Doctorat en Mathématique & Informatique
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Département de Mathématique & Informatique
Université Hassiba Benbouali de Chlef,Faculté des sciences BP 151
Hay Salem, route nationale N° 19
02000 Chlef, Algérie
Tel: 027-72-70-17
Email: Bio_uhbc@univ-chlef.dz
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Item On some classes of operators in Hilbert space and a semi-Hilbertian space(2026-04-12) Maadani MustaphaAbstract: The aim of this thesis is to introduce new classes of interaction operators, and to apply these classes to two fundamental theorems in functional analysis. The content of this work is also divided into two parts: the first part is devoted to operators defined on a Hilbert space, while the second part focuses on operators defined on a semi-Hilbertian space, which includes a positive operator. We conclude this thesis by providing examples and counterexamples illustrating the obtained results.Item Fundamental and spectral properties of some classes of non-normal operators on a Hilbert space(Aissa NASLI BAKIR / Tayeb HADJ KADDOUR, 2026) FELLAG ARIOUAT, AyyoubThe purpose of this thesis is to investigate several properties of certain classes of nonnormal linear bounded operators acting on a separable complex Hilbert space specifically, those operators that fail to commute with their adjoints. We present a collection of essential structural and spectral characteristics that extend well-known properties of normal operators. These include 1. orthogonal decompositions, 2. restrictions concerning invariant subspaces, 3. Bishop’s property , 4. the single-valued extension property, 5. isoloid and polaroid operators In addition, new results are obtained regarding invariant subspaces and the behavior of the Riesz idempotent associated with these operator classes. The methods rely mainly on the theory of orthogonal decompositions, as well as on the study of invariant and reducing subspaces, which together form the theoretical framework of this research.Item DYNAMIC ADAPTATION APPROACH FOR DISTRIBUTED SYSTEM BEHAVIOR(Mohammed Amin TAHRAOUI / Ahmed HARBOUCHE, 2026) BENDIAF, MOHAMMED LOTFIAs distributed systems (DS) evolve, managing dynamic workloads and resource availability becomes essential for maintaining optimal performance. This thesis presents a novel Dynamic Adaptation (DA) framework designed to enhance the e”ciency and responsiveness of DS, particularly in heterogeneous computing environments. The central problem addressed in this work concerns how the performance of distributed systems can be e!ectively enhanced through dynamic adaptation, particularly at the level of a single-node multiprocessor system considered as a fine-grained unit within a distributed architecture. Furthermore, what key strategies and algorithmic approaches are essential to optimize task allocation, ensure load balancing, and minimize execution time in such heterogeneous environments. A key contribution of this work is the introduction of two innovative algorithms for dynamic task scheduling. The first, DyTAg (Dynamic Task Allocation using Dynamic Programming), focuses on optimizing task allocation in heterogeneous multiprocessor systems with independent tasks. It leverages dynamic programming to minimize makespan and balance workloads e!ectively, laying the groundwork for more advanced scheduling approaches. Building on DyTAg, the thesis introduces the Knapsack-based Algorithm Co-Scheduling Task Allocation (KaCoSTA), which integrates dynamic programming with knapsack optimization techniques to address task precedence and resource constraints. KaCoSTA dynamically adapts to system states, task priorities, and processing capabilities to maximize resource utilization (RU) and minimize makespan. Extensive experiments demonstrate its superiority over existing methods, such as Min-Min, Max-Min, and HEFT (Heterogeneous Earliest Finish Time), in both static and dynamic scenarios. Results reveal significant advancements in system adaptability, load balancing, and overall e”ciency under fluctuating workloads. By combining foundational research with practical innovation, this work provides a comprehensive solution for optimizing DS behavior in real-world applications.Item On certain boundary value problems involving fractional-order differential equations(Louiza Tabharit / Benali Abdelkader, 2026) BOUZID, HouariThis thesis presents several results on the existence, uniqueness, and stability of nonlocal and boundary value problems for differential equations involving the generalized Caputo fractional derivatives. In addition, we investigate coupled systems of nonlinear fractional differential equations within the same framework. The analysis relies on fixed point theorems, including those of Krasnoselskii, Dhage, Schaefer, and the Banach contraction principle. Moreover, the study extends to Banach spaces, employing Darbo’s fixed-point theorem in conjunction with the measure of noncompactness technique. Each chapter is considered a continuation of the previous one and ends with illustrations to show the applicability of the results.Item Predictive approaches to the impact of environmental conditions on the durability of clayey soils stabilized with mineral additives.(Ali MAKHLOUF / Billal SARI-AHMED, 2025) TOUALBIA, YOUSSOUFThis doctoral thesis addresses a critical issue in geotechnical engineering: the impact of environmental conditions, particularly freeze–thaw cycles, on the effectiveness and long-term durability of stabilized clayey soils treated with mineral additives such as lime and fly ash. Numerous published experimental studies have demonstrated an initial improvement in unconfined compressive strength (UCS) following stabilization, but also a progressive deterioration of mechanical performance under repeated freeze–thaw exposure. However, experimental assessment remains challenging, due to the difficulty in realistically reproducing climatic conditions, precisely controlling curing parameters, and managing result variability. These limitations justify the adoption of predictive modeling approaches. In this context, two modeling approaches: a statistical regression and an ANN, were developed using literature-based experimental databases for soils stabilized with lime and fly ash. These models effectively simulate the evolution of UCS in stabilized soils subjected to environmental cycles. Parametric studies were also conducted to capture the influence of several key factors, such as the number of freeze–thaw cycles, water content, additive content, and curing duration. Results show that ANN models offer superior predictive performance and better capture of nonlinear relationships between input variables and soil strength response. By integrating diverse experimental data and leveraging the power of artificial intelligence, this work contributes to a better understanding of the long-term behavior of stabilized soils under harsh climatic conditions and provides reliable tools to support resilient geotechnical design in environmentally sensitive regions. Keywords: stabilized clayey soils; freeze–thaw cycles; mineral additives; statistical model; artificial neural network (ANN); unconfined compressive strength (UCS)Item A task classification strategy for IoT/Cloud collaboration(Bechar Rachid / Kadri Walid, 2026) BENABOURA, AMINAThere is a growing need for efficient computational offloading technologies that can handle large data flows while adhering to strict response time and power consumption constraints as a result of the explosive growth of Internet of Things (IoT) devices. Real-time IoT applications require scalability and responsiveness, which traditional cloud computing cannot provide. To address these issues, this thesis presents a task offloading framework based on Deep Q-Network (DQN) in a collaborative architecture between IoT, fog computing, and cloud computing levels for intelligent and adaptive decision-making that maximizes system performance. The DQNbased strategy achieves superior performance, with reductions of up to 50% in total cost, 33% in latency, and 25% in energy consumption compared to competing methods, according to extensive simulation experiments against state-of-the-art algorithms, such as bat, DJA, and DDPG-based approaches. The DQN algorithm also shows strong convergence behavior, low variance, and high reliability across multiple scenarios, confirming its robustness and adaptability in distributed computing environments. In order to improve Quality of Service (QoS) and Quality of Experience (QoE) in IoT–fog–cloud ecosystems, this work offers a scalable, data-driven offloading solution, which advances the expanding field of intelligent task management. The suggested framework opens perspectives for more independent and energy-conscious computing paradigms by laying the groundwork for future studies on multiagent deep reinforcement learning, federated offloading techniques, and optical network-enhanced IoT infrastructures.Item Design and implementation of a secure LoRaWAN protocol used in critical applications in the IoT domain(BOUMERDASSI Selma / TAHAR ABBES Mounir, 2025) Abdelouahab, NouarOf the available wireless transmission technologies, LPWANs (Low Power Wide Area Networks) are attracting increasing attention, not least because of their long radio range and low energy consumption. However, trying to minimize power consumption can sometimes compromise the resilience of data transmission in the face of environmental disturbances (interference, obstacles) and the mobility of connected objects. However, attempting to reduce power consumption can occasionally compromise the mobility of linked items and the robustness of data transmission against environmental disruptions (obstacles, interference). Additionally, each node’s duration occupancy of the frequency band is limited by the long radio range (e.g. duty cycle limited to 1%). We concentrate on LoRa/LoRaWAN technologies in this thesis. LoRaWAN may be able to accommodate a wide range of IoT applications and situations because to its numerous adjustable characteristics. It can converge to an ideal configuration to save energy due to its ADR (Adaptive Data Rate) function. More recently, LoRa and LoRaWAN have also drawn interest from applications utilizing mobile nodes. This thesis’s first contribution is its presentation of how mobility affects LoRaWAN performance. To achieve this, the research consists of two (02) parts: In the first part, we present an in-depth analysis of LoRaWAN performance evaluation in a mobility context. To do this, we consider several scenarios and performance evaluation measures using the NS − 3 simulator, based on the three mobility models most widely used in the literature, such as the Gauss Markov Mobility Model, the Random Waypoint Mobility model and the Constant Position Mobility Model. The new study presents the influence of these three models on energy consumption, PDR, network size and Radius. In order to validate the simulation results, in the second part we carried out numerous experiments with the Lora CubeCell HTCC-AB01 in various scenarios, analyzing the RSSI (Received Signal Strength Indicator) level in urban and rural areas using a large number of trajectories. The maximum distance obtained in a rural area is 1310 meters of line of sight, while in an urban area, the distance is equal to 966.97 meters of line of sight. In terms of energy consumption, the results show that the GM model is 0.1 J, and for the RWP and CP it is 0.4 J, which equals 9.6 J in 24 hours, which makes the GM model four times more efficient. The GM model with Alpha = 1 performs better than the other two models, and Alpha = 0.5 performs even better in terms of PDR. At the same time, the RWP demonstrates positive results regarding delays. In order to find the best combination for packet transmission, we provide a second contribution in this thesis to the description of the SFs allocation scheme for Lorawan. Furthermore, even though the various simulations carried out and the results obtained, we consider it more appropriate to use mathematical and logical methods to validate the ADR mechanism, and this was the subject of our third contribution. We used Event-B (the formal method) to model the protocol layers and their properties, and Event-B invariants to ensure protocol consistency, and we’ll add more guarantees to the validity of the protocol, focusing on the formal validation of the ADR mechanism on the network server side. The security aspect is studied, by presenting the physical structure of Lora packet, the Mac message types and the different LoraWan Mac commands, followed by the two activation modes of Lora End Devices including OTAA and ABP Mode. In all the literature, to our knowledge, currently the deployment of cryptography using the NS-3 simulator has not yet achieved, this is the subject of our main and last contribution, our goal is the implementation of AES-128 bits algorithm under the NS-3 simulator, and to assess the performance of the LoRaWAN network in terms of energy consumption, transmission latency, packet delivery rate, and CPU utilization. The findings indicate a shift in the transmission delay from 0.25 ms to 1.7 ms for a packet of 12 bytes and 216 bytes respectivelyItem Graph Parameters for Social Network Analysis(Tahraoui Mohammed Amin / Kheddouci Hamamache, 2025-10-15) CHENAOUI, ALIThe exponential growth of complex networks has transformed social network analysis, creating unprecedented challenges for community detection. As networks scale to billions of nodes and connections, traditional approaches struggle to capture the true nature of community structures. This thesis addresses three fundamental limitations in current methodologies: inadequate integration of node importance, limited similarity measures, and inflexible optimisation approaches. Through theoretical development and experimental validation, this research presents three innovative contributions that significantly advance community detection capabilities. The first contribution is the Neighbourhood overlap and Density (NoD) similarity measure, which combines information about shared connections with local structural density. This novel measure overcomes limitations in traditional approaches by considering both how many neighbours two nodes share and how densely those shared neighbours are connected. Experimental validation on real-world benchmark networks—including Zachary’s Karate Club, Dolphins, Football, and Polbooks—shows that NoD increases modularity by up to 0.08 (from 0.38 to 0.46 on Dolphins) while maintaining competitive accuracy with Normalised Mutual Information (NMI) values ranging from 0.71 to 0.90 across the evaluated datasets. Building upon NoD, two complementary algorithmic contributions address distinct community detection scenarios. The Heuristic Community detection algorithm based on Centrality and Similarity measures (HCCS) introduces a deterministic approach that recognises the varying importance of nodes in community formation. By systematically integrating centrality-based leader selection with similarity-driven community formation, HCCS overcomes the limitations of traditional approaches that treat these aspects separately, resulting in more accurate and robust community detection. Experimental evaluation across nine real networks—from small social graphs to large infrastructure and communication networks—using modularity and Normalised Mutual Information confirms its effectiveness, delivering the highest modularity on Karate (0.42), Football (0.60), and Uni_email (0.55) and competitive NMI scores such as 0.89 on Dolphins and 0.90 on Football while preserving reproducible results The Ant Colony Optimization Based on Centrality and NoD Similarity (ACOCNoD) algorithm extends detection capabilities to overlapping community structures, where nodes can belong to multiple communities simultaneously—a common characteristic in real-world social networks. ACO-CNoD incorporates adaptive mechanisms that automatically adjust to different network characteristics without requiring manual parameter tuning. Comprehensive evaluation on the same benchmark suite, complemented by the overlapping-heavy Pretty Good Privacy (PGP) network, shows that ACO-CNoD achieves the top overlapping modularity (Qov) on five of seven datasets (e.g., 0.72 on Karate and Dolphins, 0.709 on Jazz_collab), trailing only on PGP where COPRA reaches 0.783 compared with 0.68 for ACO-CNoD. Together, these contributions establish a new methodological foundation for community detection, offering complementary approaches for different network types and application requirements. The research balances theoretical advancement with practical applicability, bridging fundamental graph theory with real-world applications in social media analysis, biological networks, organisational studies, and infrastructure optimisation. The integrated framework significantly enhances our ability to extract meaningful community structures from complex networks, with implications for diverse domains including recommendation systems, influence maximisation, and network resilience analysis.Item POI recommendation based on social trust in LBSN(DENNOUNI Nassim / LOUKAM Mourad, 2025-07-03) MEDJROUD, SARAThis thesis addresses the challenges of point-of-interest (POI) recommendation systems in location-based social networks (LBSNs), such as Yelp or Foursquare, with a focus on data sparsity and cold-start problems. To overcome these challenges, the thesis proposes several approaches based on the exploitation of implicit trust between users. Unlike declared friendship links (explicit trust), implicit trust is inferred from users’ behavior, particularly through their check-ins and ratings. Three main models were developed to integrate this trust into recommendation systems: (1) the HRCT model (Hybrid Rating Check-in Trust), which combines ratings and check-ins to build a denser trust matrix, there by reducing data sparsity and improving recommendation accuracy; (2) the PRCT model (Propagation of Rating/Checkin for implicit Trust), an extension of the HRCT model that applies a trust propagation mechanism within the social network, helping to mitigate cold-start issues; and (3) the ITCRC model (Implicit Trust based on Combining point of interest Ratings and user Check-ins), which incorporates trust directly into the POI prediction process. The experimental results, obtained from real-world datasets such as Yelp, showed that these models help to densify the similarity matrices and improve the accuracy of POI rating predictions based on user check-ins, while also reducing the impact of sparsity and the cold start problem. In particular, approaches that incorporate check-ins into the computation of the implicit trust matrix between users proved to be more effective than those based solely on ratingsItem POI recommendation system : Formalization and evaluation(DENNOUNI Nassim / HARBOUCHE Ahmed, 2025-07-03) BETTACHE, DjelloulThe rise of location-based social networks (LBSNs) such as Foursquare, Facebook, and Geolife has revolutionized user interactions by capturing rich data such as check-ins, preferences, and movement patterns. In this context, point-of-interest (POI) recommendation systems (RS) play a crucial role in guiding users to relevant locations. However, traditional approaches, particularly collaborative filtering, have limitations in addressing the complexity of spatial, social, and temporal user behaviors. This thesis primarily addresses the inability of classical similarity measures to capture contextual proximity. Three major contributions are proposed: (1) the SPPUR model, which introduces a novel similarity measure inspired by the TF-IDF method, combining trajectory sequences with the geographic proximity between users, (2) the IPUMC model, which integrates implicit check-in similarity with an explicit measure of geographic distance, and (3) the IUPJS model, based on the Jaccard index and enhanced with a spatial component derived from users' start and end points. Empirical evaluations on Foursquare datasets (New York and Tokyo) confirm the superiority of these three models over existing approaches and highlight the importance of integrating contextual factors such as location, visit order, and social relationships into POI recommendation systemsItem Author Profiling based on Machine Learning Techniques for Modern standard Arabic language(Mourad LOUKAM, 2025-06) MANSOUR KHOUDJA, AsmaaThis thesis addresses the challenges of gender profiling and bot detection in Modern Standard Arabic (MSA) using advanced machine learning techniques, including LSTM, ARABERT, and Prompt-Based Learning. The research highlights the scarcity of resources and research in Arabic Natural Language Processing (NLP) compared to high-resource languages like English, aiming to bridge this gap by creating novel datasets and exploring innovative algorithms. Two datasets were curated: one for gender profiling (10,000 MSA texts) sourced from PAN 2018, Arabic Parallel Gender Corpus 2.0, Google Forms, while the other dataset for bot detection (1,100 MSA texts) was sourced from Fake News, and Automatically-Generated Arabic Tweets. Preprocessing steps included tokenization, balancing, and translation of dialectal Arabic to MSA. The experiments evaluated the performance of LSTM, ARABERT, and Prompt-Based Learning, with ARABERT achieving the highest accuracy (92.4% for gender profiling and 88% for bot detection), followed by Prompt-Based Learning (92.3% and 80%) and LSTM (78.5% and 66.8%). The results demonstrate the superiority of transformer-based models and the potential of prompt-based approaches for low-resource languages. Key contributions include the creation of high-quality datasets, the introduction of Prompt-Based Learning to Arabic NLP, and a comprehensive comparison of model performance. Future work include focusing on dataset expansion, optimizing prompt-based approaches, and cross-domain applications such as sentiment analysis and machine translation. This research advances Arabic NLP by providing tailored models and methodologies for author profiling and bot detection, offering valuable insights for addressing similar challenges in low-resource language settingsItem Long Time Bihavior of Some Epidemic Models(ABDELHEQ MEZOUAGHI / SALIH DJILALI, 2025-02-17) ANTOURI, ZINAThis thesis investigates an endemic model of obesity and related health issues, considering social factors and social connections. The essential goal of our proposed model is to understand and subsequently control as far as possible the evolution of each disease. Obesity and its related health issues can be transmitted through peer pressure to eat fast food and unhealthy habits learned from family and friends. In our model, the impact of these social factors is represented by the parameters βi. We provide a theoretical demonstration of the asymptotic stability of both the disease-free and endemic equilibria, as well as the solution’s existence and uniqueness, for the model of obesity and its related issues. In order to corroborate the model’s theoretical fidings through numerical simulations, to study the effects (impact)of changes in social factors on disease spread dynamicsItem Study of polyconvexity in some problems in the calculus of variations(Boussaid Omar / Kainane-Mezadek Abdelatif, 2025-04-17) Merabet, IbrahimThis study seeks to investigate the concept of symmetric polyconvex functions in higher-dimensional spaces. By advancing the methodology introduced by Boussaid et al. for two-dimensional and three-dimensional cases, we introduce an innovative characterization of symmetric polyconvex functions in higher dimensions. Our principal fiding reveals that the requisite condition for symmetric polyconvexity of a function f is its ability to be formulated as a convex function that incorporates the matrix and its second-order minors, exhibiting a non-increasing tendency in a specifi sense with respect to the second-order minor variable. Additionally, we propose and scrutinize the concept of S-positive semi-defiite matrices, which is crucial to our characterization. This new characterization also enables the identifiation of the class of symmetric polyconvex quadratic forms and demonstrates the absence of non-trivial symmetric poly-affi functions.Item PARTITIONING AND BIG GRAPH ANALYSIS(Mohammed Amin TAHRAOUI / Abdelaziz KELLA, 2025-04-20) MOULEY, KARIMAThe rapid growth of large graphs has an important role in analysing and managing their structures. The capacity to effctively analyse these graphs is crucial for many applications, including social network analysis, communication networks, and sensor networks. This thesis focuses on developing a graph partitioning based on Critical Nodes Problem (CNP) to solve several issues, including detecting communities in social networks, accurately locating the source of information in social networks, and evaluating the impact of CNP in Wireless Sensor Networks (WSNs). The fist part of this thesis describes the development of a scalable graph partitioning method that uses critical nodes to optimize vertex removal, therefore lowering network complexity while keeping critical structural characteristics and connectivity. The second part seeks to tackle the problem of community detection in social networks. We offr a unique technique that uses critical nodes to improve the quality of discovered communities. The third part emphasizes solving the problem of source detection with limited observation nodes in social networks. We propose a novel algorithm that employs critical nodes as observation nodes, it signifiantly improves source detection accuracy. The fial part of this thesis explores the impact of critical nodes in WSNs, demonstrating improved effciency, optimized performance, reduced energy consumption, and enhanced data transmission reliability. We validate the proposed approaches, according to experimental results on real-world and synthetic datasets. The results show that the algorithms outperform existing methods in terms of various performance measures, including modularity and Normal Mutual Information (NMI), accuracy, and energy consumption for WSNs.Item Towards a Multidimensional NOSQL DBMS Based on Dynamic and Scalable Data Distribution(DENNOUNI Nassim / ARIDJ Mohamed, 2025) MAABED, MohammedThis work intro duces a new paradigm for Multi-Dimensional NoSQL, namely MDNOS, which targets efficient data management in IoT, Fog, and cloud platforms. It prop oses an overall scalable and fault-tolerant architecture that includes routing in IoT clients. The fog layer namely KV-MDSS, and a cloud layer, namely SD-PGSQL. Dynamic partitioning techniques are integrated into the system for load balance and query p erformance optimization; for single-key data, RP*-SD2DS is adopted, and ZK-RP* for multi-dimensional data. It gives an overview of the basics of SDDS and then go es further to show some realizations of thes e efficiently using MD-NOS. I t presents two sp ecialized architectures: RP*-SD2DS, optimized for one-dimensional data, and ZK-RP*, which extends it further for multi-dimensional data. Exp erimental results prove the supremacy in p erformance and scalability of the system when compared to traditional NoSQL databases like MongoDB with large files and complex queries. It gives reason to b elieve that MD-NOS can enable a revolution in the management of data.Item Modéles d’ondes efficacement amorties avec mémoire non linéaire(Hadj Kaddour Tayeb /Nasli Bakir Aissa, 2025) ALIMERINA, OmarItem Etude ab-initio des propriétés électroniques et magnétiques de GaX dopé par Mn (X=N, P et As) dans le cadre de la spintronique(Mohamed BELABBAS, 2024-07-05) BOUTELDJA, NoureddineDes semi-conducteurs magnétiques dilues (DMS) à base de III-V dopés par manganèse sont très intéressants pour les applications spintroniques. Les propriétés électroniques et magnétiques de DMS à base de III-V dopé par manganèse Ga1-xMnxN, Ga1-xMnxP et Ga1- xMnxAs sont déterminées en utilisant un outil de simulation de type ab-initio (FP-LAPW) méthode des ondes planes augmentées et linéarisées à potentiel total basé sur la théorie de la densité fonctionnelle (DFT). L’objectif de ce travail est d’étudier les propriétés magnétiques de ces alliages pour déterminer lesquels présentent un intérêt dans les applications à la spintronique.Item Biometric Security in IoT Application of Smart City(TAHAR ABBES Mounir / ALLALI Mohamed Abdelmadjid, 2024) Chaib, mostefaThe Internet of Things (IoT) refers to the interconnection of various objects in our daily life, including cars, refrigerators, cell phones, smart doors, patient monitoring devices, and any other monitoring equipment. These devices are equipped with a smart sensor, an actuator, and internet connectivity, allowing them to exchange, gather, and send data to a remote server. IoT is a hybrid of various core forms of technology with varying levels of communication. Many existing IoT systems rely on a number of protocols and technologies. This causes complications with IoT connectivity and networking. Our thesis focuses on LoRaWAN networks because of their flexibility, as well as the fact that wireless communication takes advantage of the LoRa physical layer’s long-range properties. The different levels’ requirements necessitate varying levels of security. Researchers strongly recommend deploying biometric security devices at levels where direct human access is essential. Biometric security offer a scalable solution for IoT that combats unauthorized access and credential swapping. Indeed, the biometric traits of human organs serve as a unique identity for each individual since they are universal, permanent, distinct, and work perfectly. This identification will be regarded as critical data to be transmitted in the IoT network; as a result, packet loss should be minimal and packet delivery ratio high. This data will be shared over the same medium, posing a significant collision risk that must be addressed and avoided. Collisions occur in wireless communication due to the large number of nodes sharing the same channel. As a result, substantial amounts of data are lost. To avoid this issue, Networks Communications employs the CSMA method for detecting channel occupancy by measuring the carrier’s Received Signal Strength Indication (RSSI). However, the known CSMA is inefficient in LoRa-based networks, such as LoRaWAN, which employs the ALOHA protocol. Because the receiver can demodulate signals even below the noise floors, LoRa wireless communication uses the Channel Activity Detection (CAD) approach to avoid collisions. This study makes a contribution by integrating a new LoRaWAN module into the NS3 simulator and introducing a novel CSMA method called FT-CSMA, which is based on the well-known CSMA used in WIFI IEEE 802.11 and WSN IEEE 802.15.4. In this work, we describe some interesting areas of IoT use while highlighting their faults and limits. We then provide our proposal to alleviate one of these restrictions. Finally, we present the IoT applications with biometric security methods that have been developed the most by scientists.Item Sur les périodes de certaines courbes algébriques.(BERTIN.J, 1989) BENNAMA, HabibSur les périodes de certaines courbes algébriques. Memoire de doctorat en mathématiques / Option : Géométrie algébriqueItem Les polynômes orthogonaux « classiques » de dimension d.(Charles-Michel MARLE, 1988) DOUAK, KhalfaLes polynômes orthogonaux « classiques » de dimension d. Memoire de doctorat en mathématiques / Option : Analyse numérique.