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Actuators
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Sensing and Actuating Tasks in IoT Environments
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Sensing and Actuating Tasks in IoT Environments

A special issue of Actuators (ISSN 2076-0825).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 11545

Special Issue Editors

Prof. Dr. Do-Hyeun Kim

E-Mail Website
Guest Editor
Department of Computer Engineering, Jeju National University, Jeju City, Korea
Interests: Internet of Things; edge computing; artificial intelligence; deep learning; optimization; task scheduling; complex problem solving
Dr. Wenquan Jin

E-Mail Website
Guest Editor
Computer Engineering Department, Jeju National University, Jeju 63243, Korea
Interests: edge computing; internet of things; transparent computing; computational offloading; smart spaces; blockchain; federated learning; swarm learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The Internet of Things (IoT) is an emerging paradigm that inspires industries to develop intelligent and autonomous systems based on Internet-connected devices. The IoT comprises heterogeneous devices, applications, and platforms using multiple communication technologies to connect the Internet to ubiquitously provide seamless services. Leveraging cloud computing, the IoT can be supported to apply not only large-scale and personalized data, but also artificial-intelligence (AI) algorithms based on offloading AI approaches to high-performance servers to work with huge volumes of data in the cloud. Through the task scheduling of IoT services, various continuous scenarios can be deployed for controlling actuators to update the IoT environment.

Contributions from all fields related to Internet of Things are welcome to this Special Issue, and particularly the following:

  • theory, applications, case studies, and project reports related with emerging IoT standard protocols, frameworks, and platforms;
  • IoT and edge-computing solutions in smart spaces such as homes, buildings, factories, farms, and cities;
  • distributed edge computing for compuational offloading;
  • real-time inference and prediction approaches in edge computing;
  • controlling based on AI approaches;
  • energy optimization in smart spaces;
  • task scheduling for intelligent IoT services;
  • complex problem solving based on IoT and edge computing.

Prof. Dr. Do-Hyeun Kim
Dr. Wenquan Jin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • edge computing
  • task scheduling
  • control
  • optimization
  • deep learning
  • complex problem solving

Published Papers (4 papers)

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Research

16 pages, 5171 KiB  
Article
Environment Optimization Scheme Based on Edge Computing Using PSO for Efficient Thermal Comfort Control in Resident Space
by Rongxu Xu, Wenquan Jin and Dohyeun Kim
Actuators 2021, 10(9), 241; https://0-doi-org.brum.beds.ac.uk/10.3390/act10090241 - 17 Sep 2021
Cited by 5 | Viewed by 1879
Abstract
With the fast development of infrastructure and communication technology, the Internet of Things (IoT) has become a promising field. Ongoing research is looking at the smart home environment as the most promising sector that adopts IoT and cloud computing to improve resident live [...] Read more.
With the fast development of infrastructure and communication technology, the Internet of Things (IoT) has become a promising field. Ongoing research is looking at the smart home environment as the most promising sector that adopts IoT and cloud computing to improve resident live experiences. The IoT and cloud-dependent smart home services related to recent researches have security, bandwidth issues, and a lack of concerning thermal comfort of residents. In this paper, we propose an environment optimization scheme based on edge computing using Particle Swarm Optimization (PSO) for efficient thermal comfort control in resident space to overcome the aforementioned limitations of researches on smart homes. The comfort level of a resident in a smart home is evaluated by Predicted Mean Vote (PMV) that represents the thermal response of occupants. The PSO algorithm combined with PMV to improve the accuracy of the optimization results for efficient thermal comfort control in a smart home environment. We integrate IoT with edge computing to upgrade the capabilities of IoT nodes in computing power, storage space, and reliable connectivity. We use EdgeX as an edge computing platform to develop a thermal comfort considering PMV-based optimization engine with a PSO algorithm to generate the resident’s friendly environment parameters and rules engine to detects the environmental change of the smart home in real-time to maintain the indoor environment thermal comfortable. For evaluating our proposed system that maintenance resident environment with thermal comfort index based on PSO optimization scheme in smart homes, we conduct the comparison between the real data with optimized data, and measure the execution times of optimization function. From the experimental results, when our proposed system is applied, it satisfies thermal comfort and consumes energy more stably. Full article
(This article belongs to the Special Issue Sensing and Actuating Tasks in IoT Environments)
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17 pages, 5178 KiB  
Article
Distributed Secure Edge Computing Architecture Based on Blockchain for Real-Time Data Integrity in IoT Environments
by Rongxu Xu, Lei Hang, Wenquan Jin and Dohyeun Kim
Actuators 2021, 10(8), 197; https://0-doi-org.brum.beds.ac.uk/10.3390/act10080197 - 13 Aug 2021
Cited by 11 | Viewed by 3184
Abstract
The traditional cloud-based Internet of Things (IoT) architecture places extremely high demands on computers and storage on cloud servers. At the same time, the strong dependence on centralized servers causes major trust problems. Blockchain provides immutability, transparency, and data encryption based on safety [...] Read more.
The traditional cloud-based Internet of Things (IoT) architecture places extremely high demands on computers and storage on cloud servers. At the same time, the strong dependence on centralized servers causes major trust problems. Blockchain provides immutability, transparency, and data encryption based on safety to solve these problems of the IoT. In this paper, we present a distributed secure edge computing architecture using multiple data storages and blockchain agents for the real-time context data integrity in the IoT environment. The proposed distributed secure edge computing architecture provides reliable access and an unlimited repository for scalable and secure transactions. The architecture eliminates traditional centralized servers using an edge computing framework that represents cloud computing for computer and security issues. Also, blockchain-based edge computing-compatible IoT design is supported to achieve the level of security and scalability required for data integrity. Furthermore, we present the blockchain agent to provide internetworking between blockchain networks and edge computing. For experimenting with the proposed architecture in the IoT environment, we implement and perform a concrete IoT environment based on the EdgeX framework and Hyperledger Fabric. The evaluation results are collected by measuring the performance of the edge computing and blockchain platform based on service execution time to verify the proposed architecture in the IoT environment. Full article
(This article belongs to the Special Issue Sensing and Actuating Tasks in IoT Environments)
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18 pages, 2628 KiB  
Article
A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes
by Sehrish Malik, Wafa Shafqat, Kyu-Tae Lee and Do-Hyeun Kim
Actuators 2021, 10(4), 84; https://0-doi-org.brum.beds.ac.uk/10.3390/act10040084 - 20 Apr 2021
Cited by 8 | Viewed by 2419
Abstract
In today’s world, smart buildings are considered an overarching system that automates a building’s complex operations and increases security while reducing environmental impact. One of the primary goals of building management systems is to promote sustainable and efficient use of energy, requiring coherent [...] Read more.
In today’s world, smart buildings are considered an overarching system that automates a building’s complex operations and increases security while reducing environmental impact. One of the primary goals of building management systems is to promote sustainable and efficient use of energy, requiring coherent task management and execution of control commands for actuators. This paper proposes a predictive-learning framework based on contextual feature selection and optimal actuator control mechanism for minimizing energy consumption in smart buildings. We aim to assess multiple parameters and select the most relevant contextual features that would optimize energy consumption. We have implemented an artificial neural network-based particle swarm optimization (ANN-PSO) algorithm for predictive learning to train the framework on feature importance. Based on the relevance of attributes, our model was also capable of re-adding features. The extracted features are then applied as input parameters for the training of long short-term memory (LSTM) and optimal control module. We have proposed an objective function using a velocity boost-particle swarm optimization (VB-PSO) algorithm that reduces energy cost for optimal control. We then generated and defined the control tasks based on the fuzzy rule set and optimal values obtained from VB-PSO. We compared our model’s performance with and without feature selection using the root mean square error (RMSE) metric in the evaluation section. This paper also presents how optimal control can reduce energy cost and improve performance resulting from lesser learning cycles and decreased error rates. Full article
(This article belongs to the Special Issue Sensing and Actuating Tasks in IoT Environments)
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17 pages, 3576 KiB  
Article
Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory
by Sehrish Malik and DoHyeun Kim
Actuators 2021, 10(2), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/act10020027 - 31 Jan 2021
Cited by 5 | Viewed by 2593
Abstract
The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital [...] Read more.
The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate. Full article
(This article belongs to the Special Issue Sensing and Actuating Tasks in IoT Environments)
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