# Resource Indexing and Querying in Large Connected Environments
## 3 Related Work
### Indexing Approaches
In [30], the authors proposed an architecture for indexing distributed IoT sources, where different discovery services are connected using Gaussian mixture models. Gaussian mixture models are used to represent a normally distributed population in an overall population. Traditional indexes are replaced by a mathematical representation of the distribution of IoT source attributes. This approach (DSIS) relies on probabilistic indexing to determine which discovery server or gateway the query must be forwarded to. Going down through the proposed structure, the aggregated models become less sensitive to probability. The search algorithm ends when it finds the source or reaches the maximum number of permitted hops. In addition, they proposed an updating mechanism with small and large intervals. Here, capacities are defined as the number of sensors for a WSN. The data points are updated following an updating mechanism using temporal or complete updates. The queries defined in this approach are queries considered guaranteed, since, in case of a failure, a recovery process is applied from an upper level of the architecture until trying all possible paths. These type of queries will return a response at all costs but may consume a lot of resources. In [31], a tree structure called BCCF-tree was proposed having two principle layers: an internal node level (nodes that contain two pivots and two pointers and are stored in a fog computing layer) and a leaf node level (nodes contain containers and stored in cloud computing layer). The proposed indexing approach is to create clusters by determining the distance \(r\) between two pivots, then determining the distance between one of these pivots and the centroid of existing clusters (created using k-means), such that this distance is less than \(r\). This process is repeated until an indexing tree is formed. When a user issues a query, the search starts between the query point and the pivots when going down the tree. The distance between the pivots and the query point shrinks while going down. The environment is divided following resource similarities inside the environment. The authors defined the capacity as the maximum number of sensors a container can accommodate. Since the query is forwarded to the appropriate resource as the radius shrinks, queries are considered one-path queries. [PERSON] et al. [32] proposed an IoT index method named A-DBSCAN, which consists of clustering sensors using DBSCAN. DBSCAN is a famous density-based algorithm that groups data points that are close to each other. This can result in forming large-sized clusters. The same clusters will be clustered again using K-means to simplify and reduce the form of these clusters. Users can start sending queries to these clusters. In addition, in the user search method, users can send feedback on the obtained responses. Based on this feedback, the responses are updated for the next queries. Since this algorithm depends on users' feedback and directly forwards the query to the device, the queries can be considered fast. The device capacity is not considered, since this is a centralized architecture and these criteria have no importance in this type of architecture. Moreover, this algorithm does not consider that zones may be uncovered.