Abstract
Clustering, as an essential part in an hierarchy protocol that can prolong the network lifetime, is influenced by the cluster head selection and clustering scheme. A new clustering algorithm called clustering by fast search and finding of density peaks (CFSFDP) based on local density and distance is implementable and efficient. In this paper, we combine this clustering algorithm with a hierarchy protocol in wireless sensor networks (WSNs). However, energy consumption in each round is unbalanced only considering these two variables during the clustering phase, which leads to the early death of the first node. In order to solve this problem, we take residual energy into consideration in our improved CFSFDP-E (energy) algorithm so as to ultimately balance the energy consumption of the network. We analyze different forms of energy and choose a dynamic threshold for each round in the CFSFDP-E algorithm. Simulation results demonstrate that the proposed approach can not only postpone the death of the first node by almost 50% compared to LEACH, but that it also outperforms several related protocols with respect to energy efficiency.
Highlights
Wireless Sensor Networks (WSNs) are composed of a large number of multifunctional low cost sensor nodes with restricted battery power supplies, which are randomly deployed inside the sensing field [1]
Simulation Results of CFSFDP-E Compared with LEACH and CFSFDP
We can find that the FND of CFSFDP algorithm in WSN occurs much earlier than that of LEACH. The reason for this result is that the CFSFDP algorithm selects cluster heads with local density and distance, so the location of all nodes is fixed if there is no node to die and the value of local density and distance will not change, which means when a node is selected as CH, it will remain the CH in the following rounds until the energy is exhausted
Summary
Wireless Sensor Networks (WSNs) are composed of a large number of multifunctional low cost sensor nodes with restricted battery power supplies, which are randomly deployed inside the sensing field [1]. It is indispensable to balance the energy consumption among sensor nodes to prolong the network lifetime for clustered WSNs. There have been lots of clustering algorithms and schemes applied to WSNs. KM-LEACH [3] uses the K-means clustering algorithm to form clusters. The distance from the nodes to the base station, neighbors and the number of neighbors are considered for cluster head selection [7]. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy This approach is proved to have a good energy balancing performance and the network lifetime is greatly enhanced [9].
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