## 2 Underwater Acoustic Sensor Networks  
The field of underwater acoustic networks has attracted a lot of attention in the research community, recently. Many applications such as environmental and pollution monitoring [2; 19; 20], oceanographic data collection [2], distributed surveillance [17], oceanic sampling [21], offshore exploration [2; 20], disaster prevention [2], mine reconnaissance [2] among others are developed. However, there are many challenging issues that contribute to the UASNs performance degradation that include high energy consumption, loss of connectivity, security, routing and cooperation [20; 22; 23]. These issues in UASN are not well investigated in comparison to other applications such as those in Wireless Sensor Network (WSN) and Mobile ad hoc Network (MANET) applications. However, it is nearly impossible to apply those mechanism on UASNs due to their unique characteristics. Some of the distinguishing factors between terrestrial WSNs and UASNs are summarized in Table 1 and are as follows:  
1. WSNs use Radio Frequency (RF) and UASNs use acoustic signals (sound waves). 2. Due to the low bandwidth and using high power amplifiers to produce significant acoustic pressure, UASNs consume more energy compared to its counterpart WSNs for terrestrial environment. 3. The high delay of acoustic signals is five orders of magnitude greater than that of RF in WSNs. 4. Bandwidth of UASNs is limited for some certain range and depends on both frequency and distance which is contrary to the WSNs. 5. UASNs suffer more from connection problems due to high BER and multipath problems compared to WSNs. Meanwhile, several works have been conducted targeting how to achieve cooperative communication, but reliability remains a major component of secured cooperative communication that is missing [24]. Cooperative communication can be achieved once the nodes involved in the source to destination path cooperate to support the delivery of packets. However, cooperative communication does not guarantee reliability of the data packets [25]. In fact, underwater data collection and reliable communication are challenging tasks. One of the promising solutions to these problems is to use \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison Parameters** & **WSNs** & **UASNs** \\ \hline Communication Method & Radio Frequency (RF) & Acoustic Signal (Sound waves) \\ Energy Consumption & Low & High (due to energy cost of submitting packets) \\ Propagation Delay & Low & High (five orders of magnitude greater than WSNs) \\ Bandwidth & High & Low (depend on distance \& Frequency) \\ Connectivity Lost & Low & High (due to high bit error rate) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of WSNs and UASNs. underwater vehicles, which would move around the deployment area of the sensor nodes and collect their sensed data. However, planning the path for underwater vehicles to maximize the amount of data gathered and minimize the amount of time taken to travel in a particular region then becomes another challenge. The authors in [17] designed a behavioral approach based on cooperative forms of algorithms in which they used an underwater autonomous vehicle that is equipped with mobile sensor nodes. They were able to follow rules targeting a completion of a particular mission that is capable of maintaining the network connection link. Their proposed algorithm is robust and could improve the performance in terms of coverage and connection loss within the vehicle in underwater. It can also protect the communication from the denial of service (DoS) attack reactively. These types of cooperative algorithms are usually relied on availability of some information toward other types of sensors and such information may not necessarily be trustworthy and complete. However, sometimes they have been considered trustworthy for underwater oriented applications communication [17]. A schematic representation of cooperative based architecture for UASNs is illustrated in Figure 2[11; 12].