# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 4 The Non-Cross Layer Design Method
### The Energy Efficient Routing Protocol  
Since underwater sensor nodes are always deployed in harsh environments and powered by batteries, the total energy is strictly limited. In addition, it is not appropriate to change batteries for underwater sensor nodes (sometimes this is impossible). Consequently, the first important issue of an underwater sensor network is improving the energy efficiency to prolong the network lifetime. Many UASN routing protocols have been proposed to improve the energy efficiency performance, such as those described in [40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Due to the fact that depth information is much easier to get than location information in underwater environments, many routing algorithms utilize depth information instead of location information to reduce the energy consumption and propagation delay. In [40], the authors propose depth-based routing (DBR), which use depth information to reduce the invalid broadcasts. The nodes are equipped with depth sensors to calculate the node's depth. Based on the depth information, the data packets are transmitted to the sink node. Considering that the transmission model in DBR is flooding, in the depth-based multi-hop routing protocol (DBMR) [45], a multi-hop transmission model other than the flooding model is applied to reduce the energy consumption. Like DBR and DBMR, in the energy efficient routing protocol (EUROP) [42], the nodes in the network are divided into different layers based on the depth information. The nodes in the same layer can communicate with each other. The nodes can move to the surface and back to their pre-defined place. The data packets are transmitted from the deep water to the shallow water. Even these depth information-based routing algorithms can reduce energy consumption and propagation delay successfully, however, the nodes must be equipped with special sensors to calculate the depth information, which is expensive and can introduce extra energy consumption into the nodes. Aside from depth information, in some routing algorithms, AUVs or mobile nodes are used to improve the routing performance and data collection efficiency. In the mobile delay tolerant approach (DDD) [41], the authors use dolphin nodes which can move randomly or in a preplanned way to collect the data that is stored in fixed nodes. The fixed nodes use a low power communication model to detect the dolphin nodes and transmit data to the dolphin nodes through a normal transmission model. Different from DDD, in Mobicast [44], the nodes near the AUV or mobile sink can form a 3-D geographic zone. The AUVs or the mobile sinks travel along a user-defined route and continuously collect data from the nodes that are within the 3-D zone. Similar routing protocols can also be found in [47; 48]. In [47], the sensors send data to GWs, and the GWs send data to AUVs when the AUVs pass through the GWs. The AUVs can send normal data to sink nodes through acoustic channels and urgent data to satellites through RF channels. In temporary cluster-based routing (TCBR) [48], the nodes near the sink node can achieve balanced energy consumption with the help of mobile nodes. The energy consumption in AUV aided routing algorithms can be reduced seriously: (1) most nodes are fixed and can go to sleep when the AUVs are out of their transmission range; (2) the communication between the AUVs and the fixed nodes uses a low cost communication model. The disadvantage is that the travel route, the number, and the transmission ranges of AUVs all can affect the routing performance. In addition, the propagation delay of the AUV aided routing algorithm cannot be ignored. Clustering is an efficient method to reduce energy consumption and propagation delay in underwater acoustic sensor networks. In [49], the authors propose a distributed underwater clustering scheme (DUCS). The DUCS divides the nodes into different clusters for reducing energy consumption; the communications between nodes are divided into inter-cluster and intra-cluster, which is efficient.