# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 2 Related Works  
The research on ESNs includes many aspects, such as system survival time, routing selection and data fusion mechanism. Among them, how to reduce energy consumption is one of the key issues in ESN design, which directly determines the working lifespan of the network. Research shows that the communication between nodes is the main factor of the energy consumption in wireless sensor networks (WSNs), and some experts have put forward routing protocols with low energy consumption. The structure of self-organized sensor node clusters is proved to be a reasonable and effective mode to meet the need for sensors' distributed deployment, which can save energy and optimize the topology of WSNs [21], however, the reasonable distribution of cluster heads can make the network energy consumption more stable and the energy distribution more uniform. In the early clustering protocol LEACH [22], it was proposed to select a node as cluster head in a rotation way for each node, so that the nodes can be elected as cluster heads as much as possible. This way makes the sensor nodes can be self-organized by an adaptive way. However, the problem is that the imbalance in energy consumption of the network is inevitable, and residual energy of the nodes is not taken into consideration of the cluster heads' selection. Subsequently, the cluster head selection algorithm is continuously optimized. In HEED [23], cluster head rotation and topology reconstruction were carried out in the whole network for a preset time period. In EDCR [24], [PERSON] et al. presented that the calculation of the energy threshold is adopted dynamically to trigger the cluster head rotation process. Although the rotation strategy can balance the network energy consumption, excessive alternation will influence the stabilization of the nodes' organization and lead to heavy interruption of the transmission. In [25], an improvement scheme was proposed based for traditional LEACH by using multiple conditions to determine the cluster head's selection, which can reduce the energy consumption during the stage of cluster's formation, and increase the efficiency of packet transmission. LEACH-V was proposed for resolving the problem of energy imbalance between the nodes and low utilization rate of the whole network [26]. The residual energy and the average energy of the network were considered during the process of calculating the threshold for cluster head's selection. Also, a Voronoi diagram was used to restrict the situation of the cluster heads. A two levels-weighted clustering algorithm (TL-WCA) was proposed for optimizing the routing path [27]. The network is divided into several clusters based on the improved competition mechanism. Then, the elected cluster head uses greedy algorithm to find the shortest path principle and then form a chain. Next, the nodes with more than the average energy in the chain and close to the sink are selected as the cluster head, and then execute the data fusion. In [28], the possible redundancy of cluster heads was analyzed, and a clustering routing algorithm based on cluster head redundancy was proposed to apply in industrial wireless sensor networks. When the energy of the cluster head was insufficient, the redundant cluster heads can be switched to the work state. For the isolated nodes that cannot communicate with any cluster head directly, the multi-hop routing mechanism based on a probabilistic model was used for data forwarding. In [29], an analytical model for cluster head rotation was designed, which built a contention scheme for cluster head selection to remove the energy consumption in cluster head rotation. An EEHC algorithm for multi level energy heterogeneous network was proposed in [30], where the nodes with larger weights were more likely to become cluster heads. The main purpose of ESNs is that the sensors monitor the environment constantly and transmit the monitoring data to the cluster head, then forward the aggregation data to the sink through the cooperation of other nodes.