Search

International audience; This paper deals with fault detection and isolation (FDI) in complex embedded wired communication networks. The considered faults are soft faults which do not prevent the communication, but may evolve into hard faults, i.e. short or open circuit. A novel FDI method based on power line communication (PLC) transmission systems is proposed. In these PLC systems, the transmission coefficients between the source and each receiver are estimated for communication purposes using orthogonal frequency division multiplexing (OFDM). Health indicators and residuals are computed by comparing the online estimated transmission coefficients with the reference coefficients. A methodology for dealing with complex networks, such as bus networks, is proposed. It is based on the decomposition of the network into several Y-shaped sub-networks. Each of these sub-networks is monitored to detect the presence of a fault. The FDI method is first validated using real data extracted from a Y-shaped network test bench. Then, the proposed approach is validated on a more complex network using realistic simulated data.

International audience; The worldwide adoption of the Internet of things (IoT) depends on the massive deployment of sensor nodes and timely data collection. However, installing the required ground infrastructure in remote or inaccessible areas can be economically unattractive or unfeasible. Cost-effective nanosatellites deployed in low Earth orbits (LEO) are emerging as an alternative solution: on-board IoT gateways provide access to remote IoT devices, according to direct-to-satellite IoT (DtS-IoT) architectures. One of the main challenges of DtS-IoT is to devise communication protocols that scale to thousands of highly constrained devices served by likewise constrained orbiting gateways. In this paper, we tackle this issue by first estimating the (varying) size of the device set underneath the (mobile) nanosatellite footprint. Then, we demonstrate applicability of the estimation when used to intelligently throttle DtS-IoT access protocols. Since recent works have shown that MAC protocols improve the throughput and energy efficiency of a DtS-IoT network when a network size estimation is available, we present here a novel and computationally-efficient network size estimator in DtS-IoT: our optimistic collision information (OCI) based estimator. We evaluate OCI's effectiveness with extensive simulations of DtS-IoT scenarios. Results show that when using network size estimations, the scalability of a frame slotted Aloha-based DtS-IoT network is boosted 8-fold, serving up to 4 × 10 3 devices, without energy efficiency penalties. We also show the effectiveness of the OCI mechanism given realistic detection ratios and demonstrate its low computational cost implementation, making it a strong candidate for network estimation in DtS-IoT.

International audience; Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) is a recent modulation designed for communications from low-power ground end-devices to Low-Earth Orbit (LEO) satellites. To decode a frame, an LR-FHSS gateway must receive at least one header replica and a large proportion of payload fragments. However, LR-FHSS headers will likely be lost when there are many concurrent transmissions. In this paper, we motivate the header loss problem with an analytical model, propose a linear programming model to extract headerless frames and design a cost-effective sliding window heuristic. Simulation results show that our approach exhibits near-optimal headerless detection and extraction results while ensuring a low computational cost. The proposed method is, therefore, suitable for future LR-FHSS gateways located onboard resource-constrained LEO satellites.

International audience; This paper presents a new framework comprising the combination of uplink cell-free massive multiple input multiple output (mMIMO) with non-orthogonal multiple access (NOMA) for serving Internet-of-Things (IoT) devices in an uncoordinated manner. We investigate the benefits of reinforcement learning to support the massive connectivity and quality of service (QoS) requirements of IoT devices. Using the multi-armed bandit technique, the devices jointly determine their subband and transmit power, without any cooperation, in such a way as to strike a balance between the QoS and the transmit power. Applied with a dynamic cooperation clustering of serving access points (APs), the allocation technique is shown to achieve a quick convergence while having a negligible loss in performance towards a system that relies on all deployed APs for serving the devices.

International audience; In some visible light communication (VLC) systems, transmitters are light sources which support not only illumination but also information broadcast and positioning. Since transmit signals in these systems inevitably interfere with each other at the receiver side, extra devices may be added to synchronize the transmitters, but this impairs an appealing advantage in VLC systems that existing lighting infrastructure can be simply reused. This paper proposes a novel multiple access scheme to realize decentralized asynchronous VLC systems such that both the transmitters and receivers are asynchronous. For a system with N transmitters, two unique codewords of L chips are allocated to each transmitter where L is a prime number larger than 4N. Those codewords are designed to help maintain a constant transmitted average power and enhance channel estimation, as well as conveying data. Our scheme enables the receiver to obtain one channel estimate and one data symbol per L chips. Simulation results show that the proposed scheme significantly outperforms other schemes in the literature in terms of bit error rate and system throughput.

International audience; Internet of Things (IoT) technologies are increasingly used in our daily life. Among these technologies, LoRa is widely used for low power IoT data collection in wide areas. LoRa typically operates in sub-GHz bands which are region-specific, and thus has limited worldwide applications. The version of LoRa developed for the 2.4 GHz ISM band has spurred recent interest in the scientific community, as it enables the use of worldwide frequency channels. However, it requires the coexistence with numerous concurrent technologies within this 2.4 GHz band, and an evolution of the MAC and networking protocols for utilizing LoRaWAN connectivity. In this paper, we make the first experimental comparison between LoRa sub-GHz and LoRa 2.4 GHz in both indoor and outdoor scenarios. We show that LoRa 2.4 GHz can have a communication range of more than 2 km outdoor, and it can achieve a packet delivery rate of at least 0.98 for a distance of up to 39 meters indoor with the presence of obstacles. We also make an extensive discussion on the researches challenges that need to be addressed in order to fully take advantage of this new technology.

International audience; This paper proposes the first implementation of a self-stabilizing regular register emulated by n servers that is tolerant to both Mobile Byzantine Agents and transient failures in a round-free synchronous model. Differently from existing Mobile Byzantine Tolerant register implementations, this paper considers a weaker model where: (i) the computation of the servers is decoupled from the movements of the Byzantine agents, i.e., movements may happen before, concurrently, or after the generation or the delivery of a message, and (ii) servers are not aware of their failure state i.e., they do not know if and when they have been corrupted by a Mobile Byzantine agent. The proposed protocol tolerates (i) any finite number of transient failures, and (ii) up to f Mobile Byzantine agents. In addition, our implementation uses bounded timestamps from the domain and it is optimal with respect to the number of servers needed to tolerate f Mobile Byzantine agents in the given model (i.e., when , and when , where Δ represents the period at which the Byzantine agents move and δ is the upper bound on the communication latency).

The degree distributions of complex networks are usually considered to be power law. However, it is not the case for a large number of them. We thus propose a new model able to build random growing networks with (almost) any wanted degree distribution. The degree distribution can either be theoretical or extracted from a real-world network. The main idea is to invert the recurrence equation commonly used to compute the degree distribution in order to find a convenient attachment function for node connections - commonly chosen as linear. We compute this attachment function for some classical distributions, as the power-law, broken power-law, geometric and Poisson distributions. We also use the model on an undirected version of the Twitter network, for which the degree distribution has an unusual shape. We finally show that the divergence of chosen attachment functions is heavily links to the heavy-tailed property of the obtained degree distributions., Comment: 23 pages, 3 figures

International audience; Internet of Things (IoT) applications often require the processing of data streams generated by devices dispersed over a large geographical area. Traditionally, these data streams are forwarded to a distant cloud for processing, thus resulting in high application end-to-end latency. Recent work explores the combination of resources located in clouds and at the edges of the Internet, called cloud-edge infrastructure, for deploying Data Stream Processing (DSP) applications. Most previous work, however, fails to scale to very large IoT settings. This paper introduces deployment strategies for the placement of DSP applications on to cloud-edge infrastructure. The strategies split an application graph into regions and consider regions with stringent time requirements for edge placement. The proposed Aggregate End-to-End Latency Strategy with Region Patterns and Latency Awareness (AELS+RP+LA) decreases the number of evaluated resources when computing an operator’s placement by considering the communication overhead across computing resources. Simulation results show that, unlike the state-of-the-art, AELS+RP+LA scales to environments with more than 100k resources with negligible impact on the application end-to-end latency.

Hyperbolicity is a graph parameter that indicates how much the shortest-path distance metric of a graph deviates from a tree metric. It is used in various fields such as networking, security, and bioinformatics for the classification of complex networks, the design of routing schemes, and the analysis of graph algorithms. Despite recent progress, computing the hyperbolicity of a graph remains challenging. Indeed, the best known algorithm has time complexity O(n 3.69 ) , which is prohibitive for large graphs, and the most efficient algorithms in practice have space complexity O(n 2 ) . Thus, time as well as space are bottlenecks for computing the hyperbolicity. In this article, we design a tool for enumerating all far-apart pairs of a graph by decreasing distances. A node pair (u, v) of a graph is far-apart if both v is a leaf of all shortest-path trees rooted at u and u is a leaf of all shortest-path trees rooted at v . This notion was previously used to drastically reduce the computation time for hyperbolicity in practice. However, it required the computation of the distance matrix to sort all pairs of nodes by decreasing distance, which requires an infeasible amount of memory already for medium-sized graphs. We present a new data structure that avoids this memory bottleneck in practice and for the first time enables computing the hyperbolicity of several large graphs that were far out of reach using previous algorithms. For some instances, we reduce the memory consumption by at least two orders of magnitude. Furthermore, we show that for many graphs, only a very small fraction of far-apart pairs has to be considered for the hyperbolicity computation, explaining this drastic reduction of memory. As iterating over far-apart pairs in decreasing order without storing them explicitly is a very general tool, we believe that our approach might also be relevant to other problems.

International audience; Cognition of user behavior can make future mobile networks more intelligent and flexible. Knowledge about users' habits can be used to personalize services and intelligently manage network resources. However, inferring this key information with a low-cost signaling implementation, and avoiding constant user interaction, is crucial for Mobile Network Operators (MNOs). With this motivation, this paper investigates the detection of the real-life mobile user environment using contextaware detection via multi-task learning (MTL). We propose models that are able to automatically detect up to eight distinct real-life user environments. We also improve the detection accuracy with the assistance of the mobility state profiling task. We associate both environment and mobility tasks because they correspond to the main attributes of user behavior and, additionally, both of them are correlated. Using MTL, the task of detecting environment corresponds to simultaneously answering the questions: "how and where mobile user consumes mobile services?". We build models using real-life radio data which is already available in network. This data has been massively gathered from multiple diversified situations of mobile users. Simulation results support our claim to detect several environment classes in network infrastructure with improved UED accuracy.

International audience

Quantum Internetworking is a recent field that promises numerous interesting applications, many of which require the distribution of entanglement between arbitrary pairs of users. This work deals with the problem of scheduling in an arbitrary entanglement swapping quantum network - often called first generation quantum network - in its general topology, multicommodity, loss-aware formulation. We introduce a linear algebraic framework that exploits quantum memory through the creation of intermediate entangled links. The framework is then employed to mathematically derive a natural class of quadratic scheduling policies for quantum networks by applying Lyapunov Drift Minimization, a standard technique in classical network science. Moreover, an additional class of Max-Weight inspired policies is proposed and benchmarked, reducing significantly the computation cost, at the price of a slight performance degradation. The policies are compared in terms of information availability, localization and overall network performance through an ad-hoc simulator that admits user-provided network topologies and scheduling policies in order to showcase the potential application of the provided tools to quantum network design., Comment: 18 pages, 7 figures. To be submitted to the journal "IEEE Transactions on Quantum Engineering"

Long Range (LoRa) is a wireless modulation used to provide longrange connectivity to low-power devices of the Internet of Things (IoT). LoRa gateways use hardware chips called demodulators in order to demodulate concurrent frames on different channels or spreading factors. In this paper, we establish several theoretical results on the performance of fundamental demodulator allocating strategies. In the single gateway scenario, we first prove the optimality of the basic preemptive allocation strategy. Next, in the multi-gateway scenario, the greedy non-collaborative strategy, which is currently implemented in LoRa gateways, is shown to achieve a very large approximation ratio. As an alternative, we demonstrate that a preemptive non-collaborative strategy has an approximation ratio of 2, and furthermore, that a preemptive smartcollaborative strategy reaches an approximation ratio of 3/2, for two gateways. These seminal results imply that the current performance of multi-gateway LoRa deployments can be largely improved by the proposed collaborative demodulator allocation strategies.

CoNEXT 2023 - 19th Conference on emerging Networking EXperiments and Technologies Mission, December 5-8, 2023, Paris, France; One of the breaking changes induced by Industry 4.0 will be the networking of production equipment. To achieve this, the Time-Sensitive Networking (TSN) set of network standards has been developed. However, this new networking paradigm will create new challenges. For example, TSN standards allow a certain level of flexibility and modularity in the data plane, however, the configuration of these standards depends on many parameters (e.g., network topology, routing strategy, critical flows requirements, etc.) making the configuration task cumbersome. The IEEE 802.1Qbv standard is among the main TSN standards that propose a mechanism allowing to achieve deterministic latency when it is appropriately configured. Today's main approach to configure this mechanism relies on exact or heuristic methods. These are adequate for closed network (when all flows are known beforehand and the network topology is fixed). However, in open networks (where flows are added to the network in an incremental way and the network topology is dynamic), the scheduling in IEEE 802.1Qbv can lead to a NP-hard problem. In this paper, we address open networks such as TSN in industrial networks with reconfigurable production lines. We propose a solution to configure the IEEE 802.1Qbv standard by using Deep Reinforcement Learning (DRL). We use simulations to train and evaluate the configuration agent.

International audience; 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 both the relative position of an eNB-equipped LEO satellite in its scope and the future values of the Doppler shift 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.

International audience; Long-Range Wide Area Networks (LoRaWAN) enable low-power data collection over long distances, and they are thus widely used for Internet of Things applications, despite their limitations to meet some traffic requirements (e.g., reliability). To achieve the quality of service required by the applications, service differentiation is a promising approach which can be provided by network slicing. In this work, we show that related works are either incompatible with the LoRaWAN specifications, or do not isolate traffic, or assume an a priori known and stable traffic. In this paper, we propose a lightweight approach to achieve slicing in LoRaWAN, compatible with the LoRaWAN specifications. Our approach allows to isolate traffic, to protect confirmed traffic, and to deal with unsolicited traffic.

International audience; Wireless sensor networks are a precious tool for numerous use cases: studying the fauna and flora, measuring various metrics and so much more. Here we focus specifically on wireless multihop networks, in which the funneling effect is too often responsible of the unbalance of energy consumption, causing in turn nodes failures. In an attempt to mitigate the effects of funneling, the scientific literature proposes among others traffic reduction based on data estimation. The lead idea being that if two phenomena are correlated, then maybe we could estimate the way one goes based on the way the second one do. This effectively reduces the amount of data nodes may have to transmit while preserving the information. We propose in this article a similar method, but here not only limited to a pair of nodes. With our scheme we can reduce the traffic of several nodes over a whole multihop route of communications in a network. We have implemented that scheme and experimented with real hardware which shows good results.

International audience; Mobile traffic classification is a topic of interest for researchers focused on improving the network capacity or for those seeking to identify potential risks to users' privacy. In recent years, traffic classification accuracy has significantly improved thanks to machine learning techniques. These techniques allow traffic identification even if it is encrypted, as in mobile networks. In this paper, we show that it is feasible to classify mobile traffic applications with high accuracy using downlink control information (DCI) messages and machine learning. The DCI messages are collected using a sniffer located near the base station. Then we extract the statistical features of the bursts and inter-burst periods of the traffic generated by mobile applications at the physical layer. This strategy uses few features and does not require a big dataset. We have tested our approach on a 4G cellular network testbed and a commercial 4G cellular network. The results show an accuracy greater than 92% and 95% for application and category classification, respectively.

International audience; Open Radio Access Network (O-RAN) is very promising for flexible and efficient 5G and 6G wireless networks. The O-RAN architecture consists of three main units: Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU). In this paper, we study the placement of virtualized DUs. This placement has strong consequences on cost and delay, among others, and is thus an important challenge. First, we analyze the throughput between the O-RAN interfaces. Based on our analysis, we propose an efficient Integer Linear Programming (ILP) model. The objective is to minimize the O-RAN cost depending on the DU placement while respecting the delay and capacity constraints. We evaluate our model on a real topology. Our results provide interesting insights into the cost savings with regard to a legacy architecture. Moreover, the proposed model provides solutions in a configuration where a fully centralized Cloud RAN architecture would not. We also estimate the limits of capacity of a given configuration.

International audience; The growing prevalence of Internet of Things (IoT) technologies in many outdoor applications has led to an increase in the deployment of popular LoRaWAN (Long Range Wide Area Network) networks. As a result, there is increasing interest in tracking end nodes and detecting device transmission behavior. In this paper, a passive packet sniffer is demonstrated to receive LoRa (Long Range) packets in the Campus Beaulieu area of Rennes, France, over a period of two months. After processing the acquired packets, the inter-arrival times between packets of each identified device are estimated properly using a proposed technique, i.e., based on Kernel Density Estimation (KDE). Another algorithm is also employed to detect any pattern in the inter-arrival times. The study is then extended to reveal the spectrum allocation technique of a device and detect any periodic structure in the sequence of the used frequencies. Over and above, the spectrum allocation scheme is modelled as a Markov chain, allowing for the prediction of the next selected frequency using the estimated trajectory. The proposed algorithms are validated through statistical analysis of the inter-arrival times, temporal patterns, and spectrum allocation schemes' characteristics from the actual acquired data.

International audience; In grant-free random access, a key question is how devices should utilize resources without coordination. One standard solution to this problem are strategies where devices randomly select time-slots based on an optimized stochastic allocation rule. However, the optimization of this allocation rule requires accurate knowledge of which devices have been active in previous frames. As user identification algorithms are subject to errors, the expected throughput of the optimized allocation can be highly suboptimal. In this paper, we propose algorithms for optimization of device time-slot allocations that mitigate the impact of user identification errors. We show that when the activity distribution with and without errors is known, then our algorithm converges with probability one to a stationary point. When the activity distributions are not available, we introduce new theoretically-motivated heuristics which significantly improve the expected throughput over existing algorithms and approach the performance when errors are not present.

International audience; Industrial IoT (IIoT) is one of the major verticals targeted by the next generations of wireless networks. In order to provide industrial plants with features relying on wireless communications, the grant-free RA (GFRA) protocol appears to be a promising means for supporting massive ultra-reliable connectivity; at the same time, it is a critical bottleneck that requires an access point (AP) to be able to jointly perform active user detection and channel estimation (AUDaCE) to fulfill its main mission of allowing industrial wireless devices to access the core network. This mission is even harder when the GFRA requests are correlated because of event-driven activity triggers. This paper proposes a new tractable gaussian correlated activity (GCA) model for this scenario. The corresponding AUDaCE problem is then studied in the Bayesian compressed sensing (BCS) framework. An hybrid instance of the generalized AMP (GAMP) algorithm is derived and its capability to perform AUDaCE is numerically assessed by extensive Monte-Carlo simulations. The numerical results show gains of 2.5dB in channel estimation gain for twice less detection errors w.r.t. state-of-theart algorithms.

Grant-free random access (GFRA) is now a popular protocol for large-scale wireless multiple access systems in order to reduce control signaling. Resource allocation in GFRA can be viewed as a form of frame slotted ALOHA, where a ubiquitous design assumption is device homogeneity. In particular, the probability that a device seeks to transmit data is common to all devices. Recently, there has been an interest in designing frame slotted ALOHA algorithms for networks with heterogeneous activity probabilities. These works have established that the throughput can be significantly improved over the standard uniform allocation. However, the algorithms for optimizing the probability a device accesses each slot require perfect knowledge of the active devices within each frame. In practice, this assumption is limiting as device identification algorithms in GFRA rarely provide activity estimates with zero errors. In this paper, we develop a new algorithm based on stochastic gradient descent for optimizing slot allocation probabilities in the presence of activity estimation errors. Our algorithm exploits importance weighted bias mitigation for stochastic gradient estimates, which is shown to provably converge to a stationary point of the throughput optimization problem. Numerical results show significant performance improvement both over standard frame slotted ALOHA and existing algorithms that do not account for activity estimation errors.

International audience; Contact Tracing (CT) is an old, recognized epidemiological tool, and since a digital variant is now within reach, a variety of smartphone-based solutions have been rapidly developed and deployed since 2020, with mixed results and amid controversies. Yet, achieving reliable and effective digital CT at large scale is still an open problem. In this work, we contribute with an open source software platform on top of which various CT solutions can be quickly developed and tested. More specifically, we design PEPPER, which jointly leverages Bluetooth Low Energy (BLE) and Ultra Wide Band (UWB) radios for contact detection, combined with the DESIRE privacy-preserving CT protocol. We show that PEPPER+DESIRE can operate on cheap physical tokens based on low-power microcontrollers, opening new use-cases with less personal, potentially disposable devices, that could be more widely used. We also evaluate the complementarity of Bluetooth and UWB in this context, via experiments mimicking various scenarios relevant for CT. Compared to BLE-only CT, we show that UWB can decrease false negatives (e.g., in presence of human body occlusion), meaning that more actual contacts will be found, a key benefit from an epidemiological viewpoint. Our results suggest that, while PEPPER+DESIRE improves precision over state-of-the-art, further research is required to harness UWB-BLE synergy for CT in practice. To this end, our open source platform (which can run on an open-access testbed) provides a useful playground for the research community.

International audience; LoRaWAN networks can involve large numbers of wireless devices relying on batteries to sense the environment and send data to gateways. A critical trade-off for transmission performance (packet delivery ratio) versus energy conservation (and hence, the device lifespan) appears when deciding the transmission parameters, in particular, the Spreading Factor (SF) to be used by each node. In this paper, we use lightweight reinforcement learning techniques, namely multi-armed bandits, for each node to select an appropriate SF, based on preferences regarding that trade-off. Unlike previous works on that topic, we relax some assumptions to aim at a realistic implementation: our solution does not assume immediate rewards, or that each device communicates with only one gateway. Additionally, we build explicit MAC commands for the method to work in practice and implement it in the ns-3 simulator using a state-of-the-art LoRaWAN module. We share the source code of our implementation and our simulation results. Those simulations show that when energy conservation is critical for IoT nodes, such lightweight learning algorithms outperform LoRaWAN's legacy Adaptive Data Rate algorithm, both in single- and multi-gateway scenarios.

International audience; As the Information and Communication Technology (ICT) sector represents 1.8% to 3.9% of the global Green House Gas (GHG) emissions, it is of upmost importance to know how much energy is spent annually in mobile networks and how this consumption is evolving. It is quite likely that the huge energy efficiency gains achieved by technology evolution have at least been compensated by the surge in data traffic. Therefore, in this paper, we estimate the operational power consumption of cellular Base Stations (BSs) deployed in France from 2015 to 2022. However, unfortunately, the lack of openly available data hinders the estimation process. In order to work around this issue, we rely on a public dataset on radio electric installations, on widely adopted power consumption models and on a set of assumptions backed by the scientific literature. We demonstrate that, over the considered period, the numbers of BSs and transceivers have grown at a sustained Compound Annual Growth Rate (CAGR) of 7.55% and 18.27%, respectively. Within the same period, the average BS power consumption has increased at a CAGR of 9.89% while the total operational power consumption of BSs has grown at a CAGR of 18.18%. We further show that the introduction of 5G has accelerated this trend despite the recent decommissioning of 2G and 3G transceivers. These alarming figures advocate for proactive digital sobriety policies.

International audience; Experimental link quality assessment of recent Wi-Fi networks remains a challenge due to the rapid development of the Wi-Fi technology, the lack of availability of public datasets, and the difficulty to build such datasets. This paper addresses all three issues by first providing a publicly-available dataset using a custom-made Wi-Fi 5 experimental testbed. We then present an open-source framework for estimating the Frame Delivery Ratio (FDR) of a Wi-Fi link using the experimental data. The proposed solution relies on a small number of input features to build an estimation model of high accuracy, with an R2 coefficient of 0.89 and a mean absolute error of 0.06.

Distributed infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed on the Edge-to-Cloud continuum. Understanding and optimizing end-to-end performance in such a complex continuum is challenging. One crucial challenge is accurately reproducing relevant behaviors of a given application workflow and representative settings of the physical infrastructure underlying this complex continuum. This thesis is a conceptual and practical contribution to the Edge-to-Cloud continuum, proposing and applying methodologies in novel environments. Our methodologies aim at overcoming the complexity of understanding and optimizing Edge-to-Cloud workflows. As such, they enable reproducible experiment design, application optimization, efficient workflow provenance capture, and cost-effective experiment reproducibility. We validated our proposal by first developing the E2Clab framework that supports the complete analysis cycle of an application on the Edge-to-Cloud Continuum and then using E2Clab to optimize Pl@ntNet, a global plant identification application. Large-scale experimental validation on Grid'5000 shows that our methodology has proven helpful for understanding and improving the performance of Pl@ntNet.; Les infrastructures distribuées pour le calcul et l'analyse évoluent désormais vers un écosystème interconnecté permettant l'exécution d'applications complexes dans le continuum Edge-to-Cloud. Comprendre et optimiser les performances de bout en bout dans ce contexte est un défi majeur. Un besoin crucial consiste à reproduire avec précision les comportements pertinents des workflows et les paramètres représentatifs de l'infrastructure physique sous-jascente.Cette thèse est une contribution conceptuelle et pratique au continuum Edge-to-Cloud, proposant des méthodologies et les appliquant dans des environnements novateurs. Nos méthodologies ont pour but de s'affranchir de la complexité de la compréhension et de l'optimisation des workflows dans le continuum Edge-to-Cloud. Ainsi, elles permettent la conception d'expériences reproductibles, l'optimisation des applications, la capture efficace des données de provenance des exécutions de workflows, et la reproductibilité des expériences. Nous avons validé notre proposition avec, d'abord, le développement du framework E2Clab qui supporte le cycle complet d’analyse d’une application dans le continuum Edge-to-Cloud, et ensuite, l'utilisation de E2Clab pour l'optimisation Pl@ntNet, une application mondiale d'identification des plantes. La validation expérimentale à grande échelle sur Grid'5000 montre que notre méthodologie s'est avérée utile pour comprendre et améliorer les performances de Pl@ntNet.

International audience; The Internet of Things (IoT) aims for connecting Anything, Anywhere, Anytime (AAA). This assumption brings about several software engineering challenges that constitute a serious obstacle to its wider adoption. The main feature of the Internet of Things (IoT) is genericity w.r.t the variability of software and hardware technologies. Model-Driven Engineering (MDE) is a paradigm that advocates using models to address software engineering problems. It can help to meet the genericity of the IoT from a software engineering perspective. Existing MDE approaches for the IoT focus only on modeling the internal behavior of things but lack a comprehensive approach dedicated to network modeling. In the present paper, we introduce a network-oriented methodology based on MDE to unify the IoT's heterogeneous concepts. Fundamentally, we avoid the intrinsic heterogeneity of the IoT by separating the network's specification (i.e., the things, the communication scheme, and its constraints) from its concrete implementation (i.e., the low-level artifacts such as source code and documentation). Technically, the methodology relies on a model-based Domain-Specific Language (DSL) and a code generator. The former enables the modeling of the network's specification, and the latter provides a procedure to generate the low-level artifacts from this specification. Our results show that this methodology makes IoT's software engineering more rigorous, helps prevent bugs earlier, and saves time.

International audience; Discrete-time discrete-state finite Markov chains are versatile mathematical models for a wide range of real-life stochastic processes. One of most common tasks in studies of Markov chains is computation of the stationary distribution. Without loss of generality, and drawing our motivation from applications to large networks, we interpret this problem as one of computing the stationary distribution of a random walk on a graph. We propose a new controlled, easily distributed algorithm for this task, briefly summarized as follows: at the beginning, each node receives a fixed amount of cash (positive or negative), and at each iteration, some nodes receive 'green light' to distribute their wealth or debt proportionally to the transition probabilities of the Markov chain; the stationary probability of a node is computed as a ratio of the cash distributed by this node to the total cash distributed by all nodes together. Our method includes as special cases a wide range of known, very different, and previously disconnected methods including power iterations, Gauss-Southwell, and online distributed algorithms. We prove exponential convergence of our method, demonstrate its high efficiency, and derive scheduling strategies for the green-light, that achieve convergence rate faster than state-of-the-art algorithms.

International audience; Maintaining up-to-date KVM hypervisors requires regular upgrades to the host kernel, hence rebooting the physical host with the consequent termination of running Virtual Machines (VMs). Cloud platforms capable of massive large-scale live migrations evacuate VMs from the hosts before rebooting, minimizing the impact over VM up-time. However, scenarios exist where resource constraints make live migration undesirable, or the presence of fault-tolerant instances (e.g., replicated services) favors the adoption of VM termination, a simpler but more disruptive strategy. In this article, we present Hy-FiX, a fast in-place upgrade solution for KVM hypervisors. Hy-FiX preserves VM memory across host reboots, protecting the execution state of running guests while hypervisor upgrades are applied. Hy-FiX memory preservation across reboot, combined with a mixed suspend-to-disk/suspend-to-RAM technique, achieves a 2.31-second checkpoint/restore time for a 256 GB VM, and Hy-FiX lazy memory initialization reboots an enterprise-class host in constant time (7.6 seconds) regardless of its equipped memory. Hy-FiX is, therefore, a better alternative to classical VM termination and restart.

International audience; Circuits in a graph are interesting structures and identifying them is of an important relevance for many applications. However, enumerating circuits is known to be a difficult problem, since their number can grow exponentially. In this paper, we propose fast parallel approaches for enumerating elementary circuits of directed graphs based on graphics processing unit (GPU). Our algorithms are based on a massive exploration of the graph in a breadth-first search strategy. Algorithm V-FEC explores the graph starting from different vertices simultaneously. To further reduce the search space, we present T-FEC, another algorithm that uses triplets as an initial set to start exploring. To the best of our knowledge, those are the first parallel GPU-based algorithms for finding all circuits of a given graph. In addition, they find circuits of a given length and circuits with a specific vertex or edge. The evaluation results show that the proposed approaches achieve up to 190x speed-up over Johnson’s algorithm, one of the most efficient sequential algorithms for finding circuits

International audience; We introduce a model of graph-constrained dynamic choice with reinforcement modeled by positively $\alpha$-homogeneous rewards. We show that its empirical process, which can be written as a stochastic approximation recursion with Markov noise, has the same probability law as a certain vertex reinforced random walk. We use this equivalence to show that for $\alpha > 0$, the asymptotic outcome concentrates around the optimum in a certain limiting sense when 'annealed' by letting $\alpha \to \infty$ slowly.

International audience; Multi-access Edge Computing (MEC) technology has emerged to overcome traditional cloud computing limitations, challenged by the new 5G services with heavy and heterogeneous requirements on both latency and bandwidth. In this work, we tackle the problem of clustering access points in MEC environments, introducing a set of clustering models to be deployed at the pre-provisioning phase. We go through extensive simulations on real-world traffic demands to evaluate the performance of the proposed solutions. In addition, we show how MEC hosts capacity violation can be decreased when integrating access points clustering into the orchestration model, by investigating on solution accuracy when applied on heldout users traffic demands. The obtained results show that our approach outperforms two state-of-the-art algorithms, reducing both memory usage and execution time, by 46% and 50%, respectively, in comparison to a baseline algorithm. It surpasses the two methods in gaining control over MEC hosts capacity usage for different maximum achieved occupancy levels on MEC hosts.

International audience

International audience; Under the Network Function Virtualization multidomain orchestration approach, many service providers jointly manage the lifecycle of network services composed by Virtual Network Functions (VNFs). Many orchestration algorithms have been published focusing on different tasks for the lifecycle of network services. Nowadays, some general-purpose and architectural orchestration surveys have been published. However, currently, no survey classifies and evaluates distributed multidomain orchestration algorithms. In this paper, we focus on multi-domain orchestration works from an algorithm perspective by considering the cooperative and competitive approaches. Algorithms are classified and evaluated. We propose a taxonomy for works that consider different lifecycle tasks of network services. Classification and evaluation are done by identifying key requirements present in competitive and cooperative scenarios. This allows us to have a perspective on the current state of multi-domain orchestration algorithms. We also identify future research directions based on such a current perspective.

International audience; Cryptographic algorithms that can provide both encryption and authentication are increasingly required in modern security architectures and protocols (e.g. TLS v1.3). Many authenticated encryption systems have been proposed in the past few years, which has resulted in several cryptanalysis research work. In this same direction, the National Institute of Standards and Technology (NIST) is coordinating a large effort to find a new standard authenticated encryption algorithm to be used by resource-constrained and limited devices. In this paper, 12 algorithms of the 33 candidates of the Round 2 phase from NIST competition are being benchmarked on a real IoT test-bed. These 33 ciphers implement authenticated encryption with associated data which aims at preserving integrity, privacy and authenticity at the same time. In this work, we ported the 12 algorithms to different hardware platforms (an x86_64 PC, an AVR ATmega128, an MSP430F1611 and the IoT-LAB platform) and make a fair comparison between their performance. We adapted these algorithms to the Contiki operating system to evaluate the latency and efficiency of each algorithm on IoT applications deployed on a national experimental platform which is IoT-LAB. In addition, we used the FELICS-AE benchmark to quantify locally the RAM, execution time and code size of each algorithm. In fact, this work provides practical results of their performance in an IoT scenario which pave the way for further research on other algorithms, platforms or OS.

International audience; In many environments, connected devices are exposed to and must choose between multiple Wi-Fi networks. However, the procedure for selecting an access point is still based on simple criteria that consider the device to be unique in the network. In particular, the network load is not taken into account even though it is a key parameter for the quality of service and experience. In this paper, we investigate how an unmodified vanilla device could estimate the load of a network in the user space with no interventions from the access points. In this regard, we propose a novel and practical method, FAM (Frame Aggregation based Method). It leverages the frame aggregation mechanism introduced in recent IEEE 802.11 amendments to estimate the network load through its channel busy time fraction. FAM combines an active probing technique to measure the actual packet aggregation and Markovian models that provide the expected rate as a function of the volume and nature of the traffic on the network. We validate the effectiveness of FAM against both ns-3 simulations and test-bed experiments under several scenarios. Results show that our method FAM is able to infer the network load with a granularity based on six different levels of network loads for the considered scenarios.

International audience; This paper tackles online scheduling of electric vehicles (EVs) in an adaptive charging network (ACN) with local and global peak constraints. Given the aggregate charging demand of the EVs and the peak constraints of the ACN, it might be infeasible to fully charge all the EVs according to their charging demand. Two alternatives in such resource-limited scenarios are to maximize the social welfare by partially charging the EVs (fractional model) or selecting a subset of EVs and fully charge them (integral model). The technical challenge is the need for online solution design since in practical scenarios the scheduler has no or limited information of future arrivals in a time-coupled underlying problem. For the fractional model, we devise both offline and online algorithms. We prove that the offline algorithm is optimal. Using competitive ratio as the performance measure, we prove the online algorithm achieves a competitive ratio of 2. The integral model, however, is more challenging since the underlying problem is strongly NP-hard due to 0/1 selection criteria of EVs. Hence, efficient solution design is challenging even in offline setting. For offline setting, we devise a low-complexity primal-dual scheduling algorithm that achieves a bounded approximation ratio. Built upon the offline approximate algorithm, we propose an online algorithm and analyze its competitive ratio in special cases. Extensive trace-driven experimental results show that the performance of the proposed online algorithms is close to the offline optimum, and outperform the existing solutions.

International audience; Massive multiple-input multiple-output (MIMO) communication systems have a huge potential both in terms of data rate and energy efficiency, although channel estimation becomes challenging for a large number of antennas. Using a physical model allows to ease the problem by injecting a priori information based on the physics of propagation. However, such a model rests on simplifying assumptions and requires to know precisely the configuration of the system, which is unrealistic in practice. In this paper we present mpNet, an unfolded neural network specifically designed for massive MIMO channel estimation. It is trained online in an unsupervised way. Moreover, mpNet is computationally efficient and automatically adapts its depth to the signal-to-noise ratio (SNR). The method we propose adds flexibility to physical channel models by allowing a base station (BS) to automatically correct its channel estimation algorithm based on incoming data, without the need for a separate offline training phase. It is applied to realistic millimeter wave channels and shows great performance, achieving a channel estimation error almost as low as one would get with a perfectly calibrated system. It also allows incident detection and automatic correction, making the BS resilient and able to automatically adapt to changes in its environment.

This paper deals with time and frequency synchronization in LoRa system based on the preamble symbols. A thorough analysis of the maximum likelihood (ML) estimator of the delay (time offset) and the frequency offset shows that the resulting cost function is not concave. As a consequence the a priori solution to the maximization problem consists in exhaustively searching over all the possible values of both the delay and the frequency offset. Furthermore, it is shown that these parameters are intertwined and therefore they must be jointly estimated, leading to an extremely complex solution. Alternatively, we show that it is possible to recover the concavity of the cost function, from which we suggest a low-complexity synchronization algorithm, whose steps are described in detail. Simulations results show that the suggested method reaches the same performance as the ML exhaustive search, while the complexity is drastically reduced, allowing for a real-time implementation of a LoRa receiver.

International audience; Mission-critical communications (MCC) involve all communications between people in charge of the safety of the civil society. MCC have unique requirements that include improved reliability, security and group communication support. In this paper, we propose secure and robust Multiple-Input-Multiple-Output (MIMO) transceivers, designed for multiple Base Stations (BS) supporting multicast MCC in presence of multiple eavesdroppers. We formulate minimization problems with the Sum-Mean-Square-Error (SMSE) at legitimate users as an objective function, and a lower bound for the MSE at eavesdroppers as a constraint. Security is achieved thanks to physical layer security mechanisms, namely MIMO beamforming and Artificial Noise (AN). Reliability is achieved by designing a system which is robust to two types of channel state information errors: stochastic and norm-bounded. We propose a coordinate descent-based algorithm and a worst-case iterative algorithm to solve these problems. Numerical results at physical layer and system level reveal the crucial role of robust designs for reliable MCC. We show the interest of both robust design and AN to improve the security gap. We also show that full BS cooperation in preferred for highly secured and reliable MCC but dynamic clustering allows to trade-off security and reliability against capacity. Index Terms-mission critical communication (MCC), physical layer security, robust transceiver design I. INTRODUCTION Mission critical communications (MCC) are all communications between people in charge of the security and the safety of the civil society. Employees of public safety services, like policemen, firemen, rescue teams and ambulance nurses, but also from large companies managing critical infrastructures in the energy or transportation sectors require MCC for their operations [1]. MCC are conveyed by dedicated Private Mobile Radio (PMR) networks [2] that offer a group (or multicast) communication service. This is a one-to-many or many-tomany communication [3], which is one of the most important features of PMR networks and is essential to manage teams of employees. In 5G New Radio, group communication will be supported for MCC from Release R17 onwards [4]. Due to the critical aspects of their missions, MCC users also inherently require highly reliable and secure communication. In particular, sensitive information should not leak to unintended receivers although the broadcast nature of the wireless channel makes the network vulnerable to malicious eavesdroppers.

International audience; In this paper, we study non-orthogonal multiple access (NOMA) user scheduling and resource allocation problem for a generic downlink single-cell multiple input and single output (MISO) millimeter wave (mmWave) system. The larger number of packed antennas and the highly directional property of mmWave communications enable directional beamforming to achieve spatial diversity. Toward this end, we consider two different hybrid precoding schemes which are based on orthogonal matching pursuit (OMP). Users are assigned into different clusters and the base station (BS) transmits superposed signals that share the same precoding vector. Moreover, both fixed number of users per cluster and dynamic number of users per cluster are investigated. We aim to jointly optimize the user clustering, service scheduling, and power allocation strategy, in maximizing the proportional fairness (PF) among the users and exploring the multiuser diversity and multiplexing gain. Since the formulated joint user clustering, scheduling and power allocation problem is a mixed integer non-convex optimization problem, we propose a twofold methodology. First, we apply a hybrid precoding and user clustering scheme, where the hybrid precoder is constructed by singular vector division (SVD) or minimum mean square error (MMSE). Then, with the obtained result, we approximate the proportional fairness power allocation problem by a sequence of Geometric Programming (GP) problems which are solved iteratively. The proposed scheme strikes a balance between the spectral efficiency and service fairness. Results show that the proposed MISO-NOMA scheme which is based on MMSE hybrid precoder and the proposed user scheduling and power allocation strategy under proportional fairness metric can outperform various conventional MISO schemes. Furthermore, our proposed dynamic number of users per cluster scheme outperforms the fixed scheme and can be considered as an upper bound in several aspects, including spectral efficiency and fairness.

International audience; The Internet of Things (IoT) is transforming all economic sectors by connecting physical assets to the virtual world. The range of low-power connectivity options is continuously widening the range of possible IoT applications. However, too many possibilities often make it hard for industrial specialists to choose the right technology and configuration settings, yet these are crucial decisions. To deeply analyze and compare the performance and the scalability of various solution designs, one proven method is simulation. In this article, we show how IoT network simulation can help to future-proof an IoT connectivity design, without the burden of installing a lot of hardware and writing complicated scripts. Then, as the network simulation process is too complex for most IoT teams, we propose a no-code online IoT network simulation platform to make this powerful tool accessible to all. In particular, we explain how we hide the simulation workflow complexity via relevant abstractions and transform it into intuitive interactions. We illustrate the method and the usage of this promising approach, which can be integrated in a network digital twin. We show how it permits to easily evaluate what-if scenarios in order to answer a set of questions that may arise all along the life cycle of a smart connected solution.

International audience; Les blockchains font face à de multiples défis parmi lesquels l'augmentation continuelle des données à stocker par les pairs qui en supportent l'infrastructure. En particulier, Ethereum se rapproche de la barrière symbolique de 1 To de données à stocker par fullnode. Dans cet article, nous proposons une nouvelle stratégie de stockage basée sur l'exploitation de la table de hachage distribuée d'Ethereum. Nous avons implanté une preuve de concept dans Geth, et évalué que l'économie possible est d'environ 60% du stockage actuel demandé à un pair. Cela représente un gain de l'ordre d'une dizaine de pétaoctets pour le réseau pair-à-pair dans son ensemble. De plus, notre solution est compatible avec les clients actuels et peut-être déployée incrémentalement.

International audience; La largeur de coupe (cutwidth) est un paramètre utilisé dans de nombreux problèmes d'ordonnancement linéaire (layout). Calculer la largeur de coupe est un problème NP-complet, mais il est possible de concevoir des algorithmes de branch-and-bound efficaces si on dispose de bonnes bornes inférieures pour couper des branches lors de l'exploration. Savoir évaluer rapidement de bonnes bornes dans chaque noeud de l'arbre de recherche est donc crucial. Dans cet article, nous donnons de nouvelles bornes basées sur différents paramètres de densité du graphe. Nous donnons aussi une borne inférieure basée sur le groupage de requêtes sur le chemin.

International audience; Actuellement, il y a une expansion rapide de la technologie IoT (Internet of Things) et un déploiement généralisé des appareils IoT. De ce fait, assurer l'interopérabilité avec les systèmes d'information est devenu un enjeu critique dans le déploiement accéléré de ces dispositifs. Malgré la prévalence de la représentation en série chronologique des informations IoT, un format standardisé n'a pas émergé. En effet, la majorité des études de la littérature se concentrent sur le traitement, la prédiction ou la compression de séries temporelles. Dans cet article, un nouveau format de représentation de séries chronologiques pour les appareils IoT est proposé sur la base de Concise Binary Object Representation (CBOR). Le format introduit des deltas, des balises et différents modèles comme Measurements-based TS (MTS) ou Variable-based TS (VTS) pour permettre une représentation compacte des données et une intégration efficace du système d'information. De plus, la représentation proposée est capable de regrouper de manière compacte les données de séries chronologiques collectées par les appareils IoT et est capable de réduire la quantité de données transmises jusqu'à 76% et 96% par rapport à JavaScript Object Notation (JSON), ce qui entraîne des extensions potentielles de la durée de vie de la batterie, et la durée de vie utile des appareils IoT.

International audience; L'internet des objets (IoT) couvre un large éventail d'applications qui s'appuient sur une ou plusieurs technologies réseau pour échanger des données entre les différents dispositifs IoT et les applications utilisateur. Ces technologies sont très hétérogènes et sont caractérisées par un ensemble de paramètres de configuration spécifiques à chacune. Les architectes IoT peuvent avoir recours à la simulation pour sélectionner une configuration réseau qui permette d'obtenir les meilleurs performances pour une application et un contexte cibles. Mais cette approche peut s'avérer coûteuse en temps de calcul et donc en énergie. Dans cet article, nous proposons COSIMIA, une méthode s'appuyant sur la combinaison de la Simulation et de l'Apprentissage automatique afin d'accélerer la recherche d'une configuration optimisée pour un scénario IoT donné. La méthode proposée est appliquée à deux études de cas inspirées de situations industrielles réelles, utilisant des technologies IoT différentes. Nous montrons que cette méthode permet de déterminer de bonnes configurations réseau avec un nombre réduit de simulations.

National audience; La communauté dispose aujourd'hui d'outils de mesures actives permettant de scanner internet en entier en quelques minutes. Ces outils, bien qu'utiles, peuvent déclencher des mécanismes de défense des routeurs et peuvent pousser les opérateurs à filtrer définitivement ce trafic qui est néanmoins utile à tous les utilisateurs d'internet. Dans cet article, nous poussons la communauté à prendre conscience du problème éthique posé par les mesures haute fréquence et proposons des pistes pour rendre ces mesures plus éthiques.