## 5 STCDRR Protocol
### Eliminating Temporal Data Redundancy at Source Level
Here \"\(\tau\)\" is the threshold value that is decided by the type of application. Two input measures are highly correlated if and only if it satisfies Equation (8) as follows: \[\rho_{X_{i}Y_{j}}<\tau\] (8)
4. The agglomerated value is derived for every single pair of received data sets. The correlation coefficient value less than the threshold value indicates redundancy among the two data sets and repudiates such pairs containing either one or two sets. 5. Then, it computes the new weight value among the two compared input sets. Then the aggregator picks the one which has the highest cardinality among the two. Then the new aggregated value along with its latest weight is added to the record. Algorithm 2 is designed for the aggregator level aggregation using the Pearson's Correlation Coefficient method. After receiving the aggregated value of the non-redundant data set, the aggregated input value should be transmitted to the base station. ```
Input: Data measure set \(X_{i}\), \(Y_{j}\) Output: Aggregated data set along with weigh with highest cardinality Require: set of input measures \(X=\{X_{1},X_{2},X_{3},\ldots\ldots\ldots,X_{x}\}\), \(\tau\)(threshold value) Ensure: vector of selected input set \(Q\) initialize \(Q\), \(R_{1}\), \(H=\{\varnothing\}\) Start: for every input set \(X_{i}\in X\) do \(R_{1}=R_{1}\ \cup\ \{X_{i}\}\) endfor \(H=H\ \cup\ \{R_{1}\}\) return for respective pairs of input sets (\(X_{i}\), \(Y_{j}\)) \(\in\ R_{1}\) do if\(\rho_{X_{i},Y_{j}}<\tau\) then  consider \(|X_{i}|\geq|Y_{j}|\)  discard each set of input sets which include either \(X_{i}\)or \(Y_{j}\) from \(H\) \(\mathit{wt}(X_{i})=\) number of dropped pairs incremented to 1 \(Q=Q\ \cup\ \{X_{i},\mathit{wt}(X_{i})\}\) else \(H=H\ \cup\ \{X_{i}\}\ \cup\ \{Y_{j}\}\) endif endfor till none of the set \(X_{i}\) in \(H\)  return \(Q\) Stop
```

**Algorithm 2**Pearson's Correlation Coefficient

## 6 Performance Evaluation

For experimental results, we have taken the Intel Berkley Research lab data set where 46 Mica2 Dot sensors collected the different weather information which was light, humidity, voltage, temperature, pressure, along with the date and timestamp, using a 31 s interval [38]. This real data set also contains epoch and moteid. Here, the data are collected on the TinyOs [39] operating system platform using the TinyDB [40] query processing and in-network aggregation procedure. In our experiment, we have taken the temperature attribute for ease of work. We evaluated the experiment on nearly 2.5 million data readings collected from the sensors. Here, we assumed that a recurrent cluster head is situated at the center place of the laboratory and collects the data from each sensor placed inside. At first, every node reads the collected data measurement periodically and sends its data set along with its weights at the end of the step. By comparing these steps in three different algorithms, an observation can be made to check the efficiency with respect to the two other existing approaches. The implementation part is performed in the MATLAB simulator [41]. In our work, we have compared our proposed protocol STCDRR with two algorithms which are namely KAB (K-means algorithm based on the ANOVA model and Bartlett test) and ED (Euclidian distance) [42]. ED is a simple and effective technique used in various applications [43; 44; 45; 46; 47; 48]. We have the comparison results in terms of the key parameters and performance metrics which are described in Section 6.1 and 6.2. ### Key Parameters

The important parameters those can be associated with the underlined work are as follows. #### 6.1.1 Threshold Function Value

This is the threshold value that is user-defined. As our protocol STCDRR works in two levels namely the source level and the sink level. Match function threshold value is taken for the source level data aggregation where we have used the JACCARD similarity match function to reduce the temporal data redundancy.