Energy efficiency is one of the most important issues for WSNs, because the battery of each wireless sensor node cannot be recharged or replaced. Therefore, all batteries have to be well managed, in order to provide a long network lifetime, as well as to reduce energy consumption for WSNs, particularly in clustering and routing. In this paper, we propose an energy-efficient self-organized
... [Show full abstract] clustering model with splitting and merging (EECSM), which performs clustering and then splits and merges clusters for energy-efficient cluster-based routing. EECSM uses information of the energy state of sensor nodes, in order to reduce energy consumption and maintain load balance. We show the validity of splitting and merging of clusters and then compare the performance of the proposed EECSM with that of a well-known cluster-based self-organization routing protocol for WSNs. The results of our experiment show good performance of EECSM, in terms of network lifetime, residual energies, scalability, and robustness.