In the ever-evolving Industry 4.0 landscape, logs and events play a critical role in maintaining the reliability and security of complex systems. This is especially true for 5G networks, where lightweight protocols such as CoAP and LwM2M produce massive volumes of structured but often unlabeled log data. Although machine learning and deep learning methods have become common tools for anomaly detection in such environments, they are frequently hindered by practical limitations: scarce labeled data, high-dimensionality, long log sequences, and the lack of realistic attack scenarios in available datasets. To address the above challenges, we adopt a deep clustering framework that learns low-dimensional structured latent representations without requiring labels. TF-IDF statistical analysis excels at detecting repetitive attack patterns and anomalous term frequencies characteristic of threats like distributed denial-of-service attacks and message flooding, while enabling rapid compression and interpretation of large-scale log data. However, TF-IDF alone fails to capture semantic context—for instance, distinguishing between benign timeouts and attack-induced timeout cascades. Conversely, while pre-trained models like sentence-transformer capture rich semantics, their latent spaces are optimized for classification and are often unsuitable for clustering, due to their entangled and non-topological structure. In this paper, we propose a novel hybrid architecture that fuses a TF-IDF–based autoencoder with a sentence-transformer encoder through a cross-attention mechanism. This combination leverages TF-IDF’s statistical sensitivity to repetitive attack signatures while enriching it with semantic understanding, allowing the latent representation to selectively incorporate both statistical anomalies and contextual semantic signals, thereby preserving the interpretability and clusterability of the learned space. We implement this architecture through a Wazuh-based SIEM deployment at the 5G network edge, demonstrating that unsupervised hybrid clustering can deliver effective CoAP anomaly detection in industrial IoT environments without labeled data, intrusive agents, or cloud-dependent processing addressing critical gaps in operational security for private 5G networks.

Multipath cooperation technology alleviates transmission pressure by leveraging path diversity. However, in next-generation service-oriented environments with computation-intensive services, the limited computing capability of transmission paths can degrade end-to-end service quality, even when bandwidth is sufficient. This issue becomes more pronounced in dynamic mobile scenarios, where fluctuating link status and computational resources introduce new challenges in path selection. To address these challenges, we propose a novel path selection approach that jointly considers both network and computation constraints for computation-intensive services. First, we construct a computation-integrated multipath transmission framework to support real-time monitoring of both link-level computing capabilities and network conditions. Second, we introduce a packet structure embedding device identifiers and computing capability, enabling adaptive scheduling. Finally, we develop a computing capability-constrained delay-minimizing packet scheduler (C2-DMPS) to balance bandwidth and computational load, ensuring low-latency transmission for emerging service demands. The results demonstrate the critical role of computational capacity in maintaining service performance, especially under volatile network conditions, highlighting potential risks to service continuity in next-generation environments.

Machine learning is gradually transforming medical image segmentation. However, its accuracy often relies on large-scale medical datasets, while centralized data collection raises serious privacy concerns. To address this issue, federated learning (FL) enables collaborative model training without sharing raw data, thus effectively protecting patient privacy. Despite this advantage, commonly used segmentation models, such as U-Net and its variants, typically have large parameter sizes, making them inefficient for local training on FL clients. To overcome this challenge, we propose FedU-KAN, a framework built upon the lightweight U-KAN architecture, tailored for federated medical image segmentation tasks. Moreover, we design an adaptive differential privacy mechanism that dynamically adjusts gradient clipping based on feature importance. This approach helps preserve anatomical details while reducing the risk of privacy leakage. We evaluate FedU-KAN on the CVC-ClinicDB and Kvasir-SEG datasets, where it achieves IoU scores of 87.09% and 83.38%, respectively—outperforming standard FL baselines. These results demonstrate that FedU-KAN can effectively balance privacy protection and model performance in real-world medical segmentation scenarios.

As the automotive industry shifts toward Software-Defined Vehicles, seamless connectivity has become a key enabler of continuous software integration, over-the-air updates, and real-time data exchange. At the center of this transformation is the Telematic Control Unit (TCU), which orchestrates Vehicle-to-Everything (V2X) communications and unifies a wide range of connectivity interfaces. However, maintaining reliable performance under real-time and resource-constrained conditions requires advanced system-level observability. In this paper, we introduce TCU-Observer, a modular and extensible onboard observability framework specifically designed for TCU in connected vehicles. Our architecture supports real-time monitoring, telemetry analysis, and AI-based inference. In addition, we propose CLAE, a lightweight feature engineering model designed for a TCU embedded environment, enabling efficient transformation of raw telemetry into actionable insights. Extensive V2X experimentation validates the feasibility and effectiveness of our approach, demonstrating its potential to enhance the TCU’s connectivity and reliability.

The proliferation of electric vehicles (EVs) has spurred a growing demand for efficient battery exchange and charging services, making the battery swapping-charging system (BSCS) an attractive solution.The various subsystems of the BSCS exchange data in real-time through wireless communication. However, due to the openness of wireless communication, data can be easily intercepted and tampered with during transmission, which may lead to the leakage of sensitive information. To address this, we introduce a privacy-preserving distributed optimization algorithm, leveraging homomorphic encryption and multi-party secure computing in the BSCS context. Initially, we formulate the operation management problem of BSCS problem as a constrained mixed integer programming (MIP) and employ the alternating direction method of multipliers (ADMM) for optimal resolution. Subsequently, we integrate ADMM with the Paillier cryptosystem for privacy protection. Empirical validation substantiates the algorithm security and convergence, ensuring that adversaries cannot deduce private information. Notably, the proposed algorithm yields a solution closely resembling the centralized solution, with a superior convergence rate compared to alternative methods.

Irregular Repetition Slotted ALOHA (IRSA) is an effective grant-free random access scheme that is well-suited for managing the sporadic nature of IoT traffic, particularly in dense environments prone to collisions. In this paper, we evaluate the performance of IRSA under realistic conditions involving imperfect successive interference cancellation (SIC) and non-ideal physical layer environments. Specifically, we investigate the impact of various channel conditions and physical layer impairments on IRSA's performance. Previous studies on IRSA often assume ideal physical layer conditions or use simplified models for SIC errors, which fail to fully capture practical implementation complexities. To address this gap, we propose integration of practical factors, such as channel estimation imperfections, into our model of SIC failures using detailed baseband simulations. Based on that, we employ density evolution analysis to evaluate system throughput and optimize the degree distributions to enhance IRSA performance in the presence of imperfect SIC. Additionally, we analyze the power of the residual interference to assess its impact on decoding performance under realistic conditions. Our results focusing on 5G CIoT demonstrate that optimizing IRSA parameters, while accounting for SIC errors, can significantly improve system performance, resulting in notable throughput gains.

Virtual Try-On (VTO) technology, powered by Augmented Reality (AR) and Artificial Intelligence (AI), is reshaping online retail with immersive, real-time user experiences. However, the demand for high-quality, real-time VTO interactions introduces computational and latency challenges. Offloading computations to edge devices, enabled by 5 G networks, offers a promising solution to reduce end-to-end (E2E) latency and support multi-user environments. This paper proposes an optimized VTO platform tailored for edge deployment, featuring a novel knowledge distillation scheme for efficient 3D hand reconstruction. Our knowledge distillation approach reduces the model size by up to 70 % compared to the teacher model, achieving close visual accuracy in simpler tasks, such as flat hand reconstruction, ensuring it meets the quality standards of VTO applications. Experimental results demonstrate that our edge-optimized VTO architecture effectively balances quality and latency, supporting scalable, real-time interactions and advancing VTO technology for next-generation e-commerce applications.

As 5G technology is widely adopted, enterprises seek solutions for automation and rapid service delivery. Network Slicing (NS) leverages 3GPP standards to create multiple, customized network slices on shared infrastructure, serving diverse applications and user groups. This paper focuses on 3GPP 5G Core NS, particularly Release 17, and proposes Adaptive Network Slice Balancing (ANSB) to optimize resource utilization by adjusting User Equipment (UEs) and Protocol Data Unit (PDU) sessions. Extensive experimentation, with 5G OpenAirInterface (OAI) testbed, demonstrates significant improvements in UEs, PDU sessions, and maximize overall data rate consumption.

Virtual Try-On (VTO) technologies are becoming a cornerstone of immersive retail experiences in the Metaverse. However, delivering real-time, high-fidelity interactions at scale remains a significant challenge, especially for Artificial Intelligence (AI) based applications that demand substantial computational resources. While edge servers effectively reduce latency and provide greater computational power than constrained User Equipment (UE), they face scalability issues under heavy multiuser loads.In this paper, we propose a novel scheme called Hybrid-Edge-VR4Fit. It is a hybrid offloading framework designed for scalable, low-latency VTO in 5G-edge environments. The system enhances responsiveness by leveraging edge inference to refine subsequent locally processed frames, reducing offloading frequency without compromising quality of service. We then introduce a dynamic offloading strategy modeled as a finite congestion game. Each UE autonomously adjusts its behavior based on real-time edge server and 5G network conditions to balance latency and quality trade-offs. Extensive experimental evaluations demonstrate that our proposal significantly reduces server congestion and queuing delays while maintaining visual fidelity, enabling responsive VTO services across diverse user densities.

The advent of 5G has introduced complex network architectures with stringent Quality of Service (QoS) needs, requiring robust observability solutions for telco operators. This paper proposes 5GC-Tracer, a novel non-intrusive distributed tracing architecture specifically designed for cloud-native 5G Core Network (5GC). Leveraging eBPF technology, our proposal enables application-centric implicit context propagation without requiring any code instrumentation, thus minimizing operational overhead. We conducted real-world testing during the Paris 2024 Paralympic games, demonstrating the effectiveness of 5GC-Tracer in generating and collecting traces from a 5GC system. The experimental results highlight a high success rate in trace collection with low overhead (latency, CPU, memory), and our statistical approach significantly accelerates the localization of anomalies related to QoS degradation across various 5GC network functions.

Irregular Repetition Slotted ALOHA (IRSA) is a leading candidate for random access and grant-free communication in future Cellular IoT (CIoT) networks, including those envisioned for 6G and beyond. Classical analyses of IRSA typically assume ideal conditions; however, real deployments are subject to practical impairments. In particular, the capture effect enables packets to be decoded despite collisions when their signal-to-interference ratios exceed certain thresholds, and imperfect successive interference cancellation (SIC), due to channel estimation errors, further complicates decoding dynamics. In this paper, we are the first to develop a unified analytical framework that incorporates both phenomena into the IRSA design. Using a threshold-based capture model and a detailed residual interference analysis, we apply density evolution to derive asymptotic throughput bounds. Our results show that by optimizing the user degree distribution, IRSA can significantly mitigate performance loss under non-ideal SIC conditions. Extensive simulations validate our theoretical findings, revealing that performance improvements are attainable even in high-density CIoT scenarios.

In recent years, with the development of wireless communication networks, federated learning (FL) has been widely deployed in distributed scenarios as a privacy-preserving machine learning paradigm. Due to its inherent features, FL shows vulnerability to backdoor attacks. In a backdoor attack, an adversary manipulates the global model’s output by compromising the model of one or multiple participants. Existing backdoor attacks are constrained to outputting a single specified target label during the inference phase, limiting the adversary’s flexibility to alter the model’s output when different target labels are required. In this paper, we study the multi-target attack scenario within the federated learning context, where the adversary aims to manipulate the global model to output various specified labels by inserting different types of triggers. To effectively insert multiple backdoors simultaneously without reducing the attack’s effectiveness, we propose MulDoor, a novel multi-target backdoor attack scheme. MulDoor incorporates the concept of supervised contrastive learning to learn the discrepancies among different types of triggers and mitigate interference between them. The experimental results demonstrate that MulDoor achieves better attack effectiveness compared to existing backdoor attacks in a multi-target backdoor attack setting.

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security.

At present, multipath transmission realized by multi-interface devices and bandwidth aggregation technology meets user demand for high-bandwidth communication in 6G wireless networks. However, multipath transmission systems face the threat of single-point failure by multi-interface servers themselves. Existing solutions for such failure are not suitable for multipath transmission scenarios, and this seriously limits the ability to bandwidth aggregation and reduces the reliability and invulnerability of the multipath transmission system in 6G wireless networks. In this paper, we propose a novel Intelligent Device Disaster Recovery (Multi-ID2R) mechanism to solve the single-point failure in multipath and aggregated environments for the first time. In particular, we establish the Multi-Dimensional Parameter Joint Analysis model (MDPJA) and propose an algorithm for judging the running state of multi-interface devices. The algorithm takes into account the different network parameters of the paths, including delay, packet loss rate, and throughput. Moreover, an intelligent switching mechanism based on service quality is designed. Multi-ID2R comprehensively considers the characteristics of the business and the current parameters of multipath networks to determine the moment of switching to flexibly adjust switching strategies. Finally, we deploy the mechanism on multi-interface servers in actual networks. Experiments demonstrate that, compared with Virtual Router Redundancy Protocol, Gateway Load Balancing Protocol, and Hot Standby Router Protocol, Multi-ID2R effectively improves the reliability and invulnerability of multi-interface server in 6G wireless networks.

The adoption of private 5G networks and the proliferation of Industrial Internet of Things (IIoT) devices utilizing low-power protocols (such as CoAP, LWM2M, MQTT) have unveiled challenging new security vulnerabilities. The latters are particularly significant at private industrial edge servers, which are directly connected to the 5G core network via the N6 interface. Traditional security solutions fall short in effectively monitoring and analyzing industrial protocols, rendering critical systems vulnerable to cyber threats. The consequences of successful attacks on IIoT devices in these environments can result in severe operational disruptions, financial losses, safety compromises, and environmental hazards. Existing measures are inadequate for protecting the N6 interface and edge environment. In this article, we introduce a 3GPP-compliant Application Function SIEM Alerting Agent. This specialized security solution is designed to detect and mitigate malicious IIoT traffic on private 5G networks at the edge, specifically targeting low-powered industrial protocols. Our proposal makes of advanced AI/ML anomaly detection and protocol analysis algorithms adhering to 3GPP R17 standards for seamless 5G integration. Through a private 5G experimental platform replicating an industrial setting, we collect traffic and system event and logs using only industrial protocols such as CoAP, LWM2M, and MQTT. Based on extensive experimentation with private 5G AMARISOFT platform, our proposal excels at detecting threats at the N6 interface.

Observability has become a crucial aspect in cloud native environments, allowing for the measurement of internal states in IT systems. With the emergence of 5G and the anticipation of 6G technologies, the significance of observability in mobile networks has been further emphasized. This paper presents, 5GC-Analyser, a novel approach to understanding the functionality of the 5G core network (5GC) through statistical analyses. By exploring the statistical relationships and causalities among various 5GC microservices and their metrics, our 5GC-Analyser solution, enables the selection of appropriate techniques for online observability. Leveraging these analyses, our framework provides a comprehensive understanding of the intricate dynamics within the 5GC, facilitating improved network management and optimization strategies. We validate our solution in a real-world 5G scenario by simulating end users and replicating conditions observed in a real Orange's 5G gNodeBs (gNB). The obtained results pave the way for future work in proactive detection of performance degradation in the 5G core network. This research bridges the gap between theory and practice, offering valuable insights for network operators and researchers in the field of 5G technology.

In this paper, we design and implement an Intrusion Detection System (IDS) within the 5G core network, which is capable of inspecting both IP and non-IP data flows. By leveraging the Access and Mobility Management Function (AMF) Network Function (NF) communication service, our IDS can analyze all Cellular Internet of Things (CIoT) data traffic flowing across both the User and Control Planes (UP and CP), enabling the detection of malicious activities originating from or targeting IoT networks. Our proposal is aligned with the 3GPP Release 17 (R17) standard and makes use of predefined functionalities to ensure compliance. Our proposal is non-intrusive and does not interfere with the core network’s usual processes based on existing Service Based Interfaces (SBI). Additionally, we demonstrate that the classification of a data packet as malicious or benign is context-dependent using AI/ML Transformer Encoder architectures. We implement and integrate our proposed 5G-CIoT IDS as a Network Function inside the 5G Amarisoft platform for extensive experimentation. To evaluate the models’ performance, we train our models with different categories of safe and malicious generated traffic and apply them to an emulated realistic scenario. We obtained a very promising result.

In this paper, our objective is to design, develop and deploy a novel 5G-IoT IDS as a 5G core network function compliant with 3GPP R17. 5G-IoT IDS provides protection against malicious behaviors targeting IoT networks. To satisfy the 3GPP standard, our proposal respects the design architecture of the 5G system and only uses functionalities defined by the 3GPP technical specifications. Using Open5GS emulating the 5G core network, we implemented and integrated the 5G-IoT IDS as an NF to inspect IoT MQTT traffic on the user plane with common ML algorithms to demonstrate feasibility and effectiveness of our proposal. We explored a different way of handling MQTT packets, delving deeper into the structure of the packet. Based on extensive emulations, we compared our results with analogous studies focused on the MQTT protocol, and it revealed that our emulations exhibit strong performance, which aligns with those highlighted in the related studies, when up against a variant attack of the same flood-based principle. We believe our method of packet handling demonstrates a more comprehensive consideration of MQTT packet characteristics.

The 5G cellular network provides vital support for enabling fast and dependable communication in dynamic environments, which is crucial for connected autonomous vehicles. To achieve this goal, telecommunication operators must prioritize speedy and efficient radio resource management in 5G New Radio (NR) systems, achieved by dynamically adapting the configuration of the physical (PHY) layer. To address this issue, we introduce a novel method called Decision Tree Adaptive Physical Layer Configuration (DeTrAP), which utilizes machine learning and observational data to real-time fine-tune the PHY layer for efficient radio resource management. Extensive simulations demonstrate that DeTrAP achieves the expected performance for safety and non-safety traffic scenarios, while significantly reducing the convergence time.

As 5G Cellular Vehicle-to-Everything (C-V2X) technology takes the lead in V2X communication, it opens the possibility for telecommunication service providers to offer Vehicle-to-Network (V2N) services using their existing 5G network infrastructure. To enhance the security of 5G V2N services, in this paper we propose a novel collaborative V2X misbehavior detection system. This system would safeguard the V2X application servers (V2X ASs), deployed in the 5G edge network, from any malicious V2X position manipulation attacks. Our proposal includes two enhanced machine learning models. The first model utilizes historical data to conduct On-Road Plausibility Checks (ORPC), while the second model builds upon the first by enabling collaboration among edge detection nodes through the sharing of attack ratios for each vehicle. Our proposed models were tested using extensive 5G core-network emulations, yielding excellent results. The first model achieved a notable accuracy improvement from 73% to 91%, while the second model further enhanced the accuracy to an impressive 95%.

As the main goal of connected autonomous vehicles' communications is to improve the traffic safety and save lives, any design of a resource allocation scheme must consider the stringent requirements of these applications in terms of latency and reliability for a dynamic environment. For this, 5G cellular networks suitably address these challenges. This paper proposes a new mechanism for the telco operator to adapt the physical (PHY) layer configuration for efficient radio resource management in 5G New Radio (NR) based system. To tackle this issue, we propose to adjust the PHY layer numerology configuration by fine-tuning it with a Radio-Aware Adaptive PHY Layer Configuration (RA-APC) algorithm in order to maximize the efficiency of radio resource management by using the Effectively Transmitted Packet (ETP) value. Extensive simulations show that our proposal RA-APC achieves strong improvements in terms of ETP, reliability and latency while considering safety and non-safety traffic scenarios.

5G Cellular Vehicle-to-Everything (C-V2X) is expected to become the dominant technology to enable Cooperative Intelligent Transport System (C-ITS) applications. In this paper we address the problem of detecting falsified vehicle positions sent by misbehaving vehicles targeting C-ITS application servers over 5G networks. We propose a novel security system as a 5G application function. It is based on machine learning and integrated with the 5G core network to monitor, detect and prevent potential misbehavior. Based on extensive network simulations utilizing 5G network emulator, our proposal achieves very good performances, accurately reported 99% of misbehaving vehicles and scored an 86% detection rate on the messages' level.

The emergence of 5G Cellular Vehicle-to-Everything (C-V2X) has made it the predominant technology for enabling Vehicle-to-Everything (V2X) communications. As a result, this has created an opportunity for telecommunications service providers to leverage their pre-existing 5G network infrastructure, enabling them to provide Vehicle-to-Network (V2N) services. In this paper, we propose a new approach that enhances the security of 5G V2N services through the implementation of a Federated Learning V2X misbehavior detection system within the 5G core network. The proposed system aims to protect V2X application servers (V2X ASs) that are located in 5G edge networks against potential V2X attacks while leveraging the privacy and scalability advantages of Federated Learning. Our proposed model is compared, using extensive emulations, to other centralized and distributed approaches, achieving excellent results, which makes it feasible for deployment. Our proposal achieved a notable accuracy of 98.4%, while scoring an impressive 99.3% precision and 96.9% detection rate.

Telco players are accelerating their adoption of cloud native technologies. Indeed, the migration of traditional communications applications to microservice-based architectures will facilitate the development of new network services while providing a high level of granularity. However, cloud native comes with new operational challenges. Indeed, effective network service management requires novel solutions for fine-grained monitoring and tracking of widely distributed cloud native network functions. In this paper, we put forward 5GC-Observer, our proposed framework for the observability of cloud native 5G network services. 5GC-Observer leverages the eBPF technology to track network traffic circulating between 5G network functions and report telemetry data. This demo makes use of our open-source platform, Towards5GS, to implement a real cloud native 5G network on top of Kubernetes. Finally, we develop a statistical technique which leverages the collected telemetry data to detect 5G end-users’ Quality of Service degradation, based on real access network information collected from Orange’s gNB (gNodeB) located in Paris-Orly airport.

Telco stakeholders are developing a deeper understanding of cloud native technologies and adopting them faster than few years ago. It is undeniable that migrating legacy telco applications to microservice-based architectures accelerates and facilitates the development of new network services while offering a high level of granularity. However, cloud native raises new operational challenges. In order to achieve an efficient management of network services, new solutions are required to monitor and track widely distributed cloud native network functions while considering their specificity. In this paper, we propose an innovative framework, 5GC-Observer, for the observability of cloud native 5G network services. To the best of our knowledge, no such a solution has been found to date. To achieve its goal, 5GC-Observer relies on the eBPF technology to monitor the network traffic circulating between the 5G core components and report telemetry data. Besides, we leverage a statistical method to detect Quality of Service degradation based on reported telemetry data. Such an approach highlights the richness of the data acquired by our solution and its capability to detect unexpected network-related anomalies. The latter are not detectable through standard observability solutions. Performance evaluation shows that our solution generates low overhead while giving insight into the 5G core system and its internal and external exchanges.

Network slicing is a key enabler in the Next Generation 5G Radio Access Network (RAN) to build the RAN-as-a-Service concept. Cloud-RAN, Network Function Virtualization, Software Defined Network and RAN functional splits are the main pillars expected to be integrated to provide the required flexibility. One of the major concerns is to efficiently allocate RAN resources for slices, while supporting multiple use-cases with heterogeneous Quality-of-Service (QoS) requirements. Current related work is adopting radio resource allocation scheme by considering a cell-centric deployment approach for slice embedding. However, to achieve greater flexibility and fine-grained tunable resource utilization, we believe that the deployment scheme should be integrated in the slice design. In this paper, we go a step further and propose a RAN slicing approach with customized deployment scheme on user basis. As the corresponding optimization problem is NP-Hard, we propose a low-cost and efficient heuristic algorithm for RAN Slice allocation based on the Particle Swarm Optimization approach. Our proposal jointly harnesses radio, processing and link resources at user level tailored to the QoS requirements, while customizing efficiently the underlying physical RAN resource usage.

Nowadays, huge volume of traffic is generated and transported over Wide Area Networks (WAN). WAN is composed of multiple transit Autonomous Systems (ASs). Scaling traditional WAN to deal with the new application’s traffic profile and to fulfill the requested Quality of Service (QoS) requirements becomes too expensive and complicated due to physical resource limitations. In view of the above facts, in this paper we propose and implement a Software Defined Network (SDN) based WAN (SD-WAN) platform enabling the development of new generation of routing algorithms (e.g., using operational research or artificial intelligence techniques). The design makes use of several popular open-source projects such as ONOS, Docker, Mininet, Iperf and Quagga for building the emulated SD-WAN topology. We develop an external management and resolver unit with Python that (i) collects periodically useful statistics such as packet loss rate and instantaneous throughput of the transit AS links. (ii) Permits to compute route path according to some algorithm for every new flow arrival from a neighbor AS to another one. (iii) Configures the data plan according to the computed path. A network operator can implement any routing algorithm respecting the application requirements (e.g., a predefined packet loss threshold) or improving its revenue. The topology size and the traffic profile are easily changeable. For demonstration purpose of the platform usability and functionality we implement a simple shortest path routing (SPR) algorithm and leave the development of intelligent routing algorithms to future works. As expected, results from a simple SPR implementation show that SPR yields to poor performance because of the under-use of all the available resources (e.g., links not included in the shortest paths), making space for improvement and optimization.

Low latency and high-reliability communications for applications' flows is one of the main 5G cellular network objective, which is especially relevant for connected autonomous vehicles. However, efficient wireless resource allocation is a complex. To address this problem in this paper, we propose to adapt the physical (PHY) layer numerology configuration by fine-tuning it with Adaptive PHY Layer Configuration (APC) algorithm in aim to maximize the Effective Transmitted Packet (ETP). Besides, we propose an adaptive scheme to maximize the expected packet serving rate while avoiding the starvation phenomenon of low priority Logical Channels (LC). Based on extensive simulations, results show that our proposal achieves good performance in terms of ETP maximization and starvation minimization of low priority LCs.

In traditional Autonomous System$({\mathcal{A}}{\mathcal{S}})$, resources are provisioned in advance and statically based on statistical analysis of the traffic crossing every and some key parameters such as peaks. However, this approach yields to a significant wastage of resources given the continuous increasing of traffic volumes and its dynamicity. Traffic engineering can benefit of the Software Defined Network (SDN) scheme separating the control from the data plans. We tackle in this paper the traditional distributed routing limitations within a transit ${\mathcal{A}}{\mathcal{S}}$. We propose a new optimized SDN-based routing algorithm to manage incoming data flows requesting predefined throughput and a maximum accepted loss rate (i.e., application requirements) along its path. The routing problem is formulated as an Integer Linear Program (ILP). To resolve this problem, we propose a centralized SDN application named Optimal Path Routing (OPR) based on Gomory Cutting Planes and Branch-and-Bound Algorithms. Based on extensive SD-WAN emulations mainly built over ONOS/Mininet/Quagga platform, the results obtained show that our SDN based optimization solution largely outperforms the traditional shortest path algorithm in terms of packet loss, latency, jitter and throughput satisfaction rate. We show that OPR, compared with the traditional shortest path, is able to increase throughput by more than 40% and to reduce by more than 90% the latency and the packet loss rate.

Data offloading based on Device-to-Device (D2D) can support congestion-prone cellular networks in the face of traffic growth. In this paper, we tackle the design of offloading systems that are aware of the energy limitation of the complementary D2D network. We propose a novel heuristic scheme, named HERRA, using a parametric three-stage method that includes possible variations on the employed strategies in each stage. Performance evaluation, using network simulations in our extended NS-3, shows that HERRA outperforms the related work in the matter of convergence time. As a result of massive speedups, up to six orders of magnitude, HERRA scales very well in denser topologies at the price of having some performance gaps, particularly in terms of packet loss.

Cloud native paradigm gained momentum during the last few years fostering its adoption in 5G architecture. In this context, a new generation of network functions called Cloud native Network Functions (CNFs) has seen the light of day. However, despite their numerous advantages in terms of lightness and portability, CNFs raise new issues not solved yet. Specifically, these fine-grained services require an efficient orchestration system able to automate their lifecycle management while considering the stringent QoS’s requirements. In this paper, we propose a novel 5G CNFs orchestration framework addressing both IT and Network resource provisioning. Both qualitative and quantitative studies are conducted to validate our solution using the auto-scaling use-case. Our extensive experimentations show that our proposal achieves good performances in terms of: i) deployment time, ii) upgrade time, iii) packet loss rate, and iv) resource allocation balancing.

Network Function Virtualization (NFV) has already proven its efficiency to deploy networking services in large-scale. Recent advances of cloud-native applications may bring new advantage by deploying and implementing Virtual Network Function (VNFs) as cloud-native Containers rather than virtual machines. Beside remarkable advantages such as lower overhead and faster running, microservices (cloud-native containers) intend to save costs while increasing the service agility. To this end, in this paper we extend consolidated state-of-the-art tools and technologies developed in two domains cloud-native applications and Network Function Virtualization (NFV). The proposed framework chains services provisioned across Kubernetes and Contiv/VPP domains and using containers. Our orchestration framework chain services across distributed CNFs. Furthermore, we propose K -TS scheme to load balance the traffic over services replicas. K -TS is based on Ketama Consistent hashing algorithm. Experimental simulations show very good results for both the service chaining framework in term of QoS satisfaction such as: packet error rate, throughput satisfaction and jitter.

In this paper, we optimize the 5G D2D communications while considering the multicast flows and the bandwidth OFDMA resource allocation. We propose new optimized routing and resource allocation algorithm based on column- -generation. To do that, new problem formulation based on graph theory is propounded. Based on extensive network simulation in NS-3 environment, we show that our proposal achieves good performances in terms of reliability, latency, and scalability.

A Network Intrusion Detection System (NIDS) is a fundamental security tool. However, under heavy network traffic, a NIDS might become a bottleneck. In an overloaded state, incoming and outgoing packets in the network might suffer from long delays since previous packets are still being inspected, and eventually the NIDS starts to drop packets when it runs out of hardware resources. Although many solutions have been suggested in the literature to counter this problem, they are not completely reliable as each of them has limitations. This paper investigates the design of a lightweight elastic architecture which allows parallel processing in an existing NIDS while maintaining the filtering integrity. Furthermore, we propose two adaptive algorithms which dynamically adjust and divide the signature rules evenly across NIDS nodes using a node level parallelism method in order to achieve intelligent rule ordering. We test our approaches in real-life settings by implementing a functioning prototype involving different modern networking technologies. The prototype presented is a Network Function Virtualization (NFV) of an intrusion detection system which utilizes Open vSwitch and Docker containers running Snort in order to provide an elastic system. To the best of our knowledge, there has been no work that orchestrates both scaling and rule splitting and re-ordering of IDS signatures as a part of a holistic elastic IDS solution. The results of this study show that the proposed algorithms are able to equally split the IDS workload and thereby enabling the system to scale by adjusting the number of virtual components which analyse the network traffic. At the same time the experiments indicate that the algorithms can be tuned by a single parameter in order to avoid that some packets go unexamined while simultaneously craving a minimum of the dynamically available computer resources.

According to the latest statistics, the number of connected people to Internet is still exponentially growing and the high quality of cloud services is significantly requested in the incoming years. In addition to that, the appearance of big data, blockchain and bitcoin motivate more and more scientists and experts in this field to fight for the first place with the most attractive products in terms of performance and utility for their customers. But, what about the resilience and the reliability of the infrastructures accommodating these services? It is undeniable that the rapid growth of this traffic inside data centers network, caught cloud experts attention's appeal. Also, proposing algorithms and solutions for traffic management becomes essential. In this context, we study the improvement of network QoS performance for intra-data center flows in CamCube server only topology. We have already addressed in previous works the routing problem for online traffic (unicast and multicast) within CamCube topology. Our experimentations show a satisfying results. However, facing this important growth, these solutions present some weaknesses like congestion and high latency for flows treatment. To improve our solutions, we propose in this paper a new Batch routing algorithm within CamCube server only topology network that overcomes these limits. Then, we compare our proposal with the existing shortest path approach. The obtained results show that our proposal outperforms its competitor in terms of packet loss and latency.

Network slicing is proposed as a new paradigm to serve the plethora of 5G services on a shared infrastructure. Within this context, a Radio Access Network (RAN) slice is considered as the proportion of physical spectrum resources to be served to third parties. Interestingly, 3GPP standardized options of RAN processing dis-aggregation into network functions while enabling their placement whether in distributed or centralized locations. The adoption of an end-to-end RAN slicing raises new challenges related to the allocation efficiency of joint radio, link and computational resources. To deal with the stringent latency requirements of 5G services, we propose, in this paper, a Deep Learning based approach for User-centric end-to-end RAN Slice Allocation scheme. It can decide in real-time, to jointly allocate the amount of radio resources and functional split for each end- user. Our proposal satisfies end-user's requirements in terms of throughput and latency, while minimizing the infrastructure deployment cost.

In this paper, we address the optimal design of scalable offloading schemes based on the LTE-D2D standard to offload intracellular traffic, unicast and multicast, over multihop D2D networks of cooperative User-Equipments. Specifically, we deal with the problem of joint routing and OFDMA resource allocation that underlies such schemes. Considering crowded-platform use-cases, we propose a novel path-based ILP formulation in which a routing tree is formulated in terms of its constituent paths. Moreover, to boost scalability, we propose a sub-optimal solution method, named JRW-D2D-CG, based on the column-generation framework with a pricing problem. Based on extensive network simulation in NS-3 environment, we demonstrate that our proposal achieves good performances in terms of reliability, latency, and scalability.

Next Generation 5G Radio Access Network (NGRAN) is envisioned to integrate the slicing approach to build a flexible network supporting diverse use-cases with customized architectures, features and services. RAN processing functional splits have been standardized to add new deployment design capabilities and enhance cost efficiency. A further challenge consists in how to meet the multitude use-case's requirements while considering different design models in the physical infrastructure. Current related works are tackling the slice embedding problem from a cell-centric perspective. However, to achieve greater flexibility and better resource utilization, a user-centric approach should be more exploited. In this paper, we propose a SLICE-HPSO scheme that jointly harnesses radio, processing and link resources at the user level to build multiple user slices on top of the physical infrastructure. Our proposal is tailored to different user quality-of-service requirements and to the diverse functional splits resource requests. SLICE-HPSO is in compliance with the 3GPP and optimizes further the heterogeneous resource usage while meeting the scalability requirement.

5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.

Recently Mobile Edge Computing (MEC) promises a great latency reduction by pushing mobile computing and storage to the network edge. MEC solutions allows the intensive applications to be computed in nearby servers at the edge. In this work, we envision a multi-user WiFi-based MEC architecture. We tackle the problem of joint task assignment and resource allocation. The main objective of our scheme is to minimize the energy consumption on the mobile terminal side under the application latency constraint. Based on extensive simulations conducted in NS3 while considering real input traces, we show that our approach outperforms the related prominent strategies in terms of: i) energy consumption and ii) completion delay.

In this paper, we design and implement an SDN-based architecture Pgm-RAN which provides an effective representation of the radio network state at different network levels. Thanks to its programmability, Pgm-RAN enables the implementation of real-time RAN control algorithms in a modular fashion within a Cloud-RAN environment. To demonstrate the effectiveness of Pgm-RAN and assess its applicability, we design and implement a control plane application that ensures an enhanced joint Remote Radio Head assignment and physical radio resource allocation denoted by enhanced-DPS. To do so, we formulate the problem as Integer Linear Program while taking into consideration the mobile users requirements and the radio environment conditions. The problem is then resolved in a polynomial time leveraging linear relaxations and Branch & Cut algorithm. Both experimental and simulation results are provided to prove the effectiveness of our framework and to gauge the performance of enhanced-DPS. It is worth noting that our experimental platform relies on two main building blocks: i) Open Air Interface platform and ii) extended version of FlexRAN SDN controller.

Actually the important growth of intra datacenter traffic pushes researchers to optimize the resource allocation functions, mainly routing. In this work, we consider the problem of path calculation within a server only data center topology called “Camcube”. The routing function for Camcube, is based on link state protocol implemented in a distributed control plane. We rebuild the Camcube topology within a Software Defined Networking (SDN) scheme. The control plan is logically centralized thanks to a controller such as the Open Networking Operating System (ONOS). Extensive experimentation results have been presented with the shortest path algorithm showing the latency and packet loss metrics with different traffic profiles. This work is a first step toward a complete study of the resource allocation problem for CamCube based intra-datacenters.

Multicast routing provides an efficient way to support Data Center (DC) applications (e.g., replication process of MapReduce jobs, Market data and stock notification apps, and IPTV servers) as it conserves network bandwidth and reduces server load. However, a good use of multicast within the traditional DC networks requires higher performance and capacity from the network devices such as the tables capacity of access and aggregation switches. In this paper, we propose and evaluate a novel multicast routing scheme, named M-CRP, in a promising CamCube Server-Only DC architecture while considering the Software Defined Network (SDN) paradigm. To do that, first we formulate the problem as a lexicographic multi objective optimization problem. Then, we propose new optimized SDN application, M-CRP, based on Branch and Cut and monitors the CamCube DC infrastructure with OpenFlow southbound protocol. We evaluate the performance of our proposal using experimental platform built with ONOS controller and Mininet. The obtained results show that M-CRP is better in terms of packet loss, latency and jitter compared with traditional shortest path multicast routing protocol. Keywords: CamCube, Data-Center Networks, SDN, Multicast Routing, Optimization.

5G radio access networks are expected to leverage the Cloud environment for building a cost effective network infrastructure. Advanced mechanisms with functional split are introduced to split the RAN functionalities into centralized and distributed locations. This novelty has brought more flexibility to RAN deployment but is still conditioned to the radio resource availability. In this paper, we propose a new orchestration framework for joint radio and functional split scheme on user basis. Specifically, we address the orchestration of heterogeneous resources in a multi-sited Cloud-RAN infrastructure. The key idea behind our proposal is to optimize jointly the functional split and End-to-End resource allocation in order to achieve an enhanced throughput satisfaction and a low deployment cost. Results show that our approach E2E-US optimizes the heterogeneous resource usage. Indeed, the appropriate user radio load and functional split are dynamically and jointly selected, which outperforms cell centric functional split approaches.

Facing the exponential growth of the intradatacenter traffic, the traditional Data-Center Network (DCN) architectures are not capable to stay ahead of the demand in terms of scalability and lowering costs. In this paper, we address the routing problem of the intra-datacenter traffic inside CamCube-based server-only DCNs. The latter are composed of servers only and no additional network equipments are employed. Following the SDN paradigm, we firstly propose a new architecture in which the control plane is hosted in an SDN controller. Next, we propose to emulate the whole DCN using the versatile "Mininet" platform. Then, we formulate the path computation problem, considering the Quality-of-Service (QoS) in terms of the requested bandwidth, as multi-objective combinatorial optimization problem. Next, we propose a thoughtful reformulation for the problem which can be solved using the Branch-and-Cut algorithm. Our proposal, named CamCube Routing Protocol (CRP), yields the optimal routing paths for the considered traffic flows that keep a balanced load on the DCN. Based on extensive emulations using "Mininet" and the "ONOS" SDN controller, the obtained results are very good, compared with the shortest-path approach, in terms of packet error rate and latency.

In this paper, we address the scalability issue in designing LTE-D2D-based offloading schemes. We propose a scalable method, named JRW-D2D-SC, that offloads the intracellular unicast/multicast traffic using a side-links network of User Equipments (UEs). The devised scheme does this while keeping the control plane inside the LTE-A base station (eNB). By selecting relays and allocating OFDMA resources, the eNB manages to reroute the unified-model traffic from sources to destinations alleviating the cellular infrastructure from the data-plane overhead. To increase the utility of the LTE-D2D relaying network, the eNB solves the routing and the resource block allocation problem simultaneously. Like its counterpart in our previous work, JRW-D2D-SC addresses factors that limit the spectrum reuse and other LTE-D2D limitations such as half-duplex operation and contiguity in resource block allocations. However, we base our proposal on a novel formulation for the problem. The scheme employs an algorithm based on the Branch-and-Cut method to solve the resulted Mixed-Integer Linear Problem (MILP). In doing so, JRW-D2D-SC is more scalable than its counterpart in the previous work and can handle more dense deployments of UEs which is typical in the targeted crowded-platform scenarios: such as in stadiums, waiting-halls in airports and train stations. Based on our home-grown LTE-D2D module for NS-3, extensive network simulations demonstrate that JRW-D2D-SC maintains the same performance metrics or better in small-scale deployments of the LTE-D2D relays while being able to extend the advantage of the offloading system to large-scale deployments.

The high proliferation of mobile devices, deploying a myriad of application, entails an explosion of mobile traffic. Due to their resource-limitation constraint, mobile devices resort to offload computational tasks on Cloud servers and improve, hence, resource usage. Unfortunately, the conventional Mobile Cloud Computing (MCC) solution involves high transmission latency. Inspired by the visions of IoT and 5G communications, recently Mobile Edge Computing (MEC) promises a great latency reduction by pushing mobile computing and storage to the network edge (i.e., base stations and access points). The key challenge of MEC solution is to find an efficient assignment of tasks with local or remote devices while minimizing energy consumption and latency. In this paper, we propose a new joint task assignment and resource allocation approach in a multi-user WiFi-based MEC architecture. The main novelty of our work is that optimal offloading decision is jointly performed with the radio resource allocation. The objective of our scheme is to minimize the energy consumption on the mobile terminal side under the application latency constraint. To do so, we first formulate our problem as a new Integer Program (IP) while considering both delay and device computation constraints. Then, we propose a new strategy named Joint Offloading and Resource allocation in WiFi-based MEC architecture (JOR-MEC) to solve it. Based on extensive network simulations conducted with NS3 simulator while considering real input traces, we show that our proposal outperforms the related prominent baseline strategies in terms of: i) energy consumption and ii) completion delay.

The next 5G infrastructure is expected to serve a multitude of services with heterogeneous requirements, which might be potentially managed by multiple Mobile Virtual Network Operators (MVNOs) that share the same network infrastructure. The new emerging technologies, such as i) Software Defined Networking (SDN), ii) Network Function Virtualization (NFV) and iii) Network Slicing, where physical resources are partitioned and allocated in an isolated manner to a set of services or to MVNOs according to a specific Service Level Agreement (SLA), are seen as the key enabling approaches to fulfill the diversity of requirements of 5G services in a cost-effective manner. In this paper, we design and prototype a network slicing solution, which we have developed in a Cloud-RAN (C-RAN) infrastructure based on the Open Air Interface (OAI) platform and FlexRAN SDN controller. The aim of our work is to validate the feasibility of the prototype in handling the creation and configuration of network slices on-demand, taking into account some requirements that are elaborated from SDN-based slicing applications. By means of emulations, we show that our prototype reacts well to the inputs coming from the SDN application and is finally capable of providing isolation among multiple slices in a dynamic fashion.

Network slicing is considered a key technology for the upcoming 5G system, enabling operators to efficiently support multiple services with heterogeneous requirements, over a common shared infrastructure. In this demo, we present a prototype for managing network slices in the Cloud Radio Access Network (C-RAN), which is considered the reference network architecture for 5G. Our prototype deals with the spectrum slicing problematic and aims at efficiently sharing the bandwidth resources among different slices, while considering their requirements. The prototype makes use of the Open Air Interface (OAI) platform and a specific Software Defined Network (SDN) controller, known as FlexRAN. By using real smart-phones, we run experiments on stage to validate the feasibility of the prototype in configuring multiple slices on-demand, driven by the input of a northbound application.

The massive adoption of Cloud technology in mobile access networks has driven the operators and vendors to work together in order to make Radio Access Network (RAN) ecosystem more agile. In this context, the virtualization of network functions is the cornerstone of a successful Network Function Virtualization (NFV) environment. However, the stringent requirements of RAN functions make their deployment in a Cloud infrastructure more complex and prone to performance issues. In this respect, this paper puts forward a novel approach that adopts an agile orchestration of fine-grained RAN network functions in order to achieve higher flexibility and improve performances. Specifically, we address the orchestration of baseband processing network functions in a multi-sited Cloud infrastructure. To do so, we propose a user-centric solution, denoted UCS-CRAN, that optimizes the split of the baseband units, while considering both the requirements of its processing network functions and the capabilities of Cloud infrastructure. Based on extensive simulations, the results show that our proposal optimizes both processing and bandwidth usage while minimizing the energy consumption compared to cell-centric, distributed and centralized Cloud-RAN approaches.

5G aims to maximize the data rate and to handle the billions of video, voice, data and IoT flows. For this reason, the macro-cells will be very congested and may fail to satisfy the end-users. In this context, data off loading scheme is conceived to route intra-cell traffic among the D2D-enabled user equipments reusing wireless uplink resources and thus increasing the overall spectral efficiency. In this paper, we address the joint routing and OFDMA resource allocation problem in D2D network. To do so, first we formulate the problem as Mixed Integer Linear Programming. The model takes into account factors that limit spectrum reuse as well as other LTE-D2D technology constraints such as: half-duplex operation and contiguity in resource block allocations. Then, we propose a novel scheme named Joint Routing and Wireless allocation in D2D communications (JRN-D2D) which is based on the branch-and-cut algorithm. In order to gauge the effectiveness of our proposal, we implement the standard LTE- D2D protocol stack, including our scheme JRN-D2D, in the NS-3 network simulator. The results obtained are very promising in terms of reliability, ratio of admitted D2D flows and latency in comparison to other basic one-sided optimal strategies including an interference-aware heuristic scheme.

An offloading scheme based on LTE-D2D is pro­posed in this paper to route the intracellular multicast traffic via a network of D2D-enabled User Equipments (UEs). The latter are ready to cooperate under the control of the eNodeB to carry and deliver the traffic. In doing so, the UEs reuse uplink resources granted by the eNodeB and thus, increasing the overall spectral efficiency while reducing the traffic load on the eNodeB. In this paper, we address the joint multicast routing and OFDM resource allocation problem in the D2D network to accomplish the offloading task. To do so, first we formulate the problem as an Integer Linear Programming (ILP) model which takes into account factors that limit spectrum reuse in addition to other LTE-D2D limitations: half-duplex operation and contiguity in resource block allocations. Then, we propose a novel scheme named Joint Multicast Routing and Wireless allocation in D2D communications (JRW-D2D-MC). The devised scheme consists of two-stage algorithm which, first, performs a pre-admittance filtering of flows that can be routed considering the current state of the network. Then, it makes use of the branch-and-cut method to solve the reduced ILP model. To evaluate effectiveness of our proposal, we implement the LTE-D2D standard in a network simulator NS-3. The results are very good in terms of flow-acceptance rate and latency.

To support the drastically increasing data demands, Internet giants are urged to rethink their data center design. Unfortunately, the conventional wired data centers struggle to resist to the huge volume of traffic. In this regard, we investigate a radically new methodology by augmenting the wired Data Center Network (DCN) with wireless communication (60 GHz technology). Heretofore, only few researches have dealt with the optimization of multi-hop communications in such Hybrid DCN (HDCN) infrastructures. In this paper, we address the joint routing and channel allocation issue for batched flow requests within HDCN. We propose a novel strategy named Joint Batch Routing and Channel Assignment Heuristic for HDCN (JBH-HDCN). To do so, we first formulate the problem based on an advanced Multi-Commodity Flow model with interference constraints. Then, we propose i) a heuristic-based solution to find the best sequence to process the batched flow requests, and ii) an advanced Dijkstra algorithm to jointly route and assign channels. We assess the performances of our solution JBH- HDCN under real conditions, within Cisco's MSDC infrastructure, using both: i) Altoona Facebook's DCN workload and ii) uniform traces. To do so, a full protocol stack is implemented within QualNet simulator and extensive simulations are conducted. Obtained results show that our scheme outperforms the related strategies.

The massive adoption of Cloud services has led to the explosion of traffic transiting over the Cloud infrastructure. Such an impressive evolution of data demand will inevitably be the catalyst of Operator infrastructure transformation. In this context, Software Defined Networking (SDN) is the technology that is shaping the future of carriers' networks. SDN considerably reduces the complexity of managing the network infrastructure while providing tremendous computational power compared to legacy devices. In this paper, we address the resource allocation issue in Wide Area Networks (WAN) while considering the requested QoS. To do so, we design an SD-WAN architecture to enhance the network resources allocation and hence improve the QoS of distributed applications. We formulate first the path computation problem as an Integer Linear Program while taking into consideration both network application requirements and the network occupation status. The problem is then resolved in a polynomial time leveraging the Branch-and-Cut algorithm. Results obtained with our experimental platform, show that the proposed SD-WAN framework outperforms the most prominent related solutions in terms of applications' satisfaction level and consumption of network's resources.

Global IP traffic is forecast to triple by 2020 to reach 2.3 ZB per year. Such an explosion will inevitably be the catalyst of Operator infrastructure transformation. In this context, SDN is the technology that is shaping the future of carriers' networks. It offers the opportunity to implement more powerful control algorithms. In this perspective, we put forward a SD-WAN architecture to enhance the network resources allocation and hence improve the QoS of distributed applications. The main idea is to take profit from the accurate network view provided by the controller to optimize the flows routing in WAN environments. To do so, we formulate the path computation problem as an Integer Linear Program by taking into consideration both network application requirements and the network occupation status. The problem is then resolved in a polynomial time leveraging the branch-and-cut algorithm. Results obtained based an experimental platform show that our ONOS SDN framework outperforms the most prominent related work solutions in terms of network consumption and applications satisfaction level.

The explosion of traffic demands within data centers accentuates the conventional infrastructures ossification. Unfortunately, the traditional tree based architectures struggle to resist and suffer from congestion and wiring complexity which deeply deteriorate the network performance. In aim to address the QoS degradation, we put forward a Hybrid Data Center Network (HDCN) architecture based on CISCO's Massively Data Center (MSDC) model. Specifically, it combines both the wired (Fiber and/or Gigabit Ethernet) and wireless communications based on 60 GHz band (IEEE 802.11ad). Note that few related work have dealt with the optimization of multi-hop communications in HDCN infrastructures and hence the problem is still open. In this paper, we tackle the challenge of jointly i) routing and ii) allocating wireless channels for intra data centers flows, while considering beamforming antennas. For that, we propose a new strategy named Joint Routing and Channel Allocation Algorithm (JRCA-HDCN). To do so, first the problem is formulated as a Minimum Weight Perfect Matching. Then, our resolution is based on the Blossom algorithm. JRCA-HDCN aims to maximize the throughput of intra-HDCN communication over the wireless and/or wired infrastructure. Based on extensive simulations, conducted in QualNet simulator while considering the full protocol stack, the obtained results show that our proposal outperforms the related prominent strategies in terms of i) end-to-end delay, ii) throughput and iii) spectrum spatial reuse.

Data centers are dealing with a rich panoply of applications which are distributed across thousands of servers. In this context, Cloud providers seek to maximize their revenue by meeting the tremendous traffic demand. Unfortunately, the conventional wired infrastructures struggle to resist to such a traffic explosion and new innovative techniques are required. In this respect, we put forward a Hybrid Data Center Network architecture based on the CISCO's Massively Scalable Data Center model, that leverages both the wired (Fiber/Ethernet) and wireless (IEEE 802.11ad) infrastructures. In this paper, we address the problem of batch-routing jointly to spectrum allocation of intra-data center communication flows. To do so, first, we formulate the problem based on Multi-Commodity Flow problem while considering interference constraints. Then, we propose a new strategy named Joint Batch Routing and Channel assignment approach in HDCN (BR-HDCN). Based on extensive simulations conducted in QualNet simulator while considering the full protocol stack, the obtained results for both: i) real Facebook's DC, and ii) uniform, traces, show that our proposal outperforms the related prominent strategies.

As Mobile Network Operators (MNOs) are shifting towards Cloud- Radio Access Network (C-RAN), they have to upgrade their infrastructure to not only support higher processing capacities but also to be more resilient. We consider the problem where a MNO is faced with the choice of selecting virtualized Baseband Units (BBUs) from various cloud service providers, that are each characterized with distinct failure probabilities and prices. We propose to solve the BBU selection problem, formulated as an Integer Linear Program (ILP) subject to BBU capacity and virtualization cost using the Branch- and- Price algorithm. We present several schemes depicting which optimization goal the MNO can foster the most: BBU processing power minimization, resiliency, traffic handling or all. Simulation results demonstrate the good performance of our algorithm to solve the BBU selection problem for all schemes, while also emphasizing the advantages of a particular one that can realize more than 10% in virtualization cost savings.

To cope with the unprecedented traffic explosion, Internet giants are urged to rethink their data center design. Unfortunately, the conventional wired data centers struggle to support the impressive growth of both data and online services. In this regard, we resort to augmenting the wired data center network (DCN) with wireless communication (60 GHz technology). Heretofore, only few research work have dealt with the optimization of multi-hop communications in such hybrid DCN (HDCN) infrastructures. In this paper, we investigate, in HDCN, the issue of jointly: 1) routing and 2) channel allocating, in both online and batch arrival modes. First, we formulate the online joint routing and channel assignment problem as a minimum weighted perfect matching problem. We propose a new strategy named joint routing and channel allocation algorithm that makes use of the Blossom algorithm and sequentially computes the optimal routing communication paths. Second, to handle the batched arrivals of flows, we formulate the batch joint routing and channel assignment problem as an advanced multi-commodity flow model. We propose two scalable approaches: 1) a heuristic based solution, named joint routing and channel assignment heuristic and 2) an approximate solution, named scalable joint batch routing and channel allocation, based on the Lagrangian relaxation technique. Based on extensive network simulations conducted in QualNet simulator while considering the full protocol stack, the obtained results for both: 1) real Facebook's data center and 2) uniform, traces, show that our schemes outperform the related strategies.

In this paper, we tackle the virtual network (VN) embedding problem within Cloud's backbone network by taking into consideration the impact of physical equipment outages. Our main focus is to improve the Cloud provider's (CPs) revenue by: 1) maximizing the acceptance rate of VNs within the Cloud's backbone and 2) minimizing the penalties induced by service disruption due to the hardware outages. This optimization problem is non-linear multi-objective and it has been proven to be NP-hard. To cope with this complexity, we propose an advanced coordination game for VN embedding (Advanced-CG-VNE). In this mapping (i.e., embedding) game, fictitious players are playing on behalf of the CP in order to maximize the turnover. The decision makers cooperate in aim to converge to a Nash Equilibrium that we prove the existence of and the matching with a social optimum. Two variants of Advanced-CG-VNE are proposed according to the virtual links embedding approach. The first one, denoted by Advanced-CG-VNE-unsplittable, embeds each virtual link in only one substrate path. The second variant, denoted by Advanced-CG-VNE-splittable, dispatches the required bandwidth of a virtual link among a set of substrate paths. Advanced-CG-VNE does not allocate any backup to handle service interruption caused by hardware failures. Our proposal adopts preventive and reactive mechanisms to palliate substrate failures. Based on extensive simulations to gauge the effectiveness of Advanced-CG-VNE, the obtained results show that our proposal outperforms the most prominent related strategies in terms of: 1) rejection rate of VNs; 2) rate of VNs impacted by physical failures; and 3) CPs turnover.

In this paper, we address the problem of downlink resource allocation and admission control for an Orthogonal Frequency Division Multiple Access (OFDMA)-based Cloud Radio Access Network (C-RAN). Specifically, we formulate the resource allocation and admission control for mobile users in C-RAN as an optimization problem, subject to constraints on mobile users data rate requirements, maximum transmission power and fronthaul links capacity. By dropping the non-linear constraint and reformulating the problem linearly using the framework of the well-known big-M method, we propose a two-stage algorithm that can efficiently solve it. To satisfy the strict timing requirement of wireless communications in such a system, a time constraint was added to our algorithm. Numerical results demonstrate the good performance of our proposal in terms of number of accepted users and total transmission power, when compared with state-of-the-art methods used for the control admission task in C-RAN.

In this paper, we propose a novel 3D Underwater Wireless Sensor Network Deployment scheme for solid detection in rivers. Our objective is to minimize the number of deployed underwater sensors within a target field installation while ensuring i) the required Quality of Monitoring (QoM) (i.e., differentiated probabilistic detection) and ii) wireless network connectivity. To generate the best topology, we propose a novel deployment heuristic, named 3D-UWSN-Deploy, based on a subcube tessellation of the monitored field installation and a mixed integer linear program optimization. To gauge the effectiveness of 3D-UWSN-Deploy, we compare it with the most prominent related strategies. Simulation results show that our proposal is scalable and obtains the best performance in terms of cost deployment, quality of monitoring and connectivity.

Cloud-Radio Access Network (C-RAN) is a new emerging technology that holds alluring promises for Mobile network operators regarding capital and operation cost savings. However, many challenges still remain before full commercial deployment of C-RAN solutions. Dynamic resource allocation algorithms are needed to cope with significantly fluctuating traffic loads. Those algorithms must target not only a better quality of service delivery for users, but also less power consumption and better interference management, with the possibility to turn off RRHs that are not transmitting. To this end, we propose in this paper a dynamic two-stage design for downlink OFDMA resource allocation and BBU-RRH assignment in C-RAN. Specifically, we first model the resource and power allocation problem in a mixed integer linear problem for real-time fluctuating traffic of mobile users. Then, we propose a Knapsack formulation to model the BBU-RRH assignment problem. Simulation results show that our proposal achieves not only a high satisfaction rate for mobile users, but also minimal power consumption and significant BBUs savings, compared to state-of-the-art schemes.

In this paper, we propose, design and experiment an energy efficient protocol for Wireless Sensor Networks (WSNs) named Opportunistic Duty cYcle based routing protocol for wirelesS Sensor nEtworks (ODYSSE). The main key innovation of ODYSSE is that it judiciously makes use of three mechanisms. The first one is duty cycling which consists in randomly switching on/off transceivers to save energy. The second one is opportunistic routing in which the next hop is not rigidly fixed: any node closer to the destination might become a relay. The third one, is source coding using LDPC, Low-Density Parity-Check codes. With asynchronous duty cycling as a starting point, the above techniques fit perfectly, yielding a robust low complexity protocol for highly constrained nodes. ODYSSE is implemented and installed in an experimental testbed composed of 45 Arduino nodes communicating with IEEE 802.15.4 (XBee) modules deployed in the large-scale platform FIT IoT-LAB. Results show that the performance obtained is very satisfying in both following scenarios: high load (images) and light load (reporting of infrequent event).

We address the online virtual network embedding problem within the Cloud's backbone to optimally map the virtual routers and links in the substrate network in order to maximize the Cloud's provider revenue. Since the problem is NP-hard, we propose a novel approach, named VNE-GH, to significantly reduce the problem size using the Gomory-Hu transformation without losing useful information on the virtual network embedding problem. Starting from the Gomory-Hu compact tree structure, we formulate the virtual network embedding as an Integer Linear Program and resolve the reduced size problem using the branch- and-cut algorithm. Results obtained via extensive simulations show that VNE-GH outperforms the most prominent related work strategies in terms of i) acceptance rate of virtual network requests and ii) Cloud provider's revenue.

In this paper, we address the survivable batch-embedding virtual network problem within Cloud's backbone. In fact, the batch mapping of virtual networks will enhance the cumulative Cloud provider's revenue thanks to the global view of the incoming requests during a predefined time slot. Hence, the differentiation between requests can be performed and the arrival order of requests is ignored. The embedding of one virtual network is NP-hard. Adding the batch processing of the requests will further increase the complexity of the problem. In order to skirt the exponential complexity, we formulate the problem as building and researching problems within a decision tree. To resolve it, we propose a novel reliable batch-embedding virtual network strategy denoted by BR-VNE. It is based on Monte-Carlo Tree Search optimization method in which the upper confidence bounds can be reached in polynomial time. Based on extensive simulations, the results obtained show that BR-VNE outperforms the related work in terms of i) acceptance rate of virtual network requests, ii) Cloud provider's revenue and iii) rate of requests impacted by physical failures within the Cloud's backbone.

International audience

In this paper, we propose a new 2-Dimensional Underwater Barrier Deployment Algorithm (2D-UBDA) ensuring the barrier detection of toxic substances in a river. Our objective is to guarantee a full detection of chemical pollutant sources, while minimizing the deployment cost. To achieve this, first 2D-UBDA determines the potential deployment areas within a predefined target field installation and this, for each pollution source, by using a 3D-propagation model of a substance to predict its molarity in any point within the river. Then, based on an integer linear programming algorithm, 2D-UBDA selects the minimum number of sub-areas in which chemical sensors will be deployed by taking into consideration the intersections between the potential deployment zones of all pollution sources located upstream of the target field installation. To validate our proposal, the Pamplonita river located in Amazon rainforest is used as a case of study. Based on extensive simulations, 2D-UBDA outperforms the basic deployment strategies in terms of number of chemical sensors and successful detection of pollutant.

In this paper, we propose a new survivable virtual network mapping strategy within Cloud's backbone enhancing the Cloud Provider's revenue and dealing with physical failures of routers and links. In order to skirt the exponential complexity of the mapping, we propose a new reliable embedding strategy, denoted by CG-VNE, based on coordination game framework. To do so, we have formulated the problem as two interleaved coordination games. The first game addresses the virtual routers' mapping. In fact, the actions of each virtual router player strongly depend on the mapping of its attached virtual links. Hence, the second game is launched to embed the virtual links. Note that with both games, fictitious players cooperate to reach Nash Equilibrium of which we have proven the existence and it corresponds to a social optimum. CG-VNE aims to maximise the Cloud's provider revenue by maximising the acceptance rate of clients, as well as minimise the blackout rate of virtual networks caused by the outage of substrate routers and/or links. Based on extensive simulations, the results obtained show that CG-VNE has the best performance in terms of i) rejection rate of new clients, ii) Cloud's revenue and iii) rate of clients impacted by physical failures.

Cloud Computing offers elasticity and enhances resource utilisation. This is why its success strongly depends on the efficiency of the physical resource management. This paper deals with dynamic resource reconfiguration to achieve high resource utilisation and to increase Cloud providers income. We propose a new adaptive virtual network resource reconfiguration strategy named VNR-GA to handle dynamic users' needs and to adapt virtual resource allocation according to the applications' requirements. The proposed algorithm VNR-GA is based on Genetic metaheuristic and takes advantage of resources migration techniques to recompute the resource allocation of instantiated virtual networks. In order to optimally adapt the resource allocation according to customers' needs growth, the main idea behind the proposal is to sequentially generate populations of reconfiguration solutions that minimise both the migration and mapping cost and then select the best reconfiguration solution. VNR-GA is validated by extensive simulations and compared to the most prominent related strategy found in literature (i.e., SecondNet). The results obtained show that VNR-GA reduces the rejection rate of i) virtual networks and ii) resource upgrade requests and thus enhances Cloud Provider revenue and customer satisfaction. Moreover, reconfiguration cost is minimised since our proposal reduces both the amount of migrated resources and their new mapping cost.

In this paper, we study the wireless sensor network deployment for water pollution monitoring in the Amazon rainforest rivers. Our objective consists in minimising the number of deployed geographical field installations along the river, while ensuring the detection of the substance spilled in the given river regardless of the position of its source. A geographical field installation is formed by a set of barrier coverage underwater sensors which detect the pollutant if its molarity in the water is greater than a predefined threshold. Indeed, the substance molarity is inversely proportional to the moving distance. To generate the best topology, we propose a sub-optimal novel geographic Installation Field Deployment Algorithm based on the Backtracking heuristic named BT-FIDA. Since the river has a several forks, in order to reduce the number of installation fields, BT-FIDA minimises the rate of at least 2-covered river segments. The simulation results obtained show that our proposal minimises the number of field installations (i.e., deployment cost) while minimising the rate of areas which are miss-covered and over-covered.

In this paper, we propose a new preventive reliable virtual network embedding algorithm denoted by PR-VNE within the Cloud's backbone network. The proposal does not allocate any backup resources and takes into consideration the ageing of the hardware backbone network. The main objective is to maximise the number of hosted virtual networks while minimising the rate of crashed virtual networks impacted by physical (i.e., routers or links) failures. The problem is a multi-objective non-linear optimisation and classified as NP-hard. To overcome its complexity, PR-VNE is based on the artificial bee colony metaheuristic. Moreover, it makes use of a multi commodity flow algorithm in order to maximise the load balancing of bandwidth usage within the physical network. Based on extensive simulations, the performance obtained is better than the related strategies found in literature in terms of reject and blackout rates of virtual networks.

In this paper, we propose a new dynamic adaptive virtual network resource allocation strategy named Adaptive-VNE to deal with the complexity and the inefficiency of resource allocation. The proposal coordinates virtual node and virtual link mapping stages. The main idea behind the proposal is take advantage of unused bandwidth with respect to the occupancy rate of embedded virtual links. Hence, the unused bandwidth will be reassigned to incoming virtual network requests. To do so, Adaptive-VNE adopts the “divide and conquer” strategy. It divides the virtual network request topology into many star topologies. Then, the mapping of each piece within the whole topology is formulated as a K-supplier problem and resolved by an approximation bottleneck algorithm. To generate the global virtual network topology, Adaptive-VNE uses a backtracking algorithm in order to minimise the global mapping cost. Note that the proposal forecasts usage rate of virtual links and adapts their bandwidth reservation. Adaptive-VNE was validated by simulations and compared to the related strategies found in literature. The results obtained show that, contrarily to static bandwidth allocation approaches, the adaptive strategy maximises substrate bandwidth usage while the virtual links' bottleneck rate is minimised. Moreover, the congestion periods are minimised and during the bottleneck the bandwidth satisfaction is maximised. Finally, Adaptive-VNE improves performances in terms of acceptance rate of virtual networks and revenue of infrastructure providers.

This paper proposes a new joint power control and resource allocation algorithm in OFDMA femtocell networks. We consider both QoS constrained high-priority (HP) and best-effort (BE) users having different types of application and bandwidth requirements. Our objective is to minimize the transmit power of each femtocell, while satisfying a maximum number of HP users and serving BE users as well as possible. This optimization problem is multi-objective NP-hard. Hence, we propose a new scheme based on clustering and taking into account QoS requirements of users. We show by extensive network simulation results that our proposal outperforms three state of the art schemes (Centralized-Dynamic Frequency Planning, C-DFP, Distributed Random Access, DRA and Distributed Resource Allocation with Power Minimization, DRAPM as well as our previous proposal, FCRA, in both low and high density networks. The results concern the rate of rejected users, the throughput satisfaction rate, the spectrum spatial reuse, fairness, as well as computation time.

Autonomic computing is a new paradigm inspired by the biological world. It aims at making a network independent of any human monitoring. To reach such autonomy, knowledge should be disseminated over the network, which remains an open problem. Our solution consists in proposing a new model of knowledge dissemination based on three key ideas: a hierarchical architecture, a specific-service overlay network (SSON) and a multi-criteria selection of a subset of nodes responsible for knowledge management. The simulation results show that the proposed approach significantly improves performances compared to other approaches.

Recently, operators have resorted to femtocell networks in order to enhance indoor coverage and increase system capacity. Nevertheless, to successfully deploy such solution, efficient resource allocation algorithms and interference mitigation techniques should be deployed. The new applications delivered by operators require large amounts of network bandwidth. Whereas, some customers may want to pay more in exchange for a better quality of service (QoS), some others need less resources and can be charged accordingly. Hence, we consider an OFDMA femtocell network serving both QoS constrained high-priority (HP) and best-effort (BE) users. Our objective is to satisfy a maximum number of HP users while serving BE users as well as possible. This optimization problem is multi-objective NP-hard. For this aim, we propose in this paper a new resource allocation and admission control algorithm, called Q-FCRA, based on clustering and taking into account QoS requirements. We show by extensive network simulation results that our proposal outperforms two state of the art schemes (Centralized-Dynamic Frequency Planning, C-DFP, and Distributed Random Access, DRA) as well as our previous proposal, FCRA, in both low and high density networks. The results concern the number of accepted users, the fairness, the throughput satisfaction rate and the spectrum spatial reuse.

Sky computing is a promising concept enabling a flexible deployment of geographical distributed applications. Whereas, it is faced with a fundamental challenge which is: “efficient resource utilisation” within Cloud's infrastructure. Hence, a high flexible and intelligent resource allocation scheme is necessary to accommodate unpredictable and variable users demands. This paper tackles the fundamental challenge of efficient resource allocation within Cloud's backbone network. The ultimate goal is to satisfy the Cloud's user requirements while maximising Cloud provider's revenue. The problem consists in embedding virtual networks within substrate infrastructure. A new dynamic adaptive virtual network resource allocation strategy named Backtracking-VNE is investigated to deal with the complexity of resource provisioning within Cloud network. The proposal coordinates virtual nodes and virtual links mapping stages to optimise resources usage. Moreover, thanks to forecasting module, Backtracking-VNE guarantees an efficient resources share between embedded virtual links with respect to their occupancy. We demonstrate through extensive simulations that contrarily to static bandwidth allocation approaches, Backtracking-VNE enhances substrate bandwidth usage whilst minimising virtual links congestion. Acceptance rate of virtual networks and Cloud providers income are also improved compared with related strategies.

In this paper we address the problem of virtual network reconfiguration. In our previous work on virtual network embedding strategies, we found that most virtual network rejections were caused by bottlenecked substrate links while peak resource use is equal to 18%. These observations lead us to propose a new greedy Virtual Network Reconfiguration algorithm, VNR. The main aim of our proposal is to 'tidy up' substrate network in order to minimise the number of overloaded substrate links, while also reducing the cost of reconfiguration. We compare our proposal with the related reconfiguration strategy VNA-Periodic, both of them are incorporated in the best existing embedding strategies VNE-AC and VNE-Greedy in terms of rejection rate. The results obtained show that VNR outperforms VNA-Periodic. Indeed, our research shows that the performances of VNR do not depend on the virtual network embedding strategy. Moreover, VNR minimises the rejection rate of virtual network requests by at least ≃83% while the cost of reconfiguration is lower than with VNA-Periodic.

In this paper, we address a virtual network embedding problem. Indeed, our objective is to map virtual networks in the substrate network with minimum physical resources while satisfying its required QoS in terms of bandwidth, power processing and memory. In doing so, we minimize the reject rate of requests and maximize returns for the substrate network provider. Since the problem is NP-hard and to deal with its computational hardness, we propound a new scalable embedding strategy named \texttt{VNE-AC} based on the Ant Colony metaheuristic. The intensive simulations and evaluation results show that our proposal enhances the substrate provider's revenue and outperforms the related strategies found in current literature.

Recently, operators have resorted to femtocell networks in order to enhance indoor coverage and quality of service since macro-antennas fail to reach these objectives. Nevertheless, they are confronted to many challenges to make a success of femtocells deployment. In this paper, we address the issue of resources allocation in femtocell networks using OFDMA technology (e.g., WiMAX, LTE). Specifically, we propose a hybrid centralized/distributed resource allocation strategy namely Femtocell Cluster-based Resource Allocation (FCRA). Firstly, FCRA builds disjoint femtocell clusters. Then, within a cluster the optimal resource allocation for each femtocell is performed by its cluster-head. Finally, the contingent collisions among different clusters are fixed. To achieve this, we formulate the problem mathematically as Min-Max optimization problem. Performance analysis shows that FCRA converges to the optimal solution in small-sized networks and outperforms two prominent related schemes (C-DFP and DRA) in large-sized ones. The results concern the throughput satisfaction rate, the spectrum spatial reuse, and the convergence time metrics.

In this paper, we will address a WSN deployment problem. The main objectives are i) reduce the cost of deployment, ii) ensure the requested event detection probabilities, iii) guarantee the network connectivity, and iv) maximize the lifetime of the network. We will formalize the problem as multi-objective combinatorial optimization problem. To resolve the problem, we will propose a new deployment algorithm named MODA. It will be based on evolutionary and neighborhood search algorithms. The obtained results are better than the deployment strategies found in the literature.

This paper addresses the issue of wireless sensor network (WSN) deployment. We investigate this problem in the case where the monitored area is characterized by a geographical irregularity of the sensed events. Precisely, we consider that each point of the deployment area requires a minimum threshold guarantee on the event detection probability. Our proposed scalable deployment method, named potential field-based deployment algorithm (PFDA), is based on the potential field and the virtual force approaches. Our proposal is able to (1) satisfy the required event detection probability threshold for each point, in a large-scale area, while minimizing the number of deployed sensors and (2) to ensure the network connectivity. The results and evaluation analysis show that PFDA outperforms the other strategies proposed in literature.

In this paper, we address the wireless sensor network (WSN) deployment issue. Compared to similar works, we relax some assumptions that were generally considered in the literature. Precisely, instead of the classical binary detection model, we consider a distance-related probabilistic one. Moreover, we assume that the observed area is characterized by the geographical irregularity of the sensed events. Our resulting differentiated WSN deployment problem is formulated as a multi-objectives optimization one. To overcome the computational complexity of an exact resolution, we propose an original pseudo-random approach based on the tabu search heuristic. Our proposal is able to take into consideration the required detection probability threshold of each point in the monitored area while minimizing the number of deployed sensors. Performances evaluations show that our proposal achieves a much better satisfaction rate than several other approaches proposed in the literature.

In this paper, we extend the differentiated deployment method which we have proposed in our previous paper by adding the connectivity constraint. We assume a fixed communication ray and a probabilistic detection model. We also consider that to each point in the deployment area is associated a detection probability threshold, which must be satisfied by our deployment method. Finally, we suppose that the detection probabilities thresholds of the area are geographically nonuniformly distributed. Our differentiated deployment problem is modeled as multi-objectives optimization problem, which we resolve using our proposed Tabu Search-based algorithm. A comparison is made with the methods found in the literature. The performances obtained by our method are much better in term of required number of sensors, generated detection probabilities, while ensuring the network connectivity.