## 3 Basic System Operation
### SBD Routing
Figure 15b shows the total number of events finally discovered in comparison to the total number of expected events when the number of queries per batch is four. The discovery rate was 100% when the number of target sectors is one but it gradually falls with the increase in the number of target sectors. This happens due to the packet loss during the response time. When the number of target sector increases with regard to the increase of the value of \(k\), the volume of reported events for single query increases significantly. This large number of reported events created hotspot and congestion around the query node and the corresponding relay SH of its route. Figure 14: (**a**) Latency; (**b**) success rate and (**c**) total number of events. Figure 15: (**a**) Latency and (**b**) success rate. #### 5.2.4 Similarity Searching

The setting of this experiment was same as the previous experiment except the number of queries generated. The number of actual data items in the system and the number of discovered data items with no less than 50% similarity is shown in Figure 16a. This similarity is measured in terms of range query. After calculating \(h_{q}\) of a query, \(r\) is calculated as \(\pm 0.25\)\(h_{q}\). Thus, the range of the query was defined by \([h_{q}-0.25\)\(h_{q}\), \(h_{q}+0.25\)\(h_{q}]\). The target head nodes where query was forwarded were _SHj_, _SHj_+1, \(\ldots\), _SHk_, where \(P_{j}\leq h_{q\cdot r}\leq P_{j+1}\), \(P_{k}\leq h_{q\cdot r}\leq P_{k+1}\) and \(j\leq k\). From Figure 16a it is observed that DCSMSS can always discover more than 85% of this type of data events. Figure 16b shows the discovery rate of DCSMSS, SDS, DD and GHT in terms of similarity between the discovered data and the query. Discovery rate is defined as the percent of events that have certain similarity to a query and that can be discovered. In the second experiment, in total 100 queries were generated with four queries per batch. Since GHT is not locality preserving in data storage, its exact-mapping querying cannot locate similar data and thus for the GHT only 100% similar data is considered. Unlike other approaches, DD broadcasts queries to all SH and accordingly achieves 100% discovery rate. However, SDS and DCSMSS discover 85%-90% similar data. However, DCSMSS provide an optimized trade-off between energy consumption, latency and discovery rate. #### 5.2.5 Scalability

This experiment was conducted in four different network field size of 60 \(\times\) 60 m\({}^{2}\), 90 \(\times\) 90 m\({}^{2}\), 120 \(\times\) 120 m\({}^{2}\) and 150 \(\times\) 150 m\({}^{2}\) containing 80, 180, 320 and 500 nodes, respectively. In total 200 queries were generated with eight queries per batch. Figure 17a shows the total number of hops. It demonstrates that DD's total number of hop count is much higher than other approaches and grows sharply. This refers to the poor scalability of DD. The total number of hop counts for DCSMSS \(+\) SBD, DCSMSS \(+\) LEACH, SDS and GHT grows relatively slowly, which demonstrates the high scalability of these approaches. However, DCSMSS \(+\) SBD provides reasonably stable performance in terms of the total number of hops. This implies that this scheme has relatively stable routing performance for different size WSNs. Figure 17b demonstrates the latency performance of each approach for different network sizes. DD has higher latency than other approaches with a dramatically higher latency when the network size is

Figure 16: (**a**) Total number of events and (**b**) discovery rate. small (80 nodes). This happened because DD broadcasted the same number of queries in a small network creating high traffic with subsequent congestion in the network. In contrast, DCSMSS + SBD, DCSMSS + LEACH and SDS exhibit low latency across varying network sizes. This indicates the high scalability of these approaches. Figure 18 illustrates the experiments which were conducted in a scenario of 120 m \(\times\) 120 m rectangular field, in which 320 nodes are randomly and independently placed.