# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
## 1 Introduction and Motivation
JPEG2000, for example, falls into this family of image and video compression techniques. Finally, from a compressed file in which some data are lost, the recovery tools such as error concealment tools are employed to retrieve the lost data. As shown in Figure 2, there are five broad kinds of WSN data compression algorithms that we will explore in this work. Figure 1: Classification of Data Compression Techniques [9]. An overview of these methods may be found here [14]. (1) To compress the data, text data compression strategies are applied to the sensor data in string-based compression approaches. (2) To handle sensing data, image-based compression approaches first hierarchically arrange WSNs before adapting the concept from image compression techniques. (3) The Slepian-Wolf theorem is extended by distributed source coding techniques to encode and decode numerous correlated data streams independently at sensor nodes. (4) A minimal number of randomised and nonadaptive linear projection samples is used by compressed sensing techniques for data compression. (5) Some sensor nodes are responsible for merging data from other sensor nodes in the network as part of data aggregation procedures. In [15], the authors analyses some of the difficulties that might arise when running a parallel compression method on a CPU/GPU hybrid platform at the secondary sink node of a large-scale wireless sensor network (L-SWSN). It uses the matrix matching principle to dynamically split the compressed data into several dictionary strings and pre-read strings along the vertical and horizontal axes of the GPU's various blocks, allowing for the simultaneous construction of many matrices. The simulated approach reported in [16] yields reduced Root Mean Square Error (RMSE) values and larger R2 values (higher coefficient of determination) for varying compression ratios. This research presents an innovative method for enhancing network performance by utilizing Distributed Source Coding and Efficient Energy Consumption while still enabling the delivery of fundamental routing services with reduced traffic delay, end-to-end latency, and total energy consumption. Experiments on their suggested approach show that it performs well, consumes little energy, and can handle a large amount of data [17]. For taking advantage of this spatial relationship, Ref. [18] introduces a joint sparsity-based compressive sensing approach. Their method uses Bayesian inference to create a probabilistic model of the signals, and then they apply a belief propagation algorithm as a decoding technique to restore the original sparse signal. As a result, this research suggests a method for maximizing the usefulness of available sensors by consolidating and compressing data while maintaining its original quality. The primary goal, then, is to minimize this redundant data by eliminating some of the data packets and maintaining just the most crucial ones for the reconstruction. A packet of sensor data was lost during data aggregation, resulting in slower transmission and fewer packet collisions across the cellular connection. The redundancy of storing aggregated data is reduced by data compression, reducing both storage space and transmission bandwidth for use by wireless sensor nodes. As a result of these findings, the lifespan of the network has been greatly extended. The raw data is also considered to be of high quality [19]. Figure 2: Categorization of Data Compression Techniques in WSN [14]. The main motivation behind this research is to investigate power consumed during data compression and transmission. Hence, data compression techniques such as RLE and AHE and a hybrid of these techniques are investigated and implemented in different WSN scenarios to achieve higher compression ratio with less power consumption. RLE relies on correlation at the data sources to achieve compression, but AHE may guarantee a greater compression ratio even when the data sources are unknown. The primary issue with RLE is that compression outcomes are affected by the data source.