# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 2 Related Work  
Data aggregation is utilized in WSNs to diminish the resource consumption of redundant data when delivering information from sensor nodes to the sink. In specific, data aggregation schemes could be classified into three kinds, i.e., tree-based aggregation [9; 10; 11; 12], hybrid aggregation [13], and cluster-based aggregation [14; 15; 16; 17; 18; 19; 20; 21; 22]. In classical tree-based data aggregation schemes, a spanning tree rooted at the sink is constructed firstly, and then data are forwarded from leaves to root along the paths in the tree. In Tiny AGgreation (TAG) [10], after leaf nodes send their own data to their parent, the parent node aggregates data from its children and delivers the aggregated data to the root. Obviously, TAG is inefficient in case of dynamic topologies or link/device failures. In [11], [PERSON] et al. propose an aggregation tree construction/reorganization algorithm to minimize energy cost. By calculating and sending a small set of intuitive statistics, a parent node may be substituted by one of its sibling nodes based on attachment cost. In [12], an adaptive spanning tree algorithm (AST) is proposed, which adaptively builds and adjusts an aggregation spanning tree. Owing to the strategies of random waiting times and alternative father nodes, AST establishes a relatively balanced spanning tree with flexible adjustments. Considering that a single packet, as the output of aggregation algorithm at a given level of the tree, may stand for all the data coming from a subtree, if it is lost, the entropy from this subtree might be lost as well. As a typical hybrid data aggregation scheme, Tributary-Delta [13] combines the advantages of tree and multi-path by implementing them simultaneously in different regions of the network. It supports region adjustment in response to network condition changes, and determines the number of useful aggregates in the scenario. However, it may have a high overhead because of frequent update of the data gathering structure. Compared with tree-based and hybrid schemes, cluster-based aggregation schemes usually have good scalability and high energy efficiency [14; 15]. Considering that the cluster heads, which are close to the sink, relay data for others, in [16], [PERSON] et al. propose an energy-efficient unequal clustering scheme (EEUC). Cluster heads are elected by localized competition, and the competition range becomes small when it is near the base station. Therefore, those clusters closer to the sink have smaller sizes than others, and the energy consumption of cluster heads is balanced. Even though, the cluster maintenance is somewhat difficult. In recent years, some cluster-based schemes analyze various factors to select a cluster head from several candidates. In DHCR [17], energy consumption, adjustment degree and exact distance from sensors to the base station are three main parameters for cluster head selection. Multi-hop routing and clustering are combined to decrease the number of control packets. In [18], [PERSON] et al. propose REAC-IN to evenly distribute cluster heads based on the residual energy of each sensor and the average energy of sensors in the cluster. Together with isolated node checking considering power and distance, REAC-IN improves the cluster head selection process and avoids node isolation. To save energy for cluster reformation, [PERSON] and [PERSON] propose Hamilton energy-efficient routing protocol (HEER) [19]. Members in each cluster are linked on a Hamilton Path, and take turns to work as cluster head. In this way, no cluster reformation is required. However, the clusters are formed like LEACH in the first round, which may cause energy hole problem; the condition that all members in a cluster could communicate with each other is strict; the intra-cluster nodes in the Hamilton Path transmitting data in turn prolong the end-to-end delay. Moreover, some existing studies exploit spatial correlation to set aggregation areas.