# A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems
## 3 Analysis of the Data-Storage and Information-Discovery Schemes Proposed for WSANs
### Schemes Based on the Query-Driven Model  
As for the query-driven model, sensing data generated by the sensor nodes are stored in a distributed manner in WSANs, and queries are launched across the network to search the information that the consumers (sensor nodes, actuators, or other network users) are interested in. The typical storage method based on this model is data-centric storage [42], where a distributed storage system is constructed according to the data or event type. In the distributed storage system, each event type has a mapping node, also known as rendezvous node or home node. Because of the limited storage capacity of a mere sensor node, the mapping node may have one or more replicas in the storage system. When data are generated by the sensor nodes, the data will be sent to the mapping node or its replicas and stored according to the type of the data. A consumer interested in retrieving the data needs to launch and send a query to the mapping node or its replicas according to the interesting data type. In 2011, [PERSON] et al. analyzed several data-centric-storage and information-discovery schemes and found that the schemes using more than one rendezvous node perform much better than those just using a single rendezvous node for each data type in terms of minimizing the overall network traffic [42]. They classify the applications of data storage and information discovery into four profiles according to the taxonomy whether the data are aggregated or not and whether the consumption traffic dominates the production traffic or the other way around: (1) the consumption traffic dominates the production one with no data aggregation; (2) the production traffic dominates the consumption one with no data aggregation; (3) the consumption traffic dominates the production one with data aggregation; and (4) the production traffic dominates the consumption one with data aggregation. For each profile, they design a data-storage and information-discovery scheme. Specifically, in the first application profile, event data that are generated by any producer are first sent to the closest replica of the producer to get stored, and then the replica sends the copies of the event data to the remaining replicas. Any consumer just needs to send a query to the nearest replica to retrieve the data that it is interested in; in the second application profile, any producer stores its own data just at the closest replica. To discover the interesting information, any consumer first sends a query to its closest replica, and then the query will be forwarded in turn to the remaining replicas; in the third application profile, the way to store the data and discover the information is similar to that in the first profile, and the mere difference is that the event data in the third profile should be aggregated at the replicas before being forwarded to the left ones rather than forwarded directly; the last application profile is similar to the second one, the difference is that, after a consumer forwards a query along a data replication tree,which is rooted at the closest replica, all the retrieved data should be aggregated at all the replicas they pass in the replication tree. The mechanisms of data storage and information discovery in the four application profiles are illustrated in Figure 1.  
Figure 1: Data storage and information discovery in the four application profiles: (**a**) consumption traffic dominates production traffic with no data aggregation; (**b**) production traffic dominates consumption traffic with no data aggregation; (**c**) consumption traffic dominates production traffic with data aggregation; (**d**) production traffic dominates consumption traffic with data aggregation.