Publications Scientifiques

Understanding how infectious diseases spread requires models that integrate both human contact structure and environmental factors. We propose SEIRW-VN, a generalized epidemic framework that combines network heterogeneity, indirect transmission via environmental reservoirs, and vaccination. Using COVID-19 data from several European countries, the model outperforms classical homogeneous approaches, capturing more realistic epidemic peaks and timing. Our simulations reveal that highly connected individuals disproportionately sustain outbreaks, while environmental transmission can account for up to one quarter of infections, prolonging epidemic duration. Intervention analysis shows that non-pharmaceutical measures delay peaks and vaccination reduces long-term incidence, but only their combination yields strong synergistic effects, lowering both peak size and overall burden. These findings highlight the importance of integrating contact structure, environmental persistence, and vaccination into epidemic models to design robust and effective public health strategies.

The Zika virus exhibits complex transmission dynamics involving vector-borne, sexual, and vertical pathways, which complicates the design of effective control strategies. In this work, we develop an integrated SICR–SI mathematical model coupling human and mosquito populations, incorporating Wolbachia-based biocontrol, sexual prevention, and vector control as intervention strategies. The model parameters are calibrated using epidemiological time-series data from seven Pacific island archipelagos, and the basic reproduction number R0 is analytically derived. Our results show that Wolbachia substantially reduces the vector component of transmission but cannot eliminate Zika in the presence of sexual transmission, which maintains a residual epidemic risk. We demonstrate that combining Wolbachia releases with sexual prevention yields a synergistic reduction in incidence greater than the sum of their individual effects. Using optimal control theory, we identify time-dependent intervention strategies that minimize both infection burden and implementation costs. A global sensitivity analysis (Sobol indices) highlights the dominant influence of mosquito-related parameters and Wolbachia coverage on epidemic outcomes. Overall, this study underscores the necessity of multitarget and coordinated intervention strategies to effectively reduce Zika transmission and its severe neurological complications.

Assessing public health intervention strategies is crucial for effectively managing dengue. While numerous studies have explored the impact of dengue interventions on its transmission dynamics, limited research has focused on the combined effects of implementing multiple therapeutic interventions for disease control. This study presents an epidemic model for understanding dengue transmission dynamics, incorporating two critical therapeutic measures: vaccination and treatment of infected individuals. The model is characterized by ordinary differential equations involving seven-state variables. The investigation encompasses both disease-free and endemic equilibria of the model. The findings reveal that the disease-free equilibrium (only) is globally stable when the basic reproduction number is below one. Interestingly, when the vaccine’s effectiveness is low, treatment emerges as a more successful approach in reducing dengue cases than vaccination. In contrast, a highly effective vaccine alone significantly curtails dengue occurrences. Moreover, the study introduces an optimal control problem, featuring an objective function integrating two control mechanisms: vaccination and treatment. The analysis strongly suggests that implementing two control strategies outweighs the efficacy of a single approach in effectively mitigating the spread of the disease.

The automation of agriculture is increasingly incorporating advanced technologies like Internet of Things (IoT) and artificial intelligence to optimize productivity. This study focuses on a specific innovation: forecasting post-harvest tomato shelf life using electronic noses and deep learning. The proposed approach provides accurate and continuous monitoring of tomato conditions, addressing the industry’s demand for enhanced efficiency and quality. The model architecture includes four layers: two stacked recurrent layers (either long short-term memory (LSTM) or gated recurrent unit (GRU)), a dropout regularization layer, and a dense output layer. The LSTM based model achieved a mean squared error (MSE) of 0.000586, a mean absolute error (MAE) of 0.01502, and R-squared values of 0.817 (global) and 0.9870 (remaining days). Similarly, the GRU based model demonstrated superior performance with an MSE of 0.000505, an MAE of 0.01392, and R-squared values of 0.832 (global) and 0.9928 (remaining days). By effectively capturing temporal dependencies and minimizing overfitting, these models offer a robust solution for predicting tomato shelf life. This contribution advances agricultural automation, supporting sustainable practices and ensuring the delivery of fresh, high-quality produce to consumers.

The automation of agriculture is increasingly incorporating advanced technologies like Internet of Things (IoT) and artificial intelligence to optimize productivity. This study focuses on a specific innovation: forecasting post-harvest tomato shelf life using electronic noses and deep learning. The proposed approach provides accurate and continuous monitoring of tomato conditions, addressing the industry’s demand for enhanced efficiency and quality. The model architecture includes four layers: two stacked recurrent layers (either long short-term memory (LSTM) or gated recurrent unit (GRU)), a dropout regularization layer, and a dense output layer. The LSTM based model achieved a mean squared error (MSE) of 0.000586, a mean absolute error (MAE) of 0.01502, and R-squared values of 0.817 (global) and 0.9870 (remaining days). Similarly, the GRU based model demonstrated superior performance with an MSE of …

Intelligent Reflecting Surfaces or IRSs are energy-efficient technologies used as “passive” relays to increase coverage. Often fixed, they enable connectivity to users in positions that are difficult for the base station to access, or that are blind. Most studies propose fixed IRS positioning, with the disadvantage of covering only a fixed zone. Therefore, in the vehicular environment, it would be interesting to see the feasibility of placing IRSs following traffic. We, therefore, propose to study the mobile placement of IRSs in a vehicular context first by using an optimizer and then by relying on heuristics. In the first part, we compare fixed and mobile IRS positioning. Then, for the heuristic part, we present different IRS election strategies, which we have compared. Performances are compared between fixed and mobile placement in the first part, and between one and two hops in the second part, all while analyzing the impact of …

In this publication, we present a model that simulates the spread of monkeypox virus. The model takes into account the effect of interactions between human and rodent populations, as well as certain realistic factors such as the complex network with a heterogeneous mean field and certain disease control measures such as vaccination and disease therapy. Although vaccines do not offer total protection against the disease, they can reduce the transmission of monkeypox, which could help reduce the spread of the virus in a population, as our results show. So to develop efficient strategies for epidemic control and prevention, we studied global sensitivity analysis to determine the parameters most influential on disease spread. The existence and overall stability of the disease‐free equilibrium are also discussed. The basic reproduction number R0 of the model is calculated. The results show that if R0 < 1, the disease …

The topological structure of the world air transportation network has been the subject of much research. However, to better understand the reality of air networks, one can consider the traffic, the number of passengers, or the distance between flights. This paper studies the weighted world air transportation network through the component structure, recently introduced in the network literature, by using the number of flights. The component structure is based on the community or multiple core-periphery structures and splits the network into local and global components. The local components capture the regional flights of these two mesoscopic structures (dense parts). The global components capture the inter-regional flights (links between the dense parts). We perform a comparative analysis of the world air transportation network and its components with their weighted counterparts. Moreover, we explore the strength and the s-core of these networks. Results display fewer local components well delimited and more global components covering the world than the unweighted world air transportation network. Centrality analysis reveals the difference between the top airports with high traffic and the top airports with high degrees. This difference is more pronounced in the global air network and the largest global component. Core analysis shows similitude between the s-core and the k-core for the local and global components, even though the latter includes more airports. For the world air network, the North and Central America-Caribbean airports dominate the s-core, whereas the European airports dominate the k-core.

A deeper assessment of the nutritional and organoleptic potential of the items we consume is the first step toward knowing their capacity. Research is becoming more and more interested in developing quick and accurate instruments to assess touch, vision, hearing, and other senses under this impetus. This paper examines a variety of statistical and machine learning algorithms used for food quality applications with electronic nose technology. The latter was discussed, allowing us to say that different data collection devices and data analysis methods were used for each study that was undertaken. Setting up an experimental e-nose based on commercially accessible sensors would be simpler, quicker, and less expensive in a West African setting than buying a commercial e-nose to forecast local crop condition. There is a scarcity of studies that examine the connection between e-nose technologies and food quality in Africa.

Mobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, social, economic, and cultural activities. The topological structure of the physical and/or proximity contact networks depends, therefore, entirely on the mobility patterns. This paper performs an extensive comparative analysis of real-world temporal contact networks and synthetic networks based on influential mobility models. Results show that the various topological properties of most of the synthetic datasets depart from those observed in real-world contact networks because the randomness of some mobility parameters tends to move away from human contact properties. However, it appears that data generated using Spatio-Temporal Parametric Stepping (STEPS) mobility model reveals similarities with real temporal contact networks such as heavy-tailed distribution of contact duration, frequency of pairs of contacts, and the bursty phenomenon. These results pave the way for further improvement of mobility models to generate meaningful artificial contact networks.

he main objective of Zika transmission studies is to work out the simplest approach to scale back human mortality and morbidity caused by the disease. Therefore, it is essential to spot the relative importance of the various factors contributing to the transmission and prevalence of the disease. Many mathematical models have been formulated incorporating vector-to-human transmission or human-to-human transmission. However, they do not take into consideration the mixture of both sorts of transmission. It raises the question of the impact of both sorts of transmission on the disease dynamics. We develop a mathematical model of Zika with the vertical transmission in the vector population and human-to-human transmission to answer this question. It includes the immature phase of mosquitoes (eggs), adult mosquitoes (susceptible, exposed, and infectious), and human hosts (susceptible, exposed, infectious, and recovered). Results show that neglecting sexual transmission results in an understatement of the proportion of the infected population. Furthermore, it reduces the speed of disease spreading. On the other hand, vertical transmission in mosquitoes has a negligible effect on the dynamics of disease spread. We perform a sensitivity analysis of the reproductive number R0 to raise to understand the parameters driving the dynamics of the disease. It appears that the most sensitive parameters in decreasing order are as follows: the adult mosquito death rate, the sting rate, the transmission probability of mosquito to human, and, therefore, the transmission probability of human to mosquito. Furthermore, the proportion at the equilibrium of infected humans is extremely sensitive to the transition rate from the immature vector stage to the adult stage, the human-to-human transmission rate, and, therefore, the human recovery rate. These results confirm that control policies targeting the vector population and, therefore, the recovery rate of people are pretty effective solutions. To validate the model and estimate the important parameters of the model and the prediction of the disease, we consider the real cases in Colombia from 2016. In a series of graphic maps, we presented the comparative study to estimate the disease scenarios and to predict the time limit of the epidemic control measure.

The Internet of Things (IoT) has finally made its way into people’s daily lives. Multiple connected objects are present in many markets. Their rapid adoption is due to the fact that IoT applications are developing rapidly and are easily deployed. However, these applications present several security challenges, although they are based on existing network technologies, which are already more or less secure. These challenges can be broadly classified as identification, authentication, jamming, cloning, privacy, etc. To face these challenges and in the perspective of ensuring end-to-end IoT networks security, several encryption mechanisms have been adapted and widely used including public key infrastructure (PKI), AES standard and ECC (Elliptic Curve Cryptography). To correct the weaknesses and vulnerabilities that are still present, other technologies such as DLTs (Distributed Ledger Technology) have been recently adopted by the Internet of Things. This adoption is still in its infancy as there are no standards nor methods yet indicating how to manage the security of the IoT through DLTs. In this article, we review the IoT-DLT paradigm in order to identify the benefits and major challenges of the convergence of these two technologies.

Te robustness of a system indicates its ability to withstand disturbances while maintaining its properties, performance, and efciency. Tere are plenty of studies on the robustness of air transport networks in the literature. However, few works consider its mesoscopic organization. Building on the recently introduced component structure, we explore the impact of targeted attacks on the weighted world air transportation network on its components. Indeed, it contains fve local components covering diferent regions (North America•Caribbean, Europe•Russia, East and Southeast Asia•Oceania, Africa•Middle East•Southern Asia, and South America) and one global component linking these regions. We investigate targeted attacks based on infuential weighted centrality measures (strength, betweenness, and PageRank). Results show that the local components gradually separate from the world air transportation network as the fraction of removed airports grows. Te weighted betweenness attack removes fewer top airports to isolate the regions compared to its alternatives. Furthermore, it is still convenient to travel locally in the separate areas. In contrast, strength and PageRank attacks need to target more airports to split the network. However, they are more disruptive. Indeed, the size of the isolated local components reduces drastically, so it becomes more challenging to travel locally. Looking at the world air transportation network through its component structure reveals a new viewpoint on its resilence. It opens new perspectives to design more efcient attacks.

Air transportation plays an essential role in the global economy. Therefore, there is a great deal of work to understand better the complex network formed by the links between the origins and destinations of flights. Some investigations show that the world air transportation network exhibits a community and a core-periphery structure. Although precious, these representations do not distinguish the inter-regional (global) web of connections from the regional (local) one. Therefore, we propose a new mesoscopic model called the component structure that decomposes the network into local and global components. Local components are the dense areas of the network, and global components are the nodes and links bridging the local components. As a case study, we consider the unweighted and undirected world air transportation network. Experiments show that it contains seven large local components and multiple small ones spatially well-defined. Moreover, it has a main global component covering the world. We perform an extensive comparative analysis of the structure of the components. Results demonstrate the non-homogeneous nature of the world air transportation network. The local components structure highlights regional differences, and the global component organization captures the efficiency of inter-regional travel. Centrality analysis of the components allows distinguishing airports centered on regional destinations from those focused on inter-regional exchanges. Core analysis is more accurate in the components than in the whole network where Europe dominates, blurring the rest of the world. Besides the world air transportation network, this paper demonstrates the potential of the component decomposition for modeling and analyzing the mesoscale structure of networks.

Wireless Sensor Networks (WSN) consists of a set of small and resources-constrained devices. They have received much attention over the last few years for the study and development of a plethora of potential applications. However, the common denominator of all applications of these sensor networks is the vulnerability of micro-sensors because of their limited material resources the most constraining of which is energy. In fact, wireless sensors are limited in terms of calculation, storage, battery, etc. Therefore, every possible solution that aims to conserve these resources is extensively sought. Thus, a great deal of researches has been conducted leading to an effective technique answering the established problem. This solution concerns the aggregation of data which is one of the techniques that is actually considered as an essential paradigm for sensor networks since it tends to save computation and communication resources. Data aggregation allows innetwork processing which leads to lesser packet transmissions and reduces redundancy, and therefore, helps in increasing network’s overall lifetime. However, sensor networks are usually deployed in unattended and hostile environments. Thus, the designer should not only consider the limited resources of the sensor nodes but also the security threats that can occur in an easily accessible network to the attacker. In this paper, we will give an overview study of the existing data aggregation solutions in wireless sensor networks then, we will propose a new efficient and secure approach to aggregation which can be evaluated with specific criteria.

In a wireless sensor network (WSN), sensors are typically deployed in an area in order to collect events related to one or more sensing features such as temperature, humidity, light, pressure, magnetic, accelerometer, acoustic activity, seismic movements, etc. The sensors communicate with each other to relay events to a Sink node or a Base Station that communicates with the network operator. Since the most critical feature in sensor networks is the hardship of its energy resources because each sensor node has initial low resources in terms of energy and it is not often possible to replace batteries during network operations. Moreover, equipped with a small processor generally associated to a small storage unit, sensors often operate without human assistance, and could be deployed in open and hostile environments. Therefore, wireless sensor networks are subject to various types of threats, vulnerabilities and attacks which are carried out in order to disclosure sensitive information, to disrupt network operations and to exhaust the batteries of the nodes and then to reduce their lifetimes, and consequently that of the network. In this paper, we will try to give an overview study of security issues and solutions in WSN.

In Wireless Sensor Networks (WSN), the nodes are often deployed to collect environmental data related to some particular relevant events. The collected data are then sent to a sink node or a base station following operations aimed at optimizing critical network resources such as storage, computing capacities and bandwidth. However, sensors have low processing and storage capabilities, small bandwidth and relatively low energetic resources. Thus, data aggregation is one of the most powerful techniques used to optimize sensor resources. Instead of sending data directly to the base station, the sensors send it first to an aggregation node that will compute a summary avoiding then redundant data and reducing the number of transmissions. During the data aggregation process, several attacks exploiting network vulnerabilities and weaknesses threaten sensor applications. In this paper we will give an overview study of the data aggregation process security issues and solutions in WSNs.

Wireless sensor networks face significant design challenges due to limited computing and storage capacities and, most importantly, dependence on limited battery power. Energy is a critical resource and is often an important issue to the deployment of sensor applications that claim to be omnipresent in the world of future. Thus optimizing the deployment of sensors becomes a major constraint in the design and implementation of a WSN in order to ensure better network operations. In wireless networking, clustering techniques add scalability, reduce the computation complexity of routing protocols, allow data aggregation and then enhance the network performance. The well-known MaxMin clustering algorithm was previously generalized, corrected and validated. Then, in a previous work we have improved MaxMin by proposing a Single-node Cluster Reduction (SNCR) mechanism which eliminates single-node clusters and then improve energy efficiency. In this paper, we show that MaxMin, because of its original pathological case, does not support the grid deployment topology, which is frequently used in WSN architectures. The unreliability feature of the wireless links could have negative impacts on Link Quality Indicator (LQI) based clustering protocols. So, in the second part of this paper we show how our distributed Link Quality based d-Clustering Protocol (LQI-DCP) has good performance in both stable and high unreliable link environments. Finally, performance evaluation results also show that LQI-DCP fully supports the grid deployment topology and is more energy efficient than MaxMin.

In wireless sensor networks (WSN) energy efficiency of routing protocols is of primary importance. Embedded with local load balancing mechanisms, the proposed L2RP protocol is a link reliability based routing protocol which aims to help source nodes to exploit the potential capabilities of their respective neighbors. As it is a reliability-oriented protocol, L2RP discards unreliable links to avoid the substantial energy cost of packet losses. Simulation results show major efficiency benefits that stem from load balancing which helps in lengthening the network lifetime while minimizing packet losses. In WSN, the choice of a routing protocol and its key parameters depends on the nature of the application and on its primary mission. Lot of works addressed routing issues with more or less effectiveness, some of which pointed out the use of the link quality indicator (LQI) as a route selection criterion (metric). In a previous work, following an experimental study, we have shown, under some conditions, the inefficiency of the LQI based routing. In this paper, we propose through L2RP a simple way to improve reliability and efficiency of the LQI based routing in WSN. We also give a comparative study of several metrics including new definitions of LQI based metrics. Simulation results show that our adaptation of the LQI metric is among the best route selection criteria regardless of the performance criterion under consideration.