# Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)
## 2 Related Work
One such technique is discussed in [28], which is a two stage approach where in the first stage the cluster head is selected on the basis of available energy value as well as network coverage of the node, whereas in the second stage a routing tree is created that connects the cluster heads with the sink node. Another PSO based protocol is presented in [29]. In this approach, an energy conservation-based routing protocol is proposed, which selects the gateway and relay nodes that have the minimum distance from the sink nodes. Moreover, a fitness function is also proposed for load balancing in the network. Particle Swarm Optimization Reactive (PSOR) [30] is yet another PSO based protocol where optimum path is computed on the basis of energy required to reach the sink node. Particle Swarm Optimization-Energy-Efficient Cluster Head Optimization (PSO-ECHS) [31] is a scheme focusing on the efficient selection of a cluster head. For this selection, PSO-ECHS considers residual energy, distance among the nodes in a cluster, and the distance from a sink node. Similarly, clusters are formed using a weigh function with the same parameters. Ant colony optimization (ACO) is closely related to PSO and is yet another method for optimization. ACO based routing algorithms are popular in various networks, including WSN. ACOPSO [32] is a hybrid routing protocol that employs both ACO and PSO. In this scheme ACO is used for path selection by constructing a shortest path spanning tree. This is followed by PSO based path selection, where inputs are the outputs produced by the ACO. LATWSN [33] is a routing protocol developed by the ACO technique. LATWSN tries to minimize the energy consumption by selecting the node in the neighbor list that is nearest to the destination. LEACH-P [34] is an ACO based extension of LEACH protocol where the probability of energy utilization is calculated while selecting the next hop neighbor towards the sink node. IACAEO [35] is another ACO based protocol, which works by decreasing the pheromone value of a node if the energy level falls beyond a certain threshold. The same information is communicated to its neighbor nodes for non-selection of the node in future routing decisions. Just like ACO and PSO, another popular machine learning technique, neural networks (NN), is also used in WSN for development of an efficient routing algorithm. One such technique is discussed in [36] where a Hopfield NN is used to enhance the working of link state routing protocol. The weighted matrix for initialization of Nneurons and selection of the next node for routing, in this scheme, uses number of hops, load, delay, and bandwidth. SIR [36] is another NN based routing protocol, which  
Figure 1: Routing protocols for underwater sensor networks. is a modification of the classic Dijkstra algorithm for finding optimal routes from a sink to every node. The same algorithm also provides QoS and is used for recording the reading of utility meters. Reference [37] describes another technique where a three stage NN is used to select the cluster head. This is followed by a traditional routing mechanism where the distance between the sink node and NN based cluster head is calculated for selection of an optimal route. These routing protocols categorized in Figure 1.