## 1 Introduction  
This paper considers a distributed information delivery and search service for one or more applications in a Wireless Sensor Network (WSN) that utilizes in-network storage, which is known as Data Centric Storage (DCS) [1]. The applications consist of a set of producer and consumer nodes that can exchange information by relaying packets through neighboring sectors. Nodes have no explicit knowledge of each other but are aware of the applications. The distributed information delivery and search service is used to implement an information delivery and search layer between applications and nodes that provides enhanced reliability and improved flexibility. This paper introduces Data Centric Storage with Metric based Similarity Searching (DCSMSS), which is a highly scalable distributed information service based on Disk Based Data Centric Storage (DBDCS) [2] that incorporates similarity searching. A data query search for an exact match or for data within a specified similarity range is called similarity searching. Similarity searching is particularly useful where users seek data within a WSN that is either a match or close to a match.  
The member nodes in a sector or zone report the sensed event to their associated Sector Head (SH), which aggregates the received events at the end of each epoch (length of a Time Division Multiple Access (TDMA) slot assigned to each sector). The aggregated event is hashed to produce a hash key, which is mapped from a one dimensional domain into a metric space utilizing a normalized and adapted variant of _iDistance_[3]. The distance between a data point and its closest reference point plus a scaling value is called the point's _iDistance_. In this paper distances between data points and reference points in the multi-dimensional space have been mapped to one-dimensional values.  
The DCSMSS scheme presented is used to balance information transfer loads across the network, enhance reliability and provide efficient similarity searching within a distributed network for two types of queries--range query and _k_-query. DCSMSS uses a lightweight Sector Based Distance (SBD) routing algorithm, presented in [2,4], to route inter-sector storage, intra-sector storage and query traffic. The domain of the derived hash key of an aggregated sensed event, denoted by _Hd_, is mapped into the metric space of the DBDCS architecture. In order to balance the load among the sectors, a pivot point generation procedure is used dividing _Hd_ into almost equally populated sub-intervals, denoted by _hDi_, where _hDi_\(\
eq\)_hDi_ and 0 \(\leq\)_i \(\leq\)_j \(\leq\)_S; \(S\) refers to the total number of sectors. In order to store an event, the target sector is mapped based on the derived hash key and pivot points. Furthermore, the target SH distributes the load among the member nodes based on the hash key value and distance to the member nodes.  
The remainder of this paper is structured as follows: Section 2 provides an overview of the related work in the literature. Network architecture, data processing and mapping, SBD routing, insertion and querying are illustrated in Section 3. Section 4 describes the SBD analytical model. This is followed by the simulation results and performance evaluation of DCSMSS and SBD presented in Section 5. The paper is concluded in Section 6.