# An Optimized Load Balancing Using Firefly Algorithm in Flying Ad-Hoc Network
## 2 Related Work  
There are several assumptions such as device hardware, network configuration, operating setting, signal propagation models, time management, communication costs, error requirements, timing, energy requirements, and node mobility used to address the localization problem in FANET.  
Some studies formalized congestion control as an effectiveness maximization issue [8], which was then explained using optimization approaches to diminish network congestion and to propose an innovative cross-layer architecture for optimizing congestion control, direction-finding, and control in ad hoc networks, to address the routine limits imposed by the nonexistence of collaboration between layers. The research paper [9] proposed a framework for wireless networks based on the concept of utility maximization of the network that considers routing and congestion as well as power controls all at the same time while taking into account the continuing or temporary random attenuation of ad hoc networks. [PERSON] et al. [10] observed the delay in multiple access networks, addressed the delay-based effectiveness maximization issue, and tried to find the best balance of delay, PDR, and energy parameters. [PERSON] et al. [11] explained congestion control in two modes, complex mode and non-complex mode of delay traffic, and proposed an innovative decentralized system for the same problem. Under predicted contact limits, [12] investigated the combined enhancement of power control as well as congestion control in other ad hoc networks. A disseminated cross-layer enhancement framework was planned at the same time to meet the simultaneous design aim of power control and blockage control from one source to another node at the physical layer and transport layer, respectively.  
In [13], authors discussed a new paradigm, software-defined networking, for the load-balancing technique to deal with the issue of load irregularity in the organization, and they planned unified programming characterized by LTE remote access network structure. To overcome the problem of throughput expansion, [14] suggested an algorithm for congestion control-based scheduling. In heterogeneous QoS-aware applications, [15] solves the rate assignment problem to optimize the system value of closure in a certain time frame and enable every data flow to satisfy the criteria of lengthy average end-to-end delay constraints. [PERSON] et al. [16] suggested a distributed congestion control strategy that holds the congestion issue in wireless multimedia sensor networks. When it is discovered, a local binary tree is created at the congestion link to resolve the issue. The established tree is used to transfer packets to the next possible congestion node, whereas the mobile sink node is used to facilitate communication when the established tree is unable to reduce congestion.  
[PERSON] et al. [17] investigated congestion management in time lag and disruption-tolerant networks (DTNs) and a novel disruption-tolerant network congestion control system that modifies its operation dynamically based on the underlying network aspects. The authors of [18] proposed a novel, impartial and dispersed congestion control method for neighborhood area networks to assure network access equality. [PERSON] et al. [19] proposed improving throughput by applying a unique strategy for dynamically controlling the reduction factor of the congestion control algorithm in multipath TCP based on the interval between packet losses. [PERSON] et al. [20] investigated a novel congestion control policy for the Internet of Things, which aims to reduce packet drops and improve throughput by promptly adapting the data transfer rate whensoever the existing bandwidth and delay changes. The description of traffic congestion control with parameters of FANET is shown in Table 1 below: