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Recent Advances in IoT Big Data Analytics towards Smart Cities
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Recent Advances in IoT Big Data Analytics towards Smart Cities

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

Deadline for manuscript submissions: 11 June 2025 | Viewed by 3766

Special Issue Editor

Prof. Dr. Xiangjie Kong

E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: urban computing; mobile computing; network science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of Internet of Things (IoT) technology, there are various types of sensors in the city, which produce multi-modal data, e.g. GPS data, image data, video data and audio data. Artificial Intelligence provides new methods to analyze IoT big data and empowers applications in transportation, communication, industry and so on. IoT big data analytics has become one of the key technologies for realizing smart cities. By harnessing the power of IoT devices and sensors, smart cities can monitor and manage various aspects of Residential life in real-time, leading to better decision-making and resource allocation. However, data quality and integrity, computing and storage resources, data security and privacy, and social and ethical issues remain challenges for IoT big data analytics. To address these challenges, researchers are committed to finding efficient algorithms and techniques to analyze and process data, while adopting appropriate security and privacy protection measures. This special issue aims to provide a platform to discuss the latest advances in IoT big data analytics towards smart cities, as well as highlight technical innovations and future application prospects for IoT big data analytics.

Prof. Dr. Xiangjie Kong
Guest Editor

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

  • sensor data collection and storage
  • data privacy and security protection
  • IoT big data analytics based sensing techniques
  • the application of IoT big data analytics in smart city transportation, energy, environment, and health
  • IoT big data visual analytics
  • IoT big data analytical technologies with digital twin
  • intelligent services and personalized recommendations based on IoT big data
  • artificial intelligence empowered IoT big data analytics

Published Papers (3 papers)

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Research

17 pages, 894 KiB  
Article
Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data
by Jie Wang, Xuanrui Xiong, Gaosheng Chen, Ruiqi Ouyang, Yunli Gao and Osama Alfarraj
Sensors 2023, 23(17), 7434; https://doi.org/10.3390/s23177434 - 25 Aug 2023
Viewed by 747
Abstract
The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection [...] Read more.
The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment. Full article
(This article belongs to the Special Issue Recent Advances in IoT Big Data Analytics towards Smart Cities)
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21 pages, 1679 KiB  
Article
Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis
by Biao Yang, Xuanrui Xiong, He Liu, Yumei Jia, Yunli Gao, Amr Tolba and Xingguo Zhang
Sensors 2023, 23(15), 6795; https://doi.org/10.3390/s23156795 - 29 Jul 2023
Cited by 1 | Viewed by 1299
Abstract
The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of [...] Read more.
The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of the affected individuals. To address this challenge, IoT big data analytics and unmanned aerial vehicle (UAV) technologies have emerged as key elements in the solution. By analyzing large-scale sensor data, user behavior, and communication traffic, IoT big data analytics can provide real-time communication demand prediction and network optimization strategies, offering decision support for post-disaster communication reconstruction. Given the unique characteristics of post-disaster scenarios, this paper proposes a UAV-assisted communication coverage strategy based on IoT big data analytics. This strategy employs UAVs in a cruising manner to assist in communication by partitioning the target area into multiple cells, each satisfying the minimum data requirements for user communication. Depending on the distribution characteristics of users, flight–communication or hover-communication protocols are selectively employed to support communication. By optimizing the UAV’s flight speed and considering the coverage index, fairness index, and average energy efficiency of the mission’s target area, the Inner Spiral Cruise Communication Coverage (IS-CCC) algorithm is proposed to plan the UAV’s cruising trajectory and achieve UAV-based communication coverage. Simulation results demonstrate that this strategy can achieve energy-efficient cruising communication coverage in regions with complex user distributions, thereby reducing energy consumption in UAV-based communication. Full article
(This article belongs to the Special Issue Recent Advances in IoT Big Data Analytics towards Smart Cities)
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20 pages, 3805 KiB  
Article
Fasys: Visible-Light-Based Communication and Positioning Services towards Smart Cities
by Baozhu Yu, Xiangyu Liu, Lei Guo, Xuetao Wei and Song Song
Sensors 2023, 23(14), 6340; https://doi.org/10.3390/s23146340 - 12 Jul 2023
Viewed by 804
Abstract
Visible-light-based transmission application plays an important role in various types of sensor services for the Internet of Things (IoTs). However, in big data scenarios, current visible-light-based systems cannot achieve concurrent high-speed communication, low-speed communication, and positioning. Therefore, in this article, we propose a [...] Read more.
Visible-light-based transmission application plays an important role in various types of sensor services for the Internet of Things (IoTs). However, in big data scenarios, current visible-light-based systems cannot achieve concurrent high-speed communication, low-speed communication, and positioning. Therefore, in this article, we propose a smart visible-light-based fusion applications system, named Fasys, to solve the above problem for the big data traffic with heterogeneity. Specifically, for low-speed data services, we propose a novel linear block coding and bit interleaving mechanism, which enhances the LED positioning accuracy and recovers the lost data bits in the interframe gap (IFG). For high-speed data services with traffic possessing burstiness, an elegant statistical reliability analysis framework in regard to latency is proposed based on martingale theory. The backlog martingale process is constructed. Leveraging stopping time theory, a tight upper bound of unreliability is obtained. An arrival abstraction and traffic allocation scheme is designed, which contributes to decouple the reliability requirement as the maximum supportable arrival load. Finally, we implement our Fasys system, and extensive experimental results show that our system can achieve consistent high-precision positioning and low-BER data communication for low-speed data services. And the proposed martingale-based traffic allocation scheme can achieve the provisioning of reliability in regard to the latency for high-speed data services. Full article
(This article belongs to the Special Issue Recent Advances in IoT Big Data Analytics towards Smart Cities)
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