Planeación y despliegue de la red de sensores inalámbricos requerida para la medición inteligente de energía eléctrica usando restricciones de capacidad y cobertura

The electrical energy measurement (EEM), seeks to provide quality services without neglecting the reliability of the system. Therefore, a quality service must be closely linked to the wireless communication technologies, to technify the EEM, not only reading, but also cuts, reconnections, and other additional services that the intelligent measurement infrastructure provides through wireless technologies Such as cell or WiFi, increasingly common because of the reliability they provide in real-time data transmission. Wireless infrastructures allow us to provide coverage to the fixed terminals, determined by the electric meter, and in turn manage and plan the optimal deployment of wireless sensors (SI) in finite areas, whether urban, rural or suburban. This article proposes an optimal model for planning and deploying SI for the EEM in order to guarantee reliable wireless communication links at the lowest implementation cost. Therefore, the proposed algorithm gives global solutions within a finite scenario, making this a scalable model in time able to manage the use of available links. The SIs for the EEM are inserted into the Neighborhood Area Networks (NANs) covered by the mobile communications network.La medición de energía eléctrica (MEE), busca proporcionar servicios de calidad sin descuidar la confiabilidad del sistema. Por lo tanto, un servicio de calidad debe ir estrechamente ligada a las tecnologías de comunicación inalámbrica, para tecnificar la MEE, no solo lectura, sino también cortes, reconexiones, y otros servicios adicionales que la infraestructura de medición inteligente provee a través de tecnologías inalámbricas como celular o WiFi, cada vez más comunes debido a la confiabilidad que estas brindan en la transmisión de datos en tiempo real [1]. Las infraestructuras inalámbricas nos permiten brindar cobertura a los terminales fijos, determinados por el medidor eléctrico, y a su vez gestionar y planificar el óptimo despliegue de sensores inalámbricos (SI) en áreas finitas, ya sean, urbanas, rurales o suburbanas. Este artículo propone un modelo óptimo de planeación y despliegue de SI para la MEE con la finalidad de garantizar enlaces de comunicación inalámbricos confiables al menor costo de implementación. Por lo tanto, el algoritmo propuesto da soluciones globales dentro de un escenario finito, haciendo de este un modelo escalable en el tiempo capaz de gestionar el uso de enlaces disponibles. Los SI para la MEE, son insertados en las Redes de Área Vecindaria (NAN) cubiertas por la red de comunicaciones móviles

Dimensionamiento y enrutamiento de redes de sensores inalámbricos para monitoreo de tráfico vehicular

La demanda en la población urbana tiene efecto en el aumento vehicular que causa el tráfico en las ciudades. La forma de gestionar este problema es a través de la implementación de un sistema de tráfico inteligente que permita una mejor interrelación con todos los elementos que lo componen. La comunicación con los elementos se da a través de las tecnologías de la información y comunicación (TIC), las cuales se requiere para el desarrollo de ciudades inteligentes. El implementar una red de sensores inalámbricos en una ciudad permite obtener información del estado en el que se encuentra en tiempo real, de manera que la ubicación de los sensores en semáforos y señales de tráfico permitirá la obtención de los datos necesarios para la toma de decisiones correctas basadas en la percepción local. Ciudades como Quito han tenido un aumento considerable en la población, por lo que, se puede observar que uno de los principales problemas es el tráfico que enfrenta la ciudad en las horas pico, de manera que el uso de sensores inalámbricos para monitorear el tráfico es de gran ayuda. Por todo lo detallado anteriormente, este documento mostrará el dimensionamiento y enrutamiento de una red de sensores inalámbricos que ofrezca un sistema más eficiente para controlar y administrar el flujo de tráfico vehicular.The demand in the urban population affects the increase in vehicles caused by traffic in cities. The way to manage this problem is through the implementation of an intelligent traffic system that allows a better interrelation with all the elements that compose it. Communication with the elements occurs through information and communication technologies (ICT), which are required for the development of smart cities. Implementing a wireless sensor network in a city allows obtaining information on the state in which it is located in real-time so that the location of the sensors at traffic lights and traffic signals will allow obtaining the necessary data for decision-making correct based on local perception. Cities such as Quito have had a considerable increase in population, therefore, it can be seen that one of the main problems is the traffic that the city faces at peak hours, so the use of wireless sensors to monitor traffic is of great help. For everything detailed above, this document will show the dimensioning and routing of a wireless sensor network that offers a more efficient system to control and manage the flow of vehicular traffic

An Efficient Algorithm in Computing Optimal Data Concentrator Unit Location in IEEE 802.15.4g AMI Networks

With a view to achieve several goals in the smart grid (SG) such as making the production and delivery of electricity more cost-effective as well as providing consumers with available information which assists them in controlling their cost, the advanced metering infrastructure (AMI) system has been playing a major role to realize such goals. The AMI network, as an essential infrastructure, typically creates a two-way communication network between electricity consumers and the electric service provider for collecting of the big data generated from consumer’s smart meters (SM). Specifically, there is a crucial element called a data concentrator unit (DCU) employed to collect the boundless data from smart meters before disseminating to meter data management system (MDMS) in the AMI systems. Hence, the location of DCU has significantly impacted the quality of service (QoS) of AMI network, in particular the average throughput and delay. This work aims at developing an efficient algorithm in determining the minimum number of DCUs and computing their optimum locations in which smart meters can communicate through good quality wireless links in the AMI network by employing the IEEE 802.15.4g with unslotted CSMA/CA channel access mechanism. Firstly, the optimization algorithm computes the DCU location based on a minimum hop count metric. Nevertheless, it is possible that multiple positions achieving the minimum hop count may be found; therefore, the additional performance metric, i.e. the average throughput and delay, will be utilized to select the ultimately optimal location. In this paper, the maximum throughput with the acceptable averaged delay constraint is proposed by considering the behavior of the AMI meters, which is almost stationary in the AMI network. In our experiment, the algorithm is demonstrated in different scenarios with different densities of SM, including urban, suburban, and rural areas. The simulation results illustrate that the smart meter density and the environment have substantially impacted on a decision for DCU location, and the proposed methodology is significantly effective. Furthermore, the QoS in urban area, i.e. a highly populated area for SM, of the AMI network is better than those in the suburban and rural areas, where the SM density is quite sparse, because multiple available hops and routes created by neighboring meters in the dense area can help improve the average throughput and delay with the minimum hop count

Security assessment of the smart grid : a review focusing on the NAN architecture

Abstract: This paper presents a comprehensive review on the security aspect of the smart grid communication network. The paper focus on the Neighborhood Area Network (NAN) cybersecurity and it laid emphasis on how the NAN architecture is such an attractive target to intruders and attackers. The paper aims at summarizing recent research efforts on some of the attacks and the various techniques employed in tackling them as they were discussed in recent literatures and research works. Furthermore, the paper presents a detailed review on the smart grid communication layers, wireless technology standards, networks and the security challenges the grid is currently facing. The work concludes by explaining current and future directions NAN communication security could consider in terms of data privacy measures. The data privacy measures are discussed in terms of prevention and detection techniques