## 2 Related Work
According to their approach, given a set of data, a discrete wavelet transformation is performed to find out the lowest sampling rate, which meanwhile should be able to guarantee the reliability of data. Based on the characteristics of data, three level thresholds are set to derive the sensor/network level contexts as Safe, Listen and Alert. The system initially starts to function at the lowest sampling rate until the sensor/network level contexts change (i.e., from Safe to Listen, or Listen to Alert). Hence, the sampling interval would be dynamically varied depending on the sensor/network-level contexts. As a result, the energy for sensory tasks could be saved. While their strategy is in a similar fashion to ours, they only consider three fixed sampling intervals (i.e., sampling frequencies), which need predefining manually. It should be believed that much more energy could possibly be saved if the scheme for changing sampling frequency could be data-driven, rather than three predefined sampling intervals. In addition, an adaptive sampling approach for snow monitoring applications was developed in [10]. This method initializes the sampling rate by conducting a fast Fourier transform (FFT) on a sequence of pre-sensed data to acquire the maximum frequency, and depending on the subsequent data, a new maximum frequency is obtained. By comparing the variation of the current sampling rate and the new maximum frequency, a new sampling rate is made. However, this algorithm highly relies on a large number of pre-sensed data, and it also changes the sampling rate only depending on the FFT of the newly sampled data to avoid under-sampling. Nonetheless, in real environmental monitoring cases, under-sampling is acceptable if the frequency of the sensed data hardly fluctuatesand remains relatively steady within a period of interest. In this manner, in order to dynamically change the sampling frequency, properly knowing the actual value differences and stability of the sensed data makes more sense than simply using an FFT on the subsequent data. This is essentially what makes DDASA different from a traditional ASA. In [18], an adaptive sampling scheme for acquiring wind data is proposed based on energy awareness. Rather than being data accuracy oriented, this algorithm is essentially energy awareness oriented. This means when the remaining energy of the battery drops below a predetermined threshold, the sampling frequency will be decreased accordingly. The larger the gap between the remaining battery state and the predetermined threshold, the faster the drop of the sampling frequency as a result of maximizing the lifetime of the battery. Apparently, much more energy could be saved when the battery state is relatively low through this proposed scheme. However, since there is always a tradeoff between the accuracy of the sensed data and the energy conservation, a strategy that is primarily driven by the battery state will surely compromise the accuracy of the sensed data, and possibly even under-sampling can occur as a result. Hence, it is believed that the scheme DDASA proposed in this paper, which is data accuracy driven, would be more reasonable and universally applicable. Moreover, it has to be pointed out that, despite that transmitting and receiving data wirelessly also consume an enormous amount of energy, the energy consumption of sensing is not always insignificant [19]. In particular, some energy hungry sensors, for instance, the gas sensors, also consume a large amount of energy compared to that used in data transmission [10; 20; 21; 22; 23; 24]. The work presented in this paper focuses on saving energy from the sensory task within each single node in the WSN, which is typically beneficial for using energy-hungry sensors.