# Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks
## 1 Introduction  
Swarm intelligence (SI) originates from the collective behavior of life groups [1]. It is a mechanism that can overcome the limitations of the perceptions of individual agents. SI deals with complex systems, in which individual agents interact with each other with minimal communication with neighboring agents. Owing to this property, SI has been applied to several engineering applications [2; 3; 4]. The SI-based bio-inspired algorithm shows features such as stability, scalability, and adaptability in an environment where many individuals exist, the environment changes dynamically, available resources are restricted, and objects with heterogeneous characteristics remain distributed and autonomous. This is similar to a communication network environment, along with its service requirements [5; 6; 7].  
In this study, the ant colony optimization (ACO) algorithm--one of the most effective bio-inspired algorithms used in communication and networking technology--has been employed to address the limitations of wireless sensor networks (WSNs) [8; 9; 10; 11]. Accordingly, an ACO-based WSN-routing algorithm has been proposed in this paper. The basic idea of the ACO algorithm is to provide the trailing ant with decision results from the leading ant, such that the trailing ant can use this information to identify an optimal solution [12; 13; 14]. The ant system is suitable for use in large dynamic systems, such as WSNs, for two reasons. First, within an ant colony, ants search for routes while exclusively using local information (i.e., the number of pheromones). Therefore, the ACO-algorithm-based system corresponds to a distributed control system used for communication over a wireless sensornetwork. Secondly, the ACO algorithm adapts well to unpredictable changes in the environment. In WSNs, an ACO-algorithm-based system is designed to reroute data according to the network traffic, thereby improving the performance of the entire transmission network. Therefore, using SI, an algorithm that maximizes the service life of a sensor network, while simultaneously distributing data traffic across the same, can be developed.  
This paper presents the swarm-intelligence-centric routing algorithm (SICROA) for use in WSNs that aim to leverage the advantages of the ACO algorithm. The proposed algorithm considers each data packet transmitted to the base station as an ant, whereas each packet is considered the residual-energy pheromone of the sensor connected to the corresponding link upon selection of each successive hop in the path.  
Additionally, SICROA ensures efficient energy utilization by each node, thereby maximizing the sensor network's lifetime, while also allowing it to adapt to variations in the network environment [15].  
Section 2 discusses the ACO and other applied algorithms that form the basis of the proposed algorithm. Section 3 presents the realization of collision avoidance via use of interrupts, link-quality prediction, and maintenance technologies incorporated within SICROA. Using simulation results, Section 4 compares the handling of the general routing problem by the proposed and other protocols, thereby demonstrating the superiority of the proposed algorithm. Finally, Chapter 5 discusses the simulation results, conclusions, and future work.