## 2 Literature Review  
An approach to eliminate the redundant spatial and temporal data was proposed in [7] which relies on two levels. The first level minimizes the data channeling using the Kalman filter for data approximation. The second level presents the elimination of redundant data based on grouping algorithms that work at the source level and the sink level separately. It provided a clear picture of data accuracy and reliability in terms of energy consumption. A novel data aggregation named REDA (Redundancy Elimination for Data Aggregation) in WSNs was proposed in [8]. It is based on a tree-based hierarchical clustered sensor network where energy savings are up to 40% regarding CH (cluster head) selection. It uses low bandwidth for the smooth functioning of the cluster head. The CHs are selected based on the lookup table formation generated by the residual energy and changing position of the CHs yield data transmission of the changed pattern. [PERSON] et al. discussed an energy-structured data collection scheme that exploits the spatial and temporal correlation for the cluster-based sensor networks [9]. In the intra-cluster transmission dual prediction is used to deduce the redundancy temporally. A hybrid compact sensing method is used for inter-cluster data communication to reduce the data redundancy spatially. For the prediction model, an error threshold selection scheme is proposed which utilizes the combination between the energy dissipation and the recovery estimation making the proposed method fit for various sensor network applications. [PERSON] et al. proposed a data redundancy elimination approach that takes advantage of the spatial and temporal correlation among sensor observations to determine the sampling method for the distinct establishment of sensor nodes in the network area [10]. It eliminates the data emission rates by not violating the quality of the data. At the sink node, one back-end re-configuration algorithm is used which can reproduce the data to check the redundancy among the observed data set. A data aggregation based on a directional virtual backbone (DVBDAS) scheme is proposed by evaluating and balancing the energy load criteria as the virtual backbone which can build the clusters among the sensor nodes in the network [11]. The volume of energy dissipation is measured by calculating the optimized results of the cluster-based environment. [PERSON] et al. proposed a data aggregation approach based on the distributed data convergence model exploiting the spatio-temporal correlation in WSNs [12]. This incorporates the centroid distance and similarity to estimate each cluster node's attack degree. A data aggregation approach constructing an energy-efficient communication scheme is proposed in [13] by applying the GCC algorithm (Greedy Corrected Clustering) with the combination of the k-means algorithm to a LEACH (Low Energy Adaptive Clustering Hierarchy) protocol by exploiting the spatial correlation only. This algorithm limits the exploration of temporal data redundancy. This approach can reduce distortion, enhance the network lifetime and save energy. In another work, [PERSON] et al. presented a novel data redundancy reduction approach that is based on machine learning techniques [14]. It builds an aggregation tree of a defined number of sensor nodes. The Support Vector Machine (SVM) method eliminates the redundant data on the tree. Moreover, Locality Sensitive Hashing (LSH) eliminates the false data based on similarity and aggregates the non-redundant data to the superior node. [PERSON] et al. proposed a Correlation Degree based on Data Density (DDCD) clustering approach for calculating locally available data for the sink nodes in the network [15]. The sampled data is observed exploiting only the spatial correlation of the data to the sink node. No criteria have been mentioned for the temporal redundancy of the sampled data. It calculates the correlation among a sensor node's data and its adjacent sensor node's data enhancing the least contortion on their correlation.