Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Self-Organized Computing and Network Management for Intelligent Internet of Things
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Self-Organized Computing and Network Management for Intelligent Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 9669

Special Issue Editors

Dr. Wenbo Wang

E-Mail Website
Guest Editor
Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
Interests: Internet of Things; beyond 5G; multi-agent learning; game theory
Dr. Dinh Thai Hoang

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, Faculty of Electrical and Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: machine learning; ambient backscatter communications; IRS; edge intelligence; cybersecurity; IoT; and 5G/6G networks
Dr. Shaohan Feng

E-Mail Website
Guest Editor
Department of Communications and Networks, Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Connexis North, Singapore 138632, Singapore
Interests: wireless communications; wireless security; covert communications; computer networks; Internet of Things
Dr. Shimin Gong

E-Mail Website
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: Internet of Things; backscatter communications; wireless powered transfer; wireless resource optimization, machine learning in wireless systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yingbo Huang

E-Mail Website
Guest Editor
Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
Interests: adaptive control; nonlinear system control and application; parameter estimation; transient performance improvement

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) aims to enable ubiquitous wireless connections among various smart sensors, actuators and intelligent controllers, so as to integrate their functions and thereafter provide numerous novel services in both industrial and consumer domains. From the perspective of node organization, IoT can be envisioned as a highly complex network of resource-constrained devices, which demands the efficient delivery of massive data from the sensor end, intelligent data/decision processing at the service provider and controller end, and secured signaling for execution at the actuator end. As massive devices are densely deployed and IoT connections become dominated by machine terminals in proximity, infrastructure-based communications and network organization frequently face problems such as heavy loads on the centralized coordinator nodes (e.g., access points) and weak scalability. Moreover, enabling devices to directly communicate and act without relying on a fixed network infrastructure or full cloud connectivity is of the utmost importance from the perspectives of resource/cost efficiency and reliability-latency guarantee. Additionally, distributed control based on IoT technologies is promising because it can utilize the information of neighboring nodes through a sparse communication network and prevent potential risks, including cascading failures and collapses of the entire system. Thus, the modeling and design of self-organized mechanisms toward efficient/scalable network management and intelligent system control in IoTs are of great interest to both academic and industry communities.

The goal of this Special Issue is to solicit high-quality scholarly contributions, thereby providing insights and novel solutions in areas including (but not limited to) the following:

  • Novel protocols and mechanisms for self-organized access and resource management in IoTs.
  • Distributed and federated learning for data processing and networked control in IoTs.
  • Impact of self-organization on communication efficiency, data security and service provision in IoTs.
  • Game-theoretic mechanism design incentivizing self-organization for resource-constrained IoT applications.
  • Self-organized computing for wireless sensor and actuator networks.

Dr. Wenbo Wang
Dr. Dinh Thai Hoang
Dr. Shaohan Feng
Dr. Shimin Gong
Dr. Yingbo Huang
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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
  • access control and resource management
  • self-organization
  • IoT-based monitoring and control
  • decision support for IoTs
  • intelligent control for automation systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 2739 KiB  
Article
IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion
by Yi Chen, Yunfeng Yan, Xianbo Wang and Yi Zheng
Sensors 2023, 23(14), 6531; https://doi.org/10.3390/s23146531 - 19 Jul 2023
Cited by 5 | Viewed by 1727
Abstract
Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect [...] Read more.
Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON. Full article
(This article belongs to the Special Issue Self-Organized Computing and Network Management for Intelligent Internet of Things)
►▼ Show Figures

Figure 1

22 pages, 4752 KiB  
Article
Deep Reinforcement Learning for Joint Trajectory Planning, Transmission Scheduling, and Access Control in UAV-Assisted Wireless Sensor Networks
by Xiaoling Luo, Che Chen, Chunnian Zeng, Chengtao Li, Jing Xu and Shimin Gong
Sensors 2023, 23(10), 4691; https://doi.org/10.3390/s23104691 - 12 May 2023
Cited by 8 | Viewed by 2613
Abstract
Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in [...] Read more.
Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance. Full article
(This article belongs to the Special Issue Self-Organized Computing and Network Management for Intelligent Internet of Things)
►▼ Show Figures

Figure 1

20 pages, 4649 KiB  
Article
A Reinforcement Learning Handover Parameter Adaptation Method Based on LSTM-Aided Digital Twin for UDN
by Jiao He, Tianqi Xiang, Yixin Wang, Huiyuan Ruan and Xin Zhang
Sensors 2023, 23(4), 2191; https://doi.org/10.3390/s23042191 - 15 Feb 2023
Cited by 5 | Viewed by 1907
Abstract
Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to [...] Read more.
Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance. Full article
(This article belongs to the Special Issue Self-Organized Computing and Network Management for Intelligent Internet of Things)
►▼ Show Figures

Figure 1

12 pages, 5394 KiB  
Article
An Efficient Self-Organized Detection System for Algae
by Xingrui Gong, Chao Ma, Beili Sun and Junyi Zhang
Sensors 2023, 23(3), 1609; https://doi.org/10.3390/s23031609 - 1 Feb 2023
Cited by 6 | Viewed by 2290
Abstract
Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although [...] Read more.
Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 μm/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms. Full article
(This article belongs to the Special Issue Self-Organized Computing and Network Management for Intelligent Internet of Things)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop