A Data Aggregation Approach Exploiting Spatial and Temporal Correlation among Sensor Data in Wireless Sensor Networks  
[PERSON]  
1 ITER, SOYA Deemed to be University, Bhubaneswar 751030, OR, India; [EMAIL_ADDRESS] (L.  
the collision of data, idle listening of a channel, over-hearing of the data, etc. Sensor nodes can process the raw data assembled from sensors into useful information before portioning it with other nodes. In hierarchical WSNs, the sensor nodes are found in various structures which can be tree-based topology, ring-based topology, and star-based topology [2]. In our work, we have considered a tree-based clustered sensor network. In WSNs, the sensor nodes collaborate in clusters for smooth task execution. One of the sensor nodes in each cluster is assigned as a CH (cluster head). The cluster head becomes exhausted in terms of its energy resources quicker than the other sensor nodes present inside the cluster. The inter-cluster and intra-cluster configurations also consume a lot of energy for the message communication from the source node to the destination node in WSNs. Therefore, the periodic reassignment task of cluster head leads to the usage of more energy which gradually yields energy inefficiency and enhances the reduced lifetime of the network [3]. One of the major limitations of WSNs is the insufficiency of resources such as energy, route optimality, bandwidth, and network coverage. The spatial and temporal correlation among the sensor nodes is one of the distinctive features of WSNs which can be utilized effectively for enhancing the overall network lifetime thus establishing an energy-efficient communication. Spatially separated sensor data are more useful to the sink node as compared to the highly correlated data [4]. Therefore, the communication of all the sensor nodes is not required eventually reducing the energy consumption as a smaller number of measurements will be sufficient to communicate to the sink. Similarly, event tracking applications of WSNs require periodic observation of the sensor nodes. Transmission of energy radiating physical phenomena of sensor node constitutes temporal correlation among each observation. Thus, exploiting spatio-temporal correlation phenomena can effectively yield minimum energy expenditure along with the collaborative nature of WSNs [5].  
The major contributions of the paper are listed below.  
* A spatial correlation-based data aggregation protocol named STCDRR which works in two levels namely source level and aggregator level is considered [6].
* To eliminate data redundancy and to enhance the smooth functioning of WSN applications, STCDRR is implemented.
* The protocol is extensively exterminated using different parameters such as aggregation ratio, time complexity, and energy consumption.
* This protocol outperforms in the context of the above parameters compared to the KAB (K-means algorithm based on the ANOVA model and Bartlett test) and ED (Euclidian distance) techniques.  
The remainder of our paper is organized as follows: Section 2 presents the related works followed by Section 3 where background study is discussed in detail. Section 4 represents the methodologies, presumptions, and overall network representation then Section 5 represents the STCDRR (Spatial and Temporal Correlated Data Redundancy Reduction) protocol. Section 6 presents the experimental results and performance evaluation of the proposed STCDRR protocol followed by Section 7 which provides a detailed comparison of STCDRR protocol with two existing data aggregation techniques. Section 8 presents the conclusion and future work followed by the references.