# An Effective Hybrid Routing Algorithm in WSN:
## 2 Related Works
### Ant Colony Optimization Routing Algorithm  
The Ant colony optimization (ACO) algorithm was proposed by Italian scholars [PERSON] et al. [8], which imitates the behavior of ant colonies as ants search for the shortest path from their nest to the food source. The ants deposit a certain amount of pheromone on the path they traverse during the foraging process, and the posterior ants choose their path according to the pheromone intensity. Thus, the collective behavior of ant colony composed of a large number of ants shows a positive feedback of information:The shorter the path, the more ants will traverse it, producing the higher pheromone intensity, which will increase the probability of latter ants choosing the same path. Ants use this kind of information exchange among individual ants to find the shortest path for food. Classic ACO has been successfully applied in solving a number of scientific and industrial combinatorial optimization problems, such as small traveling salesman problem, vehicle routing problem, Quality of service (QoS) network routing, etc. [PERSON] and [PERSON] [9]; [PERSON] and [PERSON] [10]; [PERSON] et al. [11]; [PERSON] [12]; [PERSON] [13]. ACO routing algorithms work in a highly distributed way, and have properties such as adaptivity, robustness, and scalability. These characteristics make them particularly applicable to deal with aforementioned challenges in WSN routing. Over the last decade, a significant number of routing algorithms based on ACO have been developed to address the challenges in WSN. Examples include the approaches from various research papers [2; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Because limited energy is the most critical issue affecting WSNs performance, many of these research works focus mainly on how to use the limited energy of WSNs to maximize the network lifetime, which has been seen as the all-important problem of WSN routing design. [PERSON] et al. [18] proposed a novel multipath routing protocol (MRP) based on dynamic clustering and ACO. An improved ACO algorithm has been applied in searching for the optimal and suboptimal paths between the cluster head and the sink node. Furthermore, a load balancing function is included for dynamically selecting one path to transmit data with a probability that depends on path metrics, such as energy consumption in the path, residual energy, path length. Since the path is dynamically chosen, load balancing among the paths is guaranteed. MRP uses multiple paths to avoid a frequently used path on which theoudes with minimum energy consumption are located and keeps better balance of energy consumption among nodes, and then, prolongs the network lifetime. [PERSON] et al. [19] combined ACO and Breadth First Search (BFS) to find the best and shortest path in order to improve data transmission with the minimum energy consumption, as well as reduce the probability of data loss. By applying BFS, the accurate selection of hops in the data transmission has been enhanced with minimum transmission time and the least energy consumption. Although it increases the search accuracy in finding the shortest path, implementation of BFS in the ACO introduces additional node memory and time consumption which are the weaknesses of the method. [PERSON] et al. [20] Proposed an Inter-cluster Ant Colony Optimization algorithm (IC-ACO) that relies upon the ACO algorithm to identify the optimal path between a cluster head and the base station and effort has been made to minimize the attempts wasted in transferring the redundant data sent by the sensors which lie in the close proximity of each other in a densely deployed network. The paper provides us reference for routing of data packets in the densely deployed network. [PERSON] et al. [21] used a fuzzy system to estimate the route quality based on the number of hops and the energy level of the nodes that compose a route.