A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks
Article
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Articles and books
Abierto
Inglés
Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.
metadata
Alsukayti, Ibrahim S. and Singh, Aman
mail
UNSPECIFIED, aman.singh@uneatlantico.es
(2022)
A Lightweight Scheme for Mitigating RPL Version Number Attacks in IoT Networks.
IEEE Access, 10.
pp. 111115-111133.
ISSN 2169-3536
![]() |
Text
A_Lightweight_Scheme_for_Mitigating_RPL_Version_Number_Attacks_in_IoT_Networks.pdf Download (2MB) |
Abstract
Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Internet of Things, wireless sensor networks, RPL, network security |
Subjects: | Subjects > Engineering |
Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Articles and books |
Date Deposited: | 25 Jan 2023 23:30 |
Last Modified: | 11 Jul 2023 23:30 |
URI: | https://repositorio.unic.co.ao/id/eprint/5559 |
Actions (login required)
![]() |
View Item |
<a class="ep_document_link" href="/17788/1/s40537-025-01167-w.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis
The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.
Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,
Raza Ur Rehman
<a href="/17794/1/s41598-025-95836-8.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.
Oussama Khouili mail , Mohamed Hanine mail , Mohamed Louzazni mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Imran Ashraf mail ,
Khouili
<a href="/17593/1/s41598-025-95448-2.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Client engagement solution for post implementation issues in software industry using blockchain
In the rapidly advanced and evolving information technology industry, adequate client engagement plays a critical role as it is very important to understand the client’s concerns, and requirements, have the records, authorizations, and go-ahead of previously agreed requirements, and provide the feasible solution accordingly. Previously multiple solutions have been proposed to enhance the efficiency of client engagement, but they lack traceability, trust, transparency, and conflict in agreements of previous contracts. Due to the lack of these shortcomings, the client requirement is getting delayed which is causing client escalations, integrity issues, project failure, and penalties. In this study, we proposed the UniferCollab framework to overcome the issues of collaboration between various teams, transparency, the record of client authorizations, and the go-ahead on previous developments by implementing blockchain technology. We store the data on the permissible network in the proposed approach. It allows us to compile all the requirements and information shared by clients on permissible blockchain to secure a large amount of data which enhances the traceability of all the requirements. All the authorizations from the client generate push notifications for any changes in their current system executed through smart contracts. It removes the ambiguity between various development teams if the client has only shared the requirement with one team. The data is stored in the decentralized network from where information is gathered which resolves the traceability, transparency, and trust issues. Lastly, evaluations involved a total of 800 hypertext transfer protocol (HTTP) requests tested using Postman with blockchain block sizes ranging from 0.568 KB to 550 KB and an average size increase of 280 KB was observed as new blocks were added. The longest chain in the network was observed during 800 repetitions of blockchain operations. Latency analysis revealed that delays in processing HTTP requests were influenced by decentralized node processing, local machine response times, and internet bandwidth through various experiments. Results show that the proposed framework resolves all client engagement issues in implementation between all stakeholders which enhances trust, and transparency improves client experience and helps us manage disputes effectively.
Muhammad Shoaib Farooq mail , Khurram Irshad mail , Danish Riaz mail , Nagwan Abdel Samee mail , Ernesto Bautista Thompson mail ernesto.bautista@unini.edu.mx, Daniel Gavilanes Aray mail daniel.gavilanes@uneatlantico.es, Imran Ashraf mail ,
Farooq
<a href="/17611/1/nutrients-17-01242.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Background/Objectives: Sleep is a fundamental physiological function that plays a crucial role in maintaining health and well-being. The aim of this study was to assess dietary and lifestyle factors associated with adequate sleep duration in children and adolescents living in five Mediterranean countries. Methods: Parents of children and adolescents taking part in an initial survey for the DELICIOUS project were examined to assess their children’s dietary and eating habits (i.e., meal routines), as well as other lifestyle behaviors (i.e., physical activity levels, screen time, etc.) potentially associated with adequate sleep duration (defined as 8–10 h according to the National Sleep Foundation). The youth healthy eating index (Y-HEI) was used to assess the diet quality of children and adolescents. Multivariate logistic regression analyses were performed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs), indicating the level of association between variables. Results: A total of 2011 individuals participated in the survey. The adolescents and children of younger parents reported being more likely to have inadequate sleep duration. Among eating behaviors, having breakfast (OR = 2.23, 95% CI: 1.62, 3.08) and eating at school (OR = 1.33, 95% CI: 1.01, 1.74) were associated with adequate sleep duration. In contrast, children eating alone, screen time, and eating outside of the home were less likely to have adequate sleep duration, although these findings were only significant in the unadjusted model. After adjusting for covariates, a better diet quality (OR = 1.63, 95% CI: 1.24, 2.16), including higher intake of fruits, meat, fish, and whole grains, was associated with adequate sleep duration. Conclusions: Adequate sleep duration seems to be highly influenced by factors related to individual lifestyles, family and school eating behaviors, as well as diet quality.
Justyna Godos mail , Alice Rosi mail , Francesca Scazzina mail , Maria Antonieta Touriz Bonifaz mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Osama Abdelkarim mail , Achraf Ammar mail , Mohamed Aly mail , Evelyn Frias-Toral mail , Juancho Pons mail , Laura Vázquez-Araújo mail , Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Lorenzo Monasta mail , Ana Mata mail , Adrián Chacón mail , Pablo Busó mail , Giuseppe Grosso mail ,
Godos
<a href="/17792/1/s41598-025-97561-8.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Ensemble stacked model for enhanced identification of sentiments from IMDB reviews
The emergence of social media platforms led to the sharing of ideas, thoughts, events, and reviews. The shared views and comments contain people’s sentiments and analysis of these sentiments has emerged as one of the most popular fields of study. Sentiment analysis in the Urdu language is an important research problem similar to other languages, however, it is not investigated very well. On social media platforms like X (Twitter), billions of native Urdu speakers use the Urdu script which makes sentiment analysis in the Urdu language important. In this regard, an ensemble model RRLS is proposed that stacks random forest, recurrent neural network, logistic regression (LR), and support vector machine (SVM). The Internet Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment analysis. The Urdu hack library was used to preprocess the Urdu data, which includes preprocessing operations including normalizing individual letters, merging them, including spaces, etc. concerning punctuation. The problem of accurately encoding Urdu characters and replacing Arabic letters with their Urdu equivalents is fixed by the normalization module. Several models are adopted in this study for extensive evaluation of their accuracy for Urdu sentiment analysis. While the results promising, among machine learning models, the SVM and LR attained an accuracy of 87%, according to performance criteria such as F-measure, accuracy, recall, and precision. The accuracy of the long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) was 84%. The suggested ensemble RRLS model performs better than other learning algorithms and achieves a 90% accuracy rate, outperforming current methods. The use of the synthetic minority oversampling technique (SMOTE) is observed to improve the performance and lead to 92.77% accuracy.
Komal Azim mail , Alishba Tahir mail , Mobeen Shahroz mail , Hanen Karamti mail , Annia A. Vázquez mail annia.almeyda@uneatlantico.es, Angel Olider Rojas Vistorte mail angel.rojas@uneatlantico.es, Imran Ashraf mail ,
Azim