# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 2 Related Work
### Localization-Free Routing Protocols
To ensure the successful packet delivery to the destination,a dead-end recovery method is proposed where a node hands over its data packet to the neighbor with greater energy. Therefore, it does not perform well in sparse conditions because it cannot find neighbors. * WDFAD-DBR: The WDFAD-DBR protocol [19] is an improved version of DBR that aims to make the routing decisions more intelligent by providing two-hop information. Unlike the DBR, this scheme is based on a two-hop model, where the depth information of the next node's and expected hop is used to calculate the holding time. In addition, the forwarding area is split into three parts, i.e., one main region and two secondary regions for forwarding, so that the high priority nodes can suppress low priority nodes. WDFAD-DBR try to avoid the void hole in advance, as it considers the depth of the expected next-hop neighbor in addition to the current forwarding neighbor. WDFAD-DBR achieves a good packet delivery ratio in a sparse network; however, the forwarding strategy fails to balance network traffic and lower depth nodes are penalized which leads to the early death of the network. Because of the stretching of holding time differences among neighboring nodes, the duplicate packets are not reduced which further results in high energy consumption. * DOW-PR: The Dolphin and Whale Pod Routing protocol (DOW-PR) is introduced in [20] to achieve better performance for WDFAD-DBR. This protocol adopted the weighting depth difference for the forwarder selection, with some additional selection parameters i.e., PFN number, hop count, and the number of nodes suppressed as well as node residual energy. This hashed out the problem of void holes up to some extent by using the path from the suppressed region in the absence of nodes in the PFN region. The DOW-PR involves two processes i.e., (i) Whale Pod routing (ii) Dolphin Pod routing. In Dolphin Pod routing, all the sink nodes are installed on the water's surface, whereas only a single sink is placed inside water in the case of Whale Pod routing. Additionally, the power range is divided into power levels for better use of the node power and traffic control using the hop-count mechanism. The experimental results (simulation-based) showed significant performance in terms of higher packet delivery rate, lower APD, smaller energy tax, and better network life. * EEDDR: EEPDBR [21] algorithm is used to design an improved probabilistic DBR algorithm for underwater data collection and reporting to the surface receivers by taking node's residual energy, depth information, and the number of PFNs within the 2-hop neighborhood. This technique outperforms in terms of PDR, energy efficiency, and low average delivery time. * EBH-DBR: EBH-DBR [22] uses depth, residual energy of the node, and reliability of the link to select the next relay node for data forwarding. In this scheme, the network is divided into slices of the same width to balance energy consumption as well as to control the hop count of the sensor nodes forwarding the data. This technique works well in terms of network prolonging, energy balancing, throughput, and transmission loss. * EECMR: Energy-Efficient Clustering Multi-hop Routing (EECMR) protocol [23] aims to balance energy consumption of the data transmitting nodes and prolong their life. In this approach, the network is divided into multiple layers concerning the depth level. Data is being forwarded to the sink using a multi-hop mechanism by the cluster head selected based on the depth and residual energy of the node. The cluster head then aggregates the data packets of all cluster members and sends them to the upper layer of the sink node. This protocol is effective in terms of network lifetime and energy consumption. Several other depth-based routing protocols are also proposed for mitigating underwater network challenges, e.g., OMR [24], QERP [25], EECOR [26] and RRSS [27], UMDR [28], Stochastic modeling of depth-based routing [29], PCR [30], Energy-Efficient and Void Avoidance Routing [31].