# 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  
This section surveys the location-based routing schemes proposed for underwater sensor networks with static node deployment. JREM  
JREM [36] is designed to avoid energy holes, and to achieve a fair balance of energy consumption among sensor nodes. It assumes manual deployment of nodes based on a pre-defined pattern that is aimed at balancing energy dissipation across the network. The authors prove that the transmission load can be evenly distributed if sensor nodes adjust their transmission powers keeping in view the deployment pattern. The transmission power can be adjusted by switching between n numbers of transmission power levels. The number of transmission power levels i.e., n, helps in determining the set of potential next hops and their load weights, thus resulting in fair energy dissipation in the network. Keeping in mind the importance of the number of transmission power levels (\(n\)) for fair energy consumption, the authors derive the optimal value of \(n\) that maximizes the life span of the network by overpowering the energy holes issue. JREM improves network lifetime by distributing the network load evenly, and by avoiding the energy hole problem. However, a drawback of this scheme is that it needs pre-defined deployment patterns, thus reducing its scope. Moreover, deployment cost in deep waters may increase, as all nodes are anchored. Figure 8: The proposed scheme [45]. DCMIBM  
DCMIBM [37] is a cluster-based data gathering scheme for magnetic induction based monitoring in underwater environments. Nodes are deployed in hexagonal fashion, and clusters are formed in which certain nodes are selected as cluster heads. Cluster head selection is based on the residual energy levels of the nodes, and their location within the cluster. The node with highest energy levels and best location, i.e., center of the cluster, is chosen as the cluster head. Data collection is carried out with the help of an AUV. Ant colony optimization is used to search for shortest routes for AUV traversal, thus reducing the end-to-end delays. The proposed scheme reduces end-to-end delays, and improves network lifetime through better energy management. EBOR  
Reference [38] proposes a DS evidence theory-based opportunistic routing scheme (EBOR) to enable energy efficient forwarding with improved end-to-end delays and packet delivery ratios. The forwarding decisions are made based on tradeoff between three metrics: packet delivery probability, efficient transmission distance, and residual energy. To improve energy efficiency, the number of forwarders on each hop is reduced by optimizing the number of forwarders based on a trust-based computation. This also enables fair distribution of load between relay nodes, thus achieving uniformity in energy dissipation. EBOR reduces collisions (and therefore retransmissions) by setting different holding times for different relay nodes. This results in lower communication overhead, and better energy efficiency. Moreover, it also achieves fair energy dissipation by using residual energy as a criterion for forwarder selection, thus prolonging network lifetime. Moreover, reliability is achieved by including link quality in the forwarder selection criteria. Equation (15) presents the holding time of the \(j\)-th node in the forwarding set \(i\):  
\[T_{f_{i,j}}=\frac{d(n_{i,}f_{1,})}{C}+\sum_{p=1}^{j-1}\frac{d\left(f_{ip,}f_{ i,p+1,}\right)}{C}-\frac{d\left(n_{i,}f_{ij,}\right)}{C}+j\times T_{proc} \tag{15}\]  
where \(C\) and \(T_{proc}\) are speed of sound in water and packet processing time. ACUN  
Reference [39] proposes ACUN, which is a cluster-based routing protocol. ACUN divides the nodes into a hierarchy, taking into account the distance between CHs and sink (Figure 9). It determines the size of the competition radius of CHs based on their residual energy levels. CHs are selected based on their residual energy levels. A node that has high level of residual energy becomes a candidate clusterhead.