# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 1 Introduction  
In recent years, emerging IoT (Internet of Things) technologies have enjoyed tremendous success in varieties of applications [1; 2; 3; 4; 5; 6]. They require sensor data to be timely, reliable and suitable for decision-making [7; 8; 9]. The quality of decision-making (QoD) depends on the quality of the information from sensor data. Although sensors are available for measuring many kinds of indicators, energy consumption remains one of the major limitations of IEI (IoT of environmental intelligence). Since sensors in IoT will consume considerable energy to transmit their data, data compression to prolong system lifetime has become a hot research topic. Different types of IEI applications can impose different quality of sensor service (QoS\({}^{2}\)) requirements. For example, an air quality monitoring application gathering air parameter measurements demands less stringent timing requirements thana mobile robot navigation application. [PERSON], [PERSON] et al. [10] state that all IEI applications benefit from the QoS\({}^{2}\) metrics of higher network throughput, lower message delay and longer system lifetime. In many real-world applications, the required sensing data usually have limited error tolerance according to the QoD or QoS\({}^{2}\) required. For example, in CWB (Central Weather Bureau) Taiwan, the bounded error of QoS\({}^{2}\) is 0.1 degree Celsius for a temperature sensor [11]. As for a weather report, the bounded error of QoD is 1 degree Celsius. This motivates us to propose a system architecture for energy-efficient IEI, which is illustrated in Figure 1, to achieve better leverage between bounded error with preserved QoS\({}^{2}\)/QoD and compression ratio for different IEI applications. In this paper, the proposed BESDC (bounded-error-pruned sensor data compression) scheme can cost-efficiently prune the sensor data without QoS\({}^{2}\)/QoD deterioration in IEI applications, to prolong the sensor lifetime and save storage resource for IEI applications by reducing the size of transmitted sensor data. The system architecture of an IEI application is illustrated in Figure 1. It indicates that many sensor nodes are deployed, and the sensor nodes are divided into two categories: end device (ED) and super node (SN). SNs are usually more powerful and faster than EDs in computing and communication resources and also much more expensive than EDs. In this paper, without loss of generality, we simply assume that each SN and its EDs are homogeneous and they are deployed in the monitored environment properly for IEI applications. The application-specific bounded error is given from the server (i.e., bounded error regulator) deployed on the fog-tier according to the required QoS\({}^{2}\) or QoD for IEI applications. In this paper, we apply a two-tier scheme to assign proportionally divided error bounds (pdEB) to the related sensor-tier devices (i.e., ED and SN) to guarantee the bounded error assignment (BEA) of pdEB from the bounded error regulator on the fog-tier. Then, BESDC determines whether the new sensing data should be compressed by comparing with not only the previous sensed data for temporal correlation but also the neighboring sensed data for spatial correlation, within the tolerated BEA. Besides, after bounded-error-pruning the sensor data within the required QoS\({}^{2}\) or QoD, the dataset size for encoding can be reduced further to save resources of storage and transmission for sensor nodes and fog-computing machines. Ultimately, better sensor data compression can be further achieved in the bounded-error-pruned sensed values to save more energy consumption. Figure 1: The proposed 2-tier system architecture for energy-efficient IEI applications. Experiments on four different environmental datasets, including PM 2.5, CO, temperature and seismic wave, are presented. Performance results show that the energy consumption of IEI sensor data transmission can be highly reduced even when a small bounded error is given.