# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### MFPR
#### 3.1.2. Location-Based Routing without Mobility Consideration
ACUN avoids early demise of a CH with bigger competition radius, i.e., the proposed protocol can avoid the situation when due to greater distance between a CH head and sink node, the CH transmits with high transmission power, resulting in the CH dying earlier than the rest of the network, thus creating voids in the network. Routing is performed in a multihop fashion, instead of a single hop transmission. This multi hop routing favors nodes that are located farther from the sink, as they can transmit with smaller transmission power compared to the one hop transmission case, in which they would have required much higher transmission power, thus consuming more energy. ACUN improves the overall network level energy consumption and network lifetime. ACUN avoids the premature death of CH near the sink, by adjusting CH competition radius adaptively based on distance from the sink and the residual energy, reducing the chances of void creation, which in turn results in increased packet delivery ratio and low end-to-end delay. The drawbacks of this scheme include the requirement for location estimates, which adds overhead. Moreover, node mobility is not considered. Reference [40] presents a cross-layer quality of service routing approach. In order to overcome the QoS determination problems due to non-cross layer framework for routing, the authors propose a cross-layer approach that enables cross-layer communication between physical, datalink, and network layers. This architecture enables the identification of links that meet certain QoS requirements. These inks are used by the network layer routing protocols to establish efficient routing paths that ensure better energy efficiency and low end-to-end delays. A routing protocol termed Wireless Acoustic Line Link or WALL is proposed that establishes end-to-end routing paths based on link level information received from lower layers. The paths are established based on the end-to-end QoS requirements. The proposed protocol shows good scalability in terms of network lifetime, as overall residual energy increases with increase in the number of nodes. However, scalability suffers in the case of network throughput, which reduces with increase in the number of nodes. PCR  
PCR [41] proposes a joint design of opportunistic routing and power control aimed at achieving better end-to-end packet delivery ratios. The protocol operation can be divided into three main parts, the first of which is Neighbor discovery, during which nodes transmit beacons using every available transmit power level. This phase aims at populating tables of neighborhood based on the transmission power required to reach a neighbor. The neighbor discovery phase is followed by the candidate relay nodes selection phase. This phase selects a set of neighboring sensor nodes that can forward data to the destination node, based on every transmit power level, and the resultant energy consumption. Inclusion of energy consumption helps in cutting out high power transmissions. The third part coordinates the transmission of selected forwarders to achieve fair energy dissipation among the forwarders. In PCR, multiple beacon transmission (using all available power levels) increases communication overhead, contention, and energy consumption. Moreover, if mobility is considered, then the neighbor discovery phase should be carried out periodically. Thus, periodic transmission of beacon with all possible transmission power levels will incur even higher communication overhead, which will result in higher contention and higher energy consumption. EGBLOAD  
Reference [42] proposed three routing schemes: (1) corona-based energy grade (EG) & balance load distribution or EGBLOAD, (2) EG w/o corona, and (3) depth adjustment (DA) w/o corona. These schemes  
Figure 9: Network Model. collaborate to achieve high energy efficiency and prolong network lifetime by intelligently distributing traffic load among the nodes. The node battery is initially divided into energy grades, based on range measure between the sender and the destination.