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Artificial Intelligence and Advanced Technologies for Smart Cities and Environmental Sensing

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 6918

Special Issue Editors

Prof. Dr. Kah Phooi Seng

E-Mail Website
Guest Editor
1. School of AI & Advanced Computing, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
2. School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: artificial intelligence; deep learning; machine learning and data analytics; intelligent systems; Internet of Things and edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Li-Minn Ang

E-Mail Website
Guest Editor
School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Interests: electrical and computer engineering; intelligent and smart sensor systems; IoT and data analytics for environmental sensing; innovative technologies for real world systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junbin Gao

E-Mail Website
Guest Editor
Discipline of Business Analytics, School of Business, The University of Sydney, Sydney, Australia
Interests: big data analytics; machine learning
Dr. Hongbin Liu

E-Mail Website
Guest Editor
School of AI and Advanced Computing, Xian Jiaotong Liverpool University, Suzhou, China
Interests: artificial intelligence; spatio-temporal reasoning; computer vision

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and advanced computing technologies bring significant improvements in implementation efficiency for environmental sensing. We invite authors to contribute articles that present current trends and future perspectives for applications of AI and other advanced computing technologies in smart cities, environmental sensing and monitoring domains.

Relevant topics include but are not limited to the following:

  1. Design of AI and novel advanced computing techniques for smart cities and environmental sensing;
  2. Edge computing and architectures for smart cities and environmental sensing;
  3. Opportunistic and participatory sensing with smartphones;
  4. Cloud and ubiquitous computing architectures for smart cities and environmental sensing;
  5. Swarm intelligence approaches and techniques for smart cities and environmental sensing;
  6. Internet of Things (IoT) and wireless sensor systems for smart cities and environmental monitoring;
  7. Environmental sensing testbeds for smart cities;
  8. Crowdsourcing for smart cities and environmental sensing;
  9. Security, privacy and blockchain systems for smart cities, environmental sensing and sustainability;
  10. Trends in education with emphasis on the use of AI and advanced computing technologies including courses and teaching perspectives.

Prof. Dr. Kah Phooi Seng
Prof. Dr. Li-minn (Kenneth) Ang
Prof. Dr. Junbin Gao
Dr. Hongbin Liu
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.

Published Papers (3 papers)

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Research

24 pages, 5468 KiB  
Article
A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload
by Song Liu, Shiyuan Yang, Hanze Zhang and Weiguo Wu
Sensors 2023, 23(4), 2243; https://doi.org/10.3390/s23042243 - 16 Feb 2023
Cited by 3 | Viewed by 1847
Abstract
With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless [...] Read more.
With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Advanced Technologies for Smart Cities and Environmental Sensing)
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13 pages, 1738 KiB  
Article
Data Center Traffic Prediction Algorithms and Resource Scheduling
by Min Tan, Ruixuan Ba and Guohui Li
Sensors 2022, 22(20), 7893; https://doi.org/10.3390/s22207893 - 17 Oct 2022
Viewed by 1121
Abstract
This paper uses intelligent methods such as a time recurrent neural network to predict network traffic, mainly to solve the problems of resource imbalance and demand differentiation under the current 5G cloud-network collaborative architecture. An improved tree species optimization algorithm is proposed to [...] Read more.
This paper uses intelligent methods such as a time recurrent neural network to predict network traffic, mainly to solve the problems of resource imbalance and demand differentiation under the current 5G cloud-network collaborative architecture. An improved tree species optimization algorithm is proposed to optimize the initial network data, and the LSTM model is used to predict the data center traffic to obtain better network traffic prediction accuracy, take corresponding measures, and finally build a scheduling algorithm that integrates business cooperative caching and load balancing based on traffic prediction to reduce the peak pressure of the 5G data center network. Full article
(This article belongs to the Special Issue Artificial Intelligence and Advanced Technologies for Smart Cities and Environmental Sensing)
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14 pages, 7794 KiB  
Article
Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest
by Sanjeev Kumar Srivastava, Kah Phooi Seng, Li Minn Ang, Anibal ‘Nahuel’ A. Pachas and Tom Lewis
Sensors 2022, 22(20), 7872; https://doi.org/10.3390/s22207872 - 17 Oct 2022
Cited by 5 | Viewed by 3117
Abstract
This paper investigated the utility of drone-based environmental monitoring to assist with forest inventory in Queensland private native forests (PNF). The research aimed to build capabilities to carry out forest inventory more efficiently without the need to rely on laborious field assessments. The [...] Read more.
This paper investigated the utility of drone-based environmental monitoring to assist with forest inventory in Queensland private native forests (PNF). The research aimed to build capabilities to carry out forest inventory more efficiently without the need to rely on laborious field assessments. The use of drone-derived images and the subsequent application of digital photogrammetry to obtain information about PNFs are underinvestigated in southeast Queensland vegetation types. In this study, we used image processing to separate individual trees and digital photogrammetry to derive a canopy height model (CHM). The study was supported with tree height data collected in the field for one site. The paper addressed the research question “How well do drone-derived point clouds estimate the height of trees in PNF ecosystems?” The study indicated that a drone with a basic RGB camera can estimate tree height with good confidence. The results can potentially be applied across multiple land tenures and similar forest types. This informs the development of drone-based and remote-sensing image-processing methods, which will lead to improved forest inventories, thereby providing forest managers with recent, accurate, and efficient information on forest resources. Full article
(This article belongs to the Special Issue Artificial Intelligence and Advanced Technologies for Smart Cities and Environmental Sensing)
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