In modern Open RAN architectures, the traditional gNB protocol stack is disaggregated into virtualized components, Centralized Unit (CU), Distributed Unit (DU), and Radio Unit (RU), which are flexibly deployed across the infrastructure to meet diverse QoS requirements. This paper proposes an energyaware model that jointly optimizes user association and virtual network function (VNF) placement to minimize total system energy consumption. By dynamically consolidating workloads and selectively activating radio and compute resources, the model reduces energy usage without compromising service constraints. We formulate the problem as an integer linear problem (ILP) to obtain optimal solutions and introduce a Graph Neural Network (GNN)-based heuristic that closely approximates optimal placements in real time. Simulation results demonstrate up to 75% energy savings at low loads and show the GNN reduces execution time by over $99 \%$ while maintaining near-optimal performance.

This paper extends a framework for load balancing in large Wi-Fi networks we previously proposed. The framework partitions the network into clusters and assigns a sub-controller to each cluster. The sub-controllers employ a Deep Q-Learning-based algorithm to balance the load on the access points in the cluster. The sub-controllers collaborate by exchanging their training updates through a database. However, in highly dynamic Wi-Fi networks, the frequency of these exchanges may cause control plane overhead. In this paper, the training updates are uploaded only when their “quality” meets a condition dependent on a threshold. High threshold values reduce overhead but compromise local learning performance, and vice versa. Therefore, finding the optimal threshold values is formulated as a multi-objective optimization problem. Two metrics are designed to quantify the overhead and instability. These metrics are stochastic, scenario-dependent, and time-consuming to estimate. Randomly generated scenarios of large and dynamic 802.11ax networks are simulated to collect realizations of the metrics. The objectives are obtained by smoothing the realizations with Gaussian kernel regression. The problem is then solved with a genetic algorithm to estimate the Pareto-optimal threshold values.

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.

This paper summarizes the PhD research work conducted on resource orchestration in disaggregated Open RAN (O-RAN) architectures. In O-RAN, the gNB radio protocol stack is split into virtualized functions- Centralized Unit (CU), Distributed Unit (DU), and Radio Unit (RU)-that are dynamically deployed across edge and regional cloud infrastructures. This architectural transformation introduces new challenges in managing computing, radio, and transport resources while satisfying diverse Quality of Service (QoS) requirements. Our research is structured into three major contributions. First, the problem of CU/DU placement is addressed under fixed User Equipment (UE)-to-RU associations through an Integer Linear Programming (ILP) formulation and a Bi-directional Long Short-Term Memory (Bi-LSTM)-based Recurrent Neural Network (RNN) heuristic for scalable inference. The second contribution extends the model to jointly optimize CU/DU placement and UE-to-RU association, with a decomposition heuristic and an enhanced RNN-based solution. The proposed models significantly outperform conventional baselines in terms of user admittance and runtime. Finally, an energy-aware extension is introduced to minimize total system power consumption by consolidating workloads and managing radio and computing resources activation aiming to enhance the sustainability of$\mathbf{O}$RAN orchestration while maintaining QoS guarantees.

Open Radio Access Network (O-RAN) has recently emerged as a new trend for mobile network architecture. It is based on four founding principles: disaggregation, intelligence, virtualization, and open interfaces. In particular, RAN disaggregation involves dividing base station virtualized networking functions (VNFs) into three distinct components - the Open-Central Unit (O-CU), the Open-Distributed Unit (O-DU), and the Open-Radio Unit (O-RU) - enabling each component to be implemented independently. Such disaggregation improves system performance and allows rapid and open innovation in many components while ensuring multi-vendor operability. As the disaggregation of network architecture becomes a key enabler of O-RAN, the deployment scenarios of VNFs on O-RAN clouds become critical. In this context, we propose an optimal and dynamic placement scheme of the O-CU and O-DU functionalities on the edge or in regional O-clouds. The objective is to maximize users’ admittance ratio by considering mid-haul delay and server capacity requirements. We develop an Integer Linear Programming (ILP) model for O-CU and O-DU placement in O-RAN architecture. Additionally, we introduce a Recurrent Neural Network (RNN) heuristic model that can effectively emulate the behavior of the ILP model. The results are promising in terms of improving users’ admittance ratio by up to 10% when compared to baselines from state-of-the-art. Moreover, our proposed model minimizes the deployment costs and increases the overall throughput. Furthermore, we assess the optimal model’s performance across diverse network conditions, including variable functional split options, link capacity bottlenecks, and channel bandwidth limitations. Our analysis delves into placement decisions, evaluating admittance ratio, radio and link resource utilization, and quantifying the impact on different service types.

Developing efficient load-balancing techniques remains a persistent research challenge as modern WiFi networks evolve into increasingly complex environments, incorporating new enhancements in their standards. For instance, DQL-MultiMDP is a load-balancing algorithm that learns an optimal STA-to-AP association policy to ensure user fairness and optimize network performance in dense and dynamic WiFi networks. The algorithm leverages a Multi-Markov Decision Process (MultiMDP) strategy to accommodate the fluctuating number of devices caused by their switching on/off. However, scalability challenges arise due to the exponential expansion of the action space. In this paper, we propose a divide-and-conquer approach that extends the algorithm to operate in extremely large deployments: a dynamic partitioning mechanism divides the network into clusters and assigns a sub-controller to manage the STA-to-AP association in each cluster independently, and a coordination mechanism enables them to exchange their training updates. Experimental investigations validate the effectiveness of the approach and motivate future work.

In this paper, we present our primary version of DQL-MultiMDP, a flexible load balancing approach designed to ensure long-term user satisfaction in modern WiFi networks (WiFi 7 and beyond). The algorithm quasi-simultaneously solves multiple Markov Decision Processes (MDPs) by switching between them depending on the environmental configu-ration, specifically the number of Access Points (APs) and user Stations (STAs). Leveraging Deep Q-Learning (DQL), it utilizes sophisticated state representations with advanced metrics to determine the optimal AP-to-STA association through autonomous decision-making. Experimental results validate the effectiveness of the approach and lay the foundation for further improvements.

Congestion in the Internet of Things (IoT) networks arises when multiple flows share the same network, which can significantly impede the performance of IoT networks. This problem is exacerbated by the limitations of low-power lossy networks (LLNs), resulting in increased latency, high packet losses, reduced goodput, and other capacity-related issues. To ensure a high quality of service (QoS), and network reliability, it is crucial to implement effective congestion control mechanisms in IoT networks. Congestion in a network leads to an increase in packet losses. The loss ratio represents the proportion of lost packets to the total number of transmitted packets and is a critical metric for assessing the network's traffic load and congestion levels. This paper emphasizes the significance of studying IoT application-generated traffic to predict the loss ratio accurately. For instance, reliable data transfer is essential for IoT applications such as health monitoring, which are highly susceptible to performance degradation due to congested traffic and packet loss. This study proposes a novel approach that uses time series data and Deep Learning (DL) models to predict loss ratio in IoT networks. Our approach involves the implementation of a sliding window technique, as well as the validation and comparison of various DL models using data generated by the Cooja/Contiki framework.

Open Radio Access Network (O-RAN) is a novel architecture that enables the disaggregation and the virtualization of network components. This would provide new ways to mix and match network components by “opening up” the interfaces between them. O-RAN enables driving down the costs of network deployments and allows the entry of new players into the RAN market. It enables network operators to maximize resource utilization and deliver new network edge services at a lower cost, resulting in higher profits for operators. In this context, we consider a computing resource allocation problem for maximizing the operator’s profit. Given that an operator receives subscribers’ payments and pays the infrastructure provider's costs, we model the problem using Mixed Integer Linear Programming (MILP). Then, we propose to solve the problem using Reinforcement Learning (RL). Our simulation results demonstrate the ability of the RL agent to increase the operator's profit while reducing the algorithmic complexity of the MILP solver.

Open Radio Access Network (O-RAN) has recently emerged as a new trend for mobile network architecture. It is based on four founding principles: disaggregation, intelligence, virtualization, and open interfaces. In particular, RAN disaggregation involves dividing base station virtualized networking functions (VNFs) into three distinct components -the Open-Central Unit (O-CU), the Open-Distributed Unit (O-DU), and the Open-Radio Unit (O-RU) -enabling each component to be implemented independently. Such disaggregation aims to improve system performance and allow rapid and open innovation in many components while ensuring multi-vendor operability. As the disaggregation of network architecture becomes a key enabler of O-RAN, the deployment scenarios of VNFs over ORAN clouds become critical. In this context, we propose an optimal and dynamic placement scheme of the O-CU and O-DU functionalities either on the edge or in regional O-clouds. The objective is to maximize users’ admittance ratio by considering mid-haul delay and server capacity requirements. We develop an Integer Linear Programming (ILP) model for VNF placement in O-RAN architecture. Additionally, we introduce a Recurrent Neural Network (RNN) heuristic model that can effectively replicate the behavior of the ILP model. We get promising results in terms of improving users’ admittance ratio by up to 10% when compared to baselines from state-of-the-art. Moreover, our proposed model minimizes the deployment costs and increases the overall throughput.

Cloud Radio Access Network is a promising mobile network architecture based on centralizing the baseband processing of many cellular base stations in a BBU (BaseBand Unit) pool. Such architecture has many advantages. However, computing resources are shared among the base stations connected to the BBU pool. It is challenging to schedule the processing of users’ data, especially on overloaded BBU pools, while respecting the time constraints imposed by the Hybrid Automatic Repeat Request (HARQ) mechanism. Given that the processing time of users’ data and the computing requirement depends on the radio parameters such as the Modulation and Coding Scheme (MCS), we propose to enable the coordination between radio and computing resources schedulers; such coordination makes the selection of MCS dependent on the availability of radio and computing resources and on the ability to process data while respecting the HARQ-deadline. In this context, we propose and evaluate three Integer Linear Programming (ILP)-based schemes and three low-complexity heuristics, demonstrating their ability to reduce the wasted transmission power. Moreover, we evaluate the performance of the coordination under a multi-services scenario consisting of two services having heterogeneous requirements, enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communication (URLLC).

Cloud-RAN is a key 5G-enabler; it consists in centralizing the baseband processing of base stations by executing the baseband functions in a centralized, virtualized, and shared entity known as the Base Band Unit (BBU)-Pool. Cloud-RAN paves the way for joint management of the resources of multiple base stations. This paper aims to analyze the potential reduction in power consumption brought by the joint allocation of the radio and computing resources. We formulate a Mixed Integer Linear Programming (MILP) problem, considering the objective of power consumption minimization. For comparison, we consider the objective of throughput maximization. When the goal is power minimization, the joint allocation can minimize the total power consumption by up to 21.2%, with respect to the case where radio and computing resources in the BBU pool are allocated sequentially.

Cloud Radio Access Network (Cloud-RAN) is a novel architecture that aims at centralizing the baseband processing of base stations. This architecture opens paths for joint, flexible, and optimal management of radio and computing resources. To increase the benefit from this architecture, efficient resource management algorithms need to be devised. In this paper, we consider a coordinated allocation of radio and computing resources to mobile users. Optimal resource allocation that respects the Hybrid-Automatic-Repeat-Request deadline may require formulating high-complexity and resource-heavy algorithms. We consider two Integer Linear Programming problems (ILP) that implement a coordinated allocation of radio and computing resources with the objectives of maximizing throughput and maximizing users' satisfaction, respectively. Since solving these highly-complex problems requires a high execution time, we investigate low-complexity alternatives based on machine learning models; more precisely on Recurrent Neural Networks (RNN). These RNN models aim to depict the performance of the ILP problems with a much lower execution time. Our simulation results demonstrate the great ability of RNN models to perform very closely to the ILP problems while being able to reduce the execution time by up to 99.65%.

Mobility simulation models are important tools to the development of Intelligent Transportation Systems (ITS). These models allow to emulate the real-world characteristics of vehicular mobility operations in order to evaluate the capacity of ITS related protocols and services to deal with the highly dynamic mobility of vehicles. In this work, we provide a novel queuing theory based simulation model for vehicular mobility that functions in the Simulink/Matlab environment, and uses SimEvents toolbox. The proposed tool offers a graphic library composed of different modules each representing a mobility aspect. It is also suitable to simulate both urban and freeway scenarios under different traffic conditions. The model generates synthetic measures relevant to the assessment of road network performance such as average travel time, average delays and average queue lengths. A set of experiments are conducted to verify the ability of the model to reproduce the realistic behavior of vehicular traffic.

Vehicular Adhoc Network (VANET) is an innovative technology designed to tackle the road safety challenges by providing a communication infrastructure to vehicular environments. Safety-related applications are delay-intolerant and demand low-latency and reliable transmissions of critical data. TDMA mechanism has been proven effective for this type of applications. However, the particular features of the vehicular environment bring challenges to the applicability of TDMA in VANET. The highly dynamic nature of the network increases the likelihood of concurrent access to radio resources. Several studies addressed the access collision issue in TDMA, but they demonstrate weaknesses regarding channel utilization and negotiation overhead. In this paper, we present a traffic-aware TDMA-based (TA- TDMA) protocol which improves the VeMAC protocol to meet the real-time requirements. The proposed solution relies on traffic prediction to assign appropriate resources to each area of the network. To evaluate its validity, TA- TDMA is compared with VeMAC through simulation.

Energy Efficiency (EE) is a key performance metric to design future wireless networks. Since Directional Antennas (DAs) focus the transmission energy towards the destination, it has been shown as a cost-effective solution when used in a backhaul network. In this paper we propose a new joint optimization framework of energy consumption and throughput in backhaul Wireless Mesh Networks (WMNs) equipped with DAs. We first formulate the joint optimization problem as a Mixed Integer Linear Problem (MILP) using a weighted objective function of both the consumed energy and the throughput. Then, we propose to use the Ant-Q algorithm, a Reinforcement Learning (RL) based approach, to reduce the solution complexity and enhance its convergence. Considering a discrete power control scheme, we define a new routing scheme based on the Ant-Q heuristic to select jointly the transmission beam and the transmission power. Using ILOG Cplex to find the optimal solution and NS-3 to conduct extensive simulations, we show the effectiveness and the accuracy of the proposed routing algorithm. Moreover, we analyze the optimization tradeoff depending on the beamwidth, the network topology, the gateway position and the optimization weight factor.

Intelligent transport systems provide great possibilities to mitigate traffic congestion and intensify driving efficiency. The design of these systems requires a clear understanding of traffic dynamics. In this respect, this study focuses on describing and analysing traffic conditions at junctions in urban environments from a macroscopic level of description. As a first step, our method attempts to interpret the traffic scenario at intersections as a queuing system. Then, a Continuous Time Markov Chain to predict the future traffic condition at intersections is developed, and afterwards the pertaining steady-state probabilities are obtained. Given the equilibrium vector, significant performance measures are inferred for monitoring and planning purposes.

Wireless Sensor Networks (WSN) are considered to be a key enabler towards the Internet of Things (IoT) concept and one of its main underlying technologies. Applications built on top of IoT infrastructures such as WSNs share a set of common functions that concern not only data management such as data collection, dissemination or aggregation, but also classical networking functions, such as routing or addressing. Even though application deployment is becoming more feasible, it can still be a complex process especially in the presence of vertical solutions. WSNs should, therefore, be more flexible in order to facilitate the development of efficient applications. A promising networking paradigm which can help achieve such a requirement is the Software-Defined Networking (SDN) concept which decouples the network data plane from the control plane. To allow multiple applications to share and reuse common networking functions, we propose to implement them as SDN reusable building blocks and to instantiate and customize them through SDN rules. On the other hand, we provide a rigorous definition of these functions and of their relationships in order to make them reusable to efficiently develop the applications. We consider the smart city as a typical WSN/IoT domain of applications. Our approach aims at facilitating the development, use and management of such functions in a WSN and thus of IoT applications.

Broadcast traffic in IEEE 802.11 vehicular networks is known to suffer from poor performance due to the lack of recovery mechanisms from packet losses, based on the rules of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. This may have a detrimental impact on cooperative vehicular safety applications that build on the reliable regular broadcasting of status messages by vehicles in a local neighborhood. Full-Duplex (FD) techniques can improve the broadcast CSMA/CA performance by letting a sending vehicle sense the channel while transmitting, thus enabling “collision detection”. The vehicle, consequently, can abort the packet prone to collision and reattempt a later transmission. In this paper, we define a stochastic geometry model that captures the collision detection capability of FD-enabled vehicles, while accurately characterizing the interference power generated by other vehicles on a road segment, and the dynamics of the backoff mechanism used for broadcast packet retransmissions. The model provides helpful insights into the FD broadcast CSMA/CA behaviour, highlighting a clear relationship between the settings for the carrier sense threshold and the collision detection threshold and the number of covered receivers on the road.

5G Wireless Networks are expected to increase substantially data rates and quality of service the users will experience, with a similar or a lower power consumption as todays 4G networks. The Joint Transmission Coordinated MultiPoint (JTCoMP) is a promising scheme to enhance throughput by reducing the interference, especially for cell-edge users. However, some additional energy for hardware circuit and resource information is consumed by this technology. Meanwhile, the performance evaluation of energy efficiency (EE) in dense networks with JTCoMP approach becomes a hard task in terms of time expense to conduct simulations. To evaluate the EE metric in cellular networks with JT-CoMP scheme and to capture the major factors involved in the energy consumption process, representative and accurate models are needed. In this paper, we develop a tractable and efficient model of EE based on spatial fluid modeling when JT-CoMP is applied. Simulations results show that EE is improved with the raise of the number of coordinated BSs in case of a constant backhauling power cost. Similar EE improvement is also observed in case of variable backhauling power cost, while adding a new coordinated BS. Furthermore, the EE is significantly enhanced in femto cellular networks compared to macro cellular ones, making thereby, JT-CoMP scheme more effective in small cells.

With the increasing number of WLAN devices and application hungry resource demands, infrastructure mode WLAN networks are becoming denser and form a multi cell network. In this context, the clients should associate to one of the available Access Points (APs). The IEEE 802.11 standard association, based on the strongest RSSI (Received Signal Strength Indicator) AP, shows limited performance and does not take into account client priority levels and traffic demands. The aim of this paper is to propose jointly an offline association and resources sharing scheme implanted at a SDN central controller of a multirate WLAN. Our goal is to take into account the clients different priorities and bandwidth demands. The simulation results shown enhancements in clients satisfaction specially for the higher priority ones.

Cloud RAN (C-RAN) is a very promising architecture for future mobile network deployment, where the cloud-centric approach is useful in improving total processing load. In this context, radio and baseband network functions processing pose interesting problems that we try to expose and solve in this paper. A novel architecture for C-RAN and a first modeling of the system are proposed. Furthermore, we study the impact of many radio parameters on the processing time. Moreover, a mathematical and a deep learning model are proposed and evaluated for processing time prediction. Results show the feasibility of the proposed approaches.

Since femtocells are target to reduce the energy consumption in future cellular networks. In this paper, we analyze the joint impact of shadowing and path-loss exponent on the energy efficiency of such networks based on an analytical tractable model of the spatial fluid modeling. We first develop a closed-form expression of SINR threshold of a user equipment located at a given distance from its serving base station through a polynomial curve fitting method, while considering the impact of shadowing and path-loss exponent as well as a fixed coverage probability. Taking advantage of this expression, we then establish a tractable and efficient model based on spatial fluid framework which reduces the analysis complexity. Moreover, the effectiveness and the accuracy of the proposed model are highlighted through a comparison with the results obtained by Monte Carlo simulations. The results point out that the energy efficiency is significantly impacted by the shadow fading, and decreases with the raise of the standard deviation value of the lognormal shadowing.

Emergent WLAN applications demand more and more network resources. At the same time, the increasing number of users leads to dense WLAN networks. In this context, the standard client-Access Point association has been shown to reach far from optimal performance. Thanks to the SDN paradigm, centralised programmable strategies could be applied by the WLAN controllers to choose the best Access Point for each user when many Access Points are in the vicinity of a user. In this paper, we present an on-line centralized client-Access Point association scheme based on reinforcement learning which aims at minimizing the clients dissatisfaction in terms of obtained rate. The scheme is implemented in a SDN controller which pilots the Access Points. Simulation results show the superiority of our scheme over the IEEE standard signal strength based association in terms of aggregated throughput and users satisfaction.

Wide white areas are defined as large regions with little or no infrastructure such as deserts or large forest areas. Indeed, strategic phenomena and activities take place in these areas which necessitate data collection and analysis. However, the difficulty of access and the vastness of wide white area make the deployment of such networks hard and inefficient. In this work, we propose a new approach which aims at efficiently collecting data from networks deployed in wide white areas. The goal of our method is to improve data collection efficiency by maximizing the amount of the collected data. The proposed method uses commercial flights to collect data while they cross over an area of interest. We investigate the feasibility of such a scheme by determining the collection capacity of commercial aircrafts in different locations of the desert.

Due to the highly dynamic network topology of Vehicular Ad-hoc Networks (VANETs), an effective clustering algorithm is always required to both solve the scalability problem and enhance vehicles' connections. In this paper, a discrete- time finite-state Markov chain model is proposed based on our previous clustering framework, in order to provide a comprehensive analysis of cluster stability, including cluster's lifetime and cluster member's lifetime. Moreover, the future clustering performance can be predicted through the proposed model. Numerical results are presented to evaluate the model, which show high consistency between analytical and simulation results.

Wide white areas are defined as large regions with very little to no infrastructure. For example, deserts and large forest areas fall in this category. Many strategic phenomena and activities take place in these areas (e.g. mining, environmental monitoring) which necessitate data collection and analysis. In this context, we propose a network deployment scheme which aims at efficiently linking sparse points of interest in a very wide white area. The goal of the method is to minimize the cost of the deployment while providing a fault tolerant network. The proposed method is based on an algorithm which mimics the evolution of a type of mold called physarum. Our deployment problem is close to a Minimum Steiner Tree (MST) problem known to be NP-hard, we thus compare our results to a heuristic of MST.

The advent of the fifth generation of wireless networks forecasts an incredible increase in throughput, number of simultaneous connections and low latency to provide the full set of capabilities. But this unprecedented increase in capacity must not lead to an energy crunch. Therefore, 5G requirements specify it must be achieved at a similar or lower power consumption as todays networks. To evaluate the performance of large representative cellular networks and to capture the major factors involved in the energy consumption process, representative and accurate models are needed. In this paper, we develop a tractable and efficient model based on spatial fluid modeling which reduces the analysis complexity. Moreover, the effectiveness and accuracy of the proposed model are shown through a comparison with the results obtained by Monte Carlo simulations. Afterwards, our simulations results point out that energy efficiency is better in a small cell network compared to a macrocell one and is not related to the density of user equipments.

Effective clustering algorithms are indispensable in order to solve the scalability problem in vehicular ad hoc networks. Although current existing clustering algorithms show increased cluster stability under some certain traffic scenarios, it is still hard to address which clustering metric performs the best. In this paper, we propose a unified framework of clustering approach (UFC), composed of three important parts: 1) neighbor sampling; 2) backoff-based cluster head selection; and 3) backup cluster head based cluster maintenance. Three mobility-based clustering metrics, including vehicle relative position, relative velocity, and link lifetime, are considered in our approach under different traffic scenarios. Furthermore, a detailed analysis of UFC with parameters optimization is presented. Extensive comparison results among UFC, lowest-ID, and VMaSC algorithms demonstrate that our clustering approach performs high cluster stability, especially under high dynamic traffic scenarios.

Directional Antennas (DAs) provide higher gain, and reduce interference by directing beams toward the desired receiver. In this paper, we propose a new joint optimization framework considering the energy consumption and throughput in DAs Wireless Mesh Networks (WMNs). We formulate the joint optimization problem as a Mixed Integer Linear Problem (MILP) using a weighted objective function of both the consumed energy and the throughput. We use ILOG Cplex [1], a software based on branch and cut method, to find the optimal solution of the optimization problem. Results prove the efficiency of using DAs in WMNs in the considered scenarios and show that the consumed energy increases with the beamwidth and decreases with the number of power levels when using power control.

Road safety applications heavily rely on periodic message exchanges known as beaconing in order to gain awareness about the current situation within the vehicle communication range. The high level of transmission contention in dense vehicular networks can induce successive beacon collisions. These collisions impede the vehicles to have an up-to-date view of the environment which can lead to bad decisions from the applications and in the worst case create car crashes. Many propositions of the literature try to tackle this problem by modifying the beacon emission period and/or power either randomly or based on the network density or other measured indicators. In this paper, we explore the possibility to use bio-inspired desynchronization algorithms to avoid beacon collisions. The main idea behind these algorithms is to make nodes converging toward a consensus where they do not emit their beacon at the same time in order to avoid collisions. We show that desynchronization algorithms, if properly implemented, can significantly reduce the probability to have large inter-beacon delays.

Effective clustering algorithms are indispensable in order to solve the scalability problem in Vehicular Ad-hoc Networks (VANETs). Due to the highly dynamic network topology, an effective cluster merging scheme is always required in clustering algorithms, aiming to prevent the collapse of clusters. In the literature, there is a lack of comparison of cluster merging schemes, which makes it hard to analyze the impact of this component on clustering performance. In this paper, we analyze the existing cluster merging schemes and propose a Leadership-based Cluster Merging (LCM) scheme. Then, a comprehensive comparison of different cluster merging schemes under various traffic scenarios is presented, and our scheme is shown to achieve better performance on cluster stability.

Due to wireless propagation condition, users in a cell experience different channel condition. Thus cell-center users have optimal condition whereas cell-edge users suffer from a severe path-loss and a low bit-rate. In this paper, a fair scheme to allocate Resource Blocks (RB) on the downlink in LTE networks is proposed. The problem addresses maximising the overall system throughput while ensuring the fair resource allocation among UEs (User Equipment). We use a new fairness criterion which is based upon the concept of the Nash Bargaining Solution (NBS) model. Our idea is to bring in a compensating factor to overcome the path-loss influence. The performance evaluation confirms that our method is able to spread resources fairly over the entire cell.

Clustering in vehicular ad hoc networks (VANETs) is a challenging issue due to the highly dynamic vehicle mobility and frequent communication disconnections problems. Recent years' research have proven that mobility-based clustering mechanisms considering speed, moving direction, position, destination and density, were more effective in improving cluster stability. In this paper, we propose a new mobility-based and stability-based clustering algorithm (MSCA) for urban city scenario, which makes use of vehicle's moving direction, relative position and link lifetime estimation. We evaluate the performance of our proposed algorithm in terms of changing maximum lane speed and traffic flow rate. Our proposed algorithm performs well in terms of average cluster head lifetime and average number of clusters.

Energy Efficiency is an important feature in poor-covered areas where not only the access to a cellular network is scarce but also energy sources are limited. In this paper, we consider a wireless mesh network to act as a local backhaul network to cover rural and remote villages. The shortage of energy in these settlements motivates to optimize the energy consumption of the wireless backhaul network. Therefore, we propose to use Directional Antennas (DAs) to improve the throughput and the network energy consumption. DAs focus the RF signals toward the desired destination, to reduce the collisions and the number of hops between the source and the destination. We provide for both OAs and DAs networks, the number of links (hops) that a packet passes through to reach the destination. Using extensive simulation, we evaluate the network performance in terms of packet loss, mean throughput, mean energy consumption and energy efficiency for the chain and grid topologies. Simulations results show that using DAs improves the throughput and the energy efficiency, and reduce the mean loss ratio, and the consumed energy.

A 1-tier network composed by multimode low power nodes (LTE/Wifi) is considered as a cost-efficient solution for operators to improve services in poorly or uncovered rural areas. Using an interference coordination technique, network performance can be further improved. Stochastic geometry gives a set of tools to model the location of base stations and user equipments in such wireless networks. Using a spatial model we analyse the network performance in terms of coverage probability and data rate. To realistically model multimode node locations, a new point process model, called the r-l square point process (p.p.), is used in this work. The model of downlink communication including the coordination technique is developed and it allows to evaluate the system performances in term of coverage probability and throughput. Results show that cooperation among nodes improves the network performance.

In this paper, we propose to use the Joint Transmission approach of Coordinated Multipoint (JT-COMP) of cellular networks to reduce the interference in dense Very High Throughput (VHT) wireless LANs. VHT WLANs are based on wider channel bandwidth, efficient modulation techniques and support for spatial streams using MIMO schemes. However, the interference problem persists despite these approaches, and thereby prevents mobile stations from fully reaping the capacity improvement of such networks. In order to optimize the coverage and minimize the cell overlap in dense stadium scenario, AP locations must be planned carefully. To this end, we model positions of nodes using a spatial stochastic model called the r-l square point process. Then, we derive the coverage probability and throughput expressions and investigate the benefit of Joint Transmission coordination technique. Using simulation, we characterize the performance metrics for different sizes of coordinated set and carrier sensing domain of access points. Our results show that JT-CoMP is a promising scheme for dense WLANs.

The femtocell — Wi-Fi integration is a promising approach to solve the problem of inter-tier or intra-tier interference in heterogeneous networks. In this paper, multimode femtocell base stations are deployed which form a Wi-Fi mesh network to communicate between themselves while deploying cellular technology (e.g., LTE) for communications with mobile users, thus ensuring ultimately connectivity between mobile users and their macro base stations to improve the network coverage. We propose a tractable model for coverage/outage to evaluate the benefits of such integration in terms of SINR and received signal strength. Our work is based on point processes in two-dimensional plane that models locations of femtocell — Wi-Fi (also referred to as multimode) nodes. The proposed model is more realistic than the classical Poisson point process, as the distribution of points is more homogeneous and it ensures that the nodes are not too close to each other. The derivation of coverage/outage formula allows us to determine operational parameter ranges for the Wi-Fi network to form a mesh network. In addition, it helps in the design of the femtocell network to ensure a suitable coverage for users in terms of SINR and received signal strength.

Recent years witnessed growing concerns regarding scalability of today's Internet architecture. Universality of the single IP numbering space is one cause of unbound growth of entropy and inter-domain routing tables in the core network. Recently, the IAB proposed to review the Internet addressing architecture to separate name and location spaces. In this paper, we propose an instance of VIN-based IPv6 addressing, compatible with other Future Internet efforts, to tackle this issue with regards to the vehicular communications. We design a global hierarchical identification space of up to 235unique and distinct vehicles per manufacturer (216manufacturers). An original method of mapping these identifiers to addresses and guarantee the uniqueness conservation is proposed. The objective is to create a sustainable addressing architecture for vehicular communications and introduce the manufacturer as a new vehicular services provider. We also propose a network architecture design that allows to use pseudonyms as a way to tackle identity privacy concerns.

Transport layer performance in IEEE 802.11 multihop wireless networks (MHWNs) has been greatly challenged by wireless medium characteristics and multihop nature which induce several types of packet loss including collision, random channel errors and route failures. In this paper, we propose a novel rate control scheme, called Bi-Metric Rate Control (BMRC), which regulates efficiently the source rate in MHWNs. BMRC's design is based on two MAC metrics: the Medium Access Delay used to detect the network contention level, and the Average Transmission Time used to estimate the effective packet sending rate by which the network will not be overloaded. The simulation results show that the adapted mechanism introduces significant performance improvement in terms of fairness, packet loss rate and delay in MHWNs.

Internet predominant transport protocols, such as TCP and TFRC, face performance degradation in Multihop Wireless Networks because of the high loss and link failure rates. Many solutions have been proposed to improve the transport layer operation. These solutions are either based on network state estimation or use information from MAC layer (called MAC metrics) in a cross-layer manner to better comprehend the network state. In this paper, we define the pertinent MAC metrics to reflect the network state, and provide a comparative study among each other, their expected usage and measurement methods at MAC layer. We also investigate the behaviors of MAC metrics through several experiments in order to reveal their effectiveness in reflecting network events such as contention, collision and loss. We show that the Medium Access Delay metric is the most effective to reflect faithfully the MAC states and to detect the network congestion and contention earlier than the other.

Transport layer performance in IEEE 802.11 mul-tihop wireless networks (MHWNs) has been greatly challenged by wireless medium characteristics and multihop nature which are the sources of several types of packet loss including collision, random channel errors and route failures. Rate control transport protocols, the candidates for multimedia streaming applications suffer from high loss rates and end-to-end delay in MHWNs. A common research direction is that the rate control mechanisms at transport layer should be aware of MAC layer contention to keep the network load at a reasonable level. In this paper, we introduce a new MAC metric which reflects the contention and congestion levels more accurately. The metric is then used to improve the rate control mechanism of a rate-based transport protocol in MHWNs. The simulation results show that the adapted mechanism introduces significant performance improvement in MHWNs.

Recent vehicular networking activities include public vehicle to vehicle/infrastructure (V2X) large scale deployment, machine-to-machine (M2M) integration scenarios and more automotive applications. eHealth is about the use of the Internet to disseminate health related information, and is one of the promising Internet of Things (IoT) applications. Combining vehicular networking and eHealth to record and transmit a patient's vital signs is a special telemedicine application that helps hospital resident health professionals to optimally prepare the patient's admittance. From the automotive perspective, this is a typical Vehicle-to-Infrastructure (V2I) communication scenario. This proposal provides an IPv6 vehicular platform which integrates eHealth devices and allows sending captured health-related data to a Personal Health Record (PHR) application server in the IPv6 Internet. The collected data is viewed remotely by a doctor and supports a diagnostic decision. This paper introduces the integration of vehicular and eHealth testbeds, describes related work and presents a lightweight auto-configuration method based on a DHCPv6 extension to provide IPv6 connectivity for resource constrained devices.

The multi-user medium access mechanism OFDMA has to provide each node with a given amount of radio resources. In this paper we present a new distributed algorithm for the allocation of resource blocks in an OFDMA ad hoc network. We are principally interested in allocating resources fairly because the ad hoc networks which we work on are dedicated to be deployed in the areas of natural or man-made disasters and where the guarantee of connectivity is an important issue. Contrary to the commonly applied approach, we consider a resource allocation on the links under a two hop interference distance. The proposed allocation procedure is coupled with our other algorithm which detects and corrects two hop interferences and which has been revised and improved. The performance of our algorithm is evaluated by simulation for different topologies. We observed that simultaneous allocations in large networks allow a constant convergence time to be kept despite the networks size.

Mobile ad hoc networks are characterized by their highly dynamic, multi-hop, and infrastructure-less nature. Due to limited computing power, scarce bandwidth, high mobility and the lack of a central coordinating entity, service discovery in these network is a challenging task. The great majority of service discovery protocols developed for MANETs deal with the above issues at the application layer. In this paper, we discuss the fact of implementing service discovery at the routing layer instead of the application layer, in order to reduce Service Discovery (SD) overhead and to limit resources consumption. We develop an integrated service discovery protocol, called BF-SD-ZRP, utilizing a combination of different optimization techniques: piggi backing of SD information on the routing messages, compact description of SD using Bloom filter (BF) and service caching. Our simulation results allows to validate our scheme and demonstrate its efficiency in terms of overhead reduction and service discoverability.

MANETs are highly dynamic and suffer from frequent and unpredictable changes in the network topology due to the fact that devices are mobile and operate with low battery power. Due to limited computing power, high mobility and the lack of a central coordinating entity, service discovery in MANETs has been an attractive area of research. However, proposed solutions are broadcast-based and result in overloading the constrained network. The issue addressed in this paper is therefore to provide a cross-layer Service Discovery in order to reduce energy consumption. In this work we propose an efficient Energy-Aware Service And Route Discovery scheme called EASARD. We extended the Zone Routing Protocol in order to encapsulate service information in its routing messages. We present a performance comparison of EASARD versus another integrated protocol in terms of energy consumption. We evaluate how the different approaches affect the energy usage of mobile devices. Extensive simulation demonstrate the superiority of our proposed approach over other cross-layer schemes.

Users of today's networks expect certain services from the network. Indeed one of the purposes of the network is to enable nodes to access remote services and resources not locally available. Given the significance and potential benefits of MANETs, it is necessary to design robust protocols which ensure that users of such networks are able to find the services they require. Due to limited computing power, scarce bandwidth, high mobility and the lack of a central coordinating entity, service discovery in MANETs is a challenging task. The great majority of service discovery protocols developed for MANETs deal with the above issues at the application layer. In this paper, we focus on integrated approaches of service and route discovery. We develop an integrated service discovery protocol, called BF-SD-ZRP combining several optimization techniques: the use of Bloom Filter compression scheme providing an optimized description of service information. The subsequent piggybacking of service information into ZRP routing messages, taking advantage of its intelligent bordercasting mechanism to propagate information to remote nodes. A Caching scheme is used to obtain further reductions of both overhead and latency. We present and examine our simulation results for both BF-SD-ZRP and SD-OLSR cross-layer protocols in order to demonstrate the efficiency of our scheme in terms of overhead reduction and service discoverability using the scalable network simulator Jist/Swans.

In this paper, we present a performance analysis of a dynamic bandwidth monitoring method with flow control functionality, for intensive data-passing grid applications over wide area networks. The proposed method called flatness based trajectory tracking, adapts the grid users output rates to match expressive data-transfer SLA used to describe theirs guarantees parameters. The control considers time delay transfer by using delay predictor and provides value-added services for grid applications over Internet. This reactive monitoring uses purely control theoretic approaches which stabilize the network and avoid undesirable oscillations for the transmission of such critical flows. Moreover, discrete-time simulation under GridSim simulator illustrates its feasibility and efficiency to provide guarantees for critical grid traffics with timely execution requirements.

In this paper, we present and analyse a link management technique for pro-active routing protocols for ad-hoc networks. This mechanism is based on signal strength hence cross layer approach is used. The hysteresis mechanism provided by OLSR is improved upon by using signal strength in combination with the hello loss based hysteresis. Thus it now takes into account temporal as well as signal strength stability of a link. This not only makes the link management more robust but also helps in anticipating link breakages thereby greatly improving performance.

In grid computing, most of the SLA management systems focus on static SLAs, where the guarantee terms are defined as constants or bounds. None of them support dynamic SLAs based on analytical expressions. Therefore, to enforce dynamic SLAs and to improve services offered to applications, we propose a new monitoring method to control the resource utilization and to reconfigure them when necessary in order to match analytical expressions used to describe the service parameters. We are interested in the performance characteristics of grid computing at the network side, especially delay transfer, bandwidth and queue's lengths. The proposed method called flatness based trajectory tracking, considers time delay transfer by using delay predictor and provides value-added services for grid applications over Internet. Moreover, discrete-time simulation under GridSim simulator illustrates its feasibility and efficiency to provide guarantees for critical grid traffics with timely execution requirements and to match expressive data transfer SLAs.

Vehicular ad hoc network in a highway is composed of high speed vehicles or nodes which induce fast topology changes in their configuration. In order to solve the connectivity and scalability problems of VANETs, we introduce the architecture of a vehicular hybrid ad hoc network (VHANET). Using some routing protocol extensions, the VHANET allows ad hoc islands to be interconnected among each other using a infrastructure network. Our simulation results show that the performances, compared to a flat network, are greatly enhanced. In addition, we study a nearly optimal AP density for the use of our architecture.

In this paper, we are interested in the properties of vehicular ad hoc networks (VANET). We study the mean connection lifetimes to evaluate the feasibility to support non-safety applications. Because connection lifetimes are in the order of a few minutes, an infrastructure network is highly recommended to maintain connectivity between car clusters. Using an hybrid network, we perform comparisons of routing protocols used in the VANET. We show that reactive and geographic routing protocols are very close in terms of performance. However the reactive protocol are more accurate since it does not require neither geolocalization systems nor location services. We propose the use of hybrid networks and we give dimensioning rules to ensure connectivity of the network

In this paper, we propose a novel packet scheduling algorithm, based on the CBQ scheduling policy for QoS provisioning in multimedia cellular networks. CBQ, based on service class differentiation, aims at maximizing the use of available radio resource and meeting the QoS requirement of higher priority users as much as possible while maintaining the minimum requirements of lower priority users, especially when the system suffers from congestion. In this policy, CBQ is combined to a Dynamic Priority Function. It considers the realistic behaviour of traffic, taken into account while considering the spatial variation of the system characterizing both the user mobility and the signal propagation impairments due to the surrounding effects. The new scheduling policy provides enhanced traffic performance in heterogeneous environments and achieves a good level of capacity gain.

In this paper, we propose a combined fair packet scheduling policy and an adaptive connection admission control algorithm, to meet the rapidly increasing demand for providing multimedia services with diversified quality requirements. The new scheduling policy is based on an enhanced weighted fair queuing algorithm, while taking into account the spatial variation of the system characterizing both the user mobility and the signal propagation impairments due to the surrounding effects. The new adaptive QoS oriented CAC function, based on service class differentiation, decides whether the connection request is to be admitted into the system, on the basis of the generated interference level and the waiting time before access. The results indicate that significantly increased traffic performance can be achieved in comparison with other conventional investigated radio resource allocation policies.

In the third generation of wireless cellular communication systems, cell sizes are expected to become smaller in order to accommodate high user density and multimedia traffic. In such a system, where hierarchical cellular networks are used, microcells may coexist with macrocells. Resource allocation schemes will have to be dynamic and flexible to handle user mobility and traffic variation. In this paper, we propose a new allocation strategy to manage both fast and slow users resources request in such a network. Our contribution uses an enhanced mechanism based on preemption which can be viewed as a reversible strategy on-demand using channel re-allocation. The originality of this work is to take into account the current value of QoS criteria provided to mobile users to perform resource allocation. Our new scheme is simulated and performance is analyzed in terms of new call blocking and dropping handover probabilities and signalling load. Results show that proposed resource management policy reduces significantly call blocking and dropping probabilities compared to other proposals with lower signalling increase.