Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review

Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that vehicular congestion detection by using Internet of Vehicles based connected vehicles technology are practically feasible for congestion handling. It was found that by using Fog Computing based principles in the vehicular wireless sensor network, communication in the system can be improved to support larger number of nodes without impacting performance. In this paper, connected vehicles technology based vehicular congestion identification techniques are studied. Computing paradigms that can be used for the vehicular network are studied to develop a practically feasible vehicular congestion detection system that performs accurately for a large coverage area and multiple scenarios. The designed system is expected to detect congestion to meet traffic management goals that are of primary importance in intelligent transportation systems

Emergent situations for smart cities: A survey

A smart city is a community that uses communication and information technology to improve sustainability, livability, and feasibility. As any community, there are always unexpected emergencies, which must be treated to preserve the regular order. However, a smart system is needed to be able to respond effectively to these emergent situations. The contribution made in this survey is twofold. Firstly, it provides a comprehensive exhaustive and categorized overview of the existing surveys for smart cities.  The categorization is based on several criteria such as structures, benefits, advantages, applications, challenges, issues, and future directions. Secondly, it aims to analyze several studies with respect to emergent situations and management to smart cities. The analysis is based on several factors such as the challenges and issues discussed, the solutions proposed, and opportunities for future research. The challenges include security, privacy, reliability, performance, scalability, heterogeneity, scheduling, resource management, and latency. Few studies have investigated the emergent situations of smart cities and despite the importance of latency factor for smart city applications, it is rarely discussed

Contributions of architecture Dew Computing to the Internet of Things: comparisons between pilot implementations of both architectures

Dew computing ó la computación de rocío o lágrima ha despertado gran interés en la academia, debido a la separación de los procesos de computación distribuida; donde se encuentran las capas de cloud Computing (computación en la nube), Fog Computing (computación de niebla), Edge Computing (computación de borde) y por último Dew Computing. Estas capas están mencionadas de orden descendente (de mayor a menor) siendo Dew Computing la más cercana al usuario final. Esto se realiza para una mayor comprensión entre las tecnologías y procesos que en ellas se realizan permitiendo su diferenciación. La arquitectura de Internet of Things (IoT) es un paradigma tecnológico que se está formando dentro del ecosistema de computación distribuida, por ende, se requiere resaltar la capa de Dew Computing y su aporte al modelo tecnológico. Es por esto, que se realiza un estado del arte de las arquitecturas Dew Computing e IoT que permitan su comparación con el fin de saber su aporte de forma independiente y en dado caso, cómo podrían integrarse. Se realiza una prueba piloto entre las arquitecturas y una integración de las misma para encontrar los aportes que un modelo del entrega al otro y por último, se plantean posibles escenarios de aplicación que evidencien los beneficios y déficit de la implementación de cada arquitectura en diferentes ámbitos sociales.INTRODUCCIÓN 1. PROBLEMA, PREGUNTA E HIPÓTESIS DE INVESTIGACIÓN 11 2. JUSTIFICACIÓN 11 3. OBJETIVOS DEL PROYECTO 13 3.1 OBJETIVO GENERAL 13 3.2 OBJETIVOS ESPECÍFICOS 13 4. MARCO REFERENCIAL 14 4.1 MARCO CONCEPTUAL 14 4.1.1 Internet of Things 15 4.1.2 Cloud Computing 15 4.1.3 Fog Computing 16 4.1.4 Edge Computing 17 4.1.5 Dew Computing 20 4.2 MARCO TEÓRICO 21 4.3 ESTADO DEL ARTE 22 4.3.1 Revisión sistemática de la literatura 22 4.3.2 Análisis estado del arte 28 4.4 MARCO CONTEXTUAL Y ANTECEDENTES 28 4.5 NORMAS Y ESTÁNDARES 29 4.5.1 Normatividad colombiana 29 4.5.2 Estándares y documentos de referencia 30 4.6 EMPRESAS TECNOLÓGICAS 31 4.6.1 Microsoft Azure IoT Edge 31 4.6.2 Amazon IoT GreenGrass 32 5. DESCRIPCIÓN DEL PROCESO INVESTIGATIVO 34 5.1 ENFOQUE Y TIPO DE INVESTIGACIÓN 34 5.2 FASES Y ACTIVIDADES 34 5.2.1 Elaboración del estado del arte de Dew computing 35 5.2.2 Análisis comparativo entre frameworks para Dew Computing 35 5.2.3 Dispositivo para pruebas 36 5.2.4 Pruebas de ambas arquitecturas 40 5.2.5 Análisis de pruebas 45 6. RESULTADOS 48 6.1 REVISIÓN COMPARATIVA DE DEW COMPUTING E IOT 48 6.2 VENTAJAS Y DESVENTAJAS DE DEW COMPUTING CON IOT. 52 6.2.1 Física 53 6.2.2 Economía 54 6.2.3 Ubicación 54 6.3 OPORTUNIDADES QUE BRINDA DEW COMPUTING 55 6.3.1 Manejo de la energía 55 6.3.2 Procesamiento 55 6.3.3 Almacenamiento 55 6.3.4 Protocolos de comunicación 55 6.3.5 Lenguajes de programación 55 6.3.6 Seguridad de los datos 56 6.3.7 Visualización de los datos 56 7. CONCLUSIONES Y RECOMENDACIONES 57 8. REFERENCIAS 58MaestríaDew Computing or the dew or tear computation has aroused considerable interest in the academy, due to the separation of the processes of distributed computing; where are the layers of Cloud Computing (cloud computing), Fog Computing (fog computing), Edge Computing (edge computing) and finally Dew Computing. These layers are mentioned in descending order (from highest to lowest) with Dew Computing being the closest to the end user. This is done for a better understanding of the technologies and processes that are carried out in them, allowing their differentiation

Internet of Things Framework for Home Care Systems

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency