## 2 Related Work  
The subject of power management has been investigated by others in view of importance in various applications. In particular, the power management in WSN is a broad topic and can be studied based on various aspects. The general methodologies for managing power in a WSN can be briefly categorized as hardware and software design, network protocols and middleware services. The major energy-saving schemes designed under these four categories are mainly: radio optimization; battery repletion; sleep/wakeup schemes; energy-efficient routing, and; data reduction [11]. In terms of radio optimization, the traditional approaches focus on controlling the power used for signal transmission [12], in which the nodes in a WSN require knowing the power levels and link qualities of their neighbor nodes. Hence, the designed schemes have a spatiotemporal impact on the wireless sensor network, either locally or globally. With regard to battery repletion, in recent years applicationshave been broadly developed for remote environmental monitoring where the monitored environment is not easily accessible. Considering that it might be time- and manpower-consuming to replace the batteries, some work that focuses on the techniques of energy harvesting and wireless charging has been carried out [12]. Among the sleep/wake schemes, a typical one is duty cycling. This scheme is usually categorized as on-demand, asynchronous, and scheduled rendezvous [13]. In [5], a hybrid method based on the battery state and the stability of water quality has been proposed. The nodes can be switched on/off either depending on its remaining battery state or the need of sampled data. Moreover, in energy-efficient routing techniques, the distance between each regular node and also the sink node of the WSN plays a key factor. Hence, in order to optimally find a routing path that is energy efficient, schemes relying on single path and multiple paths have been proposed. A survey paper on this subject is available [14]. Data reduction exploits the fact that depending on the characteristics of the sampled data within the environment, some data could be redundant. For example, in water quality monitoring, if the monitored parameters remain relatively stable within the time period of interest, it is believed that no significant changes are happening. Thus, a relatively low sampling frequency may be used for the sake of saving energy. With fewer sampled data, the trend of the water quality can still be analyzed. This is the main advantage of using adaptive sampling for power management. Besides data reduction, there are some other similar approaches such as data aggregation and data compression [15] that also contribute to energy conservation by using fewer but representative data. Since the core algorithm proposed in this paper concerns adaptive sampling, a detailed review of the latest work in this topic is given next. It can be desirable to predefine a standard for the quality of data when applying adaptive sampling techniques. [PERSON] et al. [16] developed a location-aware scheme for an adaptive data collection system in vehicular networks, in which they constantly compare the travel time of a vehicle with a predefined value for deciding whether to transmit messages between a traffic management centre and a vehicle. It turns out that their developed algorithm is capable of reducing the communication load and data storage requirement while maintaining a high accuracy for estimating the fuel consumption and emission. Analogously, to ensure the quality of data, the proposed DDASA constantly compares the latest sampled data with a set of historical data to determine a new sampling frequency. However, the energy consumption in their methods is mainly reduced by limiting the number of transmission, while ours focuses on saving energy on the sensor node level. [PERSON] et al. [17], on the other hand, proposed a context aware sensing technique which could be utilized for landslide monitoring.