International audience

Delivering downlink ACKs to Class A LoRaWAN devices via LEO satellite constellations requires predicting end-to-end propagation delays of 10–500 ms to hit a 1-second receive window, a timing challenge absent from terrestrial schedulers. We present DORSAL, the first Mixed-Integer Linear Programming (MILP) scheduler for downlink delivery in direct-to-satellite LoRaWAN networks. It introduces theDownlink Arrival Receive-window Timing (DART)as the central constraint to jointly optimize gateway selection and packet injection timing across ISL-capable and bent-pipe architectures. Evaluation on a Walker 5×5 polar constellation (25 satellites, 650 km) and a commercial ground station network yields five results: (i) DART infeasibility blocks 73% of bent-pipe deliveries regardless of scheduler quality: DART-unaware approaches collapse from 93% nominal to only 20% real delivery; (ii) ISL routing raises %ACK by +67.4 pp over bent-pipe (91.6% vs. 24.2% atnGS=3 ground stations, ISM band) by eliminating the three-way simultaneous-visibility constraint, with 73% of ACKs delivered via ≥ 2-hop paths; (iii) ISL %ACK is nearly flat acrossnGS∈ {3, 6, 12} ground stations (91.6%–92.7%), showing that a minimal threestation polar network captures the full scheduling benefit of an ISL-equipped constellation; (iv) ISL hardware speed has negligible impact up to 300 ms/hop: %ACK is flat from 0 to 300 ms, then degrades in two discrete steps (−5.3 pp at 500 ms, −2 pp at 700 ms) as successive hop-count tiers lose causal injection feasibility; and (v) the MILP solves to proven optimality within seconds for up to 103devices, confirming that the sparse conflict-graph structure keeps the problem tractable at realistic IoT subscriber counts.

Using UAVs has recently emerged as a cost-efficient solution to assist first responders in search and rescue missions. Victims are usually equipped with wireless devices, which makes RF sensing an efficient solution for their localization in disaster situations. Nevertheless, the success of existing methods is highly diminished by the noisy nature of RF measurements. While leveraging recent advancements in lightweight gas sensing, we present in this ongoing work paper a novel localization approach that efficiently combines RF measurements with victim odor information while accounting for the dynamic nature of measurements' quality. We discuss the approach design, early simulation results, and ongoing experimental evaluation.

In this paper, we propose an efficient framework for monitoring pollution plumes using sensor-equipped drones. Our approach leverages the power of Reinforcement Learning and Mutual Information to strategically plan drone paths in order to maximize the informativeness of the data collected while minimizing communication costs. We propose a multi-agent Independent Q-Learning scheme, where drones act independently but share a global team reward. The reward is calculated based on both the reduction in plume estimation uncertainty and the communication costs. The proposed framework is adaptable to various problem instances, making it suitable for monitoring diverse physical phenomena. We conduct extensive simulations showing the effectiveness of our approach in achieving highquality plume monitoring, with an error in variance estimation ranging from 3% to 5% when compared with ground-truth value. Results also show that our solution offers good compromise between plume estimation and communication costs. This framework outperforms the random-walk approach up to 32.88% and genetic-based heuristics up to 4.2% in terms of total rewards under the proposed scenarios. The proposed framework is advantageous because it excels not only in providing a good solution but also in inferring it in a reasonable time especially compared to a solution provided by genetic-based heuristics.

The evolution of mobile networks towards more diverse services and open architectures has led to the emergence of mobile network function virtualization. This allows to match the reserved resources for network operation to the actual resources that are needed in the network at a certain place and time. Mobility-related network functions, specifically handover, can also be virtualized in this new paradigm. This virtualization can be facilitated by studying handover behavior at the base station level. In this work, using agglomerative hierarchical clustering, we show the existence of different base station profiles in terms of handovers, including three primary profiles: producer, receiver, and balanced. We also show that the use of these profiles, in addition to the dynamic reconfiguration enabled by virtualization, can reduce reserved resources by more than 50% compared to the current static system.

Mobile crowdsensing is a powerful paradigm that takes advantage of low-cost sensors and population density. It allows for large-scale deployments and collection of extensive data, offering a great advantage in multiple fields such as air pollution monitoring, which is a major concern worldwide. Given the mobile nature of the crowd, mobile crowdsensing platforms need to implement adequate route selection/planning solutions to better guide the crowd through the area of interest and maximize the quality of monitoring. In this paper, we propose two route selection algorithms that take into consideration the low accuracy of low-cost sensors in order to find the most informative routes. The similarity-based route selection algorithm aims to maximize spatial coverage by reducing overlaps between participant routes. The cluster-based route selection takes advantage of hierarchical clustering to build groups of similar points of the map according to explanatory variables. We compare the proposed solutions to baseline route selection algorithms, and the results show that our solutions allow for a better estimation while being efficient in terms of travel distance.

Wi-Fi is a standard off-the-shelf solution for industrial robotics. The IEEE 802.11ax amendment extends it to support the 6 GHz band, 160 MHz bandwidth and bit-rates up to 9.6 Gbps. In this article, we evaluate the performance of Wi-Fi 6 compared to Wi-Fi 5 and Wi-Fi 4. We select 9 physical layers (PHYs) representing different Wi-Fi generations and we evaluate their performance in an industrial shipyard in the presence of high radio frequency interference and metallic obstructions. We deploy setup of a robotic station (STA) and a controller STA with three applications running in parallel: a robotic STA is sending a high throughput stream to a controller, a Robotic Operating System (ROS) application is sending time-critical control commands to the robotic STA, Precision Time Protocol (PTP) keeps synchronizing the clocks between both STAs. We evaluate the performance in terms of three Key Performance Indicators (KPIs): streaming throughput, IP-level delay using PTP, and Application-level delay of ROS control packets. The networks are run in: Short range Line of Sight (LoS), Medium range LoS, Long range None-LoS (NLoS), and Long range mixed settings. We note that depending on the PHY configuration, an older Wi-Fi generation may outperform Wi-Fi 6. We further observe trade-offs between the different PHYs: wide channel PHYs (e.g. 160 MHz) had best throughput reaching up to 900 Mbps while PHYs while 80 MHz or 20 MHz bandwidth achieved as low as 9 ms delay. This motivates further research in multi-PHY adaptation for KPIs specific to industrial robotics.1

Monitoring pollution plumes is a key issue, given the harmful effects they cause. The dynamic of these plumes, which may be important due to meteorological conditions, makes their study difficult. Real-time monitoring in order to obtain an accurate mapping of the pollution dispersion is helpful and valuable to mitigate risks. In this work, we consider a fleet of cooperative drones carrying pollution sensors and operating in order to assess a pollution plume. The latter is assumed to follow a Gaussian process (GP) with varying parameters. For this use case, we propose an efficient approach to characterize spatially and temporarily the plume while optimizing the path planning of drones. In our approach, drones are guided by a deep reinforcement learning (DRL) model called the categorical deep$Q$-network (Categorical DQN) to maximize the plume coverage while considering budget constraints. Specifically, we develop a scalable independent$Q$-learning (IQL) scheme that shares team rewards based on each drone’s deployment relevance and, therefore, ensures cooperation. We evaluate the performance of the plume parameter estimation as well as the maps generated by the GP regression. By testing our framework on several plume scenarios, we show that it offers good results in terms of both estimation quality and runtime efficiency.

Indoor geolocation has witnessed a significant advancement through the refinement of the 802.11 FTM (Fine Timing Measurement) protocol. Accurate indoor geolocation has numerous applications in areas such as asset tracking, indoor navigation, and location-based services. The standard 802.11 FTM protocol enables accurate indoor positioning by measuring the time-of-flight between a mobile device and multiple access points (APs). It can be generalized to device-to-device ranging. However, the conventional implementation of FTM suffers from increased complexity as the number of devices grows, limiting its scalability. FTM indeed involves a point-to-point exchange of messages between each pair of devices, leading to a quadratic increase in the number of messages as the number of neighboring devices increases. In this article, a breakthrough method is proposed to enhance the FTM protocol by leveraging broadcast communication, resulting in a substantial reduction in message complexity from quadratic to linear. By taking into account broadcast in the protocol, our approach eliminates the need for multiple individual exchanges and devises a mechanism where a single message from the mobile device is broadcasted to all neighbors simultaneously. Each message exchanged will then be useful for computing every pairwise time-of-flight, by piggybacking all timestamps, making the protocol more efficient and scalable. We conducted extensive simulated experiments to evaluate the performance of the enhanced FTM protocol. The results demonstrated the effectiveness of the proposed method, showcasing a substantial reduction in computational overhead compared to the conventional FTM implementation.

In recent years, low-cost sensors have raised strong interest for environmental monitoring applications. These instruments often suffer from degraded data quality. Notably, they are prone to drift. It can be mitigated with costly periodic calibrations. To reduce this cost, in situ calibration strategies have emerged, enabling the recalibration of instruments while leaving them in the field. However, they rarely identify which instruments actually need a calibration because of drift, so that in situ calibration may instead degrade performances. Therefore, a novel drift detection algorithm is presented in this work, exploiting the concept of rendez-vous between measuring instruments. Its originality lies mainly in the comparisons of values determining the state of the instruments, for which the quality of the measurement results is taken into account. It defines the concept of compatibility between measurement results. A case study is developed, showing an accuracy of 88% for correct detection of drifting instruments. The results of the diagnosis algorithm are then combined with calibration approaches. Results show a significant improvement of the measurement results. Notably, an increase of 15% of the coefficient of determination of the linear regression between their true values and the measured values is observed with the correction and the error on the slope and on the intercept respectively is reduced by 50% and 60% at least. Note to Practitioners—In this paper, we investigate the problem of drift detection in sensor networks. This work was motivated by the fact that faulty nodes are rarely detected in existing in situ calibration algorithm prior to the correction of the instruments. Moreover, existing fault diagnosis algorithms for sensor networks do not specifically target drift and are often applicable to either (dense) static or mobile sensor networks but not both. We propose an algorithm designed for the detection of drift faults regardless of the type of sensor network and of the measurand. Specific attention is paid to the metrological quality of the measurement results used to carry out the diagnosis. The output of the algorithm provides information that can be exploited for the recalibration of faulty instruments. In future work, we will aim at providing tools and recommendations for the adjusment of the parameters of the diagnosis algorithm but also more elaborated approaches based on the results of our diagnosis algorithm to calibrate faulty nodes.

Data collected from mobile phones or from motion detection sensors are regularly used as a proxy for user presence in networking studies. However, little attention was paid to the actual accuracy of these data sources, which present certain biases, in capturing actual human presence in a given geographical area. In this work, we conduct the first comparison between mobile phone data collected by an operator and human presence data collected by motion detection sensors in the same geographical area. Through a detailed spatio-temporal analysis, we show that a significant correlation exists between the two datasets, which can be seen as a cross validation of the two data sources. However, we also detect some significant differences at certain times and places, raising questions regarding the data used in certain studies in the literature. For example, we notice that the most important daily mobility peaks detected in mobile phone data are not actually detected by on ground sensors, or that the end of the work-day activities in the considered area is not synchronised between the two data sources. Our results allow to distinguish the metrics and the scenarios where user presence information is confirmed by both mobile phone and sensor data.

Monitoring air pollution plumes in emergency situations (industrial accidents, natural disasters, deliberate terrorist releases, etc.) becomes an issue of utmost importance in our society given the dramatic effects that the released pollutants can cause. Considering these situations, the pollution plume is strongly dynamic leading to a fast dispersion of pollutants in the atmosphere. Thus, the need for real-time response is very strong and a solution to get precise mapping of pollution dispersion is required to mitigate risks. However, monitoring and forecasting air quality in real time in such situations remains a highly challenging endeavour. In this paper, we suggest a systemic approach for monitoring dynamic air pollution based on aerial sensing (sensors mounted on UAVs). The proposed framework consists of a cycle with feedback loop which will constantly combine a spatio-temporal forecasting model based on a convolutional long short term memory (ConvLSTM) network with a data assimilation technique to get accurate pollution maps, while adjusting at each time the trajectories of drones following uncertainty forecasts. Our solution was evaluated and validated using a highly dynamic real world data set namely Fusion Field Trial 2007 (FFT07). The proposed strategy, together with the obtained evaluation results, are presented, and carefully analyzed.

Participatory sensing leverages population density and involves citizens in the collection of extensive data in multiple fields such as air pollution monitoring, enabling large-scale deployments and improving the knowledge of air quality. This study highlights the potential of low-cost sensors through a data analysis of pollutant concentrations collected during multiple sensing campaigns we co-organized using a participatory sensing platform we designed. We first compare the estimation quality of four statistical models and investigate the impact of sampling frequency on the quality of estimation and energy consumption of the nodes using an energy model based on the sensing duty cycle. In addition, we evaluate the capacity of regression models to recover missing data of one sensor based on the other sensors. Results are satisfactory and reveal that a small decrease in the sampling frequency slightly reduces the estimation quality, but in contrast, allows the nodes to operate on a longer period.

Over the past two decades, Wi-Fi technology (defined by the IEEE 802.11 standard) has become a prominent wireless network access technology. In many situations, a device may attach to several Wi-Fi access points within the radio range. The operating system makes its choice over metrics that do not take into account the actual available capacity. To fill this gap, several proposals have been made to infer capacity, for example by estimating the occupied proportion of the channel. However, these techniques are being thwarted by the mechanisms recently introduced in 802.11 to improve transmission speeds, in particular frame aggregation. In this article, we focus on busy time inference based on frame aggregation level. We propose an analytical model based on a Markov chain which estimates the theoretical aggregation level for different amounts of cross traffic. We validate its accuracy against simulations carried out on the ns-3 network simulator and an ad-hoc simulator. Results show that the theoretical model gives an accurate estimation of the frame aggregation level and that it can be used to infer the network load.

The use of LED-to-camera communication opens the door to a wide range of use cases and applications, with diverse requirements in terms of quality of service. However, while analytical models and simulation tools exist for all the major radio communication technologies, the only way of currently evaluating the performance of a network mechanism over LED-to-camera is to implement and test it. Our work aims to fill this gap by proposing a Markov-modulated Bernoulli process to model the wireless channel in LED-to-camera communications, which is shown to closely match experimental results. Based on this model, we develop and validate CamComSim, the first network simulator for LED-to-camera communications.

Wireless sensor networks (WSNs) are widely used in environmental applications where the aim is to sense physical phenomena, such as temperature and air pollution. A careful deployment of sensors is necessary in order to get a better knowledge of these physical phenomena while ensuring the minimum deployment cost. In this paper, we focus on using WSN for air pollution mapping and tackle the optimization problem of sensor deployment. Unlike most of the existing deployment approaches that are either generic or assume that sensors have a given detection range, we define an appropriate coverage formulation based on an interpolation formula that is adapted to the characteristics of air pollution sensing. We derive, from this formulation, two deployment models for air pollution mapping using the integer linear programming while ensuring the connectivity of the network and taking into account the sensing error of nodes. We analyze the theoretical complexity of our models and propose the heuristic algorithms based on the linear programming relaxation and binary search. We perform extensive simulations on a dataset of the Lyon city, France, in order to assess the computational complexity of our proposal and evaluate the impact of the deployment requirements on the obtained results.

Air pollution has major negative effects on both human health and environment. Thus, air quality monitoring is a main issue in our days. In this paper, we focus on the use of mobile WSN to generate high spatio-temporal resolution air quality maps. We address the sensors' online redeployment problem and we propose three redeployment models allowing to assess, with high precision, the air pollution concentrations. Unlike most of existing movement assisted deployment strategies based on network generic characteristics such as coverage and connectivity, our approaches take into account air pollution properties and dispersion models to offer an efficient air quality estimation. First, we introduce our proposition of an optimal integer linear program based on air pollution dispersion characteristics to minimize estimation errors. Then, we propose a local iterative integer linear programming model and a heuristic technique that offer a lower execution time with acceptable estimation quality. We evaluate our models in terms of execution time and estimation quality using a real data set of Lyon City in France. Finally, we compare our models' performances to existing generic redeployment strategies. Results show that our algorithms outperform the existing generic solutions while reducing the maximum estimation error up to 3 times.

One of the main concerns of smart cities is to improve public health which is mainly threatened by air pollution due to the massively increasing urbanization. The reduction of air pollution starts first with an efficient monitoring of air quality where the main aim is to generate accurate pollution maps in real time. Spatiotemporally fine-grained air pollution maps can be obtained using physical models which simulate the phenomenon of pollution dispersion. However, these simulations are less accurate than measurements that can be obtained using pollution sensors. Combining simulations and measurements, also known as data assimilation, provides better pollution estimations through the correction of the fine-grained simulations of physical models. The quality of data assimilation mainly depends on the number of measurements and their locations. A careful deployment of nodes is therefore necessary in order to get better pollution maps. In this paper, we tackle the deployment problem of pollution sensors and propose a new mixed integer programming model allowing to minimize the overall deployment cost of the network while achieving a required assimilation quality and ensuring the connectivity of the network. We then design a heuristic algorithm to solve efficiently the problem in polynomial time. We perform extensive simulations on a dataset of the Lyon city, France and show that our approach provides better air quality monitoring when compared to existing deployment methods that are designed without taking into account the outputs of physical models. We also show that in terms of connectivity, the communication range of sensor nodes might have a noteworthy impact on the quality of pollution estimation.

The development of virtualization techniques enables an architectural shift in mobile networks, where resource allocation, or even signal processing, become software functions hosted in a data center. The centralization of computing resources and the dynamic mapping between baseband processing units (BBUs) and remote antennas (RRHs) provide an increased flexibility to mobile operators, with important reductions of operational costs. Most research efforts on Cloud Radio Access Networks (CRAN) consider indeed an operator perspective and network-side performance indicators. The impact of such new paradigms on user experience has been instead overlooked. In this paper, we shift the viewpoint, and show that the dynamic assignment of computing resources enabled by CRAN generates a new class of mobile terminal handover that can impair user quality of service. We then propose an algorithm that mitigates the problem, by optimizing the mapping between BBUs and RRHs on a time-varying graph representation of the system. Furthermore, we show that a practical online BBU-RRH mapping algorithm achieves results similar to an oracle-based scheme with perfect knowledge of future traffic demand. We test our algorithms with two large-scale real-world datasets, where the total number of handovers, compared with the current architectures, is reduced by more than 20%. Moreover, if a small tolerance to dropped calls is allowed, 30% less handovers can be obtained.

One of the main issues in UAVs networks design is how nodes are relocated in order to meet the desired performance objectives. In this work, we propose two UAVs fleet mobility models based on the Virtual Forces Algorithm (VFA). The application context we are interested in is the air pollution surveillance over wide areas. The first model is a centralized variant where all computations are performed in a central ground base station. While the second model is a distributed version where each node takes its own decision in collaboration with its neighbors. We evaluate our models performances and we compare them with state of the art solutions using a real data set of air pollution concentrations and according to three main metrics: the maximal estimation error, execution time and communication cost.

Wireless sensor networks (WSN) are widely used in environmental applications where the aim is to sense a physical parameter such as temperature, humidity, air pollution, etc. Most existing WSN-based environmental monitoring systems use data interpolation based on sensor measurements in order to construct the spatiotemporal field of physical parameters. However, these fields can be also approximated using physical models which simulate the dynamics of physical phenomena. In this paper, we focus on the use of wireless sensor networks for the aim of correcting the physical model errors rather than interpolating sensor measurements. We tackle the activity scheduling problem and design an optimization model and a heuristic algorithm in order to select the sensor nodes that should be turned off to extend the lifetime of the network. Our approach is based on data assimilation which allows us to use both measurements and the physical model outputs in the estimation of the spatiotemporal field. We evaluate our approach in the context of air pollution monitoring while using a dataset from the Lyon city, France and considering the characteristics of a monitoring system developed in our lab. We analyze the impact of the nodes' characteristics on the network lifetime and derive guidelines on the optimal scheduling of air pollution sensors.

Long being used for identification purposes, a new set of applications is now available thanks to the development of RFID technology. One of which is remote sensing of environmental values using passive RFID tags. This leap forward allowed a more energy efficient and cheaper solution for applications like logistics or urban infrastructure monitoring. Nevertheless, serious issues raised with the use of RFID: (i) reading collisions and (ii) gathering of tag information. Indeed, tags information retrieved by readers have to be transmitted towards a base station through a multihop scheme which can interfere with neighboring readers activity. In this paper, we propose cross-layer solutions meant for both scheduling of readers' activity to avoid collisions, and a multihop routing towards base stations, to gather read tag data. This routing is performed with a data priority aware mechanism allowing end-to-end delay reduction of urgent data packets delivery up to 13% faster compared to standard ones. Using fuzzy logic, we combine several observed metrics to reduce the load of forwarding nodes and improve latency as well as data rate. We validate our proposal running simulations on industrial and urban scenarios.

Due to the increasing industrialization and the massive urbanization, air pollution monitoring is being considered as one of the major challenges of smart cities. Many air pollution monitoring systems have been proposed in the literature, among which wireless sensor networks seem to be a leading solution thanks to sensors' low cost and autonomy as well as their finegrained deployment. A careful deployment of sensors is therefore necessary to get better performances while ensuring a minimal financial cost. In this paper, we consider citywide wireless sensor networks and tackle the minimum-cost node positioning issue for air pollution monitoring. We propose an efficient approach that aims to find optimal sensors and sinks locations while ensuring air pollution coverage and network connectivity. Unlike most of the existing methods, which rely on simple and generic detection models, our approach is based on the spatial analysis of pollution data, allowing to take into account the nature of the pollution phenomenon. As proof of concept, we apply our approach on real world data, namely the Paris pollution data, which was recorded in March 2014. We also perform extensive simulations in order to study the performance of our approach in comparison to the existing methods.

Mobile wireless sensor networks (MWSN) are widely used for monitoring physical phenomena such as air pollution where the aim is usually to generate accurate pollution maps in real time. The generation of pollution maps can be performed using either sensor measurements or physical models which simulate the phenomenon of pollution dispersion. The combination of these two information sources, known as data assimilation, makes it possible to better monitor air pollution by correcting the simulations of physical models while relying on sensor measurements. The quality of data assimilation mainly depends on the number of measurements and their locations. A careful deployment of nodes is therefore necessary in order to get better pollution maps. In this ongoing work, we tackle the placement problem of pollution sensors and design a mixed integer programming model allowing to maximize the assimilation quality while ensuring the connectivity of the network. We perform some simulations on a dataset of the Lyon city, France in order to show the effectiveness of our model regarding the quality of pollution coverage.

The popularization of RFID systems has conducted to large deployments of RFID solutions in various areas under different criteria. However, such deployments, specially in dense environments, can be subject to RFID collisions which in turn affect the quality of readings. In this paper we propose two distributed and efficient solutions for dense mobile deployments of RFID systems. mDEFAR is an adaptation of a previous work highly performing in terms of collisions reduction, efficiency and fairness in dense static deployments. CORA is more of a locally mutual solution where each reader relies on its neighborhood to enable itself or not. Using a beaconing mechanism, each reader is able to identify potential (non-)colliding neighbors in a running frame and as such chooses to read or not. Performance evaluation shows high performance in terms of coverage delay for both proposals quickly achieving 100% coverage depending on the considered use case while always maintaining consistent efficiency levels above 70%. Compared to GDRA, our solutions proved to be better suited for highly dense and mobile environments, offering both higher throughput and efficiency. The results reveal that depending on the application considered, choosing either mDEFAR or CORA helps improve efficiency and coverage delay.

Considering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field, but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000-2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Finally, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture.

With over 300 billion vehicle trips made in the United States and 64 billion in France per year, network operators have the opportunity to utilize the existing road and highway network as an alternative data network to offload large amounts of delay-tolerant traffic. To enable the road network as a large-capacity transmission system, we exploit the existing mobility of vehicles equipped with wireless and storage capacities together with a collection of offloading spots. An offloading spot is a data storage equipment located where vehicles usually park. Data is transloaded from a conventional data network to the closest offloading spot and then shipped by vehicles along their line of travel. The subsequent offloading spots act as data relay boxes where vehicles can drop off data for later pick-up by other vehicles, depending on their direction of travel. The main challenges of this offloading system are how to compute the road path matching the performance requirements of a data transfer and how to configure the sequence of offloading spots involved in the transfer. We propose a scalable and adaptive centralized architecture built on software-defined networking that maximizes the utilization of the flow of vehicles connecting consecutive offloading spots. We simulate the performance of our system using real roads traffic counts for France. Results show that the centralized controlled offloading architecture can achieve an efficient and fair allocation of concurrent data transfers between major cities in France.

Air pollution has become a major issue in the modern megalopolis because of industrial emissions and increasing urbanization along with traffic jams and the heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and the civil society. Current monitoring systems rely on reference sensing stations that are precise but massive, costly, and, therefore, seldom. In this paper, we focus on an alternative or complementary approach, with a network of low cost and autonomic wireless sensors, aiming at a finer spatiotemporal granularity of sensing. Generic deployment models in the literature are not adapted to the stochastic nature of pollution sensing. Our main contribution is to design integer linear programming models that compute sensor deployments capturing both the coverage of pollution under time-varying weather conditions and the connectivity of the infrastructure. We evaluate our deployment models on a real data set of Greater London. We analyze the performance of the proposed models and show that our joint coverage and connectivity formulation is tight and compact, with a reasonable enough execution time. We also conduct extensive simulations to derive engineering insights for effective deployments of air pollution sensors in an urban environment.

Air pollution has become a major issue of modern megalopolis because of industrial emissions and increasing urbanization along with traffic jams and heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and by the civil society. Current monitoring systems rely on reference sensing stations that are precise but massive, costly and therefore seldom. In this ongoing work, we focus on an alternative or complementary approach, using a network of low cost and autonomic wireless sensors, allowing for a finer spatiotemporal granularity of air quality sensing. We tackle the optimization problem of sensor deployment and propose an integer programming model, which allows to find the optimal network topology while ensuring air quality monitoring with a high precision and the minimum financial cost. Most of existing deployment models of wireless sensor networks are generic and assume that sensors have a given detection range. This assumption does not fit pollutant concentrations sensing. Our model takes into account interpolation methods to place sensors in such a way that pollution concentration is estimated with a bounded error at locations where no sensor is deployed.

Patrolling is mainly used in situations where the need of repeatedly visiting certain places is critical. In this paper, we consider a deployment of a wireless sensor network (WSN) that cannot be fully meshed because of the distance or obstacles. Several robots are then in charge of getting close enough to the nodes in order to connect to them, and perform a patrol to collect all the data in time. We discuss the problem of multi-robot patrolling within the constrained wireless networking settings. We show that this is fundamentally a problem of vertex coverage with bounded simple cycles (CBSC). We offer a formalization of the CBSC problem and prove it is NP-hard and at least as hard as the Traveling Salesman Problem (TSP). Then, we provide and analyze heuristics relying on clusterings and geometric techniques. The performances of our solutions are assessed in regards to networking parameters, robot energy, but also to random and particular graph models.

Monitoring air quality has become a major challenge of modern cities where the majority of population lives. In this paper, we focus on using wireless sensor networks for air pollution mapping. We tackle the optimization problem of sensor deployment and propose two placement models allowing to minimize the deployment cost and ensure an error-bounded air pollution mapping. Our models take into account the sensing drift of sensor nodes and the impact of weather conditions. Unlike most of existing deployment models, which assume that sensors have a given detection range, we base on interpolation methods to place sensors in such a way that pollution concentration is estimated with a bounded error at locations where no sensor is deployed. We evaluate our model on a dataset of the Lyon City and give insights on how to establish a good compromise between the deployment budget and the precision of air quality monitoring. We also compare our model to generic approaches and show that our formulation is at least 3 times better than random and uniform deployment.

RFID technology suffers from a recurring issue: the reader-to-reader collision. Numerous protocols have been proposed to attempt to reduce them, but, remaining reading errors still heavily impact the performances and fairness of dense RFID deployments. This paper introduces a new Distributed Efficient & Fair Anticollision for RFID (DEFAR) protocol. It reduces both monochannel and multichannel collisions as well as interference by a factor of almost 90% in comparison with the best state of the art protocols. The fairness of the medium access among the readers is improved to a 99% level. Such improvements are achieved applying a TDMA-based “serverless” approach and assigning different priorities to readers depending on their behavior over precedent rounds. A distributed reservation phase is organized between readers with at least one winning reader afterwards. Then, multiple reading phases occur within a single frame in order to obtain fast coverage and high throughput. The use of different reader priorities based on reading behaviors of previous frames also contributes to improve both fairness and efficiency. Simulation results show the robustness of the proposed solution in terms of different metrics such collision avoidance, fairness and coverage and in comparison with a centralized literature solution.

Offloading is a promising technique for alleviating the ever-growing traffic load from infrastructure-based networks such as the Internet. Offloading consists of using alternative methods of transmission as a cost-effective solution for network operators to extend their transport capacity. In this paper, we advocate the use of conventional vehicles equipped with storage devices as data carriers whilst being driven for daily routine journeys. The road network can be turned into a large-capacity transmission system to offload bulk transfers of delay-tolerant data from the Internet. One of the challenges we address is assigning data to flows of vehicles while coping with the complexity of the road network. We propose an embedding algorithm that computes an offloading overlay where each logical link spans over multiple stretches of road from the underlying road infrastructure. We then formulate the data transfer assignment problem as a novel linear programming model we solve to determine the optimal logical paths matching the performance requirements of a data transfer. We evaluate our road traffic allocation scheme using actual road traffic counts in France. The numerical results show that 20% of vehicles in circulation in France equipped with only one Terabyte of storage can offload Petabyte transfers in a week.

Recently, air pollution monitoring emerges as a main service of smart cities because of the increasing industrialization and the massive urbanization. Wireless sensor networks (WSN) are a suitable technology for this purpose thanks to their substantial benefits including low cost and autonomy. Minimizing the deployment cost is one of the major challenges in WSN design, therefore sensors positions have to be carefully determined. In this paper, we propose two integer linear programming formulations based on real pollutants dispersion modeling to deal with the minimum cost WSN deployment for air pollution monitoring. We illustrate the concept by applying our models on real world data, namely the Nottingham City street lights. We compare the two models in terms of execution time and show that the second flow based formulation is much better. We finally conduct extensive simulations to study the impact of some parameters and derive some guidelines for efficient WSN deployment for air pollution monitoring.

Providing high data rates with minimum energy consumption is a crucial challenge for next generation wireless networks. There are few papers in the literature which combine these two issues. This paper focuses on multi-hop wireless mesh networks using a MAC layer based on Spatial Time Division Multiple Access (S-TDMA). We develop an optimization framework based on linear programming to study the relationship between throughput and energy consumption. Our contributions are two-fold. First, we formulate and solve, using column generation, a new MILP to compute offline energy-throughput tradeoff curve. We use a physical interference model where the nodes can perform continuous power control and can use a discrete set of data rates. Second, we highlight network engineering insights. We show, via numerical results, that power control and multi-rate functionalities allow optimal throughput to be reached, with lower energy consumption, using a mix of single hop and multi-hop routes.

We advocate the use of a data shuttle service model to offload bulk transfers of delay-tolerant data from the Internet onto standard vehicles equipped with data storage capabilities. We first propose an embedding algorithm that computes an offloading overlay on top of the road infrastructure. The goal is to simplify the representation of the road infrastructure as raw maps are too complex to handle. In this overlay, each logical link maps multiple stretches of road from the underlying road infrastructure. We formulate then the data transfer assignment problem as a novel linear programming model that determines the most appropriate logical paths in the offloading overlay for a data transfer request. We evaluate our proposal using actual road traffic counts in France. Numerical results show that we can satisfy weekly aggregate requests in the petabyte range while achieving cumulative bandwidth above 10 Gbps with a market share of 20% and only one terabyte of storage per vehicle.

Delay-Tolerant Networks (DTN) model systems that are characterized by intermittent connectivity and frequent partitioning. Routing in DTNs has drawn much research effort recently. Since very different kinds of networks fall in the DTN category, many routing approaches have been proposed. In particular, the routing layer in some DTNs have information about the schedules of contacts between nodes and about data traffic demand. Such systems can benefit from a previously proposed routing algorithm based on linear programming that minimizes the average message delay. This algorithm, however, is known to have performance issues that limit its applicability to very simple scenarios. In this work, we propose an alternative linear programming approach for routing in Delay-Tolerant Networks. We show that our formulation is equivalent to that presented in a seminal work in this area, but it contains fewer LP constraints and has a structure suitable to the application of Column Generation (CG). Simulation shows that our CG implementation arrives at an optimal solution up to three orders of magnitude faster than the original linear program in the considered DTN examples.

This article investigates the problem of the allocation of modulation and coding, subcarriers and power to users in LTE. The proposed model achieves inter-cell interference mitigation through the dynamic and distributed self-organization of cells. Therefore, there is no need for any a prior frequency planning. Moreover, a two-level decomposition method able to find near optimal solutions is proposed to solve the optimization problem. Finally, simulation results show that compared to classic reuse schemes the proposed approach is able to pack more users into the same bandwidth, decreasing the probability of user outage.

This paper is focused on broadband wireless mesh networks based on OFDMA resource management, considering a realistic SINR model of the physical layer with a fine tuned power control at each node. A linear programming model using column generation leads to compute power efficient schedules with high network capacity. Correlation between capacity and energy consumption is analyzed as well as the impact of physical layer parameters - SINR threshold and path-loss exponent. We highlight that there is no significant tradeoff between capacity and energy when the power consumption of idle nodes is important. We also show that both energy consumption and network capacity are very sensitive to the SINR threshold variation.

In this paper, we focus on broadband wireless mesh networks like 3GPP LTE-Advanced. This technology is a key enabler for next generation cellular networks which are about to increase by an order of magnitude the capacity provided to users. Such an objective needs a significative densification of cells which requires an efficient backhauling infrastructure. In many urban areas as well as under-developed countries, wireless mesh networking is the only available solution. Besides, economical and environmental concerns require that the energy expenditure of such infrastructure is optimized. We propose a multi-objective analysis of the correlation between capacity and energy consumption of LTE-like wireless mesh networks. We provide a linear programing modeling using column generation for an efficient computation of the Pareto front between these objectives. Based on this model, we observe that there is actually no significant capacity against energy trade-off.

In this paper, we investigate on minimizing the energy consumption of a fixed broadband wireless network through a joint optimization of data routing and radio configuration. Every link holds a set of power-efficient configurations, each of them associating a capacity with its energy cost. The optimization problem involves deciding the network's configuration and flows that minimize the total energy consumption. An exact mathematical formulation of the problem is presented. It relies on a minimum cost multicommodity flow with step increasing cost functions. We then propose a piecewise linear convex function that provides a good approximation of the energy consumption on the links, and present a relaxation of the previous formulation that exploits the convexity of the cost functions. This yields lower bounds on the energy consumption, and finally a heuristic algorithm based on the fractional optimum is employed to produce feasible solutions. Our models are validated through extensive experiments.

In this paper, we address the problem of computing the transport capacity of Wireless Mesh Networks dedicated to Internet access. Routing and transmission scheduling have a major impact on the capacity provided to the clients. A cross-layer optimization of these problems allows the routing to take into account contentions due to radio interferences. We develop exact linear programs and provide an efficient column generation process computing a relaxation of the problem. It allows to work around the combinatoric of simultaneously achievable transmissions, hence computing solutions on large networks. Our approach is validated through extensive simulations. Evolution of the capacity of a mesh network with its parameters, as well as the algorithmic complexity are then discussed. We conjecture that the problem can be solved in polynomial time and that the gateway placement problem is only subject to localized constraints.

Assuming a given network topology and a routing protocol, this work is focused on the capacity evaluation of routing protocols based on either a self-organization scheme or a flat approach. To reach this goal, we propose to use linear-programming formulation to model radio resource sharing as linear constraints. Four models are detailed to evaluate the capacity of any routing scheme in wireless multihops networks. First, two models of fairness are proposed: either each node has a fair access to the channel, or the fairness is among the radio links. Besides, a pessimistic and an optimistic scenarios of spatial re-utilization of the medium are proposed, yielding a lower bound and an upper bound on the network capacity for each fairness case. Finally, using this model, we provide a comparative analysis of some flat and self-organized routing protocols

Network measurement is essential for assessing performance issues, identifying and locating problems. Two common strategies are the passive approach that attaches specific devices to links in order to monitor the traffic that passes through the network and the active approach that generates explicit control packets in the network for measurements. One of the key issues in this domain is to minimize the overhead in terms of hardware, software, maintenance cost and additional traffic.In this paper, we study the problem of assigning tap devices for passive monitoring and beacons for active monitoring. Minimizing the number of devices and finding optimal strategic locations is a key issue, mandatory for deploying scalable monitoring platforms. In this article, we present a combinatorial view of the problem from which we derive complexity and approximability results, as well as efficient and versatile Mixed Integer Programming (MIP) formulations.

We consider the problem of finding a lightpath assignment for a given set of communication requests on a multifiber WDM optical network with wavelength translators. Given such a network and w, the number of wavelengths available on each fiber, k, the number of fibers per link, and c, the number of partial wavelength translations available on each node, our problem stands for deciding whether it is possible to find a w-lightpath for each request in the set such that there is no link carrying more that k lightpaths using the same wavelength nor node where more than c wavelength translations take place. Our main theoretical result is the writing of this problem as a particular instance of integral multicommodity flow, hence integrating routing and wavelength assignment in the same model. We then provide three heuristics mainly based upon randomized rounding of fractional multicommodity flow and enhancements that are three different answers to the trade-off between efficiency and tightness of approximation, and discuss their practical performances on both theoretical and real-world instances.