## 5 STCDRR Protocol
### Eliminating Temporal Data Redundancy at Source Level
### Sink Level Experiment

#### 7.2.1 Aggregation Ratio

Figures 2-4 shows the remaining aggregated data in percentage after applying the sink level aggregation process. These aggregated data are forwarded to the cluster head after filtering. One hundred percent of the data will be forwarded to the sink if the aggregation process is not being applied. However, it is observed in the figures below that the percentage of aggregated data is subsequently low in our proposed technique as compared to the other two techniques. After each period, the sensor node sends data to the cluster head. It is noticeable that after applying the aggregation process data collection by the sensor node is reduced by 75% up to 93%. Hence, it is concluded that the STCDRR protocol is efficient in terms of aggregation ratio by eliminating the redundant data measurements after completion of each time period as compared to KAB and ED. Figure 1: Percentage of data after applying source level aggregation. Figure 2: Aggregation ratio for threshold, 0.3. Calculation of Energy Consumption:

\[E=P\times\left(\frac{t}{1000}\right) \tag{9}\]

where,

\(E\) = Energy measured in milijoules (mJ) or in Kilowatt-hours (Kwh),

\(P\) = Power units in Watts,

\(t\) = Time over which the power or energy was consumed. Example:

Let us consider 6300 rows of temperature attributes of the data set. Suppose, the average voltage of 6300 rows = 2.6

Calculating the voltage for 6300 rows of data = 6300 \(\times\) 2.6 = 16,380 which is 1.571 percentage of aggregated data. So, calculation of energy consumption will be,

\(E\) = 6300 \(\times\) 0.01571 = 98.97 (99) which is the number of aggregated data to the cluster head. So, the calculation of total energy consumption will be:

1 node consumes 2.6 mJ energy AD 99 nodes will consume 99 \(\times\) 2.6 = 257.33 mJ of V.

#### 7.2.3 Data Accuracy

The main goal is to send very less data to the destination node or sink. In the KAB technique, only one set of data elements is sent among a group of sets to the sink which in turn increases the loss of data measurements. However, in ED, the integrity of conservation of information is maintained. Figures 8-10 imply the data accuracy for three approaches with varying threshold values. It is clearly visible from the graph that the proposed protocol gives the best results in terms of measurement of the accuracy of the data. Figure 6: Energy consumption in mJ for thresholds, 0.4, 0.45. Figure 7: Energy consumption in mJ for threshold, 0.5. #### 7.2.4 Time Complexity

The execution time required for a process is the total time taken for the completion of the task. In KAB, the execution time is calculated on the basis of the number of iterations of the loops in the algorithm. In ED, the comparison between two redundant sets takes less computation time as compared to KAB. In our proposed approach the execution time taken to identify the redundant set is least as compared to KAB and ED as it uses the JACCARD Similarity Function. Complexity in computation is \(O\big{(}|X_{i}|^{2}\big{)}\) which is almost half of the data measure sensed at each period eventually reducing the energy consumption. Time complexity in KAB is in order of \(O(x)\) where \(x\) is the number of total received data sets. In, ED complexity is, at most, in order \(O\big{(}x^{2}\big{)}\). This comparison is analyzed in Figure 11.

Figure 8: Data accuracy for threshold, 0.3. Figure 10: Data accuracy for threshold, 0.5. Figure 9: Data accuracy for thresholds, 0.4, 0.45. #### 7.2.5 Discussion

ED preserves the data integration as compared to KAB and STCDRR as the distance function has the highest cardinality as compared to the other two methods. Data loss measurement is good in KAB as compared to that in ED and STCDRR as it constitutes the ANOVA model irrespective of the increase or decrease of distance and vice-versa. Data accuracy and time complexity in STCDRR are high as compared to KAB and ED.