## 2 Related Work  
Many methods for sensor data reduction in wireless sensor networks have been proposed, mainly in three categories: data aggregation, data approximation, and query-based processing. The data aggregation methods cause intermediate or energy-intensive sensor nodes to merge into one packet several sensor data in packets transmitted in the course of routing [25; 26; 27]. Consequently, by reducing the number of transmissions, they have the effect of increasing energy efficiency and prolonging network life in wireless sensor networks [25; 26; 27]. The data approximation methods make sensor nodes transmit reduced sensor data, instead of transmitting all the original sensor data [28; 29; 30; 31; 32; 33]. As a result, these methods also reduce the number of sensor data transmissions, similarly to data aggregation methods. Mostly, these methods use correlation or regression models between two or more different types of sensor data originated from different sensors. They transmit the representatives instead of all sensor data, but the untransmitted sensor data are restored to the approximate values using these correlation or regression models. The data approximation methods have overheads in that they inevitably need a priori knowledge, such as correlation or regression models, depending on the relationships among the sensor data [28; 29; 30; 31; 32; 33]. Query-based processing is an on-demand sensor data transmission method. In this category, when each sensor node receives queries from monitoring and control stations, it transmits only the resulting sensor data corresponding to each query as its responses. Thereby, this method reduces the number of sensor data transmissions, avoiding constant transmission [34; 35; 36; 37; 38]. Additionally,query aggregation and results merging methods have been presented for avoiding redundancy in the response results [37; 38]. Almost all the mechanisms in the above categories focus only on the energy efficiency resulting from reducing transmissions. Specifically, most of them are based on the premise that transmissions are in real-time and stable in communications. Therefore, in the cases of the absence of their recovery methods in these mechanisms, the sensor data sampled during a communication failure period are lost and the lost sensor data cannot be recovered [20; 21; 22; 24]. A few mechanisms to recover the lost sensor data during transmission and routing have been proposed [39; 40]. These mechanisms, however, require a priori knowledge obtained through preprocessing normal sensor data for detecting the locations of the lost sensor data and restoring them to their approximate values. Since these methods are based on real-time transmission, they too have the same problems described in the above three categories. For the data loss reduction in unstable networks, delay/disruption tolerant network (DTN) techniques have been proposed [41; 42; 43; 44]. These DTN techniques are asymmetric networking technologies that can be used in network environments where stable communication connections are not guaranteed. Generally, if sensor data were lost owing to network problems, it has been required to re-route from the source to the destination for its retransmission. Instead of doing this, these techniques preserve the transmission-intermitted sensor data packets in routers or intermediate nodes until these packets can be normally transmitted to the next node. In these methods, however, there high probabilities that, at the time of communication failure, bunches of sensor data packet streams gather at the specific router or intermediate node in a short period of time. Therefore, the routing queue capacity rapidly reaches its full state, and, thereafter, all the packets of sampled sensor data will be lost and not recovered, because of the absence of any compression and recovery method.