## 2 Related Work
Moreover, because packets with extra information such as headers or footers, and not bare data, are saved in routing queue space, the DTN techniques have high probabilities of more rapidly reaching the queue-full state, causing more data loss due to the inefficient use of memory space. One of the scenarios for avoiding or resolving the problems discussed above is described as follows. Each sensor node is aware of communication failure. Thereafter, it saves its sampled sensor data to its own memory space without depending on other nodes like routers or intermediate nodes, and compresses them into the same memory whenever its memory space researches full state, until the communication failure is recovered. As soon as the sensor node is aware of communication failure recovery, the node starts transmitting the saved original or compressed sensor data to the destination and then, after receiving them, the destination decompresses and recovers the original and lost sensor data. This is the scenario that this paper is pursuing. A framework relevant to the above scenario has been proposed in our previous work [21]. The conceptualized overview version on this framework is presented shortly in Section 3 of this paper. Meanwhile, the compression and recovery mechanisms capable of following our scenario have been presented in a small number of works in the literature. The _winavg_ and _delta_ methods are two of them and they have been introduced in [23]. Firstly, the _winavg_ method [23] calculates an average value for a given number of sensor data (that is, window size) among sensor data in the queue-full sate and saves this average value as the representative value of the sensor data in the window size. This method recovers all the lost sensor data within each window to the same unified values as the average value of the window. Accordingly, the bigger the window size and the standard deviation of the sensor data within each window, the larger the error size between the original sensor data and its recovered data is. The _delta_ method, which is also very briefly introduced in [23], selects from two sensor data adjacent to the last selected sensor data, the one of which the value difference from the last selected sensor data is bigger than the other. Thus, the unselected sensor data between these two sensor data is discarded. The _delta_ method recovers the sensor data lost between these two consecutive sensor data to the selected one. These two methods have advantages to restore the lost sensor data to the approximated values instead of losing all of them after the data queue becomes full. However, they tend to increase the error, radically, if queue overflows by communication failures last for a long time or if sensor data values change irregularly with high deviation. The Circular Queue based on Period Compression (_CQP_) method [24] is also a simple method, and one of our previous works, which is capable of following our scenario to reduce sensor data loss. The _CQP_ method is similar to but simpler than the _delta_ method. The _CQP_ method selects unconditionally the first sensor data at any time of the queue overflow. Then, it selects unconditionally the second sensor data between two sensor data located just next to the last selected sensor data in the sensor data sequence in the queue, using a circular queue. In spite of the simplest method, this method shows similar or better performance in the recovery of the lost sensor data compared with the _winavg_ and _delta_ methods. The _2 MC_ method, another one among our previous works, has been presented in [19; 20]. In the compression process, this method divides a sensor data sequence into three-point (sensor data) intervals and the last sensor data of each interval is shared with the first sensor data of its next interval. For each odd-numbered compression interval, it selects either a minimum-valued sensor data, if the sensor data shows a decreasing trend in the interval, or a maximum-valued sensor data, if they show an increasing trend.