# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 4 Experimental Verification  
### Parameter Settings  
One of the application backgrounds of this research is for forest and grassland fire risk monitoring and early warning. The precision and accuracy of forest and grassland fire risk monitoring depend on the multi-source and space-time data which are collected under the high-resolution sensor layout. How to deploy as few sensors as possible to monitor a large area of forests and grasslands and how to measure such multi-source parameters related to fire risk warning as atmospheric temperature, light, soil temperature and humidity, wind speed and rainfall are the application problems that need to be solved. To meet the application requirements, this paper takes the distance between and among the wireless sensor nodes into consideration. The service time and life of the whole network are extended to the maximum extent under such constraints as communication energy consumption and node distances. In order to verify the comprehensive performance of the algorithm proposed in this paper, we conducted simulation experiments and comparisons among the four algorithms, i.e., the greedy algorithm, the pSPIEL algorithm, the ant colony algorithm, and the improved heuristic ant colony algorithm. The BBO-based routing protocol is used for data transmission. In this paper, the forest environment monitoring area is separated into \(|V|=N=86\) locations, and a subset is selected to deploy sensors. The experimental parameter settings are shown in Table 1. We explain the experimental parameter settings as follows. In Algorithm 1, \(\alpha\) is a parameter in Equation (14). If \(\alpha\) is large, it would make the ant search according to the pheromone and fall into the local minimum easily, whereas if \(\alpha\) is small and its value is 0.1, it would increase the randomness of the search. For the same reason, \(\beta\) in Equation (14), \(\xi\) in Equation (11), and \(\rho\) in Equation (12) are all 0.1. \(w\) is the weight parameter of the heuristic function in Equation (10). To achieve equilibrium of the distance effects from node \(j\) to node \(i\) and from node \(j\) to head of adjacent cluster on the heuristic function, \(w\) takes the value of 0.5. \(Q\) is a parameter in Equation (13) and its value is 1 in order to strengthen the positive feedback mechanism of the algorithm. \(\tau_{0}\) is a parameter in Equation (11) and it takes a small value of 0.0003 to increase the probability for ant to choose an optimal path. \(\varepsilon\) is a parameter in Equation (11), which takes constant value 1. \(n_{max}\) is greater than \(N\). In Algorithm 2, \(p_{s}\) is a parameter in Equation (28). The smaller the \(p_{s}\) value is, the more it is prone to mutation. Hence the \(p_{s}\) value is 0.1. \(p_{max}\) is a parameter in Equation (28). It is the maximum value of \(p_{s}\) and it takes a value of 1. \(I\) is a parameter in Equation (26) and \(E\) is a parameter in Equation (27). In order to make both the immigration rate and the emigration rate take value at [0,1], both \(I\) and \(E\) take value 1. \(E_{0}\), \(E_{elc}\), \(E_{mp}\) and \(E_{fs}\) take the commonly used default values. The value \(round_{max}\) depends on the lifetime of all nodes of the network.