# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 2 Related Work
### ACO-Based Routing Protocols  
In Reference [17], the authors present an energy-efficient coverage optimization algorithm called ACB-SA. ACB-SA introduces ant colony optimization (ACO) to address the network coverage problem.  
In ACB-SA, each sensor can switch between the status of active and inactive freely, and the target of this work is to adopt as few active sensors as possible in order to maintain optimal network coverage area.  
In Reference [18], the authors present an energy-efficient routing schema called ACO-MNCC. It utilizes the ACO algorithm to search the maximum connected covers that disjoint with each other to enhance the lifetime of the network. The pheromone denotes the experiences of hunting, and the heuristic information represents the attraction of the target sensor. Numerous experiments illustrate the energy efficiency of the presented algorithm, especially in heterogeneous WSNs. In Reference [19], the authors present a novel sensor deployment method called ACO-MCC3D in 3-D space coordinates. ACO-MCC3D contains mainly two parts. In phase-I, an improved ACO algorithm is implemented to find the potential location for node deployment, and the heuristic denotes the number of the target points the potential sensor covers. In phase-II, the redundant potential sensors will be deleted.  
An improved version of ACO which utilizes three different pheromones is presented in Reference [20]. One of them used local pheromones to minimize the number of sensors and maintain a certain coverage rate. The other two pheromones are global pheromones used for active sensors selection. In Reference [21], the authors use ACO to avoid the communication blind district and decrease the cost of deployment. A load-balanced deployment schema is designed to improve the connectivity of the network. In Reference [22], the ACO algorithm is utilized for mobile sink moving trajectory scheduling. Nodes in the same subdomain select a CH in accordance with their residual energy, and the traveling path of the mobile sink is transformed into a TSP problem to minimize the network latency. The mobile sink needs to walk to each CH for data gathering.  
Authors in Reference [23] present an energy-aware routing method called LTAWSN. Not only the transmission path but also the residual energy among the path is considered to update the pheromone by implementing the ACO algorithm. Simulation results prove that the selected routing path is more reasonable and energy-balancing. Authors in Reference [24] present a hybrid method which combines fuzzy logic, unequal clustering, and ACO technology called FUCHAR. During the clustering phase, sensors are divided into clusters with different sizes in accordance with their residual energy, distance from the sink, neighbor distance, degree of nodes, and so on. The network topology is mainly constructed by means of ACO to hunt for an optimal path. Additionally, the clusters are marked with different levels according to their distance from the sink, and cross-level data forwarding is conducted when necessary. Authors in Reference [25] present an original routing schema called CRT2 FLACO. In CRT2 FLACO, the type-2 fuzzy logic is utilized for better clustering by considering the features of residual energy, neighbor nodes amount, and destination distance. Then, ants link all the CHs for inter-cluster transmission.