Open Access

ARTICLE

Nature Inspired Improved Firefly Algorithm for Node Clustering in WSNs

V. Manikandan^{1, *}, M. Sivaram¹, Amin Salih Mohammed², V. Porkodi³

1 Department of Computer Networking, College of Engineering and Computer Science, Lebanese French University, Erbil, 44001, Iraq.
2 College of Engineering and Computer Science, Lebanese French University, Erbil, 44001, Iraq.
3 Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, 44001, Iraq.

* Corresponding Authors: V. Manikandan. Email: ; .

Computers, Materials & Continua 2020, 64(2), 753-776. https://doi.org/10.32604/cmc.2020.010267

Received 21 February 2020; Accepted 11 April 2020; Issue published 10 June 2020

Abstract

Wireless Sensor Networks (WSNs) comprises low power devices that are randomly distributed in a geographically isolated region. The energy consumption of nodes is an essential factor to be considered. Therefore, an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime. This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication. This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies. Time Division Multiple Access (TDMA) scheduler is also improved with the characteristics/behavior of fireflies and also executed. At last, the development approach shows the progression of the network lifetime, the total number of selected Cluster Heads (CH), the energy consumed by nodes, and the number of packets transmitted. This approach is compared with Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Low Energy Adaptive Clustering Hierarchy (LEAH) protocols. Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach. The energy consumption of sensor nodes is reduced by about 50% and increases the lifetime of nodes by 78% more than AODV, DSR and LEACH protocols. The parameters such as cluster formation, end to end delay, percentage of nodes alive and packet delivery ratio, are also evaluated... The anticipated method shows better trade-off in contrast to existing techniques.

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