# Firefly Optimized Resource Control and Routing Stability in MANET +
## 3 Design of Firefly Resource-Optimized Routing (FFROR) Technique  
The FFROR technique is introduced for effective transmission of DPs with minimum resource utilization and improved routing stability. The FFROR technique uses a firefly resource optimization algorithm for finding the resource-optimized route path. With the resource-optimized route path, the proposed FFROR technique sends the DP to the receiver from the sender. Figure 1 shows the architectural diagram of the FFROR. From Figure 1, it is understood that the FFROR technique effectively finds the resource-optimized route path using the FFROR algorithm for transmitting the DP in MANET. The resource-optimized mobile nodes are selected based on the objective function (i.e., minimum energy consumption nodes and minimum bandwidth utilization). The MN, which consumes less energy and less bandwidth utilization, is taken as the resource-optimized mobile node. After that, the FFRO algorithm ranks the mobile nodes and finds the resource-optimized route path. By selecting the resource-optimized route path in MANET, the routing stability is increased. _FireFly Resource Optimization (FFRO) Algorithm for Routing Stability Improvement_  
A FFRO algorithm is a meta-heuristic approach used for solving the resource control problem and for increasing the routing stability in MANET. Using the flashing light behavior of fireflies, the proposed FFRO algorithm is presented. In the FFRO algorithm, the group of fireflies moves to brighter locations by flashing light intensity with the objective function of finding the resource-optimized path. Firefly's flash is used with the aim of operating as a signal system to attract other fireflies. The firefly algorithm is formulated by the assumptions given below:  
* All fireflies are unisexual where; each firefly is attracted to every firefly;
* Attractiveness is directly proportional to brightness; for two fireflies, a less bright firefly is attracted by the brighter one, and the intensity decreases when the distance between the two fireflies increases;  
Figure 1: FFROR technique. When no fireflies are brighter than the given firefly, it moves randomly, and the brightness of the firefly is increased or decreased based on the objective function. The firefly optimization algorithm assumption is made to find the resource-optimized firefly to increase the routing stability in MANET during the DP transmission. For improving the routing stability, the resource utilization (i.e., energy consumption and bandwidth utilization) is to be controlled. Let the objective function \(f(y)\) of the FFRO algorithm in MANET be taken as minimum energy consumption (\(y_{1}\)) and minimum bandwidth utilization (\(y_{2}\)). The energy consumption (\(y_{1}\)) of the mobile node is calculated as follows:  
\[\textbf{{Energy consumutpion }}(\textbf{{E}}_{c})=\textbf{{E}}_{t}-\textbf{{E}}_{r}. \tag{1}\]  
From (1), '\(E_{t}\)' represents the total energy, and '\(E_{r}\)' denotes the residual energy after transmitting the DP. Then, the bandwidth utilization (\(y_{2}\)) of a node is measured as below:  
\[Bandwidth\ utilization=total\ bandwidth-unused\ bandwidth. \tag{2}\]  
From (2), the minimum bandwidth utilization node is selected for DP transmission in MANET. The main aim of the FFRO algorithm is to identify the resource-optimized mobile nodes in MANET for efficient DP transmission to increase the routing stability. Initially, all fireflies are dislocated in the network randomly. In fireflies, light intensity variation and formulation of attractiveness need to be taken. The attractiveness of a firefly is evaluated using brightness or light intensity, which is related using objective function \(f(y)\). The light intensity '\(L_{l}\)' of a firefly at \(y\) is mathematically represented as follows:  
\[L_{l}(y)\alpha f(y). \tag{3}\]  
The attractiveness '\(A\)' varies with distance \(r_{pq}\) among \(p\)th and \(q\)th the fireflies. The attractiveness changes with the degree of absorption as light intensity is minimized with distance.