Energy Aware Swarm Intelligence Approach for Route Selection with Multi hopping in Wireless Sensor Network

Energy Aware Swarm Intelligence Approach for Route Selection with Multi hopping in Wireless Sensor Network

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2015 by IJETT Journal
Volume-26 Number-5
Year of Publication : 2015
Authors : Kanu Priya Sood, Saloni Singla
DOI :  10.14445/22315381/IJETT-V26P250

Citation 

Kanu Priya Sood, Saloni Singla"Energy Aware Swarm Intelligence Approach for Route Selection with Multi hopping in Wireless Sensor Network", International Journal of Engineering Trends and Technology (IJETT), V26(5),286-289 August 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Wireless sensor networks are formed by small devices communicating over wireless links without using a fixed network infrastructure. Because of limited transmission range, communication between any two devices requires collaborating intermediate forwarding network nodes, i.e. devices act as routers and end systems at the same time. Communication between any two nodes may be trivially based on simply flooding the entire network. However, more elaborate routing algorithms are essential for the applicability of such wireless networks, since energy has to be conserved in low powered devices and wireless communication always leads to increased energy consumption. Wireless Sensor Networks consisting of nodes with limited power are deployed to collect and distribute useful information from the field to the other sensor nodes. Energy consumption is a key issue in the sensor’s communications since many use battery power, which is limited. In this paper, we describe a novel energy efficient routing approach which combines swarm intelligence, especially the genetic algorithm. The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using improved swarm intelligence. The research also includes maximizing the number of `alive` particulars overtime, thus prolonging the systems lifetime. Simulation results shows that compared with the traditional Q-Leach algorithm can obviously improve adaptability and reduce the average energy consumption effectively.

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Keywords
Wireless sensor networks, Routing protocols, Energy Efficiency, Swarm Intelligence, Genetic Algorithm.