Wireless Communications and Mobile Computing

Advances in Granular Computing Methods for Evaluating IoT Platforms


Publishing date
01 Jan 2023
Status
Published
Submission deadline
19 Aug 2022

Lead Editor

1Shanxi University, Taiyuan, China

2Southwest University, Chongqing, China

3Chongqing University, Chongqing, Chongqing, China

4University of Huddersfield , Huddersfield, UK


Advances in Granular Computing Methods for Evaluating IoT Platforms

Description

As a new computing paradigm, the Internet of Things (IoT) acts as the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. In the last few years, the number of devices operating in wireless IoT has experienced tremendous growth, thus wireless IoT is taking center stage as devices are expected to form a major portion of wireless communications and mobile computing. In wireless IoT applications, millions of developers are required to select and evaluate an appropriate wireless IoT platform considering their actual demands. Therefore, how to select and evaluating appropriate ones are crucial during the development and deployment process of wireless IoT applications.

Granular computing is a newly emerged concept and computing paradigm in the field of artificial intelligence, which mainly focuses on the formation, processing, and communicating information granules. Essentially, information granules arise in the process of abstraction of data and derivation of knowledge from various real-world applications. Constructed within several specific theories including fuzzy sets, rough sets, three-way decisions, formal concept analysis, quotient space, cloud models, etc., granular computing theory and its applications have been deeply explored by scholars and practitioners over the past decades. Inspired by the merits of granular computing, individuals can observe, analyze and solve many complicated problems from diverse information granules, which is conducive to enhancing the validity and efficiency of problem-solving from the perspective of intelligent information depiction and processing. Therefore, exploring advances in granular computing methods is likely to provide an efficient way for evaluating wireless IoT platforms.

This Special Issue aims to bring together original research and review articles discussing advances in granular computing methods for evaluating wireless IoT platforms. Experimental and theoretical studies for the evaluation of wireless IoT platforms are encouraged.

Potential topics include but are not limited to the following:

  • Theory and methods of rough set theory in the evaluation of wireless IoT platforms
  • Three-way decisions in the evaluation of wireless IoT platforms
  • Fuzzy set and logic in applications of wireless communication networks and mobile computing
  • Formal concept analysis in the evaluation of wireless IoT platforms
  • Cloud models in the evaluation of wireless IoT platforms
  • Uncertainty in granular computing and complex data processing in wireless environments
  • Soft computing and its applications in wireless communication networks
  • Intelligent decision making in the evaluation of wireless IoT platforms
  • Knowledge discovery and data mining in the evaluation of wireless IoT platforms
  • Machine learning techniques in the evaluation of wireless IoT platforms

Articles

  • Special Issue
  • - Volume 2023
  • - Article ID 3267820
  • - Research Article

Exploring the Structure of IoT Data: A Symbolic Analysis Perspective

Yinghua Shen | Witold Pedrycz | ... | Yuan Chen
  • Special Issue
  • - Volume 2022
  • - Article ID 5284804
  • - Research Article

Multigranulation-Based Granularity Selection for Intuitionistic Fuzzy Weighted Neighborhood IoT Data

Wentao Li | Yue Tang | ... | Tao Zhan
  • Special Issue
  • - Volume 2022
  • - Article ID 6360553
  • - Review Article

IoT Security Review: A Case Study of IIoT, IoV, and Smart Home

Jinnan Ma | Xuekui Shangguan | Ying Zhang
  • Special Issue
  • - Volume 2022
  • - Article ID 8156917
  • - Research Article

Optimal Granularity Selection for Indoor Localization Detection with Wireless IoT Networks

Feng Cao | Jing Zhang | ... | Zhiguo Hu
  • Special Issue
  • - Volume 2022
  • - Article ID 2963195
  • - Research Article

Classifier Adaptation Based on Modified Label Propagation for Unsupervised Domain Adaptation

Yongjie Du | Deyun Zhou | ... | Yu Lei
  • Special Issue
  • - Volume 2022
  • - Article ID 3766810
  • - Research Article

Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce Network

HangYuan Du | Duo Li | WenJian Wang
  • Special Issue
  • - Volume 2022
  • - Article ID 7490656
  • - Research Article

Relative Entropy-Based Similarity for Patterns in Graph Data

Shihu Liu | Li Deng | ... | Xueyu Ma
  • Special Issue
  • - Volume 2022
  • - Article ID 9641143
  • - Review Article

The Development of Privacy Protection Standards for Smart Home

Donghang Liu | Chensi Wu | ... | Qifeng Sun
  • Special Issue
  • - Volume 2022
  • - Article ID 8081177
  • - Research Article

A Generative Clustering Ensemble Model and Its Application in IoT Data Analysis

Hangyuan Du | Wenjian Wang | ... | Jinsong Feng
  • Special Issue
  • - Volume 2022
  • - Article ID 9642617
  • - Research Article

Three-Way Group Decisions with Incomplete Spherical Fuzzy Information for Treating Parkinson’s Disease Using IoMT Devices

Chao Zhang | Jingjing Zhang
Wireless Communications and Mobile Computing
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Acceptance rate11%
Submission to final decision194 days
Acceptance to publication66 days
CiteScore2.300
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Impact Factor-

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