Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

Calibration of Smartphone-Based Weather Measurements Using Pairwise Gossip

Accurate and reliable daily global weather reports are necessary for weather forecasting and climate analysis. However, the availability of these reports continues to decline due to the lack of economic support and policies in maintaining ground weather measurement systems from where these reports are obtained. Thus, to mitigate data scarcity, it is required to utilize weather information from existing sensors and built-in smartphone sensors. However, as smartphone usage often varies according to human activity, it is difficult to obtain accurate measurement data. In this paper, we present a heuristic-based pairwise gossip algorithm that will calibrate smartphone-based pressure sensors with respect to fixed weather stations as our referential ground truth. Based on actual measurements, we have verified that smartphone-based readings are unstable when observed during movement. Using our calibration algorithm on actual smartphone-based pressure readings, the updated values were significantly closer to the ground truth values

Algorithmes de localisation distribués en intérieur pour les réseaux sans fil avec la technologie IEEE 802.15.4

The Internet of Things is finally blooming through diverse applications, from home automation and monitoring to health tracking and quantified-self movement. Consumers deploy more and more low-rate and low-power connected devices that provide complex services. In this scenario, positioning these intelligent objects in their environment is necessary to provide geo-localized services, as well as to optimize the network operation. However, indoor positioning of devices using only their radio interface is still very imprecise. Indoor wireless localization techniques often deduce from the Radio frequency (RF) signal attenuation the distances that separate a mobile node from a set of reference points called landmarks. The received signal strength indicator (RSSI), which reflects this attenuation, is known in the literature to be inaccurate and unreliable when it comes to distance estimation, due to the complexity of indoor radio propagation (shadowing, multi-path fading). However, it is the only metric that will certainly be available in small and inexpensive smart objects. In this thesis, we therefore seek algorithmic solutions to the following problem: is it possible to achieve a fair localization using only the RSSI readings provided by low-quality hardware? To this extent, we first study the behavior of the RSSI, as reported by real hardware like IEEE 802.15.4 sensor nodes, in several indoor environments with different sizes and configurations , including a large scale wireless sensor network. Such experimental results confirm that the relationship between RSSI and distance depends on many factors; even the battery pack attached to the devices increases attenuation. In a second step, we demonstrate that the classical log-normal shadowing propagation model is not well adapted in indoor case, because of the RSSI values dispersion and its lack of obvious correlation with distance. We propose to correct the observed inconsistencies by developing algorithms to filter irrelevant samples. Such correction is performed by biasing the classical log-normal shadowing model to take into account the effects of multipath propagation. These heuristics significantly improved RSSI-based indoor localization accuracy results. We also introduce an RSSI-based positioning approach that uses a maximum likelihood estimator conjointly with a statistical model based on machine learning. In a third step, we propose an accurate distributed and cooperative RSSI-based localization algorithm that refines the set of positions estimated by a wireless node. This algorithm is composed of two on-line steps: a local update of position¿s set based on stochastic gradient descent on each new RSSI measurement at each sensor node. Then an asynchronous communication step allowing each sensor node to merge their common local estimates and obtain the agreement of the refined estimated positions. Such consensus approach is based on both a distributed local gradient step and a pairwise gossip protocol. This enables each sensor node to refine its initial estimated position as well as to build a local map of itself and its neighboring nodes. The proposed algorithm is compared to multilateration, Multi Dimensional Scaling (i.e. MDS) with modern majorization problem and classical MDS. Simulation as well as experimental results obtained on real testbeds lead to a centimeter-level accuracy. Both landmarks and blind nodes communicate in the way that the data processing and computation are performed by each sensor node without any central computation point, tedious calibration or intervention from a human.L¿internet des objets se développe à travers diverses applications telles que la domotique, la surveillance à domicile, etc. Les consommateurs s¿intéressent à ces applications dont les objets interagissent avec des dispositifs de plus en plus petits et connectés. La localisation est une information clé pour plusieurs services ainsi que pour l¿optimisation du fonctionnement du réseau. En environnement intérieur ou confiné, elle a fait l¿objet de nombreuses études. Cependant, l¿obtention d¿une bonne précision de localisation demeure une question difficile, non résolue. Cette thèse étudie le problème de la localisation en environnement intérieur appliqué aux réseaux sans fil avec l¿utilisation unique de l¿atténuation du signal. L¿atténuation est mesurée par l¿indicateur de l¿intensité du signal reçu (RSSI). Le RSSI est connu dans la littérature comme étant imprécis et peu fiable en ce qui concerne l¿estimation de la distance, du fait de la complexité de la propagation radio en intérieur : il s¿agit des multiples trajets, le shadowing, le fading. Cependant, il est la seule métrique directement mesurable par les petits objets communicants et intelligents. Dans nos travaux, nous avons amélioré la précision des mesures du RSSI pour les rendre applicables à l¿environnement interne dans le but d¿obtenir une meilleure localisation. Nous nous sommes également intéressés à l¿implémentation et au déploiement de solutions algorithmiques relatifs au problème suivant : est-il possible d¿obtenir une meilleure précision de la localisation en utilisant uniquement les mesures de RSSI fournies par les n¿uds capteurs sans fil IEEE 802.15.4 ? Dans cette perspective, nous avons d¿abord étudié le comportement du RSSI dans plusieurs environnements intérieurs de différentes tailles et selon plusieurs configurations , y compris un réseau de capteurs sans fil à grande échelle (SensLAB). Pour expliquer les résultats des mesures, nous avons caractérisé les objets communicants que nous utilisons, les n¿uds capteurs Moteiv TMote Sky, par une série d¿expériences en chambre anéchoïque. Les résultats expérimentaux confirment que la relation entre le RSSI et la distance dépend de nombreux facteurs même si la batterie intégrée à chaque n¿ud capteur produit une atténuation. Ensuite, nous avons démontré que le modèle de propagation log-normal shadowing n¿est pas adapté en intérieur, en raison de la dispersion des valeurs de RSSI et du fait que celles-ci ne sont pas toujours dépendantes de la distance. Ces valeurs devraient être considérées séparément en fonction de l¿emplacement de chaque n¿ud capteur émetteur. Nous avons proposé des heuristiques pour corriger ces incohérences observées à savoir les effets de la propagation par trajets multiples et les valeurs aberrantes. Nos résultats expérimentaux ont confirmé que nos algorithmes améliorent significativement la précision de localisation en intérieur avec l¿utilisation unique du RSSI. Enfin, nous avons étudié et proposé un algorithme de localisation distribué, précis et coopératif qui passe à l¿échelle et peu consommateur en termes de temps de calcul. Cet algorithme d¿approximation stochastique utilise la technique du RSSI tout en respectant les caractéristiques de l¿informatique embarquée des réseaux de capteurs sans fil. Il affine l¿ensemble des positions estimées par un n¿ud capteur sans fil. Notre approche a été comparée à d¿autres algorithmes distribués de l¿état de l¿art. Les résultats issus des simulations et des expériences en environnements internes réels ont révélé une meilleure précision de la localisation de notre algorithme distribué. L¿erreur de localisation est de l¿ordre du centimètre sans aucun n¿ud ou unité centrale de traitement, ni de calibration fastidieuse ni d¿intervention humaine

Topology control and data handling in wireless sensor networks

Our work in this thesis have provided two distinctive contributions to WSNs in the areas of data handling and topology control. In the area of data handling, we have demonstrated a solution to improve the power efficiency whilst preserving the important data features by data compression and the use of an adaptive sampling strategy, which are applicable to the specific application for oceanography monitoring required by the SECOAS project. Our work on oceanographic data analysis is important for the understanding of the data we are dealing with, such that suitable strategies can be deployed and system performance can be analysed. The Basic Adaptive Sampling Scheduler (BASS) algorithm uses the statistics of the data to adjust the sampling behaviour in a sensor node according to the environment in order to conserve energy and minimise detection delay. The motivation of topology control (TC) is to maintain the connectivity of the network, to reduce node degree to ease congestion in a collision-based medium access scheme; and to reduce power consumption in the sensor nodes. We have developed an algorithm Subgraph Topology Control (STC) that is distributed and does not require additional equipment to be implemented on the SECOAS nodes. STC uses a metric called subgraph number, which measures the 2-hops connectivity in the neighbourhood of a node. It is found that STC consistently forms topologies that have lower node degrees and higher probabilities of connectivity, as compared to k-Neighbours, an alternative algorithm that does not rely on special hardware on sensor node. Moreover, STC also gives better results in terms of the minimum degree in the network, which implies that the network structure is more robust to a single point of failure. As STC is an iterative algorithm, it is very scalable and adaptive and is well suited for the SECOAS applications

Distributed Random Set Theoretic Soft/Hard Data Fusion

Research on multisensor data fusion aims at providing the enabling technology to combine information from several sources in order to form a unifi ed picture. The literature work on fusion of conventional data provided by non-human (hard) sensors is vast and well-established. In comparison to conventional fusion systems where input data are generated by calibrated electronic sensor systems with well-defi ned characteristics, research on soft data fusion considers combining human-based data expressed preferably in unconstrained natural language form. Fusion of soft and hard data is even more challenging, yet necessary in some applications, and has received little attention in the past. Due to being a rather new area of research, soft/hard data fusion is still in a edging stage with even its challenging problems yet to be adequately de fined and explored. This dissertation develops a framework to enable fusion of both soft and hard data with the Random Set (RS) theory as the underlying mathematical foundation. Random set theory is an emerging theory within the data fusion community that, due to its powerful representational and computational capabilities, is gaining more and more attention among the data fusion researchers. Motivated by the unique characteristics of the random set theory and the main challenge of soft/hard data fusion systems, i.e. the need for a unifying framework capable of processing both unconventional soft data and conventional hard data, this dissertation argues in favor of a random set theoretic approach as the first step towards realizing a soft/hard data fusion framework. Several challenging problems related to soft/hard fusion systems are addressed in the proposed framework. First, an extension of the well-known Kalman lter within random set theory, called Kalman evidential filter (KEF), is adopted as a common data processing framework for both soft and hard data. Second, a novel ontology (syntax+semantics) is developed to allow for modeling soft (human-generated) data assuming target tracking as the application. Third, as soft/hard data fusion is mostly aimed at large networks of information processing, a new approach is proposed to enable distributed estimation of soft, as well as hard data, addressing the scalability requirement of such fusion systems. Fourth, a method for modeling trust in the human agents is developed, which enables the fusion system to protect itself from erroneous/misleading soft data through discounting such data on-the-fly. Fifth, leveraging the recent developments in the RS theoretic data fusion literature a novel soft data association algorithm is developed and deployed to extend the proposed target tracking framework into multi-target tracking case. Finally, the multi-target tracking framework is complemented by introducing a distributed classi fication approach applicable to target classes described with soft human-generated data. In addition, this dissertation presents a novel data-centric taxonomy of data fusion methodologies. In particular, several categories of fusion algorithms have been identifi ed and discussed based on the data-related challenging aspect(s) addressed. It is intended to provide the reader with a generic and comprehensive view of the contemporary data fusion literature, which could also serve as a reference for data fusion practitioners by providing them with conducive design guidelines, in terms of algorithm choice, regarding the specifi c data-related challenges expected in a given application

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts