## 4 Node Deployment Scheme for UASNs Based on the DHFOA
### Construction of the Information Pool  
Fish, whether real or artificial, rely on their vision to perceive external conditions, as shown in Figure 2. Here, \(X_{i}\) is the current position of the artificial fish, _Visual_ is its visual range, and \(X_{h}\) is the visual position at a particular time. If the concentration of food at the visual position is greater than that of the current position, it is assumed that the fish will proceed towards the visual position, thus arriving at the next position, \(X_{next}\). Otherwise, the artificial fish continues to swim within its visual range. The more the fish swims within its visual range, the more comprehensive the understanding of the state within its visual range will become. This results in a full-scale, stereoscopic perception of the surrounding environment, which aids with corresponding judgments and decisions. The sensor node in the DHFSOA is equivalent to the artificial fish in the AFSA, the radius of the communication \(r^{c}\) is equivalent to the visual range of the artificial fish, and the event is equivalent to the food. The process of the mobile node exploring the larger network coverage in the sensor network is similar to the preying and following behaviors of individual artificial fish, and the network coverage of the sensor nodes is analogous to the food concentration in the environment of the artificial fish. However, the traditional artificial fish swarm algorithm cannot be directly applied to the underwater  
Figure 2: Concept of artificial fish vision. sensor network, mainly due to the fact that sensor nodes have limited amounts of energy. Given this limitation, excessive exploration by sensor nodes within their visual range will lead to their premature death. To enhance the global optimization and neighborhood search capabilities of the artificial fish swarm algorithm, while at the same time avoiding falling into a local optimum, an information pool is introduced here. As shown in Figure 3, it is assumed that there are five nodes in the underwater sensor network \(S=\{s_{1},\cdots,s_{5}\}\), and that each node can sense the surrounding events, here the event coverage is defined as the concentration \(F=\{f_{1},\cdots,f_{5}\}\). If all other nodes within the radius of communication of a node are referred to as neighbor nodes, then the neighbor nodes of the five nodes \(s_{i}\) (\(i=1,2,\cdots,5\)) are represented as \(g_{1}=\{s_{2},s_{3}\}\), \(g_{2}=\{s_{1}\}\), \(g_{3}=\{s_{1},s_{4}\}\), \(g_{4}=\{s_{3}\}\), and \(g_{5}=\{\phi\}\). The information pool (which can also be thought of as a set) is constructed as follows: each node \(s_{i}\) transmits data (the data mainly consist of the neighbor nodes and the number of coverage events) to each of its neighbor nodes through the network, and each neighbor node then transmits data to neighbor nodes other than the node that sent the data. Continue in this fashion until the data have traversed all the nodes in the connected state. Thus, the information pool in Figure 3 consists of node \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{4}\), that is, \(C_{sum}=\{s_{1},s_{2},s_{3},s_{4}\}\), and node \(s_{5}\) is an isolated point. The benefits of the information pool in DSFSOA do not just include an increase in the global search speed of nodes (analogous to fish), but also consist of improvements in network connectivity through collaboration between nodes. As shown in Figure 4, the isolated node \(s_{5}\) improves its isolated state through the preying behavior, establishes the connectivity between the node \(s_{5}\) and the network, and expands the amount of information in the information pool, that is, \(C_{sum}=\{s_{1},s_{2},s_{3},s_{4},s_{5}\}\), The next step will be to focus on the self-organizing deployment process of nodes. The pseudo-code of the information pool construction algorithm is in Algorithm 1.