## 2 Related Work  
A detailed literature survey that discusses key research on DCS techniques is presented in [1,5]. This section mentions researches, which are closely related to the research reported on in this paper. In order to process similarity search queries efficiently, [PERSON], _et al._[6] propose a novel framework over a data-centric storage structure, referred to as the Similarity Search Algorithm (SSA), based on the concept of a Hilbert Curve. The lack of global knowledge about the entire sensor database is identified as one of the major challenges in processing a sensor network similarity search query. However, in order to overcome this constraint, SSA presents a network layout based on a HilbertCurve, and hence, successfully avoids the need to collect data from all sensors when searching for the most similar data item. SSA divides the whole network recursively into \(\mathcal{A}^{l}\) square quadrants where \(l\) denotes the number of levels. The center (referred to indexing node and denoted by \(I\)) of each square quadrant (cell) is responsible for storing a sub-range of the entire range of an event denoted by \(R\) where \(R\)_L_ and \(R\)_U_ denotes the lower bound and upper bound, respectively. The data sub-range for which _In_ (IDth indexing node) is responsible is denoted by \((R\emph{{L}}^{\text{ID}}\), \(R\emph{{U}}^{\text{ID}})=(R\emph{{L}}+(\emph{{I}}\emph{{D}}-1)\times r\), \(R\emph{{L}}+\emph{{I}}\emph{{D}}\times r)\)**,** where \(n\times r=R\), \(n\) is the number of indexing nodes. However, SSA is not applicable in multi-dimensional range queries or similarity searching. Furthermore, statistically 80%\(\sim\)90% of the reported sensed data points tend to be very close to the mean range. But, SSA divides the domain of an attribute into equal sub-ranges and maps each sub-range to a square quadrant. This design leads to an unbalanced load distribution across the network leaving most of the quadrants empty or lightly loaded compared to few heavily loaded quadrants. Finally, storing data only in the index node is highly inefficient due to its single point of failure and quicker depletion of storage space and energy than other nodes inside the cell. [PERSON], _et al._[7] propose an efficient spatial-temporal Similarity Data Storage (SDS) scheme for both static and dynamic WSN. In SDS, a deployed large scale WSN field is considered as a rectangular field. The entire field is divided into smaller rectangular zones (number of zones--horizontally \(n_{x}\) and vertically \(n_{y}\)). A node in the zone with \(\emph{{ID}}_{i}\) can calculate its Euclidean distance from another zone \(\emph{{ID}}_{j}\) using, with \(\delta x_{i,j}=(\emph{{ID}}_{i}-\emph{{ID}}_{j})\%n_{x}\) and \(\delta y_{i,j}=(\emph{{ID}}_{j}-\emph{{ID}}_{i})/n_{x}\). A WSN application data item is characterized by attributes referred to by \(d\), and therefore, SDS uses Locality Sensitive Hashing (LSH) [8] to transform \(d\) to a series of hash values. For example, if a data item is characterized by \(d_{1}>d_{2}>d_{3}\), then their hash values conform to \(h_{d1}>h_{d2}>h_{d3}\), where \(h_{d}\) is the hash value of \(d\). In the mapping between data and zones, the _ID_ differences between zones indicate the similarity between the data stored in the zones. Apart from the research mentioned above, a few other research contributions [9, 10, 11] provide current state-of-the-art implementations of range query mechanisms. In Multidimensional Attribute (MDA) [9], a data storage and range query method for multi-dimensional attributes in WSN is proposed. MDA is built on top of three assumptions: (1) The sensors are uniformly and densely deployed; (2) each node can sense multiple events; and (3) each node maintains a neighbor table _via_ periodic beacon message exchanges and knows its own geographic location. After a source node detects an event \(E\) of \(k\) dimension, a \(k\)-bit referred to as a \"\(B\)\" code is assigned to it. \(E\) is then mapped to a range space \(R\) according to code \(B\).