Université Hassiba Benbouali de Chlef / Faculté des sciences http://dspace.univ-chlef.dz/handle/123456789/347 2026-04-05T22:03:18Z 2026-04-05T22:03:18Z TOUALBIA, YOUSSOUF http://dspace.univ-chlef.dz/handle/123456789/2412 2026-03-12T09:10:44Z 2025-01-01T00:00:00Z

Predictive approaches to the impact of environmental conditions on the durability of clayey soils stabilized with mineral additives. TOUALBIA, YOUSSOUF This 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) THESIS Submitted for the DOCTORAL DEGREE Field: Civil Engineering Specialty: Geotechnical Engineering

2025-01-01T00:00:00Z BENABOURA, AMINA http://dspace.univ-chlef.dz/handle/123456789/2411 2026-03-12T09:08:27Z 2026-01-01T00:00:00Z

A task classification strategy for IoT/Cloud collaboration BENABOURA, AMINA There 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. THESIS Submitted in partial fulfillment of the requirements for the degree of DOCTORATE Field: Computer Science Specialization: Artificial Intelligence and Software Engineering

2026-01-01T00:00:00Z Abdelouahab, Nouar http://dspace.univ-chlef.dz/handle/123456789/2217 2025-12-29T11:05:48Z 2025-01-01T00:00:00Z

Design and implementation of a secure LoRaWAN protocol used in critical applications in the IoT domain Abdelouahab, Nouar Of 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 respectively

2025-01-01T00:00:00Z CHENAOUI, ALI http://dspace.univ-chlef.dz/handle/123456789/2197 2025-11-03T13:07:42Z 2025-10-15T00:00:00Z

Graph Parameters for Social Network Analysis CHENAOUI, ALI The 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. THESIS Presented for the graduation of DOCTORATE Field: Computer science Specialty: Computer Systems

2025-10-15T00:00:00Z