sensor network (CESN), wireless sensor networks ontology (WISNO), agent-based middleware approach for mixed mode environments (A3ME) and Ontonym-Sensor.

Different sensor ontologies are developed and maintained independently by different ontology engineers. The same sensor concept might be represented with different terminologies, granularities, or contexts, which raises the heterogeneity problem to a higher level. The sensor ontology heterogeneity problem brings significant challenges to data integration, data fusion, and discovery mechanisms that require interoperable and machine-interpretable data and quality descriptions. Thus, there is an urgent need to provide mechanisms to integrate and exchange knowledge from heterogeneous sensor ontologies. In particular, we need to provide techniques to enable the processing, interpretation and sharing of sensor data from IoT which use different data models, or whose information is organized into different ontological schemes.

The aim of this Special Issue is to explore recent advancements in using Machine Learning techniques (e.g., support vector machine (SVM), decision tree (DT), random forest (RF), etc.). Submissions also discussing deep learning techniques (e.g., convolutional neural network (CNN), recurrent neural network (RNN), long-short term memory (LSTM) are also welcome. Finally, this Special Issue encourages submission including optimization techniques (e.g., evolutionary algorithms, swarm intelligence, etc.) for integrating heterogeneous sensor information and knowledge.

Potential topics include but are not limited to the following:

  • Machine Learning, deep learning and optimization-based sensor knowledge modelling and representation
  • Machine learning, deep learning and optimization-based sensors
  • Ontology engineering and sensor data annotation
  • Machine learning, deep learning and optimization-based sensor ontology alignment and linked sensor data integration
  • Machine learning, deep learning and optimization-based applications of semantic sensors data annotation and integration
  • Machine learning, deep learning and optimization-based applications of sensor data storage and management

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 4176101
  • - Research Article

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

Zhihe Wang | Yongbiao Li | ... | Xiaofen Wei
  • Special Issue
  • - Volume 2021
  • - Article ID 4912486
  • - Research Article

Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

Sapna Juneja | Abhinav Juneja | ... | Sandeep Kautish
  • Special Issue
  • - Volume 2021
  • - Article ID 5868261
  • - Research Article

Intelligent Network Office System Based on Cloud Computing and Machine Learning

Yinfang Guo | Yanjun Guo
  • Special Issue
  • - Volume 2021
  • - Article ID 6466051
  • - Research Article

Remote Sensing Data Detection Based on Multiscale Fusion and Attention Mechanism

Min Huang | Cong Cheng | Gennaro De Luca
  • Special Issue
  • - Volume 2021
  • - Article ID 3688228
  • - Research Article

Optimal Predictive Control Method of PWM Rectifiers Based on Artificial Intelligence

Yue Liu | Guojun Tan
  • Special Issue
  • - Volume 2021
  • - Article ID 6926013
  • - Research Article

University Classroom Teaching Model Based on Decision Tree Analysis and Machine Learning

Yanqi Guo
  • Special Issue
  • - Volume 2021
  • - Article ID 5169460
  • - Research Article

Research on Dynamic Path Planning of Mobile Robot Based on Improved DDPG Algorithm

Peng Li | Xiangcheng Ding | ... | Ricardo Cajo
  • Special Issue
  • - Volume 2021
  • - Article ID 9409556
  • - Research Article

Home Robot Control System Based on Internet of Things and Fuzzy Control

Qinghua Song | Li Li
  • Special Issue
  • - Volume 2021
  • - Article ID 8501990
  • - Research Article

Lightweight Neural Network-Based Viewport Prediction for Live VR Streaming in Wireless Video Sensor Network

Xiaolei Chen | Baoning Cao | Ishfaq Ahmad
  • Special Issue
  • - Volume 2021
  • - Article ID 4482578
  • - Research Article

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Xue Han | Yan Zhao | ... | Zun Liu
Mobile Information Systems
 Journal metrics
See full report
Acceptance rate51%
Submission to final decision41 days
Acceptance to publication22 days
CiteScore2.300
Journal Citation Indicator0.420
Impact Factor1.863
 Submit

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.