Energy-Efficient Data Collection in Clustered Wireless Sensor Networks employing Distributed DCT

TL;DR

This paper introduces an energy-efficient data collection method in wireless sensor networks by integrating Discrete Cosine Transform (DCT) with clustering, reducing transmission load and power consumption while ensuring data recoverability.

Contribution

It proposes a novel approach combining DCT with clustering in WSNs to minimize data transmission and power use, validated through analysis and simulations.

Findings

Significant reduction in communication cost and power consumption.

Effective data recovery using fewer DCT coefficients.

Method performs well in both noisy and noiseless environments.

Abstract

In this paper, a energy-efficient data collection method is proposed in which an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be transformed in DCT domain, we propose that each cluster from the networks only sends a small number of large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the large coefficients received at the BS. We further analyze and formulate the communication cost as the power consumption for transmitting data in such networks based on stochastic problems. Some common clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise and noiseless environments for the proposed method are considered.