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Elitist Genetic Algorithm Based Energy Efficient Routing Scheme For Wireless Sensor Networks

ISSN : 2231 - 4482
Undergraduate Theses from gdlhub / 2017-08-14 11:52:34
Oleh : Vinay Kumar Singh1, Vidushi Sharma2, International Journal of Advanced Smart Sensor Network Systems
Dibuat : 2012-07-02, dengan 1 file

Keyword : Energy Efficient, Genetic Algorithm, Elitism, Wireless Sensor Networks
Subjek : Elitist Genetic Algorithm Based Energy Efficient Routing Scheme For Wireless Sensor Networks
Url : http://airccse.org/journal/ijassn/papers/0122ijassn02.pdf
Sumber pengambilan dokumen : Internet

Wireless Sensor Networks have gained world-wide attention in recent years due to the advances made in


wireless communication, information technologies and electronics field. They consist of resource


constrained sensor nodes that are usually randomly or manually deployed in an area to be observed,


collecting data from the sensor field and transmitting the gathered data to a distant Base Station. The


nodes are energy limited sensors, and therefore it is important to increase the network lifetime. Energy


saving is one of the critical issues in the routing design in WSNs. The factors causing the unequal energy


dissipation are firstly, the distance between the nodes and base station and secondly, the distance between


the nodes themselves. Using traditional methods it is difficult to obtain the high precision of solution as the


problem is NP hard. Applying genetic algorithms (GAs) in finding energy efficient shortest route for WSNs


is emerging as an important field. The routing in WSN is a combinatorial optimization problem, hence GA


can provide optimized solution to energy efficient shortest path problem in WSN. This paper uses a forward


address based shortest path routing in the network. Genetic algorithm with elitism concept is used to obtain


energy efficient routing by minimizing the path length and thus maximizing the life of the network. The


proposed algorithm has its inherent advantage that it keeps the elite solutions in the next generation so as


to quickly converge towards the global optima. The results show that GAs are efficient for finding the


optimal energy constrained route as they can converge faster than other traditional methods used for


combinatorial optimization problems.

Deskripsi Alternatif :

Wireless Sensor Networks have gained world-wide attention in recent years due to the advances made in


wireless communication, information technologies and electronics field. They consist of resource


constrained sensor nodes that are usually randomly or manually deployed in an area to be observed,


collecting data from the sensor field and transmitting the gathered data to a distant Base Station. The


nodes are energy limited sensors, and therefore it is important to increase the network lifetime. Energy


saving is one of the critical issues in the routing design in WSNs. The factors causing the unequal energy


dissipation are firstly, the distance between the nodes and base station and secondly, the distance between


the nodes themselves. Using traditional methods it is difficult to obtain the high precision of solution as the


problem is NP hard. Applying genetic algorithms (GAs) in finding energy efficient shortest route for WSNs


is emerging as an important field. The routing in WSN is a combinatorial optimization problem, hence GA


can provide optimized solution to energy efficient shortest path problem in WSN. This paper uses a forward


address based shortest path routing in the network. Genetic algorithm with elitism concept is used to obtain


energy efficient routing by minimizing the path length and thus maximizing the life of the network. The


proposed algorithm has its inherent advantage that it keeps the elite solutions in the next generation so as


to quickly converge towards the global optima. The results show that GAs are efficient for finding the


optimal energy constrained route as they can converge faster than other traditional methods used for


combinatorial optimization problems.

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