Genetic algorithm for data aggregation trees in wireless sensor networks

Islam, Obidul; Hussain, Sajid; Zhang, Haiyi

doi:10.1049/cp:20070386

Cited by 29 publications

(19 citation statements)

References 11 publications

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“…Para la generación de la población inicial existe la condición de que los genes (nodos) consecutivos no pueden exceder una distancia umbral [11], mientras que la selección de un nodo de próximo salto depende de la distancia del enlace, del consumo de energía promedio del camino, y de la distancia del nodo actual hasta la estación base. Para la mutación se comparte información global de la población, gracias a la existencia de un registro histórico del mejor cromosoma (aquel con máximo valor de aptitud hasta el momento).…”

Section: B Trabajos Relacionadosunclassified

“…En MOR4WSN la representación del cromosoma se define como en los trabajos previos [11][12][13][14][15]; el cromosoma representa un árbol de enrutamiento donde cada nodo está etiquetado con un número entero de 0 a N, siendo N la cantidad de nodos, como se observa en la figura 2a. En la figura 2b el valor del gen en la posición 0 es 2, indicando que el nodo 0 selecciona al nodo 2 para transmitirle sus datos; el nodo 2 transmite hacia el nodo 5, y el valor en 5 es 6, indicando que el próximo nodo es la estación base.…”

Section: B Representación De La Soluciónunclassified

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Adaptación de una metaheurística evolutiva para generar árboles enrutadores en una red de sensores inalámbricos del contexto de la agricultura de precisión

Vivas

Muñoz

2016

rev.ing.univ.Medellin

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ResumenLas redes de sensores inalámbricos (WSN, por sus siglas en inglés) son ampliamente usadas para monitorizar variables de interés en fenómenos como campos de cultivo, contexto en el cual la inclusión de una WSN fortalece el quehacer de la agricultura de precisión. En este campo de aplicación surgen retos de diseño para las redes como la necesidad de alcanzar una longevidad de los nodos sensores, (que sirven como fuente de datos) de por lo menos seis meses, en correspondencia con el período de sembrado. Trabajos previos afirman que la técnica de enrutamiento usada en una red de sensores inalámbricos es un factor de alta incidencia en su longevidad. En este artículo se presenta MOR4WSN, una propuesta de enrutamiento que surge de la adaptación de un algoritmo genético multi-objetivo. A partir de despliegues de WSN, MOR4WSN crea topologías con estructura jerárquica que permitan un enrutamiento apropiado preservando la longevidad de la red. Los resultados actuales muestran resultados prometedores.Palabras clave: agricultura de precisión, algoritmos evolutivos, optimización multi-objetivo, redes de sensores inalámbricos. * La presente propuesta estuvo apoyada por el Programa para el Fortalecimiento de la Red Interinstitucional de Cambio Climático y Seguridad Alimentaria -RICCLISA; específicamente por el proyecto Servicios de generación de alertas Agroclimáticas como soporte a la toma de decisiones del sector Cafetero Colombiano -AgroCloud. Dicho proyecto es financiado por la Universidad del Cauca, el CINARA, CENICAFE, CIAT, CREPIC y el programa de Redes de Conocimiento de COLCIENCIAS para el periodo

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Section: B Trabajos Relacionadosunclassified

Section: B Representación De La Soluciónunclassified

Adaptación de una metaheurística evolutiva para generar árboles enrutadores en una red de sensores inalámbricos del contexto de la agricultura de precisión

Vivas

Muñoz

2016

rev.ing.univ.Medellin

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“…In [123] the authors present a genetic algorithm-based approach to generate balanced and energy efficient data aggregation spanning trees for WSNs. In the algorithm, the gene index determines a node and the gene's value identifies the parent node.…”

Section: Survey Of Existing Workmentioning

confidence: 99%

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Adnan

Razzaque

Ahmed

et al. 2013

Sensors

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For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

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“…In this paper, generally, two methods are considered to manage the power among the nodes. The first method is the power aware version (PEDAPPA) which has an attempt to extend the lifetime by balancing the energy consumption among the nodes, unlike in the second method, PEDAP, the non-power aware version minimizes the total energy that is consumed by the system in each data gathering round [5]. This method extends the lifetime of the last node.…”

Section: Literaturementioning

confidence: 99%

A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA

Norouzi¹,

Babamir²,

Orman³

2012

WSN

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Energy is one of the most important items to determine the network lifetime due to low power energy nodes included in the network. Generally, data aggregation tree concept is used to find an energy efficient solution. However, even the best aggregation tree does not share the load of data packets to the transmitting nodes fairly while it is consuming the lowest possible energy of the network. Therefore, after some rounds, this problem causes to consume the whole energy of some heavily loaded nodes and hence results in with the death of the network. In this paper, by using the Genetic Algorithm (GA), we investigate the energy efficient data collecting spanning trees to find a suitable route which balances the data load throughout the network and thus balances the residual energy in the network in addition to consuming totally low power of the network. Using an algorithm which is able to balance the residual energy among the nodes can help the network to withstand more and consequently extend its own lifetime. In this work, we calculate all possible routes represented by the aggregation trees through the genetic algorithm. GA finds the optimum tree which is able to balance the data load and the energy in the network. Simulation results show that this balancing operation practically increases the network lifetime

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Genetic algorithm for data aggregation trees in wireless sensor networks

Cited by 29 publications

References 11 publications

Adaptación de una metaheurística evolutiva para generar árboles enrutadores en una red de sensores inalámbricos del contexto de la agricultura de precisión

Adaptación de una metaheurística evolutiva para generar árboles enrutadores en una red de sensores inalámbricos del contexto de la agricultura de precisión

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA

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