Affinity Propagation and Chaotic Lion Swarm Optimization Based Clustering for Wireless Sensor Networks


Wireless sensor networks (WSNs) can have their energy efficiency and lifespan significantly improved through the use of clusters, which are collections of nodes that share similar characteristics. However, the total number of clusters must be determined in advance, and selecting the optimal cluster heads (CHs) can be challenging. As a result, the overall performance of the network will almost certainly suffer as a result of these two factors. In light of this, the authors of this paper propose a new clustering method that they call APCLO. This method makes use of Affinity Propagation and Chaotic Lion swarm Optimization to create the best possible clusters. When building a cluster topology in APCLO, the Affinity Propagation (AP) algorithm is used. This algorithm forms the initial clustering based on the remaining energies of the nodes and the similarity of the distances between nodes. In addition to this, AP is responsible for determining the initial CHs at the same time. Chaotic Lion Optimization (CLO) is presented to find the best CHs, in which a fitness function is set according to residual energy and distance to the BS, and chaotic map is used to speed up the convergence of CLO. This is done in order to eliminate the outliers that were present in the initial CHs. The results of the simulation show that the APCLO protocol is superior to the comparison protocols in terms of the amount of energy it consumes, the throughput of the network it operates on, the speed at which it converges, and its lifetime. When it comes to the lifespan of the network, it goes up by 20.1%, 11.2%, and 13.5% respectively.

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