arXiv subjects

Adel Larabi

Publications and source records attributed to Adel Larabi.

9 recordsLinked to original sources

arXiv2026

Large Language Model Assisted Intent-Based Satellite-Integrated Access and Backhaul FWA for Rural Areas

Rural areas exhibit low population density and highly variable connectivity needs shaped by both household usage and field operations such as planting, harvesting, and mining. These field activities often occur in isolated locations requiring temporary connectivity, whereas rural households depend on fixed broadband. During intensive outdoor activities, household fixed networks may remain underutilized, resulting in inefficient resource use and unnecessary energy consumption. The coexistence of residential and field-based communication demands creates substantial spatial and temporal fluctuations that the current rural network cannot effectively adapt to. Limited visibility into user mobility, activity patterns, and intent makes it difficult for operators to coordinate temporary and fixed networks. To address these underexplored challenges, we propose an AI driven Intent Aware Satellite Integrated Access and Backhaul (IAB) approach to connect rural areas. In our proposal, a large language model (LLM) translates users' intents into explicit network requirements. Guided by these inferred requirements, we develop a dynamic satellite IAB based Fixed Wireless Access (FWA) network approach that jointly optimizes temporary field connectivity and fixed broadband access to maximize energy efficiency while satisfying the data rate requirement. The formulated optimization problem is solved using a two stage Benders decomposition approach. The simulation results show that our approach significantly reduces energy consumption while maximizing energy efficiency.

cs.NI↗

arXiv2026

Empowering Rural Areas with Multi-radio Microwave Backhaul Supported by Digital Twin for 5G IAB-based FWA

For digital inclusion, high-capacity Internet access should be provided to rural areas to support a range of services and applications. Due to the high operating costs of fiber-optic deployment, Fixed Wireless Access (FWA) is becoming a more attractive internet solution for rural areas. However, 5G FWA is a one-hop solution with limited coverage. A multi-hop solution is needed for wider rural coverage. This work considers a unified solution combining long-haul microwave, 5G Integrated Access and Backhaul (IAB), and FWA to provide a multi-hop network for extended coverage and high network capacity in rural areas. A key challenge for such a network is that energy consumption increases with the number of hops, a problem that has been overlooked in the existing literature. To address this, we propose energy-efficiency microwave backhaul for IAB-based FWA as the Physical Twin (PT). We develop an energy-efficient strategy to optimize radio start-up, serving, sleeping, and wake-up states for microwave backhaul connecting 5G IAB-based FWA serving rural areas. By operating the network at reduced capacity during low utilization, we aim to minimize energy consumption. Then, we present a Digital Twin (DT) of PT to improve its performance. We solve the formulated optimization problem using deep Q-learning in DT and the optimization solver in PT. The simulation results show that our approach satisfies the data rate requirements while reducing energy consumption.

cs.NI↗

arXiv2025

Energy-Efficient Multi-Radio Microwave and IAB-Based Fixed Wireless Access for Rural Areas

Deploying fiber optics as a last-mile solution in rural areas is not economically viable due to low population density. Nevertheless, providing high-speed internet access in these regions is essential to promote digital inclusion. 5G Fixed Wireless Access (5G FWA) has emerged as a promising alternative; however, its one-hop topology limits coverage. To overcome this limitation, a multi-hop architecture is required. This work proposes a unified multi-hop framework that integrates long-haul microwave, Integrated Access and Backhaul (IAB), and FWA to provide wide coverage and high capacity in rural areas. As the number of hops increases, total energy consumption also rises, a challenge often overlooked in existing literature. To address this, we propose an energy-efficient multi-radio microwave and IAB-based FWA framework for rural area connectivity. When the network is underutilized, the proposed approach dynamically operates at reduced capacity to minimize energy consumption. We optimize the off, start-up, serving, deep sleep, and wake-up sates of microwave radios to balance energy use and satisfying data rate requirements. Additionally, we optimize resource block allocation for IAB-based FWA nodes connected to microwave backhaul. The formulated optimization problems aim to minimize the energy consumption of long-haul microwave and multi-hop IAB-based network while satisfying data rate constraints. These problems are solved using dual decomposition and multi-convex programming, supported by dynamic programming. Simulation results demonstrates

cs.NI↗

arXiv2023

Forensic Data Analytics for Anomaly Detection in Evolving Networks

In the prevailing convergence of traditional infrastructure-based deployment (i.e., Telco and industry operational networks) towards evolving deployments enabled by 5G and virtualization, there is a keen interest in elaborating effective security controls to protect these deployments in-depth. By considering key enabling technologies like 5G and virtualization, evolving networks are democratized, facilitating the establishment of point presences integrating different business models ranging from media, dynamic web content, gaming, and a plethora of IoT use cases. Despite the increasing services provided by evolving networks, many cybercrimes and attacks have been launched in evolving networks to perform malicious activities. Due to the limitations of traditional security artifacts (e.g., firewalls and intrusion detection systems), the research on digital forensic data analytics has attracted more attention. Digital forensic analytics enables people to derive detailed information and comprehensive conclusions from different perspectives of cybercrimes to assist in convicting criminals and preventing future crimes. This chapter presents a digital analytics framework for network anomaly detection, including multi-perspective feature engineering, unsupervised anomaly detection, and comprehensive result correction procedures. Experiments on real-world evolving network data show the effectiveness of the proposed forensic data analytics solution.

cs.CR↗

arXiv2021

Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.

cs.CR↗

arXiv2020

Cost-optimal V2X Service Placement in Distributed Cloud/Edge Environment

Deploying V2X services has become a challenging task. This is mainly due to the fact that such services have strict latency requirements. To meet these requirements, one potential solution is adopting mobile edge computing (MEC). However, this presents new challenges including how to find a cost efficient placement that meets other requirements such as latency. In this work, the problem of cost-optimal V2X service placement (CO-VSP) in a distributed cloud/edge environment is formulated. Additionally, a cost-focused delay-aware V2X service placement (DA-VSP) heuristic algorithm is proposed. Simulation results show that both CO-VSP model and DA-VSP algorithm guarantee the QoS requirements of all such services and illustrates the trade-off between latency and deployment cost.

cs.NI↗

arXiv2020

Machine Learning for Performance-Aware Virtual Network Function Placement

With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) placement problem by developing a machine learning decision tree model that learns from the effective placement of the various VNF instances forming a Service Function Chain (SFC). The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances. The benefits of using machine learning are realized by moving away from a complex mathematical modelling of the system and towards a data-based understanding of the system. Using the Evolved Packet Core (EPC) as a use case, we evaluate our model on different data center networks and compare it to the BACON algorithm in terms of the delay between interconnected components and the total delay across the SFC. Furthermore, a time complexity analysis is performed to show the effectiveness of the model in NFV applications.

eess.SP↗

arXiv2020

Edge-enabled V2X Service Placement for Intelligent Transportation Systems

Vehicle-to-everything (V2X) communication and services have been garnering significant interest from different stakeholders as part of future intelligent transportation systems (ITSs). This is due to the many benefits they offer. However, many of these services have stringent performance requirements, particularly in terms of the delay/latency. Multi-access/mobile edge computing (MEC) has been proposed as a potential solution for such services by bringing them closer to vehicles. Yet, this introduces a new set of challenges such as where to place these V2X services, especially given the limit computation resources available at edge nodes. To that end, this work formulates the problem of optimal V2X service placement (OVSP) in a hybrid core/edge environment as a binary integer linear programming problem. To the best of our knowledge, no previous work considered the V2X service placement problem while taking into consideration the computational resource availability at the nodes. Moreover, a low-complexity greedy-based heuristic algorithm named "Greedy V2X Service Placement Algorithm" (G-VSPA) was developed to solve this problem. Simulation results show that the OVSP model successfully guarantees and maintains the QoS requirements of all the different V2X services. Additionally, it is observed that the proposed G-VSPA algorithm achieves close to optimal performance while having lower complexity.

eess.SP↗

arXiv2017

An NFV and Microservice Based Architecture for On-the-fly Component Provisioning in Content Delivery Networks

Content Delivery Networks (CDNs) deliver content (e.g. Web pages, videos) to geographically distributed end-users over the Internet. Some contents do sometimes attract the attention of a large group of end-users. This often leads to flash crowds which can cause major issues such as outage in the CDN. Microservice architectural style aims at decomposing monolithic systems into smaller components which can be independently deployed, upgraded and disposed. Network Function Virtualization (NFV) is an emerging technology that aims to reduce costs and bring agility by decoupling network functions from the underlying hardware. This paper leverages the NFV and microservice architectural style to propose an architecture for on-the-fly CDN component provisioning to tackle issues such as flash crowds. In the proposed architecture, CDN components are designed as sets of microservices which interact via RESTFul Web services and are provisioned as Virtual Network Functions (VNFs), which are deployed and orchestrated on-the-fly. We have built a prototype in which a CDN surrogate server, designed as a set of microservices, is deployed on-the-fly. The prototype is deployed on SAVI, a Canadian distributed test bed for future Internet applications. The performance is also evaluated.

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