# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 2 Literature Review  
The authors of [6] presented a low-energy adaptive clustering hierarchy (LEACH), a famous disseminated heuristic approach that uses adaptive clustering. Clustering occurs in  
Figure 2: FANET in wildfire. this approach in a dynamic, unpredictable, probabilistic, and periodic manner, among other ways. LEACH is responsible for determining a threshold for CH selection. It generates a random number among [0, 1] to choose a CH throughout that period. One of the drawbacks of the LEACH protocol is the random selection of CHs, which is necessary since any node might be qualified to serve as a CH under certain conditions [40; 41; 42]. LEACH-C [7] is an improved variation of the LEACH algorithm. All of the sensor nodes broadcast information to the base station, such as position and residual energy, to maximize the network's efficiency. It has the added benefit of minimizing the energy consumption of each node in the network when LEACH-C is used as a centralized method, which is particularly advantageous. However, LEACH-C has several faults that need to be addressed. For example, when it comes to becoming a CH in the LEACH-C network, each node has an equal probability of becoming one. As the network's energy supply depletes, the nodes with the least amount of energy should be selected to serve as CHs, which will reduce the network's efficiency and place an unfair amount of energy demand on the network. TEEN (threshold-sensitive energy-efficient sensor network protocol) [8] is another hierarchical heuristic technique that uses the two-layer clustering structure to reduce energy consumption. This approach uses both a hard (HT) and a soft (ST) threshold. The threshold values determined by each CH are communicated to the CH's members. Both thresholds have been implemented to limit the number of transmissions during routing intervals and the overall amount of energy used. Since the CH selection is based on chance, the CH distribution [8] will be non-uniform. Regarding clustering, TEEN surpasses LEACH in terms of stability and energy efficiency. There is a fundamental drawback: If the requirements are not satisfied immediately, nodes will not be able to establish communication. Meta-heuristic methods for optimal CH selection have been developed to overcome the limitations of heuristic algorithms, such as the wasteful exploration of the search space [18], and obtain more outstanding performance than traditional heuristic algorithms. A key component in improving algorithm efficiency is having access to every segment of the whole search space during each period. Using an evolutionary PSO for energy-aware CH selection, the authors proposed an algorithm in [10]. This method combines the Euclidean distance between nodes and CHs, and the remaining energy from particle energy to node energy. According to the results, the addition of energy criteria in the fitness function significantly impacted the total energy efficiency of the exercise programming efforts. It should be noted that the CHs in [10] were chosen based on probabilities, and the distribution of CHs was constant throughout the group. In this method, the exploration step was likewise given precedence over the exploitation stage in terms of importance. Moreover, the authors in [9] created a hybrid approach for energy-efficient CH selection based on PSO and HS algorithms. The suggested meta-heuristic algorithm covers a global optimal in the search space (exploration) while also extending beyond the local optimal by integrating the advantages of both techniques (exploitation). PSO-HSA [9] is a search space optimization approach in which particles based on the PSO algorithm are allowed to migrate from one zone to another in the search space. As PSO faces optimization constraints at high-dimensional scales due to optimization limits, finding any viable solution in the whole search space is challenging. As a result of its great-seeking capacity, HSA aids PSO. The authors [11] offered an evolutionary HSA for CH selection influenced by musical compositions.