IoT technology is gaining popularity as more affordable, intelligent, and multifunctional IoT devices are developed. The proliferation of these IoT objects has led to their adoption in several areas, including agriculture, livestock farming, health monitoring, etc. The ultimate goal of deploying IoT networks is to collect data for decision-making. Data collection is, therefore, one of the important tasks in IoT networks. The main characteristic of IoT devices is that they are energy-constraints with finite-lifetime batteries. The batteries in IoT devices are not rechargeable, rendering them unusable when they run out. Moreover, for critical applications, the replacement of faulty IoT devices is a tricky operation. Therefore, it is important to control the energy consumption of the IoT devices during data collection in the network to ensure battery longevity. In this paper, we propose the Social Network Search-Based Data Collection Protocol (SNS-DCP), which uses the Social Network Search metaheuristic to create an optimal partition with suitable partition head node selection, thereby optimizing network lifetime and maximizing data collection in IoT networks. The simulation results show that the SNS-DCP protocol outperforms the existing PSO-UFC, PSO-ECHS, and LEACH protocols in terms of network lifetime, number of alive nodes, network energy consumption, and the amount of data collected at the BS. Concisely, the SNS-DCP protocol enhances the data collection by 43%, 68%, and 87% over the PSO-UFC. PSO-ECHS. and LEACH protocols, respectively,

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.

In this work, we investigate the enhancement of secure quality-of-experience (QoE) in unmanned aerial vehicle (UAV)-assisted multiuser rate-splitting multiple access (RSMA) networks under stringent secrecy constraints. The primary objective is to maximize the aggregate mean opinion scores (MOSs) of all legitimate users while guaranteeing robust physical-layer security against eavesdroppers. To this end, the original secure optimization problem is decomposed into two interconnected subproblems: joint secure beamforming and rate allocation, and UAV trajectory optimization. For the secure beamforming and rate allocation, we employ advanced convexification techniques, including epigraph reformulation, polynomial properties, and norm-bounded channel uncertainty modeling, to ensure both optimal user experience and resilience to information leakage. The UAV trajectory optimization, inherently nonconvex, is further addressed by transforming and approximating the secrecy-related constraints to support secure communication throughout the UAV’s path. Simulation results validate the effectiveness and robustness of the proposed framework in simultaneously enhancing user-perceived QoE and ensuring secure transmission, even under varying eavesdropping threats and network conditions.

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.

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.

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.

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.

The effectiveness of WSNs depends on the data collection scheme since the design of an energy-efficient, long-time WSN has been a challenge for over a decade. To address this issue, we propose a Cluster-tree Data Collection Protocol using a hybrid meta-heuristic algorithm termed Hybrid Bio-Inspired Protocol (HBIP). The idea consists of integrating the Bacterial Foraging optimization (BFO) swarming step into the exploitation phase in the Artificial Bee Colony algorithm (ABC). Our solution efficiently builds clusters and elects the optimal Cluster Heads (CHs). The results of the simulation show that HBIP outperforms the traditional clustering protocol LEACH and meta-heuristics like ABC and BFO in terms of throughput, energy consumption, network lifetime, and data packets received at the BS.

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.

With the diversification of 5G services especially the emergence of applications related to the Internet of Things (IoT), slicing is considered as the best way to offer the optimal Quality of Services (QoS) and Quality of Experience (QoE) using the same shared infrastructure. In this paper, we focus on the optimal resource reservation using recent methods and techniques in order to meet the highly variable and dynamic demands. That's why, we propose a taxonomy of different approaches using Artificial Intelligence/Machine Learning (AI/ML) for slicing resource optimization and orchestration. In addition, a comparison study of different solution is proposed. Finally, we highlight future challenges and discuss future research directions.

In Wireless Sensor Networks, mobile sensing refers to the presence of one or more mobile sinks or mobile sensors, which have the main role of collecting the gathered data by sensor nodes. This paper describes a new scheme of mobile sensing that aims at providing a good coverage and throughput while maintaining better energy efficiency and high network availability. To achieve this, some features of the social foraging behavior of the Escherichia coli bacteria have been used, especially the chemotaxis and swarming processes that allow bacteria to move. Particularly, a description and a formulation of a mobile sensing scheme based on an approach inspired by the Bacterial Foraging Optimization have been provided. Models that allow mobile sinks to move over the network in a self-organized and self-adaptive way have been proposed. The proposal has been experimented in order to elaborate the impact of mobility on delay, network coverage and successful amount of collected data. The obtained results demonstrate the effectiveness of the proposal.

In this paper, we propose a Proxy Mobile IPv6 (PMIPv6)-based architecture for Follow Me Cloud (FMC), a concept whereby mobile users are always connected to their optimal Data Centers via their optimal data anchor gateways, enabling not only data but also services to follow users. The approach envisioned in this paper consists of two parts: (i) a PMIPv6-based inter-domain mobility management support based on Distributed Mobility Management and (ii) a control plane based on Software Defined Networking - SDN/OpenFlow, which exploits the mobility information delivered by the PMIPv6 inter-domain mobility management approach to decide on triggering the migration of services/sessions within the cloud. Integrating SDN/Openflow rules with PMIPv6 permits removing the complexity and workload associated with tunneling in mobility management. Results obtained via analysis are encouraging and show the advantages of PMIPv6-based FMC in comparison to the state of the art mobility management protocols, such as Inter-domain PMIP (I-PMIP) and DMM-based PMIP (D-PMIP).

Follow Me Cloud (FMC) concept has emerged as a promising technology that allows seamless migration of services according to the corresponding users' mobility. Meanwhile, Software Defined Networking (SDN) is a new paradigm that permits to decouple the control and data planes of traditional network, and provides programmability and flexibility, allowing the network to dynamically adapt to changing traffic patterns and user demands. While the SDN implementations are gaining momentum, the control plane, however, is still suffering from scalability and performance concerns for a very large network. In this paper, we address these scalability and performance issues by introducing a novel SDN/OpenFlow-based architecture and control plane framework tailored for mobile cloud computing systems and more specifically for FMC-based systems where mobile nodes and network services are subject to constraints of movements and migrations. Contrary to centralized approach with single SDN controller, our approach permits to distribute the SDN/OpenFlow control plane on a two-level hierarchical architecture: a first level with a global controller G-FMCC, and second level with several local controllers L-FMCC(s). Thanks to our control plane framework and Network Function Virtual-ization concept (NFV), the L-FMCC(s) are deployed on-demand, where and when needed, depending on the global system load. Results obtained via analysis show that our solution ensures more efficient management of control plane, performances maintaining and network resources preservation.

Femtocells are an inexpensive solution to address weak indoor cellular coverage. Femtocells increase network coverage, offloaded macro cell sites, and provide high data rate services in a cost-effective manner. However, several aspects of this new technology are ambiguous or not defined in standard, such as QoS architecture, bandwidth request management and subscribers flows priority. This paper provides a new QoS management scheme for WiMAX Femtocell Access Point (WFAP). Our proposal offers three key features: prioritize femtocell's owner traffics; guarantee's the QoS for the different connections of each user, better resources management. We validate our assumptions through simulations and numerical analysis. Results show the effectiveness of our solution in public and private access methods.

The CSMA/CA protocol is well known to handle the channel access to various users in wireless ad hoc networks using IEEE 802.11 technology. This protocol requires nodes to wait for some time before initiating a transmission to avoid collisions. As a result, the greedy behavior of some misbehaving nodes can try to lower their waiting time in order to access the channel earlier and penalize the other nodes. In order to avoid this misbehavior, we propose in this paper a model based on measuring the linear regression of nodes' access time to the channel. We have demonstrated that this model exhibits a linear regression between the different nodes' access time. This result has been also confirmed by simulations. In this model, each deviation from the estimated slope is considered as a source of cheating from a corresponding node. By using this detection model, we were able to detect most of the misbehaving nodes in wireless ad hoc networks without requiring modifications to the IEEE 802.11 MAC protocol.

Mobile WiMAX is an emerging broadband wireless communication technology. The IEEE 802.16e standard for Mobile WiMAX, the enhanced version of the IEEE 802.16 standard with mobility support, defines that the implementation of hard handover is mandatory. However, the long interruption of hard handover is horrible for delay and packet loss sensitive real-time applications such as IPTV and VoIP. To solve this problem without many changes to 802.16e equipments, we propose some improvements at various stages of the handover, including the principle for selection of target base station depending on the type of traffic connection managed by subscriber station. Simulation results prove that the scheme accelerates handover process and make Real-time downlink data transmission interruption so brief that users will not perceive the interruption.

In this paper we present a new distributed group mutual exclusion (DGME) based on clients/servers model, and uses a dynamic data structures. Several processes (clients) can access simultaneously to a same opened session (server). The algorithm ensures that, at any time, at most one session is opened, and any requested session will be opened in a finite time. The number of messages is between 0 and m, where m is the number of session in the network. In the average case, O(Log(m)) messages are necessary to open a session. The maximum concurrency is n, where n is the number of processes in the network

Actually, the quality of service (QoS) provisioning in IEEE 802.11-based wireless network is assured by the enhanced coordination channel access (EDCA) mechanism, which lacks the aptitude to ensure: (i) an intra-QoS differentiation between the access class (AC), where flows belonging to the same service class are assigned the same MAC parameters regardless theirs respective bit rate, which leads to throughput fairness rather than perceived QoS fairness; (ii) an admission control mechanism that manages the network traffic. In this paper we propose a new MAC protocol featuring a dynamic reservation of the wireless channel (by using the TXOPlimit parameter) along with a distributed admission control mechanism. By monitoring the MAC queue, each flow computes at runtimes the TXOPlimit's value that satisfies the application requested data rate. Meanwhile we specify a fully distributed admission control mechanism that regulates the network load and protects the admitted flows from the new ones. Simulation results show that compared to the EDCA scheme of 802.1 Ie, our protocol excels, in terms of network utilization and ability to maintain intra-QoS data rate differentiation. Further when introducing the admission control mechanism, we ensure high protection to the admitted flows, and maintain the network in steady state

Multimedia streaming over wired network, such as the Internet, has been popular now for quite some time. Further, as the bandwidth of wireless technologies have increased, consideration has only recently turned to delivering video over wireless networks. Meanwhile, an emerging new video compression standard namely H.264 is widely accepted. The H.264 standard outperforms all previous video compression, and can deliver high video quality at low rate. However, due to wireless channel characteristics, it is insufficient to merely deliver video data; instead delivery must consider also an adaptation of the video data to the networks specific and particularly network friendliness. In this paper, we address H.264 wireless video transmission over IEEE 802.11 wireless LAN by proposing a robust cross layer architecture which exploits: (i) the new standard IEEE 802.11e MAC protocol; (ii) the H.264 error resilience tools, namely data partitioning. The performances of the proposed architecture are extensively investigated by simulations. Results obtained indicate that our architecture can reliably transport the H.264 video stream, while the actual IEEE 802.11 standard in the same conditions, degrades considerably the transmitted video.

The increase of IEEE 802.11's bandwidth led to a deployment of many multimedia applications over wireless networks. Nevertheless, these applications impose stringent constraints in QoS. In this context, a lot of works have been proposed in order to enhance the QoS-capable IEEE 802.11e MAC protocol. However, they settle for maintaining only an inter-QoS differentiation between the traffic classes, and neglect the intra-Qos differentiation. In fact, the flows belonging to the same service class are assigned the same MAC parameters regardless of their data rate, which leads to throughput fairness rather than perceived QoS fairness. On the other hand, the proposed schemes exhibit performance degradation when the number of flows increases. In this paper, we propose a new MAC protocol based on the reservation of the wireless channel through the use of transmission Opportunity (TXOPlimit) parameter. Each traffic class monitors the MAC queue and computes at runtime the TXOPlimit's value. Thus based on the class' priority and flow's data rate, we can ensure both intra and inter QoS differentiation. Additionally, we specify a distributed admission control mechanism that regulates the network load and protects the admitted flows from the new ones. Simulation results show that compared to the Enhanced Distributed Channel Access (EDCA) scheme of 802.11e, our protocol excels, in terms of network utilization and ability to maintain intra-QoS data rate differentiation. Further when introducing the admission control mechanism, we ensure high protection to the admitted flows, and maintain the network in steady state.

With the widespread IEEE 802.11 networks use, strong needs to enhance quality of service (QoS) has appeared. The IEEE 802.11 medium access control (MAC) protocol provides a contention-based distributed channel access mechanism that allow for wireless medium sharing. This protocol involves a significant collision rate as the network gets fairly loaded. Although the contention window (CW) is doubled after each collision, active stations may randomly select a backoff timer value smaller than the preceding one. This is obviously sub-optimal since the backoff values should rather increase after each collision in order to further space between successive transmissions and thus absorbing the growing contending flows. In this paper, we propose a novel backoff mechanism, namely "determinist contention window algorithm" (DCWA), which further separates between the different backoff ranges associated to the different contention stages. Instead of just doubling the upper bound of the CW, DCWA increases both backoff range bounds (i.e., upper and lower bounds). On the other hand, after each successful transmission the backoff range is readjusted by taking into account current network load and past history. Simulation results show that DCWA outperforms both the distributed coordination function (DCF) and the slow decrease (SD) scheme in terms of responsiveness to network load fluctuations, network utilization, and fairness among active stations.

Today the great challenge to the IEEE 802.11 wireless LAN is to support real time and multimedia applications. However, the characteristics of wireless channel (low and fluctuating bandwidth link and the large error rate) post different problems in supporting the requirements on QoS (bandwidth, delays, jitters) of these sensitive applications. We focus on the JVC H.26L video encoder, the wireless LAN and their interaction in QoS. The interaction is made by the guarantee of a reliable H.26L video transport over the IEEE 802.11 wireless LAN. In this context, we propose a novel cross layer architecture. This architecture is based on a marking scheme, which exploits the new propositions of the IEEE 802.11 E group for supporting QoS.

The evolution towards the broadband integrated services digital network (B-ISDN) is influenced by many factors. The most significant one is the emergence of a great number of services requiring different levels of QoS and bandwidth availability. The main services are video on demand (VoD), videoconference, data and voice transfer. It is particularly important to ensure quality of service is critical moments such as congestions in a fixed network or handover in a mobile environment. We particularly focused our work on traffic routing and we dealt with the problem of maintaining communications and sending traffic to mobile users in a WATM network. Many solutions can be adopted such as increasing the number of frequencies, choosing a transmission technique adapted to multiservice, optimizing the bandwidth on the fixed part of the mobile network. We particularly investigated the last solution and the way of making the bandwidth available by rerouting connection during a handoff execution. Our work concerns the mechanism which optimize the bandwidth utilization in a multi-services context with MSRA (multi-service rerouting algorithm). Discrete event simulations were run in order to validate our algorithm. It is shown that the use of such techniques leads to good performance results in terms of cell loss probability, new call blocking rate and dropping handover probability.

New services are introduced in the broadband network such video or multimedia. These services have some quality of service constraint such as transit delay, and cell loss probability. To improve the quality of service, it is important to optimize the bandwidth utilization. In this study we focus on this problem, and our major contribution is to provide quality of service by the development of a new rerouting algorithm. All existing routing algorithms optimize at the time of call establishment. The originality of our contribution is to optimize routing before and after call establishment. To show the benefit of our algorithm we do a performance evaluation using a new technique called SAN (stochastic automata networks), which is adapted to our problem.

The third generation of wireless networks will be broadband and thus will be able to support several services with different constraints on their QoS such as transit delay for time sensitive connections or cell loss probability for non real time applications. However, in such networks, the mobile may traverse several cells during the communication. Thus, if the newly visited cell does not have enough radio resources then some data may be lost or in a worst case the connection can be dropped. Since handovers are obviously critical moments then researchers have mainly investigated the bandwidth allocation problem on the radio interface and ignored the impact of mobility effects on the fixed part of the network. In this study we present a new rerouting algorithm for WATM networks called RA-NRTT, which aims to reroute non real time traffic data in the wired part of the mobile network while avoiding loops. Performance evaluation of this algorithm has been done while considering two buffer sharing strategies: complete sharing (CS) and complete partitioning (CP), and two scheduling policies; first in first out (FIFO) and weighted round robin (WRR).