Clustering schemes can reduce energy consumption, prolong network lifetime and improve scalability in wireless sensor networks (WSNs). In a typical cluster-based WSN, sensor nodes are organized into clusters. Each cluster elects a cluster head (CH) node. The CH is responsible for collecting the sensed data from cluster members, aggregating data and transmitting data to the sink node via a multi-hop path through intermediate CHs. Thus, the use of cluster techniques not only shortens the transmission distances for sensor nodes but also reduces energy consumption; however, each cluster imposes a larger load on the CH. Under this situation, CHs closer to the sink node tend to use up their batteries faster than those farther away from the sink node due to imbalanced traffics among CHs. To overcome this problem, we contribute to the energy balancing issues in WSNs from two aspects. In the first work, we first analyze the corona model. Based on analysis results, we found that nearly balanced energy consumption of WSNs can be achieved with the additional help of arranging different initial conditions. We then propose the Energy-balanced Node Deployment with Balanced Energy (END-BE) scheme and Energy-balanced Node Deployment with Maximum Life-Time (END-MLT) scheme, which determine the cluster density for each corona according to the energy consumption of each CH. Simulation results show that energy consumption is nearly balanced by implementing END-BE, and the network lifetime is greatly improved by adopting END-MLT. In the second work, we development a novel cluster-based routing protocol for corona-structured wireless sensor networks in order to balance the energy consumption among CHs. Based on the relaying traffic of each CH conveys, adequate radius for each corona can be determined through nearly balanced energy depletion analysis, which leads to balanced energy consumption among CHs. Simulation results demonstrate that our clustering approach effectively improves the network lifetime, residual energy and reduces the number of CH rotations in comparison with the Multi-Layer Clustering Routing Algorithm (MLCRA).