International audience

Recently, Wi-Fi signals have emerged as a powerful tool for contactless sensing. During the sensing process, obtaining target direction information can provide valuable contextual insights for various applications. Existing direction estimation methods typically rely on antenna arrays, which are costly and complex to deploy in real-world scenarios. In this paper, we present WiRainbow, a novel approach that enables single-antenna-based direction awareness for Wi-Fi sensing by leveraging the dispersion effect of frequency-scanning antennas (FSAs), which can naturally steer Wi-Fi subcarriers toward distinct angles during signal transmission. To address key challenges in antenna design and signal processing, we propose a coupled-resonator-based antenna architecture that significantly expands the narrow Field-of-View inherent in conventional FSAs, improving sensing coverage. Additionally, we develop a sensing signal-to-noise-ratio-based signal processing framework that reliably estimates target direction in multipath-rich environments. We prototype WiRainbow and evaluate its performance through benchmark experiments and real-world case studies, demonstrating its ability to achieve accurate, robust, and cost-effective direction awareness for diverse Wi-Fi sensing applications.

Forecasting future trajectories of pedestrians and vehicles is necessary for safety and efficiency maximization in connected and autonomous vehicle (CAV) networks. Existing federated learning (FL) approaches face challenges related to scalability, communication overhead, and privacy preservation. To address these challenges, we introduce a multi-level Hierarchical Decentralized Federated Learning framework tailored for trajectory prediction.Our approach organizes clients into a layered hierarchy, where communication is restricted to parent and child nodes, and synchronization across layers is both controlled and periodic. This design reduces redundant message exchanges while maintaining model consistency. We evaluate our method on two real-world trajectory datasets, Intersection Drone Dataset (inD) and Highway Drone Dataset (highD), and show that it achieves prediction accuracy comparable to Centralized Federated Learning (CFL) while reducing communication costs by approximately 25%. Our results demonstrate that hierarchical structuring in decentralized FL offers a scalable, privacy-preserving, and communication-efficient solution for real-world trajectory forecasting.

In the realm of Fifth Generation (5 G) and the upcoming Sixth Generation (6 G) networks, the efficient management of network resources becomes increasingly critical, particularly for applications that have strict Quality of Service (QoS) requirements. This paper addresses the complexities associated with Virtual Network Embedding (VNE), a vital process for establishing multiple virtual networks on shared physical infrastructure within the context of network slicing. We introduce the SetpodNet scheduler, a novel orchestration solution that leverages reinforcement learning to enhance the optimization of latency and bandwidth allocation specifically in Kubernetes environments. The SetpodNet scheduler is designed to dynamically adapt to fluctuating slice arrivals and varying resource demands, ensuring that network performance remains consistent and reliable. Through comprehensive experimental evaluations, we demonstrate improvements in slice acceptance ratios and optimizing QoS.

Wireless local area networks (WLAN) and recent Wi-Fi standards, are used since a few decades for a large variety of network applications. Since a few years, the $5^{\text {th }}$ generation wireless cellular networks (5 G) are being deployed and used, most of the time for similar applications. Both technologies and their evolution, are expected to be deployed within the wireless ecosystem by 2030 and for decades beyond. The Wi-FIP project invokes these two wireless networking solutions and their evolution, to further enhance the wireless data networks regarding sustainable and scalable quality of hybrid network services. This paper presents the visions and objectives of the Wi-FIP project, targeting to enhance synergies and integration of Wi-Fi in 5G/beyond 5G networks, through the convergent B2B/B2C usage model concept, sustainable multi-connectivity, hierarchical Software Defined Networking (SDN) and orchestration. The paper analyzes the functionalities to develop for sustainable, secure and hybrid WLAN/cellular networking.

5G and beyond networks aim to support heterogeneous services with strict QoS and isolation requirements. Network slicing addresses these challenges by creating multiple virtual networks over shared infrastructure, each tailored for specific service types. However, despite its potential, the lack of practical, open, and reproducible testbeds for 5G slicing remains a major barrier to experimentation and adoption. In this demo, we present NexSlice, an open-source, Kubernetes-Native testbed that enables the deployment and orchestration of 5G core network slices using open-source components like OpenAirInterface (OAI) and UERANSIM. Our platform supports SST-based slicing, integrates different Radio Access Networks (RANs), enables auto-scaling of user plane functions, and provides real-time monitoring with Prometheus and Grafana. NexSlice aims to bridge the gap between theoretical slicing frameworks and practical, reproducible experimentation, paving the way toward adaptive and automated slice management in future 6G networks.

The Fifth and Sixth Generation (5G/6G) networks aim to support diverse applications with specific QoS and resource needs. Network Slicing has emerged as a key paradigm to meet these demands by creating multiple Virtual Networks (VNs) over shared physical infrastructure. This process, known as Virtual Network Embedding (VNE), maps virtual nodes and links to physical resources. With Kubernetes becoming the dominant orchestration platform, most infrastructures now rely on Kubernetes clusters, which enforce direct pod-to-pod communication, necessitating a direct-link approach to VNE. However, most existing methods focus on path-based link mapping. In this paper, we present DL-ViNE, a Reinforcement Learning(RL)-based algorithm that improves slice acceptance while addressing the specific constraints of Kubernetes-hosted infrastructures.

We study the placement of virtual functions aimed at minimizing the cost. We propose a novel algorithm, using ideas based on multi-armed bandits. We prove that these algorithms learn the optimal placement policy rapidly, and their regret grows at a rate at most$O(N M \sqrt{T \ln T})$while respecting the feasibility constraints with high probability, where$T$is total time slots,$M$is the number of classes of function and$N$is the number of computation nodes. We show through numerical experiments that the proposed algorithm both has good practical performance and modest computational complexity. We propose an acceleration technique that allows the algorithm to achieve good performance also in large networks where computational power is limited. Our experiments are fully reproducible, and the code is publicly available.

The 5G/6G era has introduced a wide variety of services, including enhanced Mobile Broadband (eMBB), UltraReliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). Each service presents unique, highly diversified, and often conflicting requirements, driving the need for more flexible and intelligent solutions. In this context, Network Slicing (NS) has emerged as a prominent technology that allows multiple virtual networks to operate over a shared physical infrastructure, thereby accommodating these diverse service demands. Supported by technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), network slicing requires the efficient placement of slices to optimize resource utilization and ensure Quality of Service (QoS). We propose a native artificial intelligence (AI) architecture for end-to-end (E2E) slicing that leverages Transformer-based Deep Reinforcement Learning (DRL) to enable zero-touch, automated slice placement in future networks, such as 5 G -and-beyond systems. Our system embeds AI directly into the network fabric, supporting native AI for real-time data processing and decision-making. Results show that integrating the Transformer model with DRL effectively addresses complex optimization challenges in network slicing, outperforming other state-of-the-art learning algorithms by better balancing slice acceptance ratio and energy efficiency. This supports the sustainable management of future networks, aligns with the vision of the Next Generation Mobile Networks (NGMN) Alliance, and illustrates the evolving role of AI in next-generation communication systems.

Our main objective in this work is to address the challenge of enhancing the forecasting of agents trajectories for Connected and Autonomous Vehicles (CAVs) while prioritizing privacy. We introduce an innovative approach to Federated Learning tailored to the contextual aspects of trajectory prediction. We employ the Siamese Neural Network (SNN) to capture context similarities between clients’ environments. Subsequent cluster formation employs SNN to group clients with similar static contexts for federated training, enhancing learning efficiency.Results of our experiments on real-world datasets collected from the highway drone dataset (highD) and the intersection drone dataset (inD) combination, quantified by utilizing wellestablished metrics such as Average Displacement Error (ADE) and Final Displacement Error (FDE), validate the effectiveness of our approach, obtaining superior trajectory prediction capabilities, showcasing the successful alignment of Federated learning with the intricate challenges of trajectory forecasting, all while prioritizing privacy.

In recent years, wireless signals have been extensively investigated for contactless human respiration monitoring. However, most wireless sensing systems encounter challenges when the target exhibits body movements. In this demo, we present a solution to mitigate the impact of body motion on contactless respiration monitoring. By employing novel signal processing techniques, body motion can be first estimated and subsequently eliminated from the signal reflected signal by the chest. We prototype the proposed system using a MIMO mmWave radar. Evaluations in real-world environments demonstrate the effectiveness of the solution.

In 5G networks, physical resources can be virtualized and allocated to separate virtual networks (or network slices), with distinct requirements. The Virtual Network Embedding (VNE) problem consists in finding the optimal mapping of virtual resources (virtual links and nodes) onto a physical infrastructure. A recent trend consists in virtualizing 5G networks using Kubernates (K8s), a popular virtualization technology.In this paper we perform an experimental study to show the limit of using the standard K8s deployment strategy when dealing with dynamically arriving slices in a heavy loaded setting. By deploying the virtual components of a slice one by one, standard K8s is prone to wasting resources and energy due to partially deploying slices that, at the end, are found to be infeasible, due to lack of available resources. We propose an alternative K8s deployment strategy that first solves VNE via a Genetic Algorithm and then, for each slice, deploys either all its components or none. Our experimental results show a notable improvement in slice acceptance, energy efficiency and deployment time. Our work shows that it is necessary to adapt cloud native technologies to the specific requirements of telecommunication scenarios, as they are different from the cloud ones for which such technologies were originally developed.

Communication and computation services supporting Connected and Automated Vehicles (CAVs) are characterized by stringent requirements, in terms of response time and relia-bility. Fulfilling these requirements is crucial for ensuring road safety and traffic optimization. The conceptually simple solution of hosting these services in the vehicles increases their cost (mainly due to the installation and maintenance of computation infrastructure) and may drain their battery excessively. Such disadvantages can be tackled via Multi-Access Edge Computing (MEC), consisting in deploying computation capability in network nodes deployed close to the devices (vehicles in this case), such as to satisfy the stringent CAV requirements. However, it is not yet clear under which conditions MEC can support CAV requirements and for which services. To shed light on this question, we conduct a simulation campaign using well-known open-source simulation tools, namely OMNeT++, Simu5G, Veins, INET, and SUMO. We are thus able to provide a reality check on MEC for CAV, pinpointing what are the computation capacities that must be installed in the MEC, to support the different services, and the amount of vehicles that a single MEC node can support. We find that such parameters must vary a lot, depending on the service considered. This study can serve as a preliminary basis for network operators to plan future deployment of MEC to support CAV.

Trajectory prediction has been identified as a challenging critical task for achieving full autonomy of the connected and autonomous vehicles (CAVs). Despite the advancement of communication technologies, only few studies include the connectivity and data exchange aspects. Thus, we introduce a novel Edge-Assisted clustering architecture that takes advantage of recent deep learning models and the evolution of edge technologies to achieve better forecasting. First, the historical positions of the target vehicles are fed into the base models of all CAVs in the scene, resulting in multiple generated predictions. Then, each prediction is transmitted to an edge server where trajectories clustering is performed using DBSCAN algorithm to obtain multiple partitions with similar trajectories. The largest cluster is averaged then broadcast back to all CAVs in the scene. Our proposed method surpasses state-of-the-art results on the real world trajectory prediction nuScenes vehicles dataset, obtaining better predictions up to 21%. We also demonstrate the robustness of our method against single-agent system failures, succeeding to get very satisfactory results due to our ability to detect outliers. System practicality is studied under the current 5G/6G capabilities.

Thanks to resource virtualization, Physical Network Operators (PNOs) can share their 5G network to multiple Mobile Virtual Network Operators (MVNOs) which can leverage the shared physical infrastructure to deploy their services up to the edge. This allows much more flexibility with respect to the previous generation of cellular networks: MVNO software components can be placed at different locations, can be allocated a certain amount of virtual resources (e.g., bandwidth, CPU cycles), and be reachable via different paths. To the best of our knowledge, strategies to minimize energy consumption while satisfying Service Level Agreements (SLAs) between the PNO and the MVNOs are still largely missing, particularly if it is required to take the nonlinearity of delays into account. To fill this gap, we formulate the problem of joint placement of software components, routing of user requests and resource dimensioning. SLAs are represented in terms of latency and reliability constraints. Via Column Generation, we obtain exact solutions in real-sized networks. Our numerical results show that we can save up to 50% energy in networks with up to 30 nodes compared to the state-of-the-art algorithms, which are focused on placement or resource minimization.

Within network slicing, Virtual Network Embedding has been vastly studied, i.e., deciding in which physical nodes and links to place virtual functions and links. However, the performance of slices does not only depend on where virtual functions and links are placed, but also on how much resources they can use, which has been mostly neglected in the literature. We thus propose a method for optimal resource dimensioning, via dimensioning capacities of multiple Jackson networks (one per slice) co-existing in the same resource-constrained network. Despite the long history of Jackson networks, we are the first, to the best of our knowledge, to model such a problem. The objective is to minimize energy consumption while satisfying the latency requirements of heterogeneous service providers. We show numerically that our solution is able to achieve both goals, differently from classic approaches, which assume that the amount of resources assigned to slices is fixed a-priori.

Network slicing in the upcoming Telecom generation is a fundamental feature which is deployed to satisfy the various demands in term of data rate and latency. On the other hand, it is seen as a topic that imposes other questions such as the coexistence of physical and virtual functions. In this context, we consider the resource management problem for 5G networks slicing since the solution searches to optimally allocate multiple Virtual Network Requests (VNRs) on a substrate virtualized physical network. In this demo, we present an integrated framework that uses an agile service platform (Kube5G) to deploy one of the VNE proposed solutions with zero-touch configuration. The aim of this integration is to validate the proposed solution and to practically study the performance differences among multiple algorithms that will be conducted later as well. The overview of the process is shown in steps as exposing the resources’ availability of the Physical Nodes (PN), which will be the input of the orchestration algorithm. Successively, the last takes the suitable decision to deploy VNRs on a substrate network, based on the VNRs’ demands such as CPU and radio resources and PNs’ availability. Afterwards, the decision will be sent to the platform to host the virtual nodes on the chosen physical machines. Bearing in mind that the essential objective of this algorithm is to achieve a better resource usage and increase the VNR acceptance ratio on the physical nodes with respect to the constraints that might affect the performance.

The 5G telecommunication ecosystem is expected to dynamically support new and various applications from the industrial and the service sectors that are very heterogeneous in terms of QoS and resources’ requirements. In this context, a promising important concept for network resource management is emerging, denoted by Network Slicing. It involves decisions on embedding and managing several virtual networks on the same physical resources. This problem in its simplified form can be modeled by the Virtual Network Embedding (VNE) problem. In this paper, we propose a new resolution method, in which we first reduce the set of admitted routes and then solve an integer program. Our proposed approach is then compared to the optimal solution and to a method from the state of the art. Obtained results show that our approach provides good result in terms of slice acceptance ratio and resource consumption while reducing the overall complexity and runtime.

In this paper, we consider the Virtual Network Embedding (VNE) problem for 5G networks slicing. This consists in optimally allocating multiple Virtual Networks (VN) on a substrate virtualized physical network while maximizing among others, resource utilization, maximum number of placed VNs and network operator's benefit. We solve the online version of the problem where slices arrive over time. We propose the use of the Nested Rollout Policy Adaptation (NRPA) algorithm, a variant of the well known Monte Carlo Tree Search (MCTS). Both algorithms learn by randomly simulating the embedding, but NRPA also learns how to perform better simulations over time. Performance analysis with different scenarios, show that NRPA improves acceptance and reward ratios (by up to 69% and 65%). We also show how a smart initialization of the learning process can help improve the results furthermore (up to a 12.5% increase of acceptance ratio).

The use of Ethernet packet-switched networks for the fronthaul links with a Cloud-based Radio Access Network (C-RAN) architecture are nowadays considered. The high capacity and low latency fronthaul (FH) links requirement in the C- RAN architecture can be reduced by a flexible functional split of baseband processing between remote radio heads (RRHs) and Baseband units (BBUs). These will allow leveraging statistical multiplexing gains, infrastructure reuse and cost reduction. However, in order to satisfy latency requirements, most of the studies advocate the use of lower fronthaul split options like the eCPRI (evolved Common Public Radio Interface) Iu split, requiring huge fronthaul link capacity. In this paper, we propose an alternative uplink physical split, denoted IIU. The objective is to reduce capacity requirements on the fronthaul while meeting latency constraints. As a future work, the proposed split will be evaluted and analyzed for both fast and slow fading channel situations.

Next generation cellular networks are targeting higher bit rates, lower delays and enhanced inter-cell coordination. For that to happen, higher capacity and lower latency fronthaul solutions are required, among others. The use of Ethernet packet-switched networks for the fronthaul links with a Cloud-based Radio Access Network (C-RAN) architecture are nowadays considered. These will allow leveraging statistical multiplexing gains, infrastructure reuse and cost reduction. In the literature, different fronthaul split options are proposed. However, in order to satisfy latency requirements, most of these studies advocate the use of lower fronthaul split options like the eCPRI (evolved Common Public Radio Interface) IUsplit, requiring huge fronthaul link capacity. In this paper, we propose and evaluate an alternative uplink physical split, denoted IIU. The objective is to reduce capacity requirements on the fronthaul while meeting latency constraints. The proposed split is analyzed for both fast and slow fading channel situations. Two variants are proposed for the IIUsplit: a subframe based and slot-based multiplexing. Performance results show that the proposed IIUsplit satisfies the stringent latency requirements of next generation mobile networks while reducing the deployment cost of links and Ethernet switches of the fronthaul.

This paper presents a queuing model to evaluate the performances of cooperation mechanisms in 4G/5G cellular networks. The main contribution is the proposal of an approximation model with a closed form formula allowing fast numerical evaluation of throughput and loss probabilities per service class, while considering traffic load, radio conditions and the available resources in each collaborating cell. The proposed queuing model is successfully applied to the enhanced Inter-Cell Interference Coordination mechanism with service differentiation in the context of Heterogeneous Cloud based Radio Access Networks. Numerical results are compared to Matlab simulations with realistic radio conditions and user distributions. Through it, decisions on resource allocation among the cooperating cells to achieve the optimal capacity of the system become faster and adaptive. In conclusion, the proposed queue-based model can be used in order to tune the front-haul links and the core network resources.

In this paper, we propose mathematical models to evaluate the performances of the interference remediation technique eICIC/ABS (enhanced Inter- Cell Interference Coordination / Almost Blank Sub-frame) in the context of Heterogeneous Cloud based Radio Access Networks (H-CRAN) architecture and 5G networks. The objective is to propose a dynamic resource management tool to ease decisions on the activation/deactivation of micro cells as well as for the distributions of sub-frames among macro and micro cells. First, we propose a Markov chain based model that fits the behavior of the considered scheme and allows the analysis of the cell throughput according to traffic load, radio conditions and the distribution of available resources among macro and micro cells. Then, we propose an approximation model with a closed form formula. The two models are validated and evaluated in terms of accuracy and computation time. Numerical results are compared to Matlab simulations reproducing realistic radio conditions. Results show that both models are accurate, while the closed form approximation is less complex and provides faster results.

In this paper, we address the problem of energy-efficiency for group communications over 5G networks. We consider multicast broadcast multimedia services (MBMS) where Base Stations (BS) cooperate to convey a common information to multiple users. The problem is formulated as the global energy-efficiency (GEE) maximization under power constraints. We invoke the principle of sequential convex approximation to tackle the original non-convex problem by providing a conservative approximation. Closed-form solutions are derived and a centralized scheme is designed to find stationary solutions to the original non-convex problem. A distributed implementation with limited communication overhead among the BSs is then proposed. These theoretical findings are further validated through numerical results.

In this paper, we address the problem of minimizing channel outage probability for multi-cell multicast networks. Our main objective is to guarantee fairness and to design efficient transmit beamforming vector for physical layer multicasting. The challenging NP-hard problem is tackled by leveraging the principle of sequential parametric convex approximation. More specifically, the original nonconvex problem is replaced by a sequence of successively refined approximated problems. We derive the closed-form solution for each approximated problem. In addition to that, we design a centralized algorithm and demonstrate that it can be implemented in a distributed fashion. The proposed algorithm is guaranteed to converge to a stationary solution of the original problem. Numerical results are then provided to demonstrate the effectiveness of our theoretical findings.

This paper addresses the problem of single-cell point-to-multipoint communications. We consider a base station that needs to transmit a common information to a group of users. We propose a layered Wyner-Ziv coding (WZC) scheme based on nested lattice quantization. In our scheme, we use the base layer generated by a standard coder as the decoders side information. Then, WZC is performed for quality enhancement of the transmitted message. With the proposed layered coding, multiple refinement messages will be generated. The layered messages can be decoded each by a subgroup of users based on their channel conditions. We optimize the scheme parameters in order to maximize the end-to-end multicast rate and minimize the reconstruction distortions experienced at each subgroup of users. Numerical results show that our scheme performs well when compared with the basic multicasting scheme.

Reducing energy consumption over mobile networks and devices is an important and challenging issue. On the one hand, power is a limited resource on mobile devices and its usage should be optimized. On the other hand, energy consumption constitutes an important item within operating expenditure (OPEX) for network providers. In the literature, many studies are dedicated to evaluate scheduling mechanisms from the energy consumption perspective. However, most of these only consider few well known schemes such as Proportional Fair, Best-CQI and Round Robin. In the paper, we extend these studies to include new promising scheduling techniques such as PF, EXP-PF and MLWDF algorithm. In addition, we consider multiple metrics related to QoS, energy and fairness. Performance results show that MLWDF is more energy efficient than EXP-PF and PF schemes while providing better QoS for users.

The extensive use of IEEE 802.11 WLAN networks have taken a tremendous leap in the area of wireless communication. Its increasing PHY data rate is at par with the wired Ethernet. However, at Medium Access Control (MAC) layer, the protocol lacks to utilize its full efficiency. The IEEE 802.11 Distributed Coordination channel access Function (DCF), is still based on contention-based random channel access for medium sharing. This results in a considerable amount of resource wastage due to idle wait time and increased collision as the network gets fairly loaded. In this paper, we propose a novel mechanism : Improved DCF (I-DCF) for IEEE 802.11 which regulates the sharing of the radio channel. It assigns differentiated and unique initial backoff values to each station. The backoff values are then dynamically adapted according to the network load. The proposed I-DCF is formulated and evaluated through simulations. Results show that it outperforms the IEEE 802.11 DCF scheme in terms of increased throughput, reduced delay and minimized collisions with minimum protocol modifications.

Demands for QoS challenging wireless multimedia services are continuously increasing for both professional and entertainment needs. Efficient wireless technologies, able to offer the corresponding throughput and QoS are required. The IEEE 802.11n is the latest wireless standard. It introduces many improvements at both Physical and MAC layers. However, and despite the increase of the physical data rate, the large overhead associated with channel access and packet transmission schemes reduces its overall efficiency, mainly at the MAC layer. In this paper, we propose QoS-HAN: a new scheme for the 802.11n aggregation process. QoS-HAN is mainly designed to meet the requirements of multimedia services over Home Automated Networks (HAN). It takes into consideration the size of the packet during aggregation. The proposed scheme is evaluated and examined for different types of traffic. Simulation results demonstrate the efficiency of the proposed scheme in terms of throughput, delay and PDR.

To meet the continuously increasing demands and requirements of mobile users, next generation wireless systems are more and more relaying on the coexistence of heterogeneous wireless networks. The objective is to provide any time and anywhere connectivity to users. One of the main challenges in such a heterogeneous environment is mobility management, including network selection and Vertical Handover (VHO) procedures. In this paper, we propose a VHO management solution combining the use of reputation as a Quality of Experience (QoE) indicator for fast decision-making and the Stream Control Transmission Protocol (SCTP) as a mobility protocol. This reputation-based Vertical Handover solution operates on the previous observed experiences of users within the different available networks to make fast decisions and uses the SCTP protocol that provides multihoming facilities. Performance results demonstrate that the proposed handover scheme provides seamless mobility, with low latency, good throughput and almost no packet loss.

Radio resource and mobility managements are becoming more and more complex within nowadays rich and heterogeneous wireless access networking systems. Multiple requirements, challenges and constraints, at both technical and economical perspectives have to be considered. While the main objective remains guaranteeing the best Quality of Service and optimal radio resource utilization, economical aspects have also to be considered including cost minimization for users and revenue maximization for network providers. In this paper, we propose a game theoretic scheme where each available network plays a Stackelberg game with a finite set of users, while users are playing a Nash game among themselves to share the limited radio resources. A Nash equilibrium point is found and used for vertical handover decision making and admission control. We also introduce in the proposed model the user's requirements in terms of quality of service according to its running application and the network reputation that is conducted from the users' quality of experience and we study the effect of these parameters on the network pricing and revenue maximization problems.

Virtualization is a promising approach to meet the objectives of next generation networks. It will enable building pervasive systems, with the possibilities of automated adaptation and provision of personalized services for users with different needs and contexts. In this paper, we propose the use of Avatars: software delegates to hide heterogeneity between entities such as networks, users, services and any resource or device. Avatars act at an abstraction overlay level, on behalf of their represented entities. They carry information and intelligence (workflows) and cooperate to make decisions like the reconfiguration of the entity they stand for. A software agents-based implementation is then proposed and evaluated.

Provisioning continuous services while moving through heterogeneous wireless networks is a main issue in the fourth generation wireless Networks. An efficient vertical handover decision making algorithm that takes into account services' requirements, considers users' preferences and guaranties seamless handover over heterogeneous technologies is required. In this paper, we propose an intelligent context-aware solution that considers both users and services requirements. It is based on advanced decision approaches like fuzzy logic and analytic hierarchy processes.

Recent developments in computer science and networking technologies motivate new families of applications that are sensitive to user and ambient contexts. The objective is to offer adaptive services that can be personalized to users' needs within heterogeneous, complex and dynamic environments. This requires enhanced and generic solutions for context representation and retrieval as well as for the interactions between heterogeneous systems and components. In this paper, we propose a conceptual model and a software framework using ontologies to build semantic context databases. Contextual information is modeled and implemented using first order logic and OWL to promote expressiveness and interoperability. The different steps to build the framework are detailed. The approach is then applied to the heterogeneous wireless access networks' use case. Measurements and performance results are provided and discussed.

Beamforming techniques can enhance both capacity and coverage in wireless networks. To be efficient, most of these techniques require accurate estimation of the destination position. In vehicular environments, standard beamforming is not suitable when continuous tracking of node location is required. To handle inaccuracies in location estimation, this work uses Linearly Constrained Minimum Variance (LCMV) beamforming to form beams in adjoining directions to facilitate tracking. The directions are selected using the last accurate destination direction and movement parameters such as direction and speed variations of the source. Performance evaluations show that the proposed approach enhances system capacity and connectivity while reducing localization overhead.

This paper introduces I-MAC a new hybrid medium access control protocol for wireless sensor networks (WSNs). I- MAC combines both CSMA and TDMA techniques while introducing prioritization. It aims to improve energy efficiency as well as channel utilization. I-MAC is composed of two phases: a setup phase and a transmission phase. During the setup phase, several operations such as neighborhood discovery, slot assignment, local framing and global synchronization are run. During the transmission phase, a priority scheme is investigated to control the access to the channel. We focus in this paper on the description and evaluation of the different setup phase operations particularly DNIB, a new TDMA free collision slot assignment algorithm proposed for WSNs.

This paper introduces I-MAC, a new medium access control protocol for wireless sensor networks. I-MAC targets at improving both channel utilisation and energy efficiency while taking into account traffic load for each sensor node according to its role in the network. I-MAC reaches its objectives through prioritized and adaptive access to the channel. I-MAC performances obtained through simulations for different network topologies, scenarios and traffic loads show significant improvements in energy efficiency, channel utilization, loss ratio and delay compared to existing protocols.

A slot assignment (or scheduling) in TDMA based wireless sensor networks (WSNs) considering energy efficiency, scalability and complexity is presented. The algorithm named DNIB is a new TDMA free collision distributed slot assignment algorithm for WSNs. DNIB relies on two-hop neighborhood information. Performance analysis and simulations show that DNIB is scalable and outperforms existing mechanisms.

In this paper, we consider the multiple sinks placement problem in large-scale wireless sensor networks (WSN). The objective is to determine optimal sinks' positions that maximize the network lifetime by reducing energy consumption related to data transmissions from sensor nodes to different sinks. Balanced graph partitioning techniques are used to split the entire WSN into connected sub-networks. Smaller subnetworks are created, having similar characteristics and where energy consumption can be optimized independently but in the same way. Therefore, different approaches and mechanisms that enhance the network lifetime in small-size WSN can be deployed inside each sub-network. In this paper, we propose a simple and efficient approach for the placement of multiple sinks within large-scale WSNs. Performance results show that the proposed technique significantly enhances the network lifetime.

In this paper we propose the Mobile Party protocol, a new scheme for mobility management in the context of mesh networks. While most of proposed solutions for mobility management issue are routing protocol-independent mechanisms, our proposal integrates routing and mobility management functionalities together in the same scheme. This enhances considerably the performance of our proposal as shown in this paper. Through simulations and performance studies, we demonstrate that the proposed scheme provides a viable solution for mobility and routing management, assuring scalability and seamless communications.

In this paper we study a self organizing network architecture, party. Party is a new routing protocol intended to be applied in environments with large number of nodes where the scalability of the routing protocol plays an important role. Party's routing is unique and only depends on the current node's neighborhood. Routing tables are created on the basis of the first hop neighborhood only. We will show the protocol performance with a large number of nodes in the network, and compare it to the legacy ad hoc routing protocols. Results show a large improvement in terms of overhead and throughput.

Ensuring seamless mobility for users is becoming one of the main objectives of ongoing research activities in the field of data telecommunications. More over, if some proposals are efficient in reducing handoff latency for single mobile hosts, adequate solutions are still required for moving networks. The NEtwork MObility protocol (NEMO), designed to provide network mobility, is not efficient to offer low latency handoff in the case of nested mobile networks because it uses fairly sub-optimal routing. In this paper, an optimized solution to reduce handoff latency for Nested Mobile Networks is proposed. This solution minimizes the registration delay component of the overall handoff latency. The paper describes the new architecture and mechanisms and provides simulation results indicating performance compared to the basic NEMO solution. Index Terms—low latency handoff, MIPv6, NEMO, Nested mobile network.

In emerging personal area networks (PANs), consisting of heterogeneous devices and nodes, dynamic intra-PAN and PAN-to-infrastructure connectivity should be achieved transparently to end users and applications. This paper proposes a distributed virtual network interface (DVNI) solution for PAN device discovery, seamless handover and routing. DVNI is an extension of the VNI concept designed for seamless vertical handover in the case of a single device with multiple interfaces. The DVNI concept consists of making all PAN interfaces available via a common distributed virtual interface to mask changes in the PAN dynamics and connectivity

An analytical model to evaluate the impact of TCP on the UMTS-HSDPA capacity is presented. A method to minimize the effect of TCP on wireless networks using shared channels is also proposed. HSDPA (high speed downlink packet access) achieves higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid ARQ, fast scheduling, fast cell selection and MIMO (space time coding and BLAST) techniques. The proposed model is used to evaluate the effect of the TCP protocol on the bit rate of various data services (at 64 and 128 Kbps). As expected, the bit rate per flow decreases strongly if congestion in the wired network increases. However, the capacity achieved by HSDPA is not as affected by TCP. Using this result, a method to maintain the bit rate per TCP flow at a given value without losing much cell capacity is proposed. The findings are supported by simulation results.

The sliding window (SW) mechanism is widely used for policing schemes in the frame relay network. Many performance studies have already been performed. All of them have considered the algorithmic description, the only one available till now for the SW. A queueing model is given for the SW mechanism. Its conformity to the algorithmic behaviour is proved through simulation. A diffusion approximation method is then used to resolve the queueing model. The aim of this approximation is to build up analytical equations describing the behaviour of the SW mechanism with a general input process.