Low-Power Wide-Area Networks (LPWANs) provide connectivity to widely-spread battery-powered devices. In such networks, very large numbers of terminals compete for radio access. Frame collisions naturally occur in the absence of coordination, which is detrimental to network performance. However, each terminal transmits relatively small and sporadic amounts of data. The design of strategies to operate large-scale LPWAN is challenging. In fact, any introduction of protocol overhead for terminal coordination purposes has a detrimental impact on device energy efficiency and ultimately battery life. Herein, this paper summarizes a doctoral thesis [1] focused on the design of scalable yet energy efficient access schemes for LPWAN. One of the key takeaways of this work is the need for dynamic access protocols, capable of adapting their behavior to the traffic load while minimizing the amount of control plane messages. In order to take a step back from the work accomplished, methodological insights about the approaches and performance evaluation tools used during the thesis are also provided. Finally, research perspectives are discussed. More specifically, the application of such schemes to Direct-to-Satellite IoT networks is foreseen as a means to enable worldwide and efficient low-power networks.

The Global Navigation Satellite System (GNSS) is the most widely used technology for localization, offering realtime, high-accuracy positioning with global coverage. However, its limitations, such as high energy consumption and signal obstruction in certain environments, make it unsuitable for some emerging applications. One such challenge is the localization of very low-power devices, which require alternative positioning solutions. As a result, research efforts are increasingly focused on improving localization precision and addressing challenges related to energy efficiency, coverage limitations, and signal reliability. This paper provides an overview of existing Internet of Things (IoT) solutions, with a particular emphasis on LoRaWAN (Long Range Wide Area Networks) as the deployed technology and Time Difference of Arrival (TDoA) as the primary localization technique.

Low-Power Wide-Area Networks (LPWANs) provide a low-cost solution for connecting low-power devices over long distances. Among LPWAN technologies, Narrowband IoT (NB-IoT) has gained prominence because of its wide coverage and high reliability. Recently, as the demand for IoT connectivity in remote and underserved regions continues to grow, integrating NB-IoT with Low-Earth Orbit (LEO) satellite networks has gained increasing attention. Due to satellite communication's dynamic nature, this integration poses significant challenges, including synchronization issues and managing high-density User Equipment (UE) during the random access procedure. This digest paper summarizes key contributions from a doctoral thesis that addresses these challenges. A systematic framework based on key performance indicators (KPIs) is proposed to evaluate and optimize satellite IoT communication, addressing reliability, latency, throughput, and energy efficiency trade-offs. A lightweight downlink synchronization method is proposed, which reduces device complexity in LEO satellite environments. In addition, a GNSS-free wake-up strategy is developed to improve energy efficiency, particularly in scenarios with intermittent satellite coverage. To address the challenge of random access in high-density environments, an early detection method is introduced. This method reduces collisions during the random access procedure and improves the network capacity to support many UEs. These contributions provide a comprehensive set of solutions to address the limitations of current NB-IoT and LEO satellite integration strategies, paving the way for efficient and scalable IoT networks in challenging environments.

Direct-to-satellite (DtS) communications are becoming increasingly popular in the field of connected objects, since Low Earth Orbit (LEO) satellites can be easily deployed at lower and lower costs. In fact, a DtS Internet of Things (IoT) paves the way for the development of an incredibly vast gamut of monitoring applications for very inaccessible areas, e.g., oceans, mountains, and deserts. In this context, the medium access protocol used for Long Range Wide Area Network (LoRaWAN) has recently been proposed as a viable solution for policing the communication between ground low power devices and LEO satellites equipped with LoRaWAN gateways. However, the default LoRaWAN medium access scheme is based on a “transmit first” policy that inevitably augments the collision rate among the increased number of concurrent devices in the satellite coverage. Instead, this paper focuses on a “listen first” policy enabled through LoRaWAN Class $\mathbf{B}$ beacons and the sole adoption of Activation-By-Personalization (ABP). In more detail, a wake-up strategy allowing ground LoRaWAN devices to intermittently switch on and off their own radio for listening to incoming beacons is studied and analyzed through simulations. By means of this investigation, it is possible to find the best timer settings guaranteeing a good compromise between energysaving and the need to reduce the time to catch the first beacon. Some preliminary results related to the availability of a single LoRaWAN-enabled LEO satellite show that a reduction of 33 % in energy consumption can be achieved at the cost of a slightly increased “first catch” time.

The latest release by the 3rd generation partnership project (3GPP) defines how a nonterrestrial narrowband IoT (NB-IoT) link may be set up between user equipments (UEs) on the ground and low Earth orbit (LEO) satellites equipped with evolved nodes B (eNB). However, a strong assumption is undertaken. Each UE must have global navigation satellite systems (GNSSs) capabilities to properly precompensate the Doppler frequency shift and the propagation delay according to the time-varying relative motion of satellites. Additionally, although Release 18 accounts for discontinuous coverage by LEO satellites, the management of next passes over any spot on the Earth is undefined, thus affecting the system scalability. Remarkably, this contribution enables GNSS-free NB-IoT direct-to-satellite communications with sparse LEO satellite constellations. To do that, the UE periodically wakes up until it detects a satellite pass in its range. By listening to several NB-IoT beacons, the estimated Doppler curve is used to precompensate ongoing communications in frequency and time. Furthermore, the UE uses the standard information sent from the eNB, together with its own estimated location, to guess the next satellite pass without using GNSS. Simulation results reveal that the introduced wake-up strategy allows GNSS-free UEs to save more energy than if equipped with the most power-efficient GNSS chipsets surveyed in 3GPP specifications, promoting the broader deployment of Internet of Things (IoT) devices in remote and underserved areas.

In this paper, we propose a controllable virtual network service that can be provided on a multi-administrative multi-domain network, and whose behaviour can be programmed and customized according to user needs. A resource allocation algorithm is proposed to compute the resources, from different domains, that are needed to support the virtual network with the required QoS (Quality of Service) and capabilities. An implementation of the service on an OpenFlow-enabled multi-domain network is described. The service is then applied in the context of coalition military network to show its benefits and potential.

5G is a new key technology for future communication networks. It aims at providing a broad range of new services and capabilities for users as well as facilitating its management for network operators (NetOps). Whereas networks still have monolithic architectures, 5G design takes advantage of softwarization and virtualization of its functionalities, for this purpose. However, 5G is also raising a lot of critics, especially related to its energy consumption. This paper then deals with assessing the energetic cost of the future softwarized 5G facilities. To this aim, an experimental platform has been setup taking advantage of the software 5G OpenAirlnterface (OAI) implementation. This paper shows the complexity of designing energy consumption measurement tools. It then exhibits the level of energy consumption of the main 5G components, pointing out the ones that need to be optimized.

In the last decade, it has been quickly recognized that backhauling Low Power Wide Area Networks (LPWAN) through Low Earth Orbit (LEO) satellites paves the way to the development of novel applications for a truly ubiquitous Internet of Things (IoT). Among LPWAN communications technologies, Narrowband IoT (NB-IoT) does not suffer from interference by other concurrent technologies since it works on a licensed frequency spectrum. At the same time, thanks to its medium access scheme based on contention resolution and resource allocation, NB-IoT is a key enabler for the specific market slice of IoT applications requiring a good level of reliability. In the architectural configuration analyzed throughout this contribution, an NB-IoT low power User Equipment (UE) can communicate with a LEO satellite equipped with an Evolved Node B (eNB) for a time limited to the visibility window of that satellite from the UE position on the Earth. However, the Doppler effect inherent to the time-varying relative speed of the eNB needs to be dealt with additional resources. The solutions proposed until now are non-trivial, thus making the use of NB-IoT for ground-to-satellite communications still expensive and energetically inefficient. Timely, this contribution proposes a procedure for a UE to infer the future values of the Doppler shift from the beacon signals so that frequency pre-compensation can be easily applied in the following interactions during the visibility time. The presented simulation results show that a UE needs to listen to about 10 beacon signals in 1 second to accurately and robustly predict the Doppler curve, thus enabling a lightweight (and eventually truly energy-efficient) implementation of NB-IoT over ground-to-satellite links.

The Long Range (LoRa) modulation keeps gaining relevance in the landscape of low-power sensor networks. Most models used to evaluate the performances of LoRa deployments are based on the assumption that two colliding frames are necessarily lost. Recent findings have shown that the capture effect occurs in these networks, allowing the receiver to sometimes demodulate the frame featured with the highest signal power. This finding notably improves the overall throughput compared to expectations, but in turn decreases the network fairness. In this paper, we analyze the benefits and drawbacks of such an effect. We therefore provide new throughput models for LoRa networks operating Pure and Slotted ALOHA access schemes. For this purpose, an experimental testbed has been setup and used to measure the occurrence probabilities of capture events in several transmission scenarios. The resulting models are validated with real-life data gathered on the same setup. We additionally analyze the fairness in our deployment, showing that the devices featured with the highest average power at the receiver benefit from a higher success rate than others. By computing Jain's index, we show that this unfairness gets more pronounced as the traffic load increases.

We consider the SFC embedding (SFCE) problem in the Slice as a Service (SlaaS) model. In this model, a slice provider leases resources from multiple cloud and network providers in order to instantiate the Service Function Chain (SFC) requested by a slice tenant. As the slice provider has no visibility on the infrastructures of the resource providers, in which resources may be purchased and released quite rapidly, it has to query them to determine what are the possible allocations and their costs. We show that when there are many resource providers and many VNFs composing the SFC, the number of queries to be made for discovering a minimum cost SFC embedding grows quickly, leading to excessively long deployment times. In order to reduce the latter quantity, we propose to query resource providers strategically, rather than collecting the information on all possible allocations at once. We provide bounds on the number of queries to be made in this approach, and propose to exploit a Shortest Path Discovery algorithm in order to reduce this number of queries and thus the SFC deployment time. Our numerical results suggest that this algorithm is fairly efficient, and that the deployment times can be significantly shortened, in particular when initial estimates of allocation costs can be provided by the slice provider.

The emerging LoRa technology is quickly becoming the de facto standard for Low Power Wide Area Networks upon unlicensed frequencies. Herein, the LoRaWAN medium access sets up a lightweight network architecture able to connect very low power devices to the Internet. Traffic flows in such deployments can be variable, or even unpredictable, depending on the needs of the monitoring applications using the network. As an example, to track air quality in cities, some applications can trigger an increased need of fine grained pollution data during the daytime. However, the network capacity is currently limited by the default LoRaWAN pure ALOHA access scheme. A time synchronized scheduled access would considerably improve the achievable throughput, at the cost of an increased power consumption for synchronization duties. In such a context, this contribution introduces the traffic-aware energy efficient Medium Access Control (TREMA) protocol for LoRa networks, capable of seamlessly switching between asynchronous and synchronous schemes according to the probed traffic variations. TREMA ultimately increases the maximum capacity of LoRa deployments while always selecting the most energy efficient access scheme.

With the recent integration of Non Terrestrial Networks (NTNs) into 3GPP Release 17, 5G networks are expected to benefit from the NTN large coverage area. This integration will help mobile terrestrial networks reach a worldwide coverage. However, this ultimate ubiquity also comes with its set of challenges to overcome. One of the main issues is the seamless integration of NTNs into the existing mobile network standard. In this paper, we propose a comprehensive architecture integrating NTNs as slice-aware backhaul links. This architecture remains fully compliant with the 3GPP standard. For this purpose, we propose an end-to-end slice model integrating NTNs and 5G networks. Then, we implement this model on a 5G-satellite testbed, adding new functional components to interconnect both networks at the control and data plane levels. Lastly, we evaluate the performances of our method using the aforementioned testbed by monitoring each slice and their related Quality of Service requirements.

Long Range Wide Area Network (LoRaWAN) technology is knowing an impressive growth. It is considered by many researchers as the new era of Internet of Things (IoT) communication. Several studies focus on evaluating the LoRaWAN performance according to many features such as coverage, scalability, and communication reliability. However, these studies assume that LoRaWAN end-devices are already activated by the network server. Thus, the performance of LoRaWAN activation procedure, referred as Over-The-Air Activation (OTAA), is not widely treated.In this paper, we elaborate an experimental analysis of the OTAA procedure performance using a real field LoRaWAN deployment. Our objective is to analyse the end-device activation delay and power consumption at large scale LoRaWAN. To achieve this goal, we design an experimental scenario of 30 end-devices competing for being activated by sending network join-requests. Upon its activation, each end-device transmits unconfirmed data at high rate, which simulates a large-scale LoRaWAN where hundreds of end-devices send their join-requests concurrently. Results show that OTAA procedure incurs high activation delays and power consumption, especially in large scale where the network traffic is high. This is due to three main factors: collisions, the back-off retransmission mechanism and join-request duty-cycle.

The growing demand and the diverse traffic patterns coming from various heterogeneous Internet of Things (IoT) systems place an increasing strain on the IoT infrastructure at the network edge. Different edge resources (e.g. servers, routers, controllers, gateways) may illustrate different execution times and energy consumption for the same task. They should be capable of achieving high levels of performance to cope with the variability of task handling. However, edge nodes are often faced with issues to perform optimal resource distribution and energy-awareness policies in a way that makes effective run-time trade-offs to balance response time constraints, model fidelity, inference accuracy, and task schedulability. To address these challenging issues, in this paper we present a SDN-based dynamic task scheduling and resource management Deep Reinforcement Learning (DRL) approach for IoT traffic scheduling at the network edge. First, we introduce the architectural design of our solution, with the specific objective of achieving high network performance. We formulate a task assignment and scheduling problem that strives to minimize the network latency while ensuring energy efficiency. The evaluation of our approach offers better results compared against both deterministic and random task scheduling approaches, and shows significant performances in terms of latency and energy consumption.

Long Range Wide Area Networks (LoRaWANs) have recently emerged as a hot research topic for their capability to collect sporadic data from a great number of widely spread low power devices. By enabling low-cost low-traffic wireless communications at large scale, such networks can be adopted in many application domains, including smart agriculture, logistics, and emergency detection among others. LoRaWAN employs a pure ALOHA default medium access scheme, that limits the maximum achievable throughput to 18%. Increasing the number of terminals would lead to more frame collisions, and eventually to network collapse. The easiest way to allow bigger network sizes is to synchronize devices, so that they can discretize time into slots, and use them to schedule frame transmissions. Herein, a Time Division Multiple Access (TDMA) scheme on the top of LoRaWANs is highly desired. However, since keeping synchronization can lead to heavy energy consumption, the design of such scheme should enforce energy-efficiency. This contribution describes the technical issues related to the implementation of such mechanism, that as a matter of fact represents the cornerstone on which more sophisticated random access protocols or even scheduling techniques can be designed. In dense networks, the proposed scheme combined with a very simple slotted ALOHA mechanism has also been shown to outperform the default LoRaWAN access protocol in terms of expected energy consumption.

In cellular networks, the emergence of machine communications such as connected vehicles increases the high demand of uplink transmissions, thus, degrading the quality of service per user equipment. Enforcing quality-of-service in such cellular network is challenging, as radio phenomena, as well as user (and their devices) mobility and dynamics, are uncontrolled. To solve this issue, estimating what the quality of transmissions will be in a short future for a connected user is essential. For that purpose, we argue that lower layer metrics are a key feature whose evolution can help predict the bandwidth that the considered connections can take advantage of in the following hundreds of milliseconds. The paper then describes how a 4G testbed has been deployed in order to investigate throughput prediction in uplink transmissions at a small time granularity of 100 ms. Based on lower layer metrics (physical and mac layers), the main supervised machine learning algorithms are used, such as Linear Regressor and Random Forest to predict the uplink received bandwidth in different radio phenomena environment. Hence, a deep investigation of the impact of radio issues on bandwidth prediction is conducted. Further, our evaluation shows that the prediction is highly accurate: at the time granularity of 100 ms, the average prediction error is in the range of 6% to 12% for all the scenarios we explored.

The channel access technique of LoRaWAN hinders the thorough utilization of channel capacity since it was not designed to exclusively limit or even avoid collisions, but rather with simplicity and energy consumption in mind. As a consequence, in presence of plenty of active nodes, the overall performance offered by a LoRaWAN Network can be poor. In this paper, we propose a new synchronized Uplink transmission scheme to improve the performance of data transmissions for LoRaWAN class B end-devices which are suitable for IoT applications with low latency requirements and regular traffic. In contrast to related works, our proposal is defined without modifying the fundamentals of the original ALOHA based channel access technique, but simply takes advantage of Class B downlink synchronization. Our preliminary analysis based on an analytic model shows an enhancement of performances regarding data transmission success.

Fog computing has recently emerged as a new cyber foraging technique to offload resource-intensive tasks from mobile devices to mobile cloudlets in close proximity to endusers. Since the one-hop communication in the network edge is predominantly wireless, Wireless Mesh Networks (WMNs) are being considered to build wireless fog networks. However, WMNs use distributed hop-by-hop routing protocols to reflect a partial visibility of the network, which limits their ability to perform global network management and monitoring needed by fog networks. Software Defined Networking (SDN) provides a centralized control and management of the entire network, which makes it a good candidate to support fog communication. Unfortunately, the SDN OpenFlow protocol does not support any functionalities for wireless fog networks as it is primarily targeted to wired networks. To address these issues, this paper presents a SDN-enabled wireless fog architecture that combines both OpenFlow and distributed wireless protocols. The proposed solution provides lower latency and efficient load balancing to offload the network load by enabling programmable fog routers.

This work proposes an approach based on Steiner trees to efficiently support multipoint communications in a multi-domain context, where each domain exposes a synthetic and aggregated view of its network. The approach that we propose is based on two pillars: The adoption of a topology aggregation of each domain's network as a Steiner tree and the use of a shortest path heuristic for the calculation of these aggregated networks as well as the global Steiner tree. An extensive experimental study on random and real network topologies shows the gains made by our approach in terms of both accuracy and computational complexity.

Assigning network resources to Virtual Links (VLs) efficiently and on-demand is a challenging problem for any network virtualization solution. Known as the Virtual Link Mapping (VLM) problem, its objective is to compute the appropriate network paths with the required network resources that meet the quality of service expectations of arriving VLs while spreading the load over all nodes to maximise the admissibility of forthcoming VLs. Despite the efficiency of existing VL mapping algorithms, when resources are allocated and released over time due to the arrivals and departures of VLs, the network inevitably drift into a fragmented state (with nodes with very different loads) often causing a VLs request rejection that could have been avoided with a different resource allocation. In practice, defragmentation algorithms are used in complement to VL mapping algorithms to proactively or reactively (on the event of a VLs request refusal) trigger some VLs reallocation (or migration). In this paper, we propose an Integer-Linear program (ILP) based reactive defragmentation and VLs mapping algorithm for an SDN/OpenFlow network. In addition to selecting the VLs that should be migrated to reduce network defragmentation, our algorithm also computes the paths (and the associated resources) that support the previously rejected VLs. Our solution was evaluated on a real network topology and the experiments showed that our proposal outperforms existing approaches from the literature by about 12% in terms of acceptance rate with a gain on migration costs around 40%.

Network simulators are often used for their simplicity and cost regarding wireless networks. However, their realism is often criticized and their results challenged. The main concern comes from the modeling of the PHY and MAC layers. To assess the performances of these simulators and their models, the results of simulations are often compared with experimental results. However, the comparison methodologies used in these studies may introduce biases. This work focuses on accurately discovering and analyzing the reasons for the calibration problems or implementation bugs in simulators and experimental devices. For this purpose, we leverage the famous Root Cause Analysis (RCA) technique for comparing traces issued from different simulations and real experiments, that includes the study of the root causes of dissimilarities. Throughout the paper, our RCA-based method has been applied to detect and analyze a performance anomaly between NS-3 simulation and our lab wireless testbed when transmitting data over a WIFI 802.11 link. It especially details how low level traffic traces have been generated in both environments for similar scenarios, and how they can accurately be compared and their differences analyzed.

In order to address the performance problems that many business applications are experiencing, network vendors and Network Service Providers are reconsidering the integration of some form of application awareness in the way their networks forward user traffic. Their ultimate goal is to devise new network service models that are dedicated and customized to applications. This trend is mainly enabled by the emergence of software-Defined networking (SDN), which allows for flexible flow-based forwarding. This paper proposes an SDN-based Application Driven Network (ADN) that deploys customized data paths on an application basis, that the ADN is able to adapt to meet the, potentially, varying Quality of Service (QoS) needs of applications. These needs are, either, explicitly expressed and submitted by the middleware or by an application agent, or inferred by the proposed ADN with DPI (Deep Packet Inspection) based traffic classification techniques. This paper presents the general architecture of our proposed ADN by describing its main components, their requirements as well as their main algorithms. The proposed ADN has been implemented, evaluated and applied to the OMG Data Distribution Service (DDS) based distributed applications in an enterprise network context.

For years, Virtual Network Operators (VNOs) are a major player in the satellite communication market landscape. Typically, they repackage services leased from Satellite Network Operators (SNOs) to provide their customers with added-value end-to-end services that mostly go beyond the satellite network and cover terrestrial networks. However, the level of control and visibility that VNOs have on their purchased satellite services (and the underlying satellite resources) is limited mainly because of SNOs' protective policies and the closed nature of satellite devices. From the VNO perspective, this refrains the development of novel satellite communication services and complicates the provision process of the services that they offer to their customers. To address these limitations, this paper proposes and elaborates on the idea of exposing to VNOs, an Openflow based switch abstraction of the satellite segment. The feasibility and the opportunities brought by such an abstraction to VNOs are presented as well as the technical requirements to make it a reality. Insights on how it can be implemented are presented complemented with a proof of concept implementation on the OpenSAND satellite system emulation platform.

Network virtualization is seen as a key networking paradigm for building diverse network services and architectures over a shared network infrastructure. Assigning network resources to virtual links and, more generally to virtual network topologies, efficiently and on-demand is one of the most challenging components of any network virtualization solution. This paper addresses the problem of on-line resource allocation of multiple virtual links on a Software Defined Network (SDN) infrastructure. The application context that is targeted is primarily the online provisioning of virtual overlay networks even if it can be broadened to address more general virtual networks. Considering an SDN physical infrastructure allows a complete freedom in choosing the optimal physical paths and the associated resources that support the virtual links with no interference from any other network function (such as routing). However, for the time being, forwarding in an SDN network is resource consuming with a noticeable impact on the size of the flow tables. Hence, forwarding (i.e. switching) resources should be carefully considered by the network resource allocation algorithm. This paper proposes a novel Integer-Linear formulation of the above cited problem by taking into account: (1) point-to-point as well as point-to-multipoint virtual links, each with an associated bandwidth requirement and a maximum transfer delay requirement, (2) two types of network resources, namely network links' bandwidth and nodes' switching resources, and (3) optionally, path splitting which allows a virtual link to be established on multiple physical paths. We report preliminary experimental results on a real network topology in an overloaded scenario (bandwidth requests largely exceed network capacity); they show that our algorithm outperforms shortest path heuristics with a gain on the admission rate (of virtual links requests) that ranges from 5 to 15% (compared to the most efficient heuristic) and with a computation time less than a few seconds.

Many distributed real-time applications have dynamic requirements regarding communication delay and bandwidth. The Data Distribution Service (DDS) middleware is a key enabling technology used to support such applications. Indeed, the publish/subscribe distribution model of DDS with the ability to assign dynamic QoS (Quality of Service) parameters to DDS distribution services is able to take into account changes in the exchanged data flows and in the required QoS. This variability is taken into account at the middleware level to adjust some of DDS QoS mechanisms but is rarely propagated to the network layer to provide dynamic network communication services that fit the varying DDS distribution service needs. Usually, an over provisioned network is used, leading to network resource wastage. This paper addresses this issue and proposes a communication architecture that combines DDS with a new emerging class of communication networks named Software Defined Networks (SDN) to support efficiently dynamic distributed applications. SDN bring flexibility to the network and enable the provision of on-demand dynamic network communication services.

The last decade has seen an increasing interest for wireless communications. With the current use of smart-phones and tablets, together with the rise of the Internet of Things, the number of mobile nodes in networks is significantly changing the way they are managed. Indeed, these wireless networks are highly dynamic, mainly, regarding topology and traffic matrices. The high dynamism of mobile nodes can for instance impact the connectivity of the network, or the role of the nodes in the routing graph. Given this increasing complexity, network's service management requires to be as autonomous as possible. However, autonomy can not be achieved unless the network is able to assess and understand its own behavior. In this paper, we propose an assessment index (SA) based on nodes' satisfaction and its self-estimation algorithm for wireless mobile networks.We then provide events' collection and distributed mining methods allowing nodes to analyze the evolution of this index.We illustrate our framework and characterize the estimation error for various network properties under NS3 simulations.

Supporting end-to-end quality-of-service (QoS) in Large-scale distributed interactive simulations (DIS) is hard due to the heterogeneity and scale of communication networks, transient behavior, and the lack of mechanisms that holistically schedule different resources end-to-end. This paper aims to cope with these problems in the context of wide area network (WAN)-based DIS applications that use the OMG Data Distribution Service (DDS) QoS-enabled publish/subscribe middleware. First, we show the design and implementation of the QoS framework, which is a policy-driven architecture that shields DDS-based DIS applications from the details of network QoS mechanisms by specifying per-flow network QoS requirements, performing resource allocation and validation decisions (such as admission control), and enforcing per-flow network QoS at runtime. Second, we evaluate the capabilities of the framework in an experimental large-scale multi-domains environment. The evaluation of the architecture shows that the proposed QoS framework improves the delivery of DDS services over heterogeneous IP networks, and confirms its potential impact for providing network-level differentiated performance.

Dead Reckoning mechanisms are usually used to estimate the position of simulated entity in virtual environment. However, this technique often ignores available contextual information that may influence to the state of an entity, sacrificing remote predictive accuracy in favor of low computational complexity. A novel extension of Dead Reckoning is suggested in this paper to increase the network availability and fulfill the required Quality of Service in large scale distributed simulation application. The proposed algorithm is referred to as ANFIS Dead Reckoning, which stands for Adaptive-Network-based Fuzzy Inference Systems Dead Reckoning is based on a fuzzy inference system which is trained by the learning algorithm derived from the neuronal networks and fuzzy inference theory. The proposed mechanism is based on the optimization approach to calculate the error threshold violation in networking games. Our model shows it primary benefits especially in the decision making of the behavior of simulated entities and preserving the consistence of the simulation.

Wireless networks have become increasingly popular for the past few years, especially IEEE 802.11 as the most widespread wireless technology. This article proposes a new architecture for broadband 802.11 networks to support distributed multimedia applications. After discussing the differences between multimedia and traditional applications, we show how the 802.11b mechanism ensuring enhanced robustness connectivity for low signal to noise ratio (SNR) areas, penalizes multimedia stream transmissions. A new multimedia architecture disabling the dynamic rate shifting mechanism and using a performance enhancing proxy is then proposed.