# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 2 Related Work
The sensor energy level limits the period of time that a sensor behaves as a CH. The mechanism of DEEC considers that high energy elements generate stable transmission behaviors. As an alternative to traditional schemes, the design of clustering protocols has also been conducted considering metaheuristic algorithms [41; 42; 43]. According to the existent literature, metaheuristic approaches have exhibited better performance levels than those schemes based on classical computing strategies in terms of robustness and accuracy. Under the metaheuristic methodology, the problem of clustering protocol is turned into an optimization formulation where an objective function is modeled to estimate the quality of a solution. With the information provided by the objective function, the metaheuristic method tries different sensor configurations until finding the solution that obtains a longer lifetime of the network [44; 45]. In the literature, different clustering protocols have been designed following metaheuristic concepts. Some representative schemes involve the Energy Centers using Particle Swarm Optimization (EC-PSO) [46]. Under EC-PSO, the CHs are initially determined considering a geometric approach. Then, the PSO method is applied to select the sensor that will assume the role of the CH node from the network. The EC-PSO scheme also considers a process to avoid the selection of nodes that maintain low energy levels. Another important approach is the Genetic-Algorithm-Based Energy-Efficient Clustering (GAEEC) [47]. Under this protocol, a Genetic Algorithm is considered to select the CH nodes through a similarity metric. Therefore, the GA method employs as an objective function a model that evaluates the transmission cost produced by each node according to its energy level. In recent years, a routing protocol that considers the Grey Wolf Optimizer has been also introduced. Under this protocol, distinct objective functions are considered to evaluate the node properties of each sensor. The values provided by each objective function represent weights that are dynamically adapted according to the distance from the nodes of the network. Under this approach, it is identified the node configuration that reduces the total sum of weights. In spite of their interesting results, these metaheuristic methods present a critical disadvantage, such as premature convergence. It refers to the act of detecting a suboptimal node configuration as the best solution for an optimization problem. Different from other protocols based on metaheuristic principles, our proposed approach is the mechanism to determine the CH nodes. In our LS-II scheme, candidate solutions are encoded to automatically identify the number of CHs. Under this approach, it is avoided the consideration of a fixed percentage of CHs or a predefined probability to obtain the CHs. Since the LS-II method prevents premature convergence, our technique ensures that the optimal number of CHs is selected and not a suboptimal configuration. The proposed scheme models a distinct objective function to guide the search process towards the node configuration, which better fits the constraints imposed by energy savings and load balancing. Due to the large number of acronyms involved, a list of them and their meaning is presented in Table 1.