Algorithmes pour l'estimation des données dans les réseaux de capteurs

International audienceLa collecte des données est un des enjeux majeurs dans les réseaux de capteurs. En effet, les communications induites par la transmission de données réduisent considérablement la durée de vie du réseau. Une des techniques utilisées pour réduire la quantité de données transférées est l'agrégation et selon le type des données étudiées, une des possibilités est l'utilisation de série temporelle ARMA. Dans cet article, nous proposons quatre algorithmes d'agrégation de données s'appuyant sur le modèle AR permettant ainsi la diminution de la consommation d'énergie dans les réseaux de capteurs et augmentant la durée de vie de ceux-ci

Investigating Data Similarity and Estimation Through Spatio-Temporal Correlation to Enhance Energy Efficiency in WSNs

International audienceWireless sensor networks are of energy-constrained nature, which calls for energy efficient protocols as a primary design goal. Thus, minimizing energy consumption is a main challenge.We are concerned in howcollected data by sensors, can be processed to increase the relevance of certain mass of data and reduce the overall data traffic. Since sensor nodes are often densely deployed, the data collected by nearby nodes are either redundant or correlated. One of the great challenges for the aforementioned problem is to exploit temporal and spatial correlation among the source nodes. Our work is composed of two main tasks: 1- A predictive modeling task that aims to capture the temporal correlation among collected data. 2- A data similarity detection task that measures the data similarity based on the spatial correlation

Amélioration de la qualité d'agrégation par l'analyse de données dans les réseaux de capteurs

L'adoption des réseaux de capteurs sans fil (WSNs) dans divers secteurs continuent à croître, comme la médecine, la domotique, le contrôle de processus industriels, la localisation des objets, etc. Cela revient à l'émergence de capteurs de plus en plus petits et de plus en plus intelligents dans notre vie quotidienne. Ces dispositifs interagissent avec l'environnement ou d'autres périphériques, pour analyser les données et produire de l'information. En plus de créer de l'information, ils permettent, une intégration transparente de la technologie virtuelle autour de nous. En effet, ces objets sont de faible puissance et fonctionnent sur batterie. Ils sont souvent utilisé dans des zones géographiques dangereuse et peu accessible, tels que les volcans actifs, les champs de bataille, ou après une catastrophe naturelle etc. Ces zones critiques rendent le remplacement ou la recharge des batteries de chaque capteur difficile voire impossible. Ainsi, leur consommation énergétique devient le principale verrou technologique empêchant leur déploiement à grande échelle. Nous sommes intéressés à partie la plus consommatrice d'énergie dans les réseaux de capteurs: la communication ou l'envoi/la réception de données. Nous proposons des méthodes pour réduire les transmissions des nœuds en réduisant le volume de données à transmettre. Notre travail s'articule autour de trois axes fondamentaux: la prédiction des données, la détection de similarité des données et la détection des comportements anormaux.The promise and application domain of Wireless Sensor Networks (WSNs) continue to grow such as health care, home automation, industry process control, object tracking, etc. This is due to the emergence of embedded, small and intelligent sensor devices in our everyday life. These devices are getting smarter with their capability to interact with the environment or other devices, to analyze data and to make decisions. They have made it possible not only gather data from the environment, but also to bridge the physical and virtual worlds, assist people in their activities, while achieving transparent integration of the wireless technology around us. Along with this promising glory for WSNs, there are however, several challenges facing their deployments and functionality, especially for battery-operated sensor networks. For these networks, the power consumption is the most important challenge. In fact, most of WSNs are composed of low-power, battery-operated sensor nodes that are expected to replace human activities in many critical places, such as disaster relief terrains, active volcanoes, battlefields, difficult terrain border lands, etc. This makes their battery replacement or recharging a non-trivial task. We are concerned with the most energy consuming part of these networks, that is the communication. We propose methods to reduce the cost of transmission in energy-constrained sensor nodes. For this purpose, we observe the way data is collected and processed to save energy during transmission. Our work is build on three basic axis: data estimation, data similarity detection and abnormal behaviors detection

EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs

The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV’s flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV’s mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information

PPS: Energy-Aware Grid-Based Coverage Path Planning for UAVs Using Area Partitioning in the Presence of NFZs

Area monitoring and surveillance are some of the main applications for Unmanned Aerial Vehicle (UAV) networks. The scientific problem that arises from this application concerns the way the area must be covered to fulfill the mission requirements. One of the main challenges is to determine the paths for the UAVs that optimize the usage of resources while minimizing the mission time. Different approaches rely on area partitioning strategies. Depending on the size and complexity of the area to monitor, it is possible to decompose it exactly or approximately. This paper proposes a partitioning method called Parallel Partitioning along a Side (PPS). In the proposed method, grid-mapping and grid-subdivision of the area, as well as area partitioning are performed to plan the UAVs path. An extra challenge, also tackled in this work, is the presence of non-flying zones (NFZs). These zones are areas that UAVs must not cover or pass over it. The proposal is extensively evaluated, in comparison with existing approaches, to show that it enables UAVs to plan paths with minimum energy consumption, number of turns and completion time while at the same time increases the quality of coverage

CMHWN: coverage maximization of heterogeneous wireless network

Two of the main challenges in wireless sensor networks (WSNs) are connectivity and coverage. Connectivity keeps different nodes in the network linked and to exchange data. Coverage affects the efficiency of the operating sensors used in the network. This paper proposes a novel resilient incremental algorithm that improves the coverage of randomly distributed mobile devices within a heterogeneous or homogeneous environment. This algorithm guarantees connectivity by ensuring at least 2-connected neighbors for any device in the network. Results showed up to 89% coverage improvement in a heterogeneous environment and up to 99% coverage improvement in a homogeneous environment

MCCM: an approach for connectivity and coverage maximization

The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for “Maintaining Connectivity and Coverage Maximization” called MCCM . The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes’ movement over existing algorithms in the state of the art

Amélioration de la qualité d'agrégation par l'analyse de données dans les réseaux de capteurs

L'adoption des réseaux de capteurs sans fil (WSNs) dans divers secteurs continuent à croître, comme la médecine, la domotique, le contrôle de processus industriels, la localisation des objets, etc. Cela revient à l'émergence de capteurs de plus en plus petits et de plus en plus intelligents dans notre vie quotidienne. Ces dispositifs interagissent avec l'environnement ou d'autres périphériques, pour analyser les données et produire de l'information. En plus de créer de l'information, ils permettent, une intégration transparente de la technologie virtuelle autour de nous. En effet, ces objets sont de faible puissance et fonctionnent sur batterie. Ils sont souvent utilisé dans des zones géographiques dangereuse et peu accessible, tels que les volcans actifs, les champs de bataille, ou après une catastrophe naturelle etc. Ces zones critiques rendent le remplacement ou la recharge des batteries de chaque capteur difficile voire impossible. Ainsi, leur consommation énergétique devient le principale verrou technologique empêchant leur déploiement à grande échelle. Nous sommes intéressés à partie la plus consommatrice d'énergie dans les réseaux de capteurs: la communication ou l'envoi/la réception de données. Nous proposons des méthodes pour réduire les transmissions des nœuds en réduisant le volume de données à transmettre. Notre travail s'articule autour de trois axes fondamentaux: la prédiction des données, la détection de similarité des données et la détection des comportements anormaux.The promise and application domain of Wireless Sensor Networks (WSNs) continue to grow such as health care, home automation, industry process control, object tracking, etc. This is due to the emergence of embedded, small and intelligent sensor devices in our everyday life. These devices are getting smarter with their capability to interact with the environment or other devices, to analyze data and to make decisions. They have made it possible not only gather data from the environment, but also to bridge the physical and virtual worlds, assist people in their activities, while achieving transparent integration of the wireless technology around us. Along with this promising glory for WSNs, there are however, several challenges facing their deployments and functionality, especially for battery-operated sensor networks. For these networks, the power consumption is the most important challenge. In fact, most of WSNs are composed of low-power, battery-operated sensor nodes that are expected to replace human activities in many critical places, such as disaster relief terrains, active volcanoes, battlefields, difficult terrain border lands, etc. This makes their battery replacement or recharging a non-trivial task. We are concerned with the most energy consuming part of these networks, that is the communication. We propose methods to reduce the cost of transmission in energy-constrained sensor nodes. For this purpose, we observe the way data is collected and processed to save energy during transmission. Our work is build on three basic axis: data estimation, data similarity detection and abnormal behaviors detection.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

Algorithm for temporal anomaly detection in WSNs

International audienceKnowledge discovery and data analysis in resource constrained wireless sensor networks faces different challenges. One of the main challenges is to identify misbehaviors or anomalies with high accuracy while minimizing energy consumption in the network. In this paper, we extend a previous work of us and we present an algorithm for temporal anomalies detection in wireless sensor networks. Our experiments results show that our algorithm can efficiently and accurately detect anomalies in sensor measurements. It also pro- duces low false alarm rate for slow variation time series measurements without harvesting the source of energy

Algorithm for data similarity measurements to reduce data redundancy in wireless sensor networks.

International audienceExtending the lifetime of wireless sensor networks remains the most challenging and demand- ing requirement that impedes large-scale deploy- ments. The basic operation in WSNs is the systematic gathering and transmission of sensed data to a base station for further processing. During data gathering, the amount of data can be large sometimes, due to re- dundant data combined from different sensing nodes in the neighborhood. Thus the data gathered need to be processed before being transmitted, in order to detect and remove redundancy, which can impact the communication traffic and energy consumption of the network in a negative way. In this paper, we propose an algorithm to measure similarity between the data collected toward the base station(relative to a specific event monitoring), so that an aggregator sensor sends a minimum amount of information to the base station in a way that the latter can deduce the source information of sensing neighbors nodes. Further, our experimental results demonstrate that the communication traffic and the number of bits transmitted can be minimized while preserving ac- curacy on the base station estimations