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Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities

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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 (31 August 2023) | Viewed by 15925

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Special Issue Editors

Prof. Dr. Habib Hamam

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Faculty of Engineering, Moncton University, Moncton, NB E1A3E9, Canada
Interests: optical telecommunications; wireless Communications; diffraction; fiber components; RFID; information processing; data protection; deep learning
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Dr. Ateeq Ur Rehman

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Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
Interests: social Internet of Things; AI; big data
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mohamed Tahar Ben Othman

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Guest Editor

Department of Computer Science, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia
Interests: bioinformatics; cryptography and steganography; computer networks; wireless systems; artificial intelligence; optimization; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the Internet of Things has been a notably evolving interdisciplinary research area for both academia and industry professionals. IoT devices yield massive amounts of structured and unstructured data and, to deal with this huge amount of data, Big Data Analytics (BDA) technology must be adapted. This Special Issue invites submissions wirh the scope and exploitation/fusion of Big Data Analytics (BDA) and the Internet of Things (IoT) and their applications for future smart cities. Big data analytics has been gaining much attention from diverse disciplines such as computer science, information technology, social sciences, etc. The intermingling of BDA and IoT results in more advanced, intelligent, smart and efficient systems, which are meeded at present and the future. The use of Big Data tools and softwares can better process the massive volumes of data and could help to propose new services for a smart city.

Futhermore, this Special Issue invites the extended version of accepted manuscripts from the International Conference on Smart Computing and Application (ICSCA 2022). The conference will be organized by the College of Computer Science and Engineering, University of HA’IL, Saudi Arabia and will be held in hybrid (onsite and virtual) from 4-5 December 2022. This Special Issue is open to high-quality research contributions from a wide range of professions including scholars, researchers, academicians, and industry people. In this Special Issue, we hope to promote discussion and contributions towards innovative solutions. Furthermore, the aim is to focus on original research papers of high quality, providing practical applications for real-world problems and bridging the gap between industry and academia. Topics of interest include, but are not limited to, the following:

Big data analytics and social media
Machine learning and AI for big data
Smart computing models, tools, and devices
Smart computing models, devices, tools and systems for big data
Techniques, models and algorithms for big data
Security and privacy for big data
Infrastructure and platform for big data and smart computing
Data and information quality
Big data and Cloud Computing
Big data analytics and stock market
Software Engineering for big data analytics
Big data analytics for smart cities
Big data analytics for e-healthcare
Big data analytics for water management
Big data analytics for waste management
Healthcare services and health informatics
Remote pain/patient monitoring using IoT
Big data analytics for urban traffic management
IoT and BDA for Smart Grid, Smart Home, Connected Car, Connected Health, Smart Farming, Smart Education, etc.
Deployment of big data for security, privacy, and trust for smart future networks
Big data analytics for mitigating traffic accidents, congestion, environmental pollution, etc.
IoT-based sensing, mining, processing, and communication
IoT-based weather monitoring and forecasting

Prof. Dr. Habib Hamam
Dr. Ateeq Ur Rehman
Prof. Dr. Mohamed Tahar Ben Othman
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. Applied Sciences 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 2400 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

big data analytics and social media
machine learning and AI for big data
smart computing models, tools, and devices
smart computing models, devices, tools and systems for big data
techniques, models and algorithms for big data
security and privacy for big data
infrastructure and platform for big data and smart computing
data and information quality
big data and cloud computing
big data analytics and stock market
software engineering for big data analytics
big data analytics for smart cities
big data analytics for e-healthcare
big data analytics for water management
big data analytics for waste management
healthcare services and health informatics
remote pain/patient monitoring using IoT
big data analytics for urban traffic management
IoT and BDA for smart grid, smart home, connected car, connected health, smart farming, smart education, etc.
deployment of big data for security, privacy, and trust for smart future networks
big data analytics for mitigating traffic accidents, congestion, environmental pollution, etc.
IoT-based sensing, mining, processing, and communication
IoT-based weather monitoring and forecasting

Published Papers (7 papers)

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Research

11 pages, 601 KiB

Open AccessArticle

Content-Based Approach for Improving Bloom Filter Efficiency

by Mohammed Alsuhaibani, Rehan Ullah Khan, Ali Mustafa Qamar and Suliman A. Alsuhibany

Appl. Sci. 2023, 13(13), 7922; https://doi.org/10.3390/app13137922 - 06 Jul 2023

Viewed by 988

Abstract

Bloom filters are a type of data structure that is used to test whether or not an element is a member of a set. They are known for being space-efficient and are commonly employed in various applications, such as network routers, web browsers, and databases. These filters work by allowing a fixed probability of incorrectly identifying an element as being a member of the set, known as the false positive rate (FPR). However, traditional bloom filters suffer from a high FPR and extensive memory usage, which can lead to incorrect query results and a slow performance. Thus, this study indicates that a content-based strategy could be a practical solution for these challenges. Specifically, our approach requires less bloom filter storage, consequently decreasing the probability of false positives. The effectiveness of several hash functions on our strategy’s performance was also evaluated. Experimental evaluations demonstrated that the proposed strategy could potentially decrease false positives by a substantial margin of up to 79.83%. The use of size-based content bits significantly contributes to the decrease in the number of false positives as well. However, as the volume of content bits rises, the impact on time is not considerably noticeable. Moreover, the evidence suggests that the application of a singular approach leads to a more than 50% decrease in false positives. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities)

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24 pages, 2899 KiB

Open AccessArticle

Early Forest Fire Detection Using a Protocol for Energy-Efficient Clustering with Weighted-Based Optimization in Wireless Sensor Networks

by Puneet Kaur, Kiranbir Kaur, Kuldeep Singh and SeongKi Kim

Appl. Sci. 2023, 13(5), 3048; https://doi.org/10.3390/app13053048 - 27 Feb 2023

Cited by 5 | Viewed by 2278

Abstract

Wireless sensor networks (WSNs) have proven to be incredibly useful for forest applications that rely on sensing technologies for event detection and monitoring. This radical sensing technology has revolutionized data gathering, analysis, and application. Despite the many advantages of this technology, one key drawback is the rapid drain on sensor batteries caused by their intensive processing activities and communication processes. The effectiveness of sensor nodes is strongly influenced by two factors: the amount of energy they consume and the length of their coverage lifetimes. Using our proposed method, we can find fire zones in a forest, detect and monitor battlefield surveillance, combat monitoring and intruder detection, and then wirelessly send all the information to a central station. So, extending the life of WSNs is essential to ensure that Sensor Nodes (SN) will always be available. Our proposed EEWBP (energy-efficient weighted-based protocol) technique uses a composite weighted metric that includes system elements such as the node degree, residual energy, the number of neighbors’ nodes, average flying speed, and trust value, which are evaluated separately and then added together to help in cluster-building and node-scheduling processes. Our proposed protocol makes it easy to set up many clusters of SNs, each with their own cluster head (CH). This way, data can be sent between clusters in a way that uses the least amount of energy and makes coverage last longer. After putting our cluster-based routing strategy in place, we tested how it worked and evaluated it with different network parameters. The simulation results show that EEWBP consumes less energy and maintains a higher level of consistency in the CH than coverage preserving clustering protocol (CPCP), coverage clustering protocol (CACP), coverage aware unequal clustering algorithm (CUCA), and low-energy adaptive clustering hierarchy (LEACH). EEWBP also shows a better packet delivery rate and an improvement in first-node death. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities)

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21 pages, 17899 KiB

Open AccessArticle

A Cloud Intrusion Detection Systems Based on DNN Using Backpropagation and PSO on the CSE-CIC-IDS2018 Dataset

by Saud Alzughaibi and Salim El Khediri

Appl. Sci. 2023, 13(4), 2276; https://doi.org/10.3390/app13042276 - 10 Feb 2023

Cited by 18 | Viewed by 2712

Abstract

Cloud computing (CC) is becoming an essential technology worldwide. This approach represents a revolution in data storage and collaborative services. Nevertheless, security issues have grown with the move to CC, including intrusion detection systems (IDSs). Intruders have developed advanced tools that trick the traditional IDS. This study attempts to contribute toward solving this problem and reducing its harmful effects by boosting IDS performance and efficiency in a cloud environment. We build two models based on deep neural networks (DNNs) for this study: the first model is built on a multi-layer perceptron (MLP) with backpropagation (BP), and the other is trained by MLP with particle swarm optimization (PSO). We use these models to deal with binary and multi-class classification on the updated cybersecurity CSE-CIC-IDS2018 dataset. This study aims to improve the accuracy of detecting intrusion attacks for IDSs in a cloud environment and to enhance other performance metrics. In this study, we document all aspects of our experiments in depth. The results show that the best accuracy obtained for binary classification was 98.97% and that for multi-class classification was 98.41%. Furthermore, the results are compared with those from the related literature. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities)

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17 pages, 4513 KiB

Open AccessArticle

Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques

by Alaeddine Mihoub, Moez Krichen, Mohannad Alswailim, Sami Mahfoudhi and Riadh Bel Hadj Salah

Appl. Sci. 2023, 13(2), 683; https://doi.org/10.3390/app13020683 - 04 Jan 2023

Cited by 4 | Viewed by 1431

Abstract

The state of roads may sometimes be difficult to perceive due to intense climate conditions, absence of road signs, or simply human inattention, which may be harmful to both vehicles and drivers. The automatic monitoring of the road states represents a promising solution to warn drivers about the status of a road in order to protect them from injuries or accidents. In this paper, we present a novel application for data collection regarding road states. Our application entitled “Road Scanner” allows onboard users to tag four types of segments in roads: smooth, bumps, potholes, and others. For each tagged segment the application records multimodal data using the embedded sensors of a smartphone. The collected data concerns mainly vehicle accelerations, angular rotations, and geographical positions recorded by the accelerometer, the gyroscope, and the GPS sensor, respectively, of a user phone. Moreover, a medium-size dataset was built and machine learning models were applied to detect the right label for the road segment. Overall, the results were very promising since the SVM classifier (Support Vector Machines) has recorded an accuracy rate of 88.05%. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities)

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37 pages, 3415 KiB

Open AccessArticle

Environment-Aware Energy Efficient and Reliable Routing in Real-Time Multi-Sink Wireless Sensor Networks for Smart Cities Applications

by Fatma H. El-Fouly, Mnaouer Kachout, Yasser Alharbi, Jalawi Sulaiman Alshudukhi, Abed Alanazi and Rabie A. Ramadan

Appl. Sci. 2023, 13(1), 605; https://doi.org/10.3390/app13010605 - 02 Jan 2023

Cited by 7 | Viewed by 1585

Abstract

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities)

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19 pages, 3914 KiB

Open AccessArticle

Lung Nodules Localization and Report Analysis from Computerized Tomography (CT) Scan Using a Novel Machine Learning Approach

by Inayatul Haq, Tehseen Mazhar, Muhammad Amir Malik, Mian Muhammad Kamal, Inam Ullah, Taejoon Kim, Monia Hamdi and Habib Hamam

Appl. Sci. 2022, 12(24), 12614; https://doi.org/10.3390/app122412614 - 09 Dec 2022

Cited by 16 | Viewed by 1729

Abstract

A lung nodule is a tiny growth that develops in the lung. Non-cancerous nodules do not spread to other sections of the body. Malignant nodules can spread rapidly. One of the numerous dangerous kinds of cancer is lung cancer. It is responsible for taking the lives of millions of individuals each year. It is necessary to have a highly efficient technology capable of analyzing the nodule in the pre-cancerous phases of the disease. However, it is still difficult to detect nodules in CT scan data, which is an issue that has to be overcome if the following treatment is going to be effective. CT scans have been used for several years to diagnose nodules for future therapy. The radiologist can make a mistake while determining the nodule’s presence and size. There is room for error in this process. Radiologists will compare and analyze the images obtained from the CT scan to ascertain the nodule’s location and current status. It is necessary to have a dependable system that can locate the nodule in the CT scan images and provide radiologists with an automated report analysis that is easy to comprehend. In this study, we created and evaluated an algorithm that can identify a nodule by comparing multiple photos. This gives the radiologist additional data to work with in diagnosing cancer in its earliest stages in the nodule. In addition to accuracy, various characteristics were assessed during the performance assessment process. The final CNN algorithm has 84.8% accuracy, 90.47% precision, and 90.64% specificity. These numbers are all relatively close to one another. As a result, one may argue that CNN is capable of minimizing the number of false positives through in-depth training that is performed frequently. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities)

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14 pages, 565 KiB

Open AccessArticle

Deep Transformer Language Models for Arabic Text Summarization: A Comparison Study

by Hasna Chouikhi and Mohammed Alsuhaibani

Appl. Sci. 2022, 12(23), 11944; https://doi.org/10.3390/app122311944 - 23 Nov 2022

Cited by 9 | Viewed by 3721

Abstract

Large text documents are sometimes challenging to understand and time-consuming to extract vital information from. These issues are addressed by automatic text summarizing techniques, which condense lengthy texts while preserving their key information. Thus, the development of automatic summarization systems capable of fulfilling the ever-increasing demands of textual data becomes of utmost importance. It is even more vital with complex natural languages. This study explores five State-Of-The-Art (SOTA) Arabic deep Transformer-based Language Models (TLMs) in the task of text summarization by adapting various text summarization datasets dedicated to Arabic. A comparison against deep learning and machine learning-based baseline models has also been conducted. Experimental results reveal the superiority of TLMs, specifically the PEAGASUS family, against the baseline approaches, with an average F1-score of 90% on several benchmark datasets. Full article

(This article belongs to the Special Issue Emerging Trends and Applications of Big Data Analytics and the Internet of Things for Future Smart Cities)

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