An adaptive clustering algorithm for dynamic heterogeneous wireless sensor networks

In the heterogeneous wireless sensor networks, most algorithms assume that nodes are heterogeneous in terms of their initial energy (we refer to as static energy heterogeneity). However, little research focuses on dynamic energy heterogeneity, which means that energy heterogeneity of nodes results from adding a percentage of the population of sensor nodes to the network when the operation of the network evolves. In this paper, we combine the idea of static energy heterogeneity with that of dynamic energy heterogeneity and then propose a dynamic model for heterogeneous wireless sensor networks. We refer to this dynamic model as dynamic heterogeneous wireless sensor networks (DHWSNs). Furthermore, we give a detailed estimation and analysis of this dynamic model in terms of the lifetime and data packets of the network. Moreover, we optimize the number of clusters for DHWSNs. In order to adapt the dynamic change of topology in DHWSNs, an adaptive clustering algorithm for dynamic heterogeneous wireless sensor networks (ACDHs) is proposed. In ACDHs, the cluster head is elected according to the initial energy in each node, the remaining energy in each node, and the average energy of the network. Simulations show that by adjusting dynamic parameters and heterogeneity parameters, ACDHs yields longer lifetime and more data packets of the network compared with current homogeneous and heterogeneous clustering algorithms.