## 4 Data Aggregation
### Spatial  
Cluster-based data aggregation architecture for Structural Health Monitoring (SHM) is presented by [106]. The paper points out the importance of data aggregation in the SHM application because of the large volume of data generated. The main objective of the paper is to leverage intelligent monitoring by organizing the aggregation method into a three-tier hierarchy. The three-level structure of data aggregation consists of localized computation, data aggregation and distributed computation. In localized computation, every sensor node executes filtering to remove the invalid measurements, and feature extraction to perform pattern recognition of signals and data compression. At the data aggregation stage, the cluster members' data received by CHs go through three steps of filtering, spatial and semantic correlations. While filtering does the same as the previous stage to remove the unreliable results and trivial events, spatial and semantic correlation summarizes the results by their types and explores the contextual relation of different types. The provided regional views of monitored area or objects by parallel clusters are fused at the CH-CH distribution level by location combination, information confidence estimation and inference merging. Filtering is the basic technique used in different levels of the protocol to eliminate noises at the sensor node level and to increase the confidence of the intermediary results at the CH level. Therefore, the paper integrates five filters: threshold filter, deviation filter, quality of information filter, semantic filter and location filter. Through these three steps, raw data generated by each node is condensed into a smaller size and more valuable data until reaching the BS and hence, a great amount of energy is saved in the data communication phase and the reliability of the received information is improved. The authors of [107] combine Direct Diffusion [108] with clustering and introduce new features of layered data aggregation and dynamic data aggregation points. The so-called Clustered Diffusion with Dynamic Data Aggregation (CLUDDA) protocol constructs the clusters at the initial stage and then uses direct diffusion to route back the aggregated data towards the BS. Although broadcasting of interest messages throughout the network using flooding in Direct Diffusion is an intensive energy consuming operation, the number of nodes involved in this process in clustering protocols is limited to CHs and gateway nodes. To allow the intermediary nodes to do the layered data aggregation, the entire query definitions are provided within interest messages. The queries definitions contain all the required components and describe the operations that need to be performed on components. Each CH or gateway node maintains a query cache including the different data components that were aggregated. This list is used to obtain the final data. Thus using this layered process, data are aggregated in small steps and are reduced along the initial stages of the data propagation path from the node servicing the query towards the node, which propagated the query. Besides, by the change of the location of the source nodes, new CHs or gateways closer to the source which are equipped with the new query definition performs data aggregation and in this way, the aggregation points dynamically change throughout the network, which leads to the even distribution of energy expenditure throughout the network. Grid-based Routing and Aggregator Selection Protocol (GRASS) is presented in [109] to maximize network lifetime by minimizing the aggregation points while routing data to the BS. The paper combines two issues of the selection of the data aggregation points and the optimal routing route of the aggregated data to the BS, and addresses two issues jointly to achieve the optimum solution. GRASS divides a network into a Virtual Grid Architectures (VGAs). Each VGA consists of several nodes and a CH, which is selected periodically, while the square zones are considered fixed.