## 4 Data Aggregation  
In a typical wireless sensor network, a large number of sensor nodes are redundantly scattered to collect the application specific information from the monitoring environment. This redundancy is for two reasons: first, sensing devices are usually low-powered sensor nodes with limited memory, computation ability, sensing and communication ranges. These inherent characteristics of WSNs necessitate a level of redundancy in implementation to achieve the expected level of QoS. Another reason is the common deployment of WSNs in remote and hostile environments, where the possibility of node failure due to environmental conditions is high and each individual node is usually not accessible after deployment. However, this redundancy entails generation of large numbers of highly correlated or even analogous data, which imposes high level of energy expenditure into the network to be processed and forwarded to the BS. Therefore, to save limited battery power of WSNs, Data aggregation mechanisms are proposed.  
Data aggregation can be defined as _\"a systematic distributed in-network collection, processing and combining of sensed data from several nodes that sense the same phenomenon\"_. The main objective of data aggregation is to eliminate redundant transmission and provide fused information to increase network lifetime. Although data aggregation may degrade some QoS characteristics of the network such as latency and data accuracy, achieving a trade-off between energy saving and expected level of QoS can be the proper solution. To achieve the optimal trade-off, data aggregation techniques should be closely coupled with data routing protocols to have complete domination on different forwarding paradigms to promote in-network data aggregation efficiency.  
Consequently, data aggregation protocols are usually categorized according to the network architecture as flat and hierarchical (grid, tree, chain and cluster based) techniques. There are couples of existing survey papers on data aggregation [101; 102; 103; 104], which investigate several data aggregation techniques including cluster-based protocols. Following the same grouping merit, all the surveyed algorithms in this paper can be considered as cluster-based data aggregation algorithms. since one of the fundamental objectives of cluster-based algorithms is to achieve higher level of energy saving by minimizing transmission costs through data aggregation in CHs and forwarding path. Nevertheless, cluster-based protocols generally focus on improving the routing aspect of protocols and accept simple aggregation operators like MIN, MAX, AVG and XOR to fuse data or to identify the identical received packets [105]. However, in this section, we survey several protocols, which specifically investigate data aggregation process in details in homogeneous cluster-based WSNs. We classify the surveyed cluster-based data aggregation protocols based on the class of data correlation and compression into:  
* Spatial: this class exploits the data correlation and redundancy of the readings of a node as a function of the readings at nearby sensors.
* Temporal: in this class, the data correlation of sensor readings as a function of its reading in the past is put to use.  
Figure 8: The taxonomy of the data aggregation phase.  
* Spatiotemporal: is the combination of the above two classes. It is interpreted as the utilization of readings correlation of sensor nodes in both spatial and temporal aspects.  
The taxonomy of data aggregation mechanisms based on the classes of data correlation is given in Figure 8.