Formal Probabilistic Analysis of a Wireless Sensor Network for Forest Fire Detection

Wireless Sensor Networks (WSNs) have been widely explored for forest fire detection, which is considered a fatal threat throughout the world. Energy conservation of sensor nodes is one of the biggest challenges in this context and random scheduling is frequently applied to overcome that. The performance analysis of these random scheduling approaches is traditionally done by paper-and-pencil proof methods or simulation. These traditional techniques cannot ascertain 100% accuracy, and thus are not suitable for analyzing a safety-critical application like forest fire detection using WSNs. In this paper, we propose to overcome this limitation by applying formal probabilistic analysis using theorem proving to verify scheduling performance of a real-world WSN for forest fire detection using a k-set randomized algorithm as an energy saving mechanism. In particular, we formally verify the expected values of coverage intensity, the upper bound on the total number of disjoint subsets, for a given coverage intensity, and the lower bound on the total number of nodes.Comment: In Proceedings SCSS 2012, arXiv:1307.802

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

Local user mapping via multi-modal fusion for social robots

User detection, recognition and tracking is at the heart of Human Robot Interaction, and yet, to date, no universal robust method exists for being aware of the people in a robot surroundings. The presented work aims at importing into existing social robotics platforms different techniques, some of them classical, and other novel, for detecting, recognizing and tracking human users. These algorithms are based on a variety of sensors, mainly cameras and depth imaging devices, but also lasers and microphones. The results of these parallel algorithms are then merged so as to obtain a modular, expandable and fast architecture. This results in a local user mapping thanks to multi-modal fusion. Thanks to this user awareness architecture, user detection, recognition and tracking capabilities can be easily and quickly given to any robot by re-using the modules that match its sensors and its processing performance. The architecture provides all the relevant information about the users around the robot, that can then be used for end-user applications that adapt their behavior to the users around the robot. The variety of social robots in which the architecture has been successfully implemented includes a car-like mobile robot, an articulated flower and a humanoid assistance robot. Some modules of the architecture are very lightweight but have a low reliability, others need more CPU but the associated confidence is higher. All configurations of modules are possible, and fit the range of possible robotics hardware configurations. All the modules are independent and highly configurable, therefore no code needs to be developed for building a new configuration, the user only writes a ROS launch file. This simple text file contains all wanted modules. The architecture has been developed with modularity and speed in mind. It is based on the Robot Operating System (ROS) architecture, a de facto software standard in robotics. The different people detectors comply with a common interface called PeoplePoseList Publisher, while the people recognition algorithms comply with an interface called PeoplePoseList Matcher. The fusion of all these different modules is based on Unscented Kalman Filter techniques. Extensive benchmarks of the sub-components and of the whole architecture, using both academic datasets and data acquired in our lab, and end-user application samples demonstrate the validity and interest of all levels of the architecture.La detección, el reconocimiento y el seguimiento de los usuarios es un problema clave para la Interacción Humano-Robot. Sin embargo, al día de hoy, no existe ningún método robusto universal para para lograr que un robot sea consciente de la gente que le rodea. Esta tesis tiene como objetivo implementar, dentro de robots sociales, varias técnicas, algunas clásicas, otras novedosas, para detectar, reconocer y seguir a los usuarios humanos. Estos algoritmos se basan en sensores muy variados, principalmente cámaras y fuentes de imágenes de profundidad, aunque también en láseres y micrófonos. Los resultados parciales, suministrados por estos algoritmos corriendo en paralelo, luego son mezcladas usando técnicas probabilísticas para obtener una arquitectura modular, extensible y rápida. Esto resulta en un mapa local de los usuarios, obtenido por técnicas de fusión de datos. Gracias a esta arquitectura, las habilidades de detección, reconocimiento y seguimiento de los usuarios podrían ser integradas fácil y rápidamente dentro de un nuevo robot, reusando los módulos que corresponden a sus sensores y el rendimiento de su procesador. La arquitectura suministra todos los datos útiles sobre los usuarios en el alrededor del robot y se puede usar por aplicaciones de más alto nivel en nuestros robots sociales de manera que el robot adapte su funcionamiento a las personas que le rodean. Los robots sociales en los cuales la arquitectura se pudo importar con éxito son: un robot en forma de coche, una flor articulada, y un robot humanoide asistencial. Algunos módulos de la arquitectura son muy ligeros pero con una fiabilidad baja, mientras otros requieren más CPU pero son más fiables. Todas las configuraciones de los módulos son posibles y se ajustan a las diferentes configuraciones hardware que puede tener el robot. Los módulos son independientes entre ellos y altamente configurables, por lo que no hay que desarrollar código para una nueva configuración. El usuario sólo tiene que escribir un fichero launch de ROS. Este sencillo fichero de texto contiene todos los módulos que se quieren lanzar. Esta arquitectura se desarrolló teniendo en mente que fuese modular y rápida. Se basa en la arquitectura Robot Operating System (ROS), un estándar software de facto en la robótica. Todos los detectores de personas tienen una interfaz común llamada PeoplePoseList Publisher, mientras los algoritmos de reconocimiento siguen una interfaz llamada PeoplePoseList Matcher. La fusión de todos estos módulos se basa en técnicas de filtros de Kalman no lineares (Unscented Kalman Filters). Se han realizado pruebas exhaustivas de precisión y de velocidad de cada componente y de la arquitectura completa (realizadas sobre ambos bases de datos académicas además de sobre datos grabados en nuestro laboratorio), así como prototipos sencillos de aplicaciones finales. Así se comprueba la validez y el interés de la arquitectura a todos los niveles.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Fernando Torres Medina.- Secretario: María Dolores Blanco Rojas.- Vocal: Jorge Manuel Miranda Día

The Internet of Things and The Web of Things

International audienceThe Internet of Things is creating a new world, a quantifiable and measureable world, where people and businesses can manage their assets in better informed ways, and can make more timely and better informed decisions about what they want or need to do. This new con-nected world brings with it fundamental changes to society and to consumers. This special issue of ERCIM News thus focuses on various relevant aspects of the Internet of Things and the Web of Things

Nonparametric Message Passing Methods for Cooperative Localization and Tracking

The objective of this thesis is the development of cooperative localization and tracking algorithms using nonparametric message passing techniques. In contrast to the most well-known techniques, the goal is to estimate the posterior probability density function (PDF) of the position of each sensor. This problem can be solved using Bayesian approach, but it is intractable in general case. Nevertheless, the particle-based approximation (via nonparametric representation), and an appropriate factorization of the joint PDFs (using message passing methods), make Bayesian approach acceptable for inference in sensor networks. The well-known method for this problem, nonparametric belief propagation (NBP), can lead to inaccurate beliefs and possible non-convergence in loopy networks. Therefore, we propose four novel algorithms which alleviate these problems: nonparametric generalized belief propagation (NGBP) based on junction tree (NGBP-JT), NGBP based on pseudo-junction tree (NGBP-PJT), NBP based on spanning trees (NBP-ST), and uniformly-reweighted NBP (URW-NBP). We also extend NBP for cooperative localization in mobile networks. In contrast to the previous methods, we use an optional smoothing, provide a novel communication protocol, and increase the efficiency of the sampling techniques. Moreover, we propose novel algorithms for distributed tracking, in which the goal is to track the passive object which cannot locate itself. In particular, we develop distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Finally, the last part of this thesis includes the experimental analysis of some of the proposed algorithms, in which we found that the results based on real measurements are very similar with the results based on theoretical models

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

New Frameworks for Secure Image Communication in the Internet of Things (IoT)

The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG design, it can be observed that the power consumption is substantially reduced, up to 19%