Big Data Analytics for Internet of Things Enabled Smart Cities and Societies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 12441

Special Issue Editors

Robotics and Internet of Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia
Interests: big data analytics; machine learning; internet of things (IoT); intelligent transportation system (ITS); smart cities
Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad 44000, Pakistan
Interests: IoT; software defined networks; flying ad hoc networks (FANETS); mesh networks, resource allocation, and routing
Department of Computer Science, Allama Iqbal Open University (AIOU), Islamabad 44000, Pakistan
Interests: image processing; internet of things; video processing; data analytics; data compression

Special Issue Information

Dear Colleagues,

A smart city is a viewing platform where IoT-enabled services relate to all facets, including smart health, smart parking, smart traffic, smart surveillance, and so forth. More than 100 billion IoT devices are expected to be wired this year, and the IoT standard will increase with 5G. The IoT comprises a network connected with many varieties of sensors and devices. The 5G abilities are a deep-seated accumulation of the band that is 200 times quicker than 4G. Smart city attention schemas entail being technologically advanced and armor-plated in a way that will appease today’s populace and near-term age group anxieties. Massive quantities of data are formed each day from IoT devices, personnel archives, societal nets, businesses, IoT, and cyberspace. It assuredly bears a crucial trial to the production of current set-ups and schemes.

This special collection seeks to realize current expansions and disseminate up-to-date methods on IoT-enabled smart cities producing a huge amount of big data fulfilling social, financial, and environmental sustainability. All the papers will be assessed based on the exclusive contribution to the IoT-enabled solutions for the smart city using big data. The manuscripts will be peer-reviewed. The topics include, but are not limited to:

  • IoT-enabled smart city schemes using big data analytics;
  • Big data processing schemes for IoT-enabled smart societies;
  • Big data analytics using machine learning for smart environment;
  • Big data analytics-based preventive care for social sustainability;
  • Management of IoT devices using machine learning;
  • Management of IoT devices using big data;
  • Energy efficiency of IoT devices for smart environment;
  • IoT-based mass observation in the intelligent smart city;
  • Smart city architectures for socio-economic encounters;
  • Parallel computation for big data powered smart transportation;
  • Prediction using big data analytics in the smart health;
  • Data acquisition using big data analytics for sensors;
  • Remote citizen monitoring using big data and machine learning;
  • Extrapolative schemes for improved e-services using big data.

Dr. Muhammad Babar
Dr. Saleem Iqbal
Dr. Aftab Khan
Guest Editors

Manuscript Submission Information

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Keywords

  • big data analytics
  • machine learning
  • smart city
  • IoT
  • AI

Published Papers (6 papers)

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Research

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23 pages, 2972 KiB  
Article
Performance Evaluation of an IEEE 802.15.4-Based Thread Network for Efficient Internet of Things Communications in Smart Cities
by Sohaib Bin Altaf Khattak, Moustafa M. Nasralla, Haleem Farman and Nikumani Choudhury
Appl. Sci. 2023, 13(13), 7745; https://doi.org/10.3390/app13137745 - 30 Jun 2023
Cited by 8 | Viewed by 1813
Abstract
The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, [...] Read more.
The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, among which the Thread protocol has emerged as a promising option. Thread is IEEE 802.15.4 based and has advanced capabilities like mesh networking, IPv6 support, and multiple gateways providing no single point of failure. This paper presents the design and evaluation of a low-cost mesh network using Raspberry Pi, nRF52840 dongle, and OpenThread 1.2 (i.e., an open-source software implementation of the Thread protocol stack). The research elaborates on the hardware and software solutions used, as well as the network topologies adopted. To evaluate the performance of the developed system, extensive real-time tests are performed, considering parameters, such as jitter, packet loss, and round trip time. These tests effectively demonstrate the effectiveness of the Thread network. Furthermore, the impact of varying payload size and bitrate on the network is analyzed to understand its influence. The behavior of the multi-hop network is also examined under link failure scenarios, providing insights into the network’s robustness. Our findings provide valuable insights for researchers interested in designing low-cost and efficient mesh networks for various IoT applications, including home automation, building/campus monitoring systems, distributed industrial IoT applications, and smart city infrastructure. Full article
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24 pages, 8332 KiB  
Article
Machine Learning-Based DoS Amplification Attack Detection against Constrained Application Protocol
by Sultan M. Almeghlef, Abdullah AL-Malaise AL-Ghamdi, Muhammad Sher Ramzan and Mahmoud Ragab
Appl. Sci. 2023, 13(13), 7391; https://doi.org/10.3390/app13137391 - 21 Jun 2023
Viewed by 1047
Abstract
This paper discusses the Internet of Things (IoT) and the security challenges associated with it. IoT is a network of interconnected devices that share information. However, the low power and resources of IoT devices make them vulnerable to attacks. Using heavy protocols like [...] Read more.
This paper discusses the Internet of Things (IoT) and the security challenges associated with it. IoT is a network of interconnected devices that share information. However, the low power and resources of IoT devices make them vulnerable to attacks. Using heavy protocols like HTTP for IoT devices can prove costly and using popular lightweight protocols like CoAP can invite attacks such as DoS (Denial-of-Service). While security models such as DTLS and LSPWSN can secure IoT against such attacks, they also have limitations. To overcome this problem, this paper proposes a machine learning model that detects DoS amplification attacks against CoAP with 99% accuracy. To the best of our knowledge, this research is the first to use the multi-classification process to detect and classify the different types of the DoS amplification techniques that attack CoAP client use against victim CoAP clients. Full article
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21 pages, 632 KiB  
Article
ADAL-NN: Anomaly Detection and Localization Using Deep Relational Learning in Distributed Systems
by Kashan Ahmed, Ayesha Altaf, Nor Shahida Mohd Jamail, Faiza Iqbal and Rabia Latif
Appl. Sci. 2023, 13(12), 7297; https://doi.org/10.3390/app13127297 - 19 Jun 2023
Cited by 3 | Viewed by 1103
Abstract
Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid in the localization and detection of [...] Read more.
Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid in the localization and detection of anomalies. Current methods for achieving this are insufficient for overcoming the following obstacles: (1) Log processing is performed in a separate component apart from log mining. (2) In modern software systems, log format evolution is ongoing. It is hard to detect latent technical issues using simple monitoring techniques in a non-intrusive manner. Within the scope of this paper, we present a reliable and consistent method for the detection and localization of anomalies in interleaved unstructured logs in order to address the aforementioned drawbacks. This research examines Log Sequential Anomalies (LSA) for potential performance issues. In this study, IDs are used to group log messages, and ID relation graphs are constructed between distributed components. In addition to that, we offer a data-driven online log parser that does not require any parameters. By utilizing a novel log parser, the bundled log messages undergo a transformation process involving both semantic and temporal embedding. In order to identify instance–granularity anomalies, this study makes use of a heuristic searching technique and an attention-based Bi-LSTM model. The effectiveness, efficiency, and robustness of the paper are supported by the research that was performed on real-world datasets as well as on synthetic datasets. The neural network improves the F1 score by five percent, which is greater than other cutting-edge models. Full article
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12 pages, 2006 KiB  
Article
Understanding Seasonal Indoor Radon Variability from Data Collected with a LoRa-Enabled IoT Edge Device
by Leonel J. R. Nunes, António Curado and Sérgio Ivan Lopes
Appl. Sci. 2023, 13(8), 4735; https://doi.org/10.3390/app13084735 - 09 Apr 2023
Cited by 3 | Viewed by 1471
Abstract
The long-term assessment of radon (Rn) is a critical factor in evaluating the exposure risk faced by building occupants, and it plays a significant role in determining the implementation of Rn remediation strategies aimed at enhancing indoor air quality (IAQ). Meteorological parameters, such [...] Read more.
The long-term assessment of radon (Rn) is a critical factor in evaluating the exposure risk faced by building occupants, and it plays a significant role in determining the implementation of Rn remediation strategies aimed at enhancing indoor air quality (IAQ). Meteorological parameters, such as temperature, relative humidity, and atmospheric pressure, as well as geological factors, such as soil properties, uranium content, rock formations, parent rock weathering, and water content, can significantly impact the assessment of Rn exposure risk and the selection of appropriate mitigation measures. A continuous monitoring campaign of a National Architectural Heritage building serving as a museum open to the public for a period of 546 consecutive days was conducted. The results of the in situ investigation revealed a broad range of seasonality in indoor Rn emission, with a negative correlation observed between Rn concentration and air temperature. The data indicated that indoor Rn concentration increases in the winter months as a result of reduced indoor air temperature and decreased air exchange, while it decreases in the summer months due to increased air temperature and enhanced natural ventilation. However, the implementation of high ventilation rates to improve IAQ may result in significant heat losses, thereby affecting the thermal comfort of building occupants during the winter months. Therefore, it is imperative to achieve a balance between ventilation practices and energy efficiency requirements to ensure both IAQ and thermal comfort for building occupants. Full article
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19 pages, 2190 KiB  
Article
A Smart Framework for Managing Natural Disasters Based on the IoT and ML
by Fares Hamad Aljohani, Adnan Ahmed Abi Sen, Muhammad Sher Ramazan, Bander Alzahrani and Nour Mahmoud Bahbouh
Appl. Sci. 2023, 13(6), 3888; https://doi.org/10.3390/app13063888 - 18 Mar 2023
Cited by 7 | Viewed by 2101
Abstract
Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same [...] Read more.
Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage. Full article
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Review

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26 pages, 14015 KiB  
Review
Target Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis
by Sundas Iftikhar, Muhammad Asim, Zuping Zhang, Ammar Muthanna, Junhong Chen, Mohammed El-Affendi, Ahmed Sedik and Ahmed A. Abd El-Latif
Appl. Sci. 2023, 13(6), 3995; https://doi.org/10.3390/app13063995 - 21 Mar 2023
Cited by 11 | Viewed by 3109
Abstract
In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the [...] Read more.
In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people’s lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, which results in a deficient performance of actual recognition models. To tackle these issues, many deep learning (DL)-based approaches have been proposed. In this review paper, we study an end-to-end target detection paradigm based on different DL approaches, which includes one-stage and two-stage detectors from UAV images to observe the target in traffic congestion under complex circumstances. Moreover, we also analyze the evaluation work to enhance the accuracy, reduce the computational cost, and optimize the design. Furthermore, we also provided the comparison and differences of various technologies for target detection followed by future research trends. Full article
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