Optimal Cluster Head in DTN Routing Hierarchical Topology (DRHT)

In delay tolerant networking (DTN), nodes are autonomous and behave in an unpredictable way. Consequently, a control mechanism of topology is necessary. This mechanism should ensure the overall connectivity of the network taking into account nodes’ mobility. In this paper, we study the problem of data routing with an optimal delay in the bundle layer, by exploiting: the clustering, the messages ferries and the optimal election of cluster head (CH). We first introduce the DTN routing hierarchical topology (DRHT) which incorporates these three factors into the routing metric. We propose an optimal approach to elect a CH based on four criteria: the residual energy, the intra-cluster distance, the node degree and the head count of probable CHs. We proceed then to model a Markov decision process (MDP) to decide the optimal moment for sending data in order to ensure a higher delivery rate within a reasonable delay. At the end, we present the simulation results demonstrating the effectiveness of the DRHT. Our simulation shows that while using the DRHT which is based on the optimal election of CH, the traffic control during the TTL interval (Time To Live) is balanced, which greatly increases the delivery rate of bundles and decreases the loss rate

improving parking availability prediction in smart cities with iot and ensemble based model

Abstract Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity

L'utilisation de la blockchain pour la sécurité de l'internet des objets.

International audienceD'ici 2020, l'Institut Gartner, la célèbre compagnie de recherche en technologie de l'information, estime que le nombre des objets connectés sur le marché pourrait atteindre 50 milliards. Les maisons intelligentes, comme une application typique d' IdO, fournissent des dispositifs avec diverses applications pratiques, mais sont confrontés à des problèmes de sécurité et de confidentialité. la technologie Blockchain (BC) a apporté une solution potentielle au problème de la sécurité IdO. L'émergence de cette technologie a provoqué un changement de la gestion décentralisée, fournissant une solution efficace pour la protection de la sécurité du réseau et la confidentialité. Dans cet article, nous proposons une modélisation de la blockchain par la théorie des hypergraphes. Les objectifs de ce modèle sont de réduire la consommation de stockage et de résoudre les problèmes de sécurité supplémentaires

A Quantitative Approach to Road Safety in Morocco: Reducing Accidents through Predictive Modeling

This paper uses machine learning to predict road accidents in Morocco, a country marked by high annual accident rates. Our model employs data such as weather, time of day, and road conditions, derived from historical accidents and environmental records. Findings suggest that such predictive modeling can enable traffic authorities to anticipate high-risk situations and enact pre-emptive safety measures, contributing to significant reductions in road accidents. This study provides a data-driven approach towards policy implementation for road safety, with insights applicable to global road safety initiatives

Prédiction du temps d'attente des bus dans les villes intelligentes à l'aide du Machine Learning avec l'Internet des objets

International audienceLe temps d'attente à une station de bus est parfois long, ou peut paraître très long pour les voyageurs. Ce ressenti est causé par les multiples arrêts dans l'itinéraire du bus ainsi que l'intervalle de temps de déplacement entre les différentes stations. En effet, les gens cherchent toujours à planifier leurs déplacements en ville d'une façon optimale et n'aiment pas attendre de longues heures dans les arrêts de bus pour atteindre leur destination. Dans ce papier nous allons présenter une solution afin de prédire le temps d'attente des bus en utilisant l'internet des objets, l'ITS et les techniques de machine learning. Plusieurs algorithmes sont utilisés afin de donner une estimation précise du temps d'attente dans les arrêts de bus.

A Game Theoretic Approach to Analyse Security between Smart Vehicles and Parcels in Smart Cities

The Internet of Things (IoT) is considered as a modern concept that will revolutionize the near future. Its interest is to create an environment of combined intelligent devices and systems, communicating with each other through wireless networks. Urban logistics are an applicative field of this new technology, especially for smart parcels and vehicles. Actually, in the context of economy development, the competitiveness between companies and territories necessarily involves an improvement of logistics services. Although these gains offered by IoT, there are significant obstacles to counter. One of the important obstacles to consider is the security. In this paper, we will analyze the interaction between selfish smart vehicles/parcels and malicious smart vehicles/parcels, that was formulated as a game model. As a result, we have calculated the Nash equilibrium and the utilities for the both selfish smart vehicles/parcels and malicious smart vehicles/parcels, evaluated the parameters that can maximize the selfish smart vehicles/parcels’s utility when the smart parcels are transported by vehicles between different centers (shops, supermarket, etc) was planned and identify the potential malicious smart vehicles/parcels

Using Machine Learning in WSNs for Performance Prediction MAC Layer

To monitor environments, Wireless Sensor Networks (WSNs) are used for collecting data in divers domains such as smart factories, smart buildings, etc. In such environments, different medium access control (MAC) protocols are available to sensor nodes for wireless communications and are of a paramount importance to enhance the network performance. Proposed MAC layer protocols for WSNs are generally designed to achieve a good performance in packet reception rate. Once chosen, the MAC protocol is used and remains the same throughout the network lifetime even if its performance decreases over time. In this paper, we adopt supervised machine learning techniques to predict the performance of CSMA/CA MAC protocol based on the packet reception rate. Our approach consists of three steps: experiments for data collection, offline modeling and performance evaluation. Our analysis shows that XGBoost prediction model is the better supervised machine learning technique to enhance network performance at the MAC layer level. In addition, we use SHAP method to explain predictions

An empirical assessment of ensemble methods and traditional machine for web-based attack detection in 5.0

Cybersecurity attacks that target software have become profitable and popular targets for cybercriminals who consciously take advantage of web-based vulnerabilities and execute attacks that might jeopardize essential industry 5.0 features. Several machine learning-based techniques have been developed in the literature to identify these types of assaults. In contrast to single classifiers, ensemble methods have not been evaluated empirically. To the best of our knowledge, this work is the first empirical evaluation of both homogeneous and heterogeneous ensemble approaches compared to single classifiers for web -based attack detection in industry 5.0, utilizing two of the most realistic public web-based attack data -sets. The authors divided the experiment into three main phases: In the first phase, they evaluated the performance of five well-established supervised machine learning (ML) classifiers. In the second phase, they constructed a heterogeneous ensemble of the three best-performing ML algorithms using max vot-ing and stacking methods. In the third phase, they used four well-known homogeneous ensembles to evaluate the performance of the bagging and boosting method. The results based on the ECML/PKDD 2007 and CSIC HTTP 2010 datasets revealed that bagging, particularly Random Forest, outperformed sin-gle classifiers in terms of accuracy, precision, F-value, FPR, and area of the ROC curve with values of 99.597%, 98.274%, 99.129%, 0.523%, 100 and 99.867%, 99.867%, 99.867%, 0.267%, 100, respectively. In con-trast, single classifiers performed better than boosting and stacking. However, in terms of FPR, the boost-ing exceeded single classifiers. Max voting is appropriate when accuracy, precision, and FPR are the primary concerns, whereas single classifiers can be employed when recall, FNR, training, and prediction times are critical elements. In terms of training time, ensemble approaches are more likely to be affected by data volume than single classifiers. The papers findings will help security researchers and practition-ers identify the most efficient learning techniques for securing web applications. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)