Wireless Mesh Networks to Support Video Surveillance: Architecture, Protocol, and Implementation Issues

Current video-surveillance systems typically consist of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper is to present an experience of implementation of a large-scale video-surveillance system based on a wireless mesh network infrastructure, discussing architecture, protocol, and implementation issues. More specifically, the paper proposes an architecture for a video-surveillance system, and mainly centers its focus on the routing protocol to be used in the wireless mesh network, evaluating its impact on performance at the receiver side. A wireless mesh network was chosen to support a video-surveillance application in order to reduce the overall system costs and increase scalability and performance. The paper analyzes the performance of the network in order to choose design parameters that will achieve the best trade-off between video encoding quality and the network traffic generated

Advanced Topics in Systems Safety and Security

This book presents valuable research results in the challenging field of systems (cyber)security. It is a reprint of the Information (MDPI, Basel) - Special Issue (SI) on Advanced Topics in Systems Safety and Security. The competitive review process of MDPI journals guarantees the quality of the presented concepts and results. The SI comprises high-quality papers focused on cutting-edge research topics in cybersecurity of computer networks and industrial control systems. The contributions presented in this book are mainly the extended versions of selected papers presented at the 7th and the 8th editions of the International Workshop on Systems Safety and Security—IWSSS. These two editions took place in Romania in 2019 and respectively in 2020. In addition to the selected papers from IWSSS, the special issue includes other valuable and relevant contributions. The papers included in this reprint discuss various subjects ranging from cyberattack or criminal activities detection, evaluation of the attacker skills, modeling of the cyber-attacks, and mobile application security evaluation. Given this diversity of topics and the scientific level of papers, we consider this book a valuable reference for researchers in the security and safety of systems

Hierarchical representations for spatio-temporal visual attention: modeling and understanding

Mención Internacional en el título de doctorDentro del marco de la Inteligencia Artificial, la Visión Artificial es una disciplina científica que tiene como objetivo simular automaticamente las funciones del sistema visual humano, tratando de resolver tareas como la localización y el reconocimiento de objetos, la detección de eventos o el seguimiento de objetos....Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luis Salgado Álvarez de Sotomayor.- Secretario: Ascensión Gallardo Antolín.- Vocal: Jenny Benois Pinea

Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

acceptedVersio

Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term “Big Video Data” (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this paper, we draw researchers’ attention towards the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook towards future research directions derived from our critical assessment of the efforts invested so far in this exciting field

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

A holistic review of cybersecurity and reliability perspectives in smart airports

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport\u27s networks

Soft computing and non-parametric techniques for effective video surveillance systems

Esta tesis propone varios objetivos interconectados para el diseño de un sistema de vídeovigilancia cuyo funcionamiento es pensado para un amplio rango de condiciones. Primeramente se propone una métrica de evaluación del detector y sistema de seguimiento basada en una mínima referencia. Dicha técnica es una respuesta a la demanda de ajuste de forma rápida y fácil del sistema adecuándose a distintos entornos. También se propone una técnica de optimización basada en Estrategias Evolutivas y la combinación de funciones de idoneidad en varios pasos. El objetivo es obtener los parámetros de ajuste del detector y el sistema de seguimiento adecuados para el mejor funcionamiento en una amplia gama de situaciones posibles Finalmente, se propone la construcción de un clasificador basado en técnicas no paramétricas que pudieran modelar la distribución de datos de entrada independientemente de la fuente de generación de dichos datos. Se escogen actividades detectables a corto plazo que siguen un patrón de tiempo que puede ser fácilmente modelado mediante HMMs. La propuesta consiste en una modificación del algoritmo de Baum-Welch con el fin de modelar las probabilidades de emisión del HMM mediante una técnica no paramétrica basada en estimación de densidad con kernels (KDE). _____________________________________This thesis proposes several interconnected objectives for the design of a video-monitoring system whose operation is thought for a wide rank of conditions. Firstly an evaluation technique of the detector and tracking system is proposed and it is based on a minimum reference or ground-truth. This technique is an answer to the demand of fast and easy adjustment of the system adapting itself to different contexts. Also, this thesis proposes a technique of optimization based on Evolutionary Strategies and the combination of fitness functions. The objective is to obtain the parameters of adjustment of the detector and tracking system for the best operation in an ample range of possible situations. Finally, it is proposed the generation of a classifier in which a non-parametric statistic technique models the distribution of data regardless the source generation of such data. Short term detectable activities are chosen that follow a time pattern that can easily be modeled by Hidden Markov Models (HMMs). The proposal consists in a modification of the Baum-Welch algorithm with the purpose of modeling the emission probabilities of the HMM by means of a nonparametric technique based on the density estimation with kernels (KDE)