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Mobile Ad Hoc Networks (MANETs) in the Era of Cutting-Edge Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 17251

Special Issue Editor

Prof. Dr. Abdelhafid Abouaissa

E-Mail Website
Guest Editor
IRIMAS, University of Haute Alsace, 68000 Colmar, France
Interests: ad hoc networks; resource management; data science; data processing and storage; blockchains

Special Issue Information

Dear Colleagues,

In the last two decades, the integration of smart sensing devices into mobile objects has led to a revolution in mobile ad hoc networks (MANETs). This has enabled the deployment of new services and applications that have made our lives smarter and safer. However, datasets collected by MANETs are characterized by their massive size, high speed generation and heterogeneous type, which require complicated processing techniques and substantial storage facilities. This underlines the importance of using cutting-edge technologies (mainly artificial intelligence (AI), data science, edge/fog, cloud, etc.) in MANETs. Using such technologies, the processing and analysis of mobile data can yield benefits to MANETs and optimize services and applications in places such as cities. Additionally, they ensure efficient architectures for storing and handling big data collected in MANETs.

This Special Issue presents the latest findings that highlight how cutting-edge technologies such as AI, machine/deep learning, edge and fog computing, and cloud computing can be used to push the current state of the art with respect to MANETs.

Prof. Dr. Abdelhafid Abouaissa
Guest Editor

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Keywords

  • MANET
  • cutting-edge technology
  • artificial intelligence
  • data science
  • data processing
  • storage architectures

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Published Papers (3 papers)

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Research

35 pages, 607 KiB  
Article
Toward a New Era of Smart and Secure Healthcare Information Exchange Systems: Combining Blockchain and Artificial Intelligence
by Joseph Merhej, Hassan Harb, Abdelhafid Abouaissa and Lhassane Idoumghar
Appl. Sci. 2024, 14(19), 8808; https://doi.org/10.3390/app14198808 - 30 Sep 2024
Cited by 2 | Viewed by 4709
Abstract
Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information [...] Read more.
Healthcare Information Exchange (HIE) is becoming a fundamental operation in current healthcare systems. In such systems, electronic health records (EHRs) are digitally stored inside each medical centers and, sometimes, are required to be shared between various healthcare facilities (HCFs). Indeed, sharing patient information is crucial and might be vulnerable to power outages, data misuse, privacy or security violations, and an audit trail. Hence, researchers have focused recently on cutting-edge technologies to develop secure HIE systems and ensure data privacy during transactions. Among such technologies, blockchain and artificial intelligence (AI) occupy a vital role in researchers’ focuses and efforts to detect risky transactions in HIE systems, thus enhancing their security and privacy. While the blockchain allows HCFs to link to each other without requiring a central authority, AI models offer an additional security layer when sharing patient data between HCFs. This paper presents a survey about HIE systems, and the aim is two-fold: we first present the architecture of HIE systems along with their challenges; then, we categorize and classify the current state-the-art-techniques that show the potential of using blockchain and AI technologies in such systems. Full article
(This article belongs to the Special Issue Mobile Ad Hoc Networks (MANETs) in the Era of Cutting-Edge Technologies)
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26 pages, 3174 KiB  
Article
Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
by Ersin Elbasi, Nour Mostafa, Chamseddine Zaki, Zakwan AlArnaout, Ahmet E. Topcu and Louai Saker
Appl. Sci. 2024, 14(17), 8018; https://doi.org/10.3390/app14178018 - 7 Sep 2024
Cited by 10 | Viewed by 9769
Abstract
The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing [...] Read more.
The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively. Full article
(This article belongs to the Special Issue Mobile Ad Hoc Networks (MANETs) in the Era of Cutting-Edge Technologies)
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17 pages, 799 KiB  
Article
Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing
by Alwin M. Kurian, Munachimso J. Onuorah and Habib M. Ammari
Appl. Sci. 2024, 14(3), 960; https://doi.org/10.3390/app14030960 - 23 Jan 2024
Cited by 9 | Viewed by 1801
Abstract
Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary [...] Read more.
Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage. Full article
(This article belongs to the Special Issue Mobile Ad Hoc Networks (MANETs) in the Era of Cutting-Edge Technologies)
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