# Cognitive Routing in Software-Defined Underwater Acoustic Networks
## 2 Related Work  
Routing in UCANs is challenging as compared to conventional routing protocols in UANs. [PERSON] et al. [7] proposed depth-based routing (DBR) for underwater sensor networks to provide scalable and efficient services for dense networks. DBR is based on a greedy algorithm in which each sensor node makes a decision by comparing its own depth with the depth of the previous node. The protocol achieved a 95% packet delivery ratio without considering recovery algorithms to bypass the connectivity voids. Pressure routing for underwater sensor networks [8] addressed the local maxima by maintaining a recovery route. In this algorithm, a route discovery method was provided that implements hop-limited two-dimensional (2D) flooding. To select a set of forwarding nodes that maximizes greedy progress and limits co-channel interference, an opportunistic routing mechanism and a dead-end recovery method were used. To bypass connectivity voids in a network, [PERSON] et al. [9] proposed void-aware pressure routing (VAPR), a modified version of pressure routing, to ensure loop freedom for mobile networks. VAPR is a soft-state protocol that uses _enhanced beaconing_ to propagate the data from sonobuoys to sensor nodes, and _opportunistic directional data forwarding_ then builds a directional trail to the closest sonobuoy. [PERSON] et al. [10] proposed two distributed geographic routing algorithms for delay-insensitive and delay-sensitive applications in underwater environments to investigate the problem of data gathering. They achieved high acoustic channel efficiency by limiting the error rate for each packet. The objective of these protocols was to minimize energy consumption by jointly selecting the best next hop, the optimal transmit power, and the best forward error correction (FEC) rate. The channel-aware routing protocol (CARP) [11] is another robust relay-selection mechanism to achieve high-throughput efficiency. The protocol exploits link quality information to route around connectivity voids and shadow zones. [PERSON] et al. [12] proposed an AUV-aided underwater routing protocol (AURP) to maximize the data delivery ratio and minimize the energy consumption of underwater sensor nodes. The scheme not only uses heterogeneous acoustic channels but also controls the mobility of the AUVs. This was the first protocol to employ multiple AUVs as relay nodes in a multi-hop underwater acoustic sensor network. [PERSON] et al. [13] designed a reactive, link-state mobile ad hoc network routing protocol--a form of location-aware source routing (LASR) that considers the characteristics of underwater acoustic networks. LASR uses the idea of dynamic source routing (DSR) for communications between AUVs. The protocol achieved greater reliability than both DSR and blind-flooding routing protocols. The diagonal and vertical routing protocol (DVRP) [14] is another method proposed for underwater wireless sensor networks to improve throughput and to reduce network load by calculating a flooding zone. The protocol increases the reliability of the network by preventing flooding in the whole network. [PERSON] et al. [15] proposed a geographic and opportunistic routing protocol with depth adjustment-based topology control for communication recovery (abbreviated as GEDAR) to improve the data packet delivery ratio in mobile underwater sensor networks. The protocol uses greedy opportunistic forwarding to route packets and to move void nodes to new depths in order to adjust the topology. [PERSON] et al. [16] proposed an AUV-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in underwater sensor networks. AEDG employs an AUV for data collection from gateways; to associate sensor nodes with gateways, it uses a shortest path tree (SPT) algorithm. The protocol achieved better performance in terms of data gathering and energy consumption, even in harsh oceanic conditions. [PERSON] and [PERSON] [17] proposed an energy-efficient chain-based routing protocol for data gathering in underwater sensor networks.