A Redundancy based Protocol for Safety Message Dissemination in Vehicular Ad Hoc Networks

International audienceThe diversity of applications types in vehicular ad hoc networks (VANETs) has spawned a large variety of messages that need to be disseminated in vehicle to vehicle (V2V) communication mode. The most critical messages are those dedicated for safety applications such as accident warning, road hazardous warning, signal violation warning, etc. The dissemination of this sort of messages is a challenging task in VANETs since they should be efficiently transmitted i.e. by achieving high packet delivery within a certain time limit and an acceptable overhead. In this work, we propose a robust and an original data dissemination protocol called Redundancy-based Protocol (RBP). Contrary to most of the density based protocols, the protocol is beaconless. It takes into account the surrounding vehicle density during the broadcasting process through a specific metric, named “packet redundancy ratio”, calculated locally at each vehicle. Based on this metric, each vehicle is able to dynamically determine the probability of rebroadcast in order to mitigate the broadcast storm problem. The simulation results prove that the proposed protocol outperforms the slotted 1- persistence scheme in terms of packet drop ratio, and network load while still maintaining a low End-to-End delay and high packet reachability. This scheme is suitable for safety applications, as well as for further kinds of application by saving the network capacity consumption

Delay-based strategy for safety message dissemination in Vehicular Ad hoc NETworks: Slotted or continuous?

International audience—The diversity of applications' types in Vehicular Ad hoc NETworks (VANETs) has spawned a large variety of messages that need to be efficiently disseminated between connected vehicles. The most critical messages are those dedicated for safety applications such as road hazardous warning, signal violation warning, etc. The dissemination of this sort of messages is considered as a challenging task in mobile networks where the topology changes dynamically. Indeed, transmitted messages should achieve a high data reachability within a limited transmission delay and an acceptable overhead in a Vehicle to Vehicle (V2V) communication mode. In this work, we focus on a special type of data dissemination protocols based on the delay strategy. The purpose of this paper is to compare two basic distinguished techniques, namely the slotted technique and the continuous technique, and study in depth their impact on the data dissemination performance. A proper selection of the convenient technique according to the application's requirements is consequently deduced. For a faithful and rigorous study, simulations are performed by means of ns-3 simulator under a realistic VANET environment in terms of map layout, mobility pattern and radio model. Simulation results show that contrary to the theoretical reflection, slotted technique is approved as the most appropriate one for safety message dissemination. This technique achieves the same packet data ratio and redundancy ratio, compared to the continuous one, while reducing the data transmission delay

SEAD: A simple and efficient adaptive data dissemination protocol in vehicular ad-hoc networks

International audienceVehicular ad-hoc network (VANET) is becoming a promising technology for improving the efficiency and the safety of intelligent transportation systems by deploying a wide variety of applications. Smart vehicles are expected to continuously exchange a huge amount of data either through safety or non-safety messages dedicated for road safety or infotainment and passenger comfort applications, respectively. One of the main challenges posed by the study of VANET is the data dissemination design by which messages have to be efficiently disseminated in a high vehicular speed, intermittent connectivity, and highly dynamic topology. In particular, broadcast mechanism should guarantee fast and reliable data delivery within a limited wireless bandwidth in order to fit the real time applications’ requirements. In this work, we propose a simple and efficient adaptive data dissemination protocol called “SEAD”. On the one hand, the originality of this work lies in its simplicity and efficiency regardless the application’s type. Simplicity is achieved through a beaconless strategy adopted to take into account the surrounding vehicles’ density. Thanks to a metric locally measured, each vehicle is able to dynamically define an appropriate probability of rebroadcast to mitigate the broadcast storm problem. Efficiency is manifested by reducing excessive retransmitted messages and hence promoting the network capacity and the transmission delay. The simulation results show that the proposed protocol offers very low packet drop ratio and network load while still maintaining a low end-to-end delay and a high packet delivery. On the other hand, SEAD protocol presents a robust data dissemination mechanism which is suitable either for safety applications or for other kinds of application. This mechanism is able to adapt the protocol performance in terms of packet delivery ratio to the application’s requirements

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.Open Access funding provided by the Qatar National Library. The authors are grateful to Qatar National Research Fund (QNRF) for funding and supporting the M-NEX Project (Grant No. BFSUGI01-1120-170005) in Qatar. The M-NEX is a project of the Collaborative Research Area Belmont Forum (Grant No. 11314551)

A Redundancy based Protocol for Safety Message Dissemination in Vehicular Ad Hoc Networks

International audienceThe diversity of applications types in vehicular ad hoc networks (VANETs) has spawned a large variety of messages that need to be disseminated in vehicle to vehicle (V2V) communication mode. The most critical messages are those dedicated for safety applications such as accident warning, road hazardous warning, signal violation warning, etc. The dissemination of this sort of messages is a challenging task in VANETs since they should be efficiently transmitted i.e. by achieving high packet delivery within a certain time limit and an acceptable overhead. In this work, we propose a robust and an original data dissemination protocol called Redundancy-based Protocol (RBP). Contrary to most of the density based protocols, the protocol is beaconless. It takes into account the surrounding vehicle density during the broadcasting process through a specific metric, named “packet redundancy ratio”, calculated locally at each vehicle. Based on this metric, each vehicle is able to dynamically determine the probability of rebroadcast in order to mitigate the broadcast storm problem. The simulation results prove that the proposed protocol outperforms the slotted 1- persistence scheme in terms of packet drop ratio, and network load while still maintaining a low End-to-End delay and high packet reachability. This scheme is suitable for safety applications, as well as for further kinds of application by saving the network capacity consumption

Algorithmic adaptations schemas on the new parallel platforms

Avec la multitude des plates-formes parallèles émergentes caractérisées par une hétérogénéité sur le plan matériel (processeurs, réseaux, …), le développement d'applications et de bibliothèques parallèles performantes est devenu un défi. Une méthode qui se révèle appropriée pour relever ce défi est l'approche adaptative consistant à utiliser plusieurs paramètres (architecturaux, algorithmiques,…) dans l'objectif d'optimiser l'exécution de l'application sur la plate-forme considérée. Les applications adoptant cette approche doivent tirer avantage des méthodes de modélisation de performance pour effectuer leurs choix entre les différentes alternatives dont elles disposent (algorithmes, implémentations ou ordonnancement). L'usage de ces méthodes de modélisation dans les applications adaptatives doit obéir aux contraintes imposées par ce contexte, à savoir la rapidité et la précision des prédictions. Nous proposons dans ce travail, en premier lieu, un framework de développement d'applications parallèles adaptatives basé sur la modélisation théorique de performances. Ensuite, nous nous concentrons sur la tâche de prédiction de performance pour le cas des milieux parallèles et hiérarchiques. En effet, nous proposons un framework combinant les différentes méthodes de modélisation de performance (analytique, expérimentale et simulation) afin de garantir un compromis entre les contraintes suscités. Ce framework profite du moment d'installation de l'application parallèle pour découvrir la plate-forme d'exécution et les traces de l'application afin de modéliser le comportement des parties de calcul et de communication. Pour la modélisation de ces deux composantes, nous avons développé plusieurs méthodes s'articulant sur des expérimentations et sur la régression polynômiale pour fournir des modèles précis. Les modèles résultats de la phase d'installation seront utilisés (au moment de l'exécution) par notre outil de prédiction de performance de programmes MPI (MPI-PERF-SIM) pour prédire le comportement de ces derniers. La validation de ce dernier framework est effectuée séparément pour les différents modules, puis globalement pour le noyau du produit de matrices.With the multitude of emerging parallel platforms characterized by their heterogeneity in terms of hardware components (processors, networks, ...), the development of performant applications and parallel libraries have become a challenge. A method proved suitable to face this challenge is the adaptive approach which uses several parameters (architectural, algorithmic, ...) in order to optimize the execution of the application on the target platform. Applications adopting this approach must take advantage of performance modeling methods to make their choice between the alternatives they have (algorithms, implementations or scheduling). The use of these modeling approaches in adaptive applications must obey the constraints imposed by the context, namely predictions speed and accuracy. We propose in this work, first, a framework for developing adaptive parallel applications based on theoretical modeling performance. Then, we focuse on the task of performance prediction for the case of parallel and hierarchical environments. Indeed, we propose a framework combining different methods of performance modeling (analytical, experimental and simulation) to ensure a balance between the constraints raised. This framework makes use of the installing phase of the application to discover the parallel platform and the execution traces of this application in order to model the behavior of two components namely computing kernels and pt/pt communications. For the modeling of these components, we have developed several methods based on experiments and polynomial regression to provide accurate models. The resulted models will be used at runtime by our tool for performance prediction of MPI programs (MPI-PERF-SIM) to predict the behavior of the latter. The validation of the latter framework is done separately for the different modules, then globally on the matrix product kernel

Schémas d'adaptations algorithmiques sur les nouveaux supports d'éxécution parallèles

With the multitude of emerging parallel platforms characterized by their heterogeneity in terms of hardware components (processors, networks, ...), the development of performant applications and parallel libraries have become a challenge. A method proved suitable to face this challenge is the adaptive approach which uses several parameters (architectural, algorithmic, ...) in order to optimize the execution of the application on the target platform. Applications adopting this approach must take advantage of performance modeling methods to make their choice between the alternatives they have (algorithms, implementations or scheduling). The use of these modeling approaches in adaptive applications must obey the constraints imposed by the context, namely predictions speed and accuracy. We propose in this work, first, a framework for developing adaptive parallel applications based on theoretical modeling performance. Then, we focuse on the task of performance prediction for the case of parallel and hierarchical environments. Indeed, we propose a framework combining different methods of performance modeling (analytical, experimental and simulation) to ensure a balance between the constraints raised. This framework makes use of the installing phase of the application to discover the parallel platform and the execution traces of this application in order to model the behavior of two components namely computing kernels and pt/pt communications. For the modeling of these components, we have developed several methods based on experiments and polynomial regression to provide accurate models. The resulted models will be used at runtime by our tool for performance prediction of MPI programs (MPI-PERF-SIM) to predict the behavior of the latter. The validation of the latter framework is done separately for the different modules, then globally on the matrix product kernel.Avec la multitude des plates-formes parallèles émergentes caractérisées par une hétérogénéité sur le plan matériel (processeurs, réseaux, …), le développement d'applications et de bibliothèques parallèles performantes est devenu un défi. Une méthode qui se révèle appropriée pour relever ce défi est l'approche adaptative consistant à utiliser plusieurs paramètres (architecturaux, algorithmiques,…) dans l'objectif d'optimiser l'exécution de l'application sur la plate-forme considérée. Les applications adoptant cette approche doivent tirer avantage des méthodes de modélisation de performance pour effectuer leurs choix entre les différentes alternatives dont elles disposent (algorithmes, implémentations ou ordonnancement). L'usage de ces méthodes de modélisation dans les applications adaptatives doit obéir aux contraintes imposées par ce contexte, à savoir la rapidité et la précision des prédictions. Nous proposons dans ce travail, en premier lieu, un framework de développement d'applications parallèles adaptatives basé sur la modélisation théorique de performances. Ensuite, nous nous concentrons sur la tâche de prédiction de performance pour le cas des milieux parallèles et hiérarchiques. En effet, nous proposons un framework combinant les différentes méthodes de modélisation de performance (analytique, expérimentale et simulation) afin de garantir un compromis entre les contraintes suscités. Ce framework profite du moment d'installation de l'application parallèle pour découvrir la plate-forme d'exécution et les traces de l'application afin de modéliser le comportement des parties de calcul et de communication. Pour la modélisation de ces deux composantes, nous avons développé plusieurs méthodes s'articulant sur des expérimentations et sur la régression polynômiale pour fournir des modèles précis. Les modèles résultats de la phase d'installation seront utilisés (au moment de l'exécution) par notre outil de prédiction de performance de programmes MPI (MPI-PERF-SIM) pour prédire le comportement de ces derniers. La validation de ce dernier framework est effectuée séparément pour les différents modules, puis globalement pour le noyau du produit de matrices

Estimating Forest Losses Using Spatio-temporal Pattern-based Sequence Classification Approach

Consistent forest loss estimates are important to enforce forest management regulations. In Tunisia, recent evidence has suggested that the deforestation rate is increasing, especially since the 2011’s Revolution. However, no spatially explicit data on the extent of deforestation before and after the Revolution exists. Here, we quantify deforestation in the country for the period 2001–2014 and we propose a novel spatio-temporal pattern-based sequence classification framework for forest loss estimation. To do so, expert knowledge and spatial techniques are applied to identify deforestation drivers. Then, we adopt sequential pattern mining to extract sets of patterns sharing similar spatiotemporal behavior. The sequence miner generates multidimensional-closed sequential patterns at different time granularities. Then, a discriminative filter is employed to decide on patterns to use as relevant classification features. Lastly, the classifier is trained using random forest and shows an improved result

Estimating Forest Fire Losses Using Stochastic Approach: Case Study of the Kroumiria Mountains (Northwestern Tunisia)

Kroumiria Mountains (northwestern Tunisia) have experienced major fires, making them the main loss reason of Tunisian forested areas. The ability of accurately forecasting or modeling forest fire areas may significantly aid optimizing fire-fighting strategies. However, there are still limitations in the empirical study of forest fire loss estimation because the poor availability and low quality of fire data. In this study, a stochastic approach based on Markov process was developed for the prediction of burned areas, using available meteorological data sets and GIS layers related to the forest under analysis. The Self-organizing map (SOM) was initially used to classify spatiotemporal factors influencing the fire behavior. Subsequently, the SOM clusters were incorporated into a Hidden Markov Model (HMM) framework to model their corresponding burned areas. Results achieved using a database of 829 forest fires records between 1985 and 2016, showed the appropriateness of the HMM approach for the prediction of burned areas compared with a state-of-the art machine learning methods. The transition probability matrix (TPM) and the emission probability matrix (EPM) were also analyzed to further understand the spatiotemporal patterns of fire losses

On the use of performance models for adaptive algorithm selection on heterogeneous clusters

International audienceDue to the increasing diversity and the continuous evolution of existing parallel systems, solving efficiently a target problem by using a single algorithm or writing efficient and portable programs is becoming a challenging task. In this paper, we present a generic framework that integrates performance models with adaptive techniques in order to design efficient parallel algorithms in heterogeneous computing environments. To illustrate our approach, we study the matrix multiplication problem, where we compare different parallel algorithms. Experiments demonstrate that accurate performance predictions obtained from analytical performance models allow us to select the most appropriate algorithm to use depending on the problem and the platform parameters