# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 2 Related Works  
IEI applications need rapid information delivery from environmental transitions. They require a timely, reliable and suitable process in collecting diversified sensor data. As sensor data communication dominates the energy consumption in IoT, many compression mechanisms have been applied to prolong the IoT lifetime [12]. Data compression schemes applied for energy-saving in IoT can be divided into two categories: lossy and lossless. Lossy compression schemes usually give much better compression ratios but introduce some signal fidelity in required QoS2/QoD lost from sensor data. Schemes in this category include JPEG [13; 14; 15], discrete cosine transform (DCT) [16], wavelet-based [17], vector quantization (VQ) [18], and multi-resolution spatial and temporal coding (MRCQ) [19]. As the errors between the compressed and original data are unbounded, these works are unable to support real-world IEI applications to make proper decisions for users. Modern IEI applications are usually based on lossless compression schemes including Huffman coding (HC) [20], static HC (SHC) [21], adaptive HC (AHC) [22], modified AHC (MAHC) [23] and improved MAHC (IMAHC) [24]. HC refers to the use of a variable-length codebook where symbols with higher probabilities to appear are encoded with fewer bits. Given statistics information of source symbols, SHC is proposed to construct a codebook with an optimal prefix code. AHC separates encoded data into prefixes and suffixes based on the weight of the data. Prefix can be redefined as adaptive to have a better compression ratio. MAHC merges the codebook with the optimal prefix code of SHC and the codebook with dynamic suffix of AHC to have a better compression ratio. IMAHC applies dynamic codebook to adjust to rapid environmental changes for IoT. These lossless compression schemes maintain complete data integrity but have poor compression ratios compared with lossy compression. Correlations considered in data compression can be spatial correlation, data correlation and temporal correlation. Spatial correlation suggests that the sensor nodes located closely in an intensive area usually have similar sensed data. Thus, we can apply the spatial correlation to compress transmission data. Spatial correlation compression schemes include minimum fusion Steiner tree (MFST) [25], adaptive fusion Steiner tree (AFST) [26], data fusion (DFuse) [27], context adaptive clustering (CAC) [28], CODA [29], clustered aggregation (CAG) [30], prediction-based monitoring (PREMON) [31], and data fusion using temporal and spatial correlations (DF-TS) [32]. Different from spatial correlation, data correlation uses information theory to encode high probability data with short length output. An example of a data correlation compression scheme is decentralized data fusion (DDF) [33]. Temporal correlation is based on the idea that sensor nodes usually have similar sensed data in a short period of time. Temporal correlation compression schemes include multi-level fusion synchronization (MFS) [34], temporal coherency-aware in-network aggregation (TiNA) [35], CAG, PREMON, and DF-TS. Usually, IEI applications will tolerate limit errors for their required QoD and QoS2. Bounded error can be considered valuable information to reduce the communication cost. The simplest way, as in the TiNa scheme, is to use the bounded error between current and former data to decide whether or not to transmit the current data. In this paper, we use bounded error in advance for data compression to not only reduce the communication/storage cost for sensors and fog-computing machines but also cost-efficiently extend the sensors' lifetime. A tolerable bounded error is helpful not only to improve the data compression ratio [36; 37; 38; 39] for energy-efficient IoT applications but also to reduce the query response time for big sensor data [40; 41].