Multilevel Clustering Protocol Research Articles

CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs

Artificial bee colony or ABC is an interesting meta-heuristic algorithm designed to solve various continuous optimization problems. However, it cannot be directly applied to solve discrete problems such as clustering of sensor nodes in the wireless sensor networks (WSNs). For this purpose, in this paper, we present a chaotic discrete version of the ABC algorithm, denoted as chaotic discrete ABC (CDABC). By using the CDABC algorithm, we propose a novel clustering protocol that can be used to organize WSNs into multiple levels of clusters to reduce their energy consumption. The main objective of this protocol is to improve WSN’s lifetime by selecting appropriate nodes as cluster heads in each clustering level and reducing the energy costs of the inter-cluster and intra-cluster communications. Extensive simulations results validate the effectiveness of the proposed CDABC-based multi-level clustering protocol in improving the network lifetime.

Multi-level clustering protocol for load-balanced and scalable clustering in large-scale wireless sensor networks

The advent of wireless sensor networks (WSNs) has revolutionized the field of smart applications. In order to improve the performance of WSNs, refinement of clustering and routing protocols can make a vast difference. Existing classical and evolutionary optimization technique-based protocols have high computational complexity since clustering and routing problems are solved separately. Moreover, these protocols suffer from hot-spot problem due to uneven formation of clusters. In this paper, we propose a multi-level clustering protocol (MLCP) for energy-efficient data gathering in large-scale WSNs. Additionally, a hierarchical clustering architecture is designed in MLCP to jointly solve the problems of clustering and routing. Further, for the purpose of cluster head selection, a hybrid dragonfly algorithm-based particle swarm optimization technique is proposed which combines the exploration and exploitation capabilities of dragonfly algorithm and particle swarm optimization, respectively. MLCP considers intra-cluster distance, node degree and inter-cluster distance for the formation of scalable, load-balanced and energy-efficient clusters. To demonstrate the full potential of MLCP, network simulations have been carried out in diverse network conditions. MLCP has shown up to 90% increase in the network lifetime and an improvement of 19.36% in conservation of energy in comparison with the competent protocols. The comparison of obtained results with state-of-the-art clustering protocols clearly establishes the superiority of MLCP in achieving load-balanced, scalable and energy-efficient clustering.

An Energy-Efficient Multilevel Clustering Algorithm for Heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are generally believed to be homogeneous, but some sensor nodes of higher energy can be used to prolong the lifetime and reliability of WSNs. This gives birth to the concept of Heterogeneous Wireless Sensor Networks (HWSNs). Clustering is an important technique to prolong the lifetime of WSNs and to reduce energy consumption as well, by topology management and routing. HWSNs are popular in real deployments (Corchado et al., 2010), and have a large area of coverage. In such scenarios, for better connectivity, the need for multilevel clustering protocols arises. In this paper, the authors propose an energy-efficient protocol called heterogeneous multilevel clustering and aggregation (HMCA) for HWSNs. HMCA is simulated and compared with existing multilevel clustering protocol EEMC (Jin et al., 2008) for homogeneous WSN. Simulation results demonstrate that the proposed protocol performs better.