sensors-logo

Journal Browser

Journal Browser

Edge Computing in IoT Networks Based on Artificial Intelligence

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 3064

Special Issue Editors

1. AIR Institute, Deep Tech Lab, Paseo de Belén 9A, 47011 Valladolid, Spain
2. BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
3. Higher School of Engineering and Technology, International University of La Rioja (UNIR), Logroño, Spain
Interests: Internet of Things; edge computing; distributed ledger and blockchain technologies; embedded systems; indoor location systems; cloud computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; neural networks; evolutionary computation; distributed computing; transfer learning; swarm robotics; collective behavior; smart grids

Special Issue Information

Dear Colleagues,

Edge computing and Internet of Things (IoT) are synergic disruptive computational technologies that are already having a massive impact in a wide span of application areas, including connected Industry 4.0, precision agriculture and smart farming, robotics, transportation, energy and smart grids, health, and Fintech, among others. Furthermore, synergies between edge and IoT in such applications are enhanced with more cohesive functioning and improved functionalities by means of advanced processing based on artificial intelligence (AI) methods: for example, edge architectures for processing data from IoT devices can be leveraged by federated learning methods (both hierarchical and P2P), to optimize local AI analysis models while providing enhanced privacy by restricting the communication of raw data outside of edge nodes. These synergies between such edge techniques and architectures in IoT networks using AI have set exceptional grounds for impactful advances in many areas, which motivates this Special Issue, with the goal of providing a platform for researchers to contribute findings on these technologies and establish synergies to further advance these joint disciplines and their application. Authors are invited to submit high-quality research articles on topics including (but not restricted to):

  • Internet of Things (IoT) and Industrial IoT;
  • Sensor and actuator distributed networks;
  • Edge computing, edge architectures;
  • Edge continuum (edge/fog/cloud computing);
  • Machine learning for IoT sensor networks;
  • Federated learning;
  • Knowledge representation and AI processing in IoT networks;
  • Distributed learning AI methods and transfer learning;
  • Industrial applications leveraging edge, IoT and AI;
  • Adversarial learning and security in edge architectures and IoT networks.

Dr. Ricardo S. Alonso Rincón
Dr. Iñaki Fernández Pérez
Dr. Sara Rodriguez
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

  • edge computing
  • Internet of Things
  • artificial intelligence
  • federated learning
  • distributed learning

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 2471 KiB  
Article
Digital Twin Platform for Water Treatment Plants Using Microservices Architecture
by Carlos Rodríguez-Alonso, Iván Pena-Regueiro and Óscar García
Sensors 2024, 24(5), 1568; https://doi.org/10.3390/s24051568 - 29 Feb 2024
Viewed by 698
Abstract
The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing [...] Read more.
The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant’s systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making. Full article
(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)
Show Figures

Figure 1

20 pages, 4608 KiB  
Article
A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks
by Aakanksha Sharma, Venki Balasubramanian and Joarder Kamruzzaman
Sensors 2024, 24(4), 1216; https://doi.org/10.3390/s24041216 - 14 Feb 2024
Viewed by 488
Abstract
With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The [...] Read more.
With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward–punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach—tDQN—effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss. Full article
(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)
Show Figures

Figure 1

16 pages, 3076 KiB  
Article
Statement Recognition of Access Control Policies in IoT Networks
by Li Ma, Zexian Yang, Zhaoxiong Bu, Qidi Lao and Wenyin Yang
Sensors 2023, 23(18), 7935; https://doi.org/10.3390/s23187935 - 16 Sep 2023
Viewed by 712
Abstract
Access Control Policies (ACPs) are essential for ensuring secure and authorized access to resources in IoT networks. Recognizing these policies involves identifying relevant statements within project documents expressed in natural language. While current research focuses on improving recognition accuracy through algorithm enhancements, the [...] Read more.
Access Control Policies (ACPs) are essential for ensuring secure and authorized access to resources in IoT networks. Recognizing these policies involves identifying relevant statements within project documents expressed in natural language. While current research focuses on improving recognition accuracy through algorithm enhancements, the challenge of limited labeled data from individual clients is often overlooked, which impedes the training of highly accurate models. To address this issue and harness the potential of IoT networks, this paper presents FL-Bert-BiLSTM, a novel model that combines federated learning and pre-trained word embedding techniques for access control policy recognition. By leveraging the capabilities of IoT networks, the proposed model enables real-time and distributed training on IoT devices, effectively mitigating the scarcity of labeled data and enhancing accessibility for IoT applications. Additionally, the model incorporates pre-trained word embeddings to leverage the semantic information embedded in textual data, resulting in improved accuracy for access control policy recognition. Experimental results substantiate that the proposed model not only enhances accuracy and generalization capability but also preserves data privacy, making it well-suited for secure and efficient access control in IoT networks. Full article
(This article belongs to the Special Issue Edge Computing in IoT Networks Based on Artificial Intelligence)
Show Figures

Figure 1

Back to TopTop