Steganography: A Secure way for Transmission in Wireless Sensor Networks

TL;DR

This paper proposes a steganography-based security method for wireless sensor networks, embedding sensitive data within images to protect against attacks during transmission over the Internet.

Contribution

It introduces a novel steganography approach tailored for WSN data security, enhancing confidentiality during data transmission over public networks.

Findings

High PSNR and SSIM indicate good image quality.

Effective data hiding with minimal distortion.

Enhanced security for sensitive WSN data.

Abstract

Addressing the security concerns in wireless sensor networks (WSN) is a challenging task, which has attracted the attention of many researchers from the last few decades. Researchers have presented various schemes in WSN, addressing the problems of processing, bandwidth, load balancing, and efficient routing. However, little work has been done on security aspects of WSN. In a typical WSN network, the tiny nodes installed on different locations sense the surrounding environment, send the collected data to their neighbors, which in turn is forwarded to a sink node. The sink node aggregate the data received from different sensors and send it to the base station for further processing and necessary actions. In highly critical sensor networks such as military and law enforcement agencies networks, the transmission of such aggregated data via the public network Internet is very sensitive and vulnerable to various attacks and risks. Therefore, this paper provides a solution for addressing these security issues based on steganography, where the aggregated data can be embedded as a secret message inside an innocent-looking cover image. The stego image containing the embedded data can be then sent to fusion center using Internet. At the fusion center, the hidden data is extracted from the image, the required processing is performed and decision is taken accordingly. Experimentally, the proposed method is evaluated by objective analysis using peak signal-to-noise ratio (PSNR), mean square error (MSE), normalized cross correlation (NCC), and structural similarity index metric (SSIM), providing promising results in terms of security and image quality, thus validating its superiority.