# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
## 2 Related Work  
Wireless sensors are increasingly being used for a variety of new tasks requiring vision, surveillance, object detection, tracking, and geolocation [20]. Long-term environmental monitoring is the primary function of WSN applications, whereas sensor nodes are often powered by batteries. This necessitates that batteries be conserved in order to extend the life of the sensors [21]. The majority of energy consumption in sensor nodes occurs during transmission. Data compression is one way to economise on transmission power. The development of data compression algorithms has become crucial in a variety of applications, particularly in multimedia. One of the most challenging aspects of developing large-scale wireless sensor networks that have real-world applications is coming up with techniques that will enable the network to function over extended periods of time using just the minimum amount of energy that can be stored in or acquired by individual wireless sensor nodes. Since sensor node data transmission is a major drain on the network's energy reserves, methods for reducing the quantity of data sent between nodes are highly sought after. As a result of this study, network data transfer has been reduced by compressing data locally before it is transferred. In spite of the fact that the topic of data compression has been around for a considerable amount of time, the vast majority of the known methodologies are unable to be immediately transferred to wireless sensor nodes because to the restricted hardware resources, in particular programmed and data memory. Even though compression techniques could be implemented on current wireless sensor nodes, doing so would leave little room for other processes such as sensing and transmission. As a result, these nodes would be less likely to enter deep sleep states, preventing them from realising the energy savings that inspired them to choose a compression strategy. There has been a proliferation of new data compression techniques in recent years for WSNs. Many of these methods can achieve high compression ratios with minimal computing expense, and they can also correlate data collected by sensor nodes. Among the most popular compression techniques are run-length encoding (RLE), Huffman encoding, Golomb-rice encoding, Lempel-ziv-welch (LZW), and wavelet compression [22]. Text, images, video, and audio are just some of the forms of data that sensors may capture [23]. It is possible that various compression techniques will need to be applied to various forms of telemetry data. [PERSON] et al. [24] combined LZW and Flate algorithms with JPEG coding and discrete Fourier transforms (DFTs) for textual data compression. For the telemetry data generated by hyperspectral sensors mounted on a satellite, two nearly lossless compression techniques have been presented [25]. Using combined source and channel coding, [PERSON] et al. [26] developed a distributed compression architecture. Both quantized and correlated side information are used to reduce the amount of inter-node communication required for compression in this method. [PERSON] et al. [27] present an architecture for distributed matched source-channel communication and an algorithm for reconstructing noisy random projections from the data. A similar approach can be found in [28], which uses a gossip communication system. Power-distortion-latency trade-offs exist, despite the fact that universality is stated. In addition, there is no consideration given to the correlation that exists between the data. It was proposed by [PERSON] et al. [29] and [PERSON] et al. [30] that a dis-tributed wavelet analysis architecture be developed that does not rely on grid regularity. How to decide on the optimal path for compression is unclear, and the spatial relationship has not been well investigated. Sensor data from satellite launch vehicles is compressed using a two-stage Lempel-Ziv lossless data compression technique [31].