## 1 Introduction  
There are wireless networks in which communications are typically unsafe. Most wireless sensor networks belong to this category of networks. Usually, they have several shortcomings caused by limited resources, such as short-range and low-speed communications, limited-power batteries, low memory capacity, and low-speed processors [1; 2; 3; 4; 5]. In particular, their communications are vulnerable to environmental changes, such as foggy, rainy, hot, cold, or dry weather conditions, or location, among other factors [6; 7; 8; 9; 10; 11; 12]. Another example of an unsafe communication network is an underwater acoustic sensor network (UWASN). In underwater acoustic communication, there are several barriers that need to be overcome: low bandwidth, high propagation delay, high error rate, multi-path propagation, strong signal attenuation, and time-variant channels [13; 14; 15; 16; 17; 18]. The time variation of channels in particular is one of primary causes of unsafe underwater communications. Underwater environmental changes, such as temperature, salinity, pressure, underwater fluid, bubbles, and noise, among others, are the main causes of time variations of channels [13; 14; 15; 16; 17; 18], but many other factors also influence channel time variations. Owing to the time-variant channels, communication failures occur frequently and the failure time durations may range from seconds up to a few hours, days, or even weeks. After the failure has ended, in most cases the communication resumes, but during the communication failure time certain problems may occur. One of these is the loss of sensor data sampled during the failure. Although senor data can be saved temporally in routers in the form of packets during the course of routing, most or all of them are typically lost. Because the routing queue is not designed for long-time data saving, but rather for short-time saving to facilitate fast routing service, its capacity is insufficient to include many packets. Moreover, even sensor data packets saved in the router will be lost as the saving time increases and the number of new packets for routing increases more and more. On the other hand, no routing can be so much as attempted in a case in which the sensor node does not communicate with any neighbor node. Nonetheless, there are many cases in which it is very important for sensor nodes to save sensor data sampled during communication failure and to resume transmitting them when the failure is recovered. The saved sensor data can also be read out after the sensor node is retrieved by its operators. By doing this, we can detect several facts or forecast future trends such as following, using the sensor data saved during communication failures:  
1. _Detection of accidents or events_: What accidents or events occurred? 2. _Main agents of accidents or events_: What or who brought about the accidents or events? 3. _Cause, time, or place of accidents or events_: Why, when, or where did the accidents or events occur? 4. _Progress of accidents or events_: How did the accidents or events proceed? 5. _Analysis of the cause of accidents or events_: What or who was the cause of accidents or events? 6. _Analysis of effects of accidents or events_: What or whom did the accidents or events have effects on and what were these effects? 7. _Prediction of future trends_: What will be changed by accidents or events, and how, when, where, or why will these changes occur? For longer communication failure durations, in many applications it is more important for us to know some or all of the above. However, as the failure duration is longer, networks or nodes have higher probabilities of sensor data loss in the course of transmission [19; 20]. Accordingly, long-period communication failures cause a large amount of sensor data loss on sensor networks and such sensor data loss may cause disadvantages for human beings.