Energy-efficient image compression algorithm for wireless sensor networks in structural health monitoring applications

Abstract

In Wireless Sensor Networks (WSN), transmitting an uncompressed image consumes more energy than a compressed image, and it is, therefore, the prime requirement to establish energy-aware compression methods to extend the life of the sensor node, and ultimately the network as a whole. This work suggests an image compression algorithm with a low degree of complexity for WSNs in structural health monitoring applications. This algorithm represents a pruning approach to a Discrete Cosine Transform approximation transform in which the transformation matrix is modified to reduce the series of steps and the compression ratio achieved is better compared to the actual image which makes it easy for the data storage. Because of the reduction in the number of data bits, it enhances the lifetime of the network by reducing the number of node failures caused by resource scarcity. The implementation is tested by capturing real-time images of concrete walls in buildings using Raspberry Pi3B + WSN gateway fitted with camera modules. The scheme is also investigated in terms of a variety of parameters like Peak Signal to Noise Ratio, Mean Square Error, Structural SIMilarity Index and Compression Ratio. This technique achieves the best arbitration of energy consumption and image quality.