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IoT and Big Data Applications in Smart Cities: Recent Advances,...
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IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 665

Special Issue Editors

Dr. Diego Gabriel Rossit

E-Mail Website
Guest Editor
INMABB, Engineering Department, Universidad Nacional del Sur and CONICET, 1253 Avenida Alem, Buenos Aires, Bahía Blanca 8000, Argentina
Interests: operations research; smart cities; production planning; industrial engineering; transport planning; optimization; simulation
Prof. Dr. Pedro Moreno-Bernal

E-Mail Website
Guest Editor
Faculty of Accounting, Administration, and Informatics, Autonomous University of Morelos State (UAEM), Avenida Universidad 1001 Colonia Chamilpa, Cuernavaca C.P. 62209, Morelos, Mexico
Interests: smart cities; parallel computing; cloud computing; optimization; smart manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue titled "IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues" aims to provide a comprehensive overview of how Internet of Things (IoT) and big data technologies are shaping the future of smart cities. It brings together research and practical insights into how these technologies are enhancing urban management, improving the quality of life, and promoting sustainable development. Key areas of focus include real-time monitoring systems, energy-efficient solutions, traffic management, smart grids, and public safety.

This Special Issue highlights recent technological advancements, including the integration of AI with IoT for predictive analytics, data-driven decision making, and automation. It also addresses critical challenges like data privacy and security, the complexity of integrating heterogeneous systems, managing massive data streams, and ensuring the scalability of smart city solutions. Additionally, this issue discusses regulatory and ethical considerations surrounding data collection and usage in urban environments.

By analyzing both technical innovations and obstacles, this Special Issue provides insights into the future potential of IoT and big data for building resilient, efficient, and citizen-centric cities while also outlining the current gaps that need to be addressed to fully realize their benefits.

Dr. Diego Gabriel Rossit
Prof. Dr. Pedro Moreno-Bernal
Guest Editors

Manuscript Submission Information

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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. Data 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 1600 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
  • smart cities
  • wireless connections
  • optimizations
  • urban management
  • big data
  • visualization
  • real-time information
  • image processing
  • decision-making process
  • simulation

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Published Papers (1 paper)

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21 pages, 6066 KiB  
Article
Algorithm for Trajectory Simplification Based on Multi-Point Construction in Preselected Area and Noise Smoothing Processing
by Simin Huang and Zhiying Yang
Data 2024, 9(12), 140; https://doi.org/10.3390/data9120140 - 29 Nov 2024
Viewed by 447
Abstract
Simplifying trajectory data can improve the efficiency of trajectory data analysis and query and reduce the communication cost and computational overhead of trajectory data. In this paper, a real-time trajectory simplification algorithm (SSFI) based on the spatio-temporal feature information of implicit trajectory points [...] Read more.
Simplifying trajectory data can improve the efficiency of trajectory data analysis and query and reduce the communication cost and computational overhead of trajectory data. In this paper, a real-time trajectory simplification algorithm (SSFI) based on the spatio-temporal feature information of implicit trajectory points is proposed. The algorithm constructs the preselected area through the error measurement method based on the feature information of implicit trajectory points (IEDs) proposed in this paper, predicts the falling point of trajectory points, and realizes the one-way error-bounded simplified trajectory algorithm. Experiments show that the simplified algorithm has obvious progress in three aspects: running speed, compression accuracy, and simplification rate. When the trajectory data scale is large, the performance of the algorithm is much better than that of other line segment simplification algorithms. The GPS error cannot be avoided. The Kalman filter smoothing trajectory can effectively eliminate the influence of noise and significantly improve the performance of the simplified algorithm. According to the characteristics of the trajectory data, this paper accurately constructs a mathematical model to describe the motion state of objects, so that the performance of the Kalman filter is better than other filters when smoothing trajectory data. In this paper, the trajectory data smoothing experiment is carried out by adding random Gaussian noise to the trajectory data. The experiment shows that the Kalman filter’s performance under the mathematical model is better than other filters. Full article
(This article belongs to the Special Issue IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues)
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