Title: ST-DAM: exploiting spatial and temporal correlation for energy-efficient data aggregation method in heterogeneous WSN
Authors: Khushboo Jain; Anoop Kumar
Addresses: Department of Computer Science & Engineering, Banasthali Vidyapith, Tonk, Rajasthan, India ' Department of Computer Science & Engineering, Banasthali Vidyapith, Tonk, Rajasthan, India
Abstract: In Wireless Sensor Networks (WSNs), the continuous monitoring application senses data which generally exhibits high spatial and temporal redundancies. A huge amount of energy is consumed in communicating this redundant data which makes it very difficult to attain a satisfactory network lifetime and thus creates a bottleneck in scaling WSN applications. Recent cited literature has suggested that many spatial and temporal models for data aggregation have successfully reduced the data transmission overheads, but have their constraints. Thus, a data aggregation method is proposed for heterogeneous WSN applications that have configured a two-stage model of data. The first stage is for exploiting Temporal Correlations (TCs) by applying Adaptive Vector Method (AVM) and Relative Variation (RV) at the Sensor Nodes (SNs), and the second stage is for exploiting Spatial Correlations (SCs) by applying RV and AVM at the Cluster Heads (CHs). Experiments have demonstrated improved proficiency when compared with other state-of-art methods.
Keywords: adaptive vector method; data aggregation method; relative variation; spatial correlations; temporal correlations; network lifetime.
International Journal of Wireless and Mobile Computing, 2021 Vol.21 No.3, pp.285 - 294
Accepted: 31 Dec 2021
Published online: 16 Feb 2022 *