Optimal positioning of GPRS concentrators for minimizing node hops in smart grids considering routing in mesh networks

Souza, Gustavo Batista de Castro; Vieira, Flávio Henrique Teles; Lima, C.R.; Júnior, Getúlio Antero de Deus; Castro, Marcelo Stehling de; Araujo, S.G.

doi:10.1109/isgt-la.2013.6554419

Cited by 15 publications

(15 citation statements)

References 3 publications

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“…Las características de la medición inteligente advierte que los sensores-medidores inteligentes son fijos y, por tanto, la colocación de sitios de concentración y agregación de datos resulta más simple; sin embargo, se debe garantizar su cobertura, con limitantes en la capacidad de cada tecnología que pudiera ser empleada [11][12].…”

Section: Dimensionamiento Y Enrutamiento De La Red De Sensores Inalámunclassified

“…La capacidad de cobertura de un punto de agregación tiene una forma específica omnidireccional por lo que si se colocan en posiciones clave permitirán la recolección de datos eficientemente; por ello la facilidad que ofrecen los sistemas de posicionamiento global e información geográfica (GIS) mejorará la precisión para la implantación suponiendo un avance en las técnicas de optimización haciendo además a la presente propuesta universal [5,11].…”

Section: Introductionunclassified

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Óptimo despliegue y enrutamiento de UDAP para infraestructura de medición avanzada basada en el algoritmo MST

Ganán

Inga

Hincapié

2017

Ingeniare. Rev. chil. ing.

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RESUMENEl presente artículo permite optimizar el costo por despliegue de los puntos de agregación de datos universales (UDAP) para la infraestructura avanzada de medición inteligente (AMI) considerando cobertura y capacidad. Se presenta un modelo de enrutamiento entre UDAPs basado en minimum spanning tree (MST) para encontrar el mínimo número de enlaces en una zona georreferenciada específica. Al estar georreferenciada se logra que la localización de UDAP y la topología de la red sean reales. La búsqueda de los lugares óptimos de un conjunto de sitios candidatos advierte la necesidad de un algoritmo de optimización que permita minimizar los sitios candidatos y los convierta en sitios factibles activos en el diseño previo al despliegue de la red. Los resultados obtenidos implican un punto base firme para que las empresas de distribución eléctrica establezcan el número adecuado de concentradores (UDAP). Dentro del trabajo se consideran ciertas tecnologías de comunicación aplicadas en AMI como: redes celulares (CDMA, GPRS, LTE) o el estándar IEEE 802.15.4 g. El menor costo basado en el uso óptimo de recursos para despliegue de infraestructura toma gran importancia para la planificación y escalabilidad de la red de AMI.Palabras clave: Redes eléctricas inteligentes, infraestructura de medición avanzada, punto de agregación de datos universal, mínimo árbol de expansión, optimización, costos, red de sensores inalámbricos. ABSTRACT This paper optimizes the cost of deployment of universal data aggregation points (UDAP) for advanced smart metering infrastructure (AMI), considering coverage and capacity. We propose a routing model between UDAPs, based on minimum spanning tree algorithm (MST) to find the minimum number of links in a specific geo-referenced area. Because it is geo-referenced, the location of the UDAP and the topology

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Section: Dimensionamiento Y Enrutamiento De La Red De Sensores Inalámunclassified

Section: Introductionunclassified

Óptimo despliegue y enrutamiento de UDAP para infraestructura de medición avanzada basada en el algoritmo MST

Ganán

Inga

Hincapié

2017

Ingeniare. Rev. chil. ing.

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“…This step of their heuristic algorithm has a high complexity, because every pole that is not selected is checked to see if it can replace a subset of two or more selected poles. In our preliminary work [23], we have proposed a modified K-means algorithm for DAP placement in the single-hop communication scenario only DRAFT February 20,2018 considering network coverage, assuming SMs and poles are uniformly distributed through the area. The K-means algorithm chooses random locations as primary potential locations for DAP placement and all the network construction is conducted based on these locations.…”

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confidence: 99%

“…Therefore, in this paper, we apply a different and more suitable heuristic algorithm by which the network is constructed from pole locations. In [20], the authors develop a K-means based algorithm for placing a fixed number of aggregators on selected utility poles with the objective of minimizing the total number of hops SMs require to access the selected data aggregators. This work is among the first to consider multi-hop communication and minimize the experienced delays by minimizing the total number of hops.…”

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Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

Aalamifar

Lampe

2018

IEEE Trans. Commun.

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In an advanced metering infrastructure (AMI), data acquisition points (DAPs) are responsible for collecting traffic from several smart meters and automated devices and transmitting them to the utility control center. Although the problem of optimized data collector placement has already been addressed for wireless broadband and sensor networks, DAP placement is quite a new research area for AMIs. In this paper, we investigate the minimum required number of DAPs and their optimized locations on top of the existing utility poles in a distribution grid such that smart grid quality of service requirements can best be provided. In order to solve the problem for large-scale AMIs, we devise a novel heuristic algorithm using a greedy approach for identifying potential pole locations for DAP placement and the Dijkstra's shortest path algorithm for constructing reliable routes. We employ the characteristics of medium access schemes from the IEEE 802.15.4g smart utility network (SUN) standard, and consider mission-critical and non-critical smart grid traffic. The performance and time-complexity of our algorithm are compared with those obtained by the IBM CPLEX software for small scenarios. Finally, we apply our devised DAP placement algorithm to examples of realistic smart grid AMI topologies. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible 1 I. INTRODUCTION As part of the broader concept of smart grids, advanced metering infrastructures (AMIs) are being massively deployed almost everywhere in the world. AMIs are responsible for reading the energy consumption from thousands of smart meters (SMs) [1]-[4], monitor the last-mile automated devices for reporting emergency events [3], [4] such as electricity blackout and also unauthorized access to the power system [5]. In an AMI, due to the large number of devices and their distances, data collectors are installed to collect traffic from several endpoints and transmit them to the utility control center on their behalf.The placement of collector nodes, which are known as data acquisition points (DAPs) [4], [6] or aggregators [7], [8] in smart grid communication networks (SGCNs) and as relay station [9], gateway [10] or sink [11], [12] in broadband wireless access networks and sensor networks, respectively, has previously been investigated [13]- [16]. However, there is a combination of features and requirements in AMI that render the problem sufficiently different from the data collector placement in other types of networks so that a new problem formulation and solution for network planning are needed. For example, in sensor networks, the collector nodes can be placed on selected endpoint nodes [10] or in arbitrary locations [13]. Different from this, in a distribution grid with overhead powerlines, the utility poles are ideal locations for DAP placement [4], since this extends network coverage and also eliminates the cost of new tower installations. Moreover, since...

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“…Los modelos matemáticos o algoritmos para encontrar las distancias más cortas son varios, pero para nuestro caso hemos tomado como base los trabajos de investigación de [16], [17] o [18]que han usado k-means como método de conglomeración. En [2] se propone un problema de optimización que a más de usar el algoritmo dijkstra para las rutas más cortas, aglutina las poblaciones de medidores inteligentes más cercanos a una estación base a través del algoritmo k-means y de este modo clusteriza de forma idónea los dispositivos que se requieren en AMI.…”

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Shortest path for optimal routing on Advanced Metering Infrastructure using cellular networks

Inga

Hincapié

Suarez

et al. 2015

IEEE Colombian Conference on Communication and Computing (IEEE COLCOM 2015)

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In order to deploy an Electrical Energy Advanced Metering Infrastructure (AMI), Electrical Enterprises traditionally have to rent the infrastructure of a primary Mobile Network Operator (MNO). This paper presents a model for optimizing the Mobile Virtual Network Operator (MVNO) infrastructure employed for the communication between the Central Office with the Smart Meters (SM). It is proposed a model which minimizes the cost of the primary network operator infrastructure and reduces the expenses of the secondary network operator while saves and facilitates the spectrum utilization. The algorithm employed in the optimization model searches the best scenario for the SM clustering with purpose of minimizing the distance among Cellular Base Stations. In this paper the Smart Meters (SM) are considered fixed data terminals placed in Neighborhood Area Networks (NAN) covered by the mobile communications network.

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Optimal positioning of GPRS concentrators for minimizing node hops in smart grids considering routing in mesh networks

Cited by 15 publications

References 3 publications

Óptimo despliegue y enrutamiento de UDAP para infraestructura de medición avanzada basada en el algoritmo MST

Óptimo despliegue y enrutamiento de UDAP para infraestructura de medición avanzada basada en el algoritmo MST

Cost-Efficient QoS-Aware Data Acquisition Point Placement for Advanced Metering Infrastructure

Shortest path for optimal routing on Advanced Metering Infrastructure using cellular networks

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