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Journal of Sensors
Volume 2016, Article ID 5836913, 19 pages
http://dx.doi.org/10.1155/2016/5836913
Research Article

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

1Punjab Technical University, Jalandhar 144001, India
2Department of Electronics and Communication Engineering, S.L.I.E.T., Longowal 148106, India

Received 3 July 2015; Revised 19 August 2015; Accepted 26 August 2015

Academic Editor: Pietro Siciliano

Copyright © 2016 Rajeev Kumar and Dilip Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.