Trail-Using Ant Behavior Based Energy-Efficient Routing Protocol in Wireless Sensor Networks | Zendy

Soon-gyo Jung | Zendy; Byungseok Kang | Zendy; Sanggil Yeoum | Zendy; Hyunseung Choo | Zendy

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Trail-Using Ant Behavior Based Energy-Efficient Routing Protocol in Wireless Sensor Networks

Author(s) -

Soon-gyo Jung,

Byungseok Kang,

Sanggil Yeoum,

Hyunseung Choo

Publication year - 2016

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2016/7350427

Subject(s) - computer science , zone routing protocol , wireless routing protocol , routing protocol , computer network , ant colony optimization algorithms , dynamic source routing , overhead (engineering) , distributed computing , swarm intelligence , wireless sensor network , link state routing protocol , enhanced interior gateway routing protocol , distance vector routing protocol , routing (electronic design automation) , particle swarm optimization , algorithm , operating system

Swarm Intelligence SI observes the collective behavior of social insects and other animal societies. Ant Colony Optimization ACO algorithm is one of the popular algorithms in SI. In the last decade, several routing protocols based on ACO algorithm have been developed for Wireless Sensor Networks WSNs. Such routing protocols are very flexible in distributed system but generate a lot of additional traffic and thus increase communication overhead. This paper proposes a new routing protocol reducing the overhead to provide energy efficiency. The proposed protocol adopts not only the foraging behavior of ant colony but also the trail-using behavior which has never been adopted in routing. By employing the behaviors, the protocol establishes and manages the routing trails energy efficiently in the whole network. Simulation results show that the proposed protocol has low communication overhead and reduces up to 55% energy consumption compared to the existing ACO algorithm.

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