An Improved HAAR Particle Swarm Optimization Integrated with a Dual-Stage Huffman Coding Framework for Energy Efficient Wireless Sensor Networks
Received: 9 December 2025 | Revised: 9 February 2026, 5 March 2026, and 15 March 2026 | Accepted: 18 March 2026 | Online: 6 June 2026
Corresponding author: Nalina Santanahalli Basavarajappa
Abstract
The conservation and efficient utilization of energy are crucial in Wireless Sensor Networks (WSNs) due to the limited battery power, bandwidth, and processing capacity of sensor nodes. To address the high energy cost of data transmission, image compression techniques are employed to minimize the amount of data transmitted while maintaining acceptable image quality. This study presents a hybrid image compression approach that combines the HAAR wavelet transform, Particle Swarm Optimization (PSO), and Huffman coding for efficient image transmission in WSNs. The proposed method first applies a 2D HAAR wavelet transform to decompose the image and reduce spatial redundancy. Huffman coding is then used to assign shorter codewords to frequently occurring coefficients, further enhancing compression efficiency. PSO is utilized to optimize critical parameters, such as quantization thresholds and wavelet decomposition levels, achieving an optimal trade-off between compression ratio and image quality. Simulation studies were conducted to validate the efficacy of the proposed PSO-HAAR-Huffman approach, demonstrating considerable gains in Peak Signal-to-Noise Ratio (PSNR) and memory savings while significantly minimizing transmission energy consumption. PSNR, SSIM, and MSE were measured to evaluate system performance, and the experimental results show that the proposed method exhibits superior performance. Energy consumption was reduced, which confirmed effectiveness during image transmission while preserving reconstruction quality. The proposed optimization-based compression technique offers an intelligent and energy-efficient solution for image handling in resource-constrained WSN environments.
Keywords:
particle swarm optimization, Huffman compression, image compression, compression ratio, space saving, peak signal to noise ratioReferences
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