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

Joint routing protocol and image compression algorithm for prolonging node lifetime in wireless sensor network

Wireless sensor network (WSN) are among the emerging modern technologies, with a vast range of application in different areas. However, the current WSNs technology faces a key challenge in terms of node lifetime and network connectivity due to limited power resource of the node. The conventional data routing protocols do not consider the power available at the node on the path from source to sink, thus they result in the exhaustion and eventual death of nodes surrounding the sink node, thus generating routing holes reducing the network throughput. In order to address the issue in this research presents a novel protocol based on equal power consumption at all network nodes. The consume power fairly (CPF) protocol achieves a high power efficiency by distributing power consumption equal on all the network nodes. The protocol compares the power available on all the paths from source to sink and then selects the path with highest power. Additionally in order to reduce the transmitted data size, a lossy image compression technique based on adaptive Haar wavelet transform has been implemented. The simulation designs based on MATLAB consists of 100 randomly distributed nodes over an area of 100 m2, with 30 Kbits and 40 Kbits of packet sizes. The comparison between the proposed CPF protocol and the energy aware protocol has been carried out on the basis of number of iterations and the dead nodes in the network. Thorough simulations have been carried out based on different number of network iterations to validate the potential of the proposed solution. Moreover the implemenetation of multiscale retinex technique results in image enhancement and impoved classification. An implementation of the CPF protocol and image compression technique on a 100 node network with 500 iterations, results in the death of 13 nodes as compard to 38 dead nodes with energy aware protocol for the same network. Thus the performance comparision of CPF and energy aware protocol demonstrates an improvement of 81.19% for the energy consumption of the network. Thus the proposed algorithm prolongs the network under consideration by 57 – 62% as compared to networks with conventional routing protocols

An Efficient Analysis on Performance Metrics for optimized Wireless Sensor Network

Wireless Sensor Networks have the revolutionary significance in many new monitoring applications and self-organized systems. Based on the nature of application WSN are needed to support various levels of Quality of Services. Quality of service parameters are most significant aspect in WSN during data transmission from sensor nodes to sink. This paper surveys the factor on reliability, predictability, sustainability, optimal clustering and scheduling by analyzing various models existing in WSN. A network that satisfies all these Qos parameters ensures outstanding throughput in performance. We concluded by exploring some of the dimensions for research interest and addressed open issues ahead to enhance the performance of WSNs

FEATURE SELECTION FOR INTRUSION DETECTION SYSTEM IN A CLUSTER-BASED HETEROGENEOUS WIRELESS SENSOR NETWORK

Wireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment.  Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period

Despliegue óptimo de redes de distribución eléctricas soterradas usando métodos metaheurísticos y simulación

This document presents a planning model that allows optimizing the deployment of underground electrification networks for distribution considering the number of users simultaneously connected to a transformer. We present a model based on a heuristic process that seeks to reduce costs by using the resources required for a minimum cost routing on a geo-referenced scenario. The model is scalable because it allows the population density of the studied georeferenced area to be varied, that is, it adjusts to the use of resources required for different population quantities. Additionally, a simulation process is presented, articulated to the planning model using the Cymdist software, contemplating elements of a real underground electrification network, in order to verify voltage problems, failures, overloads, etc. The obtained results allow to quickly diagnose the possible deployment and routing options of underground networks for distribution, warning to decrease the times for deployment of new networks, in addition the work successfully explores the optimality principle and makes the heuristic process computationally useful. Finally, the proposal provides a road map with a view to the optimal planning of underground electrification networks for distribution.Este documento presenta un modelo de planeación que permite optimizar el despliegue de redes de electrificación soterradas para distribución considerando la cantidad de usuarios conectados simultáneamente a un transformador. Se presenta un modelo basado en un proceso heurístico que busca reducir costes por uso de recursos requeridos para un enrutamiento de mínimo costo sobre un escenario georreferenciado. El modelo es escalable pues permite que se varíe la densidad poblacional del área georreferenciada estudiada, es decir, se ajusta al uso de recursos requeridos para diferentes cantidades poblacionales. Adicionalmente se presenta un proceso de simulación articulado al modelo de planeación mediante el software Cymdist, contemplando elementos de una red de electrificación soterrada real, con la finalidad de verificar problemas de tensión, fallos, sobrecargas, etc. Los resultados obtenidos permiten diagnosticar rápidamente las posibles opciones de despliegue y enrutamiento de redes soterradas para distribución, advirtiendo disminuir los tiempos por despliegue de nuevas redes, además el trabajo explora con éxito el principio de optimalidad y hace que el proceso heurístico sea computacionalmente útil. Finalmente, la propuesta brinda un mapa de ruta con visión hacia la óptima planeación de redes de electrificación soterradas para distribución

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out

Compressive Data Gathering in Wireless Sensor Networks

The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs). Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compressed or weighted sums from sensors, and eventually recovers the original data. To prolong the lifetime of WSN, in this thesis, we present data gathering methods based on CDG. More specifically, we propose data gathering schemes using CDG by building up data aggregation trees from sensor nodes to a central unit (sink). Our problem aims at minimizing the number of links in the forwarding trees to minimize the number of overall transmissions. First, we mathematically formulate the problem and solve it using optimization program. Owing to its complexity, we present real-time algorithmic (centralized and decentralized) methods to efficiently solve the problem. We also explore the benefits one may obtain when jointly applying compressive data gathering with network coding in a wireless sensor network. Finally, and in the context of compressive data gathering, we study the problem of joint forwarding tree construction and scheduling under a realistic interference model, and propose some efficient distributed methods for solving it. We also present a primal dual decomposition method, using the theory of column generation, to solve this complex problem