Despliegue de MDMS para ami basado en árboles de expansión usando Minimun Steiner Tree

This purpose of this article is to perform an optimum deployment of MDMS for AMI, based in the theory of expansion trees through Minimum Steiner Tree (SMT), for a geographic area of network users, randomly distributed. For this purpose, it is firstly needed to group together each one of the users (Meters) in a uniform way in clusters, distributed in length and width of the geographical area. Later, an algorithm of cluster named K-means is used, which is in charge to group the network elements (Meters) in K central groups, the distance of the elements to the nearest centroid determines the cluster that is formed (MDMS). A solution to the MDMS optimum deployment problem for Communication networks in AMI is proposed, based on graph theory, which can make a coverage of all the MDMS inside AMI, through a topology of expansion trees. For this purpose, the Minimum Steiner Tree (SMT) has been used, which determines the topology of optimum network for the link´s minimization distance between MDMS, given as the MDMS cost-Development Function.En el presente artículo se propone realizar un despliegue óptimo de MDMS para AMI basado en la teoría de árboles de expansión mediante Mínimum Steiner Tree (SMT) para un área geográfica determinada en donde los usuarios están distribuidos aleatoriamente. Para lo cual se busca primero agrupar a cada uno de los usuarios (Medidores) de manera uniforme en clústeres distribuidos a lo largo y ancho del área geográfica. A continuación se procede a emplear un algoritmo de clusterización llamado K-means que se encarga de conglomerar los elementos (Medidores) de la red en K grupos centrales, la distancia de los elementos al centroide más cercano determina el clúster que se forma (MDMS). Se propone una solución al problema de despliegue óptimo de MDMS para redes de comunicación en AMI basado en teoría de grafos, que realice una cobertura de todos los MDMS dentro de AMI a través de una topología de árboles de expansión. Para lo cual se ha empleado el algoritmo de Mínimum Steiner Tree (SMT) que determina la topología de red óptima para la minimización de la distancia de enlace entre MDMS dada como la Función costo-Despliegue de MDMS

Energy efficient multi channel packet forwarding mechanism for wireless sensor networks in smart grid applications

Multichannel Wireless Sensor Networks (MWSNs) paradigm provides an opportunity for the Power Grid (PG) to be upgraded into an intelligent power grid known as the Smart Grid (SG) for efficiently managing the continuously growing energy demand of the 21st century. However, the nature of the intelligent grid environments is affected by the equipment noise, electromagnetic interference, and multipath effects, which pose significant challenges in existing schemes to find optimal vacant channels for MWSNs-based SG applications. This research proposed three schemes to address these issues. The first scheme was an Energy Efficient Routing (ERM) scheme to select the best-optimized route to increase the network performance between the source and the sink in the MWSNs. Secondly, an Efficient Channel Detection (ECD) scheme to detect vacant channels for the Primary Users (PUs) with improved channel detection probability and low probability of missed detection and false alarms in the MWSNs. Finally, a Dynamic Channel Assignment (DCA) scheme that dealt with channel scarcities by dynamically switching between different channels that provided higher data rate channels with longer idle probability to Secondary Users (SUs) at extremely low interference in the MWSNs. These three schemes were integrated as the Energy Efficient Multichannel Packet Forwarding Mechanism (CARP) for Wireless Sensor Networks in Smart Grid Applications. The extensive simulation studies were carried through an EstiNet software version 9.0. The obtained experimental simulation facts exhibited that the proposed schemes in the CARP mechanism achieved improved network performance in terms of packets delivery ratio (26%), congestion management (15%), throughput (23%), probability of channel detection (21%), reduces packet error rate (22%), end-to-end delay (25%), probability of channel missed-detection (25%), probability of false alarms (23.3%), and energy consumption (17%); as compared to the relevant schemes in both EQSHC and G-RPL mechanisms. To conclude, the proposed mechanism significantly improves the Quality of Service (QoS) data delivery performance for MWSNs in SG