Abstract:
In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.
Machine summary:
Hybrid Bio-Inspired Clustering Algorithm for Energy Efficient Wireless Sensor Networks Amirhossein Barzin PhD Candidate, Industrial Engineering, Azadi Pardis of Yazd University, Yazd University, Yazd, Iran.
In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks.
The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round.
Similarly, clustering and routing are two renowned optimization problems, which are researched broadly for developing many bio-inspired based algorithms in the field of wireless sensor networks (Pratyay & Prasanta, 2014).
The simulations outcomes showed that the proposed approach outperformed existing distributions based clustering algorithms without GT, such as LELC and LEACH in terms of saving energy and increasing the number of data packets received by the sink.
A multi-objective fitness function has been formulated, to choose the proper CHs. The centralized location-unaware clustering algorithm is executed at the sink, using energy levels and the adjacency information of the sensor nodes as input parameters.
Hybrid swarm intelligence- based clustering algorithm for energy management in wireless sensor networks.
Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach.
Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks.