# Inspired from Ants Colony: Smart Routing Algorithm of Wireless Sensor Network
## 1 Introduction  
In recent years, smaller electronics devices have appeared, so much so that the different wireless networks have emerged. The improvement of data rates and the reduction of energy consumption are the challenges to improve these networks. Higher data rates are required as internet access becomes more popular. That is why, the allocation of more bandwidth, or the processing of advanced signals with spectral efficiency are required. As a matter of fact, the major problem in wireless networks was still the energy consumption [1]. Some emerging wireless network applications are designed to require low data rates. Therefore, the energy consumption concern is greater than that of the high data rates. The wireless sensor network is another type of emerging wireless network where low power consumption is more critical than high data rates. Actually, wireless sensor networks have become a growing field of research and application, with the advancement of information technology [2; 3; 4; 5; 6; 7]. A sensor array is composed of numbers of sensor nodes scattered randomly in certain regions. These sensor nodes have detection, communication, calculation and power functions [8; 9; 10; 11]. WSNs have been applied in many different applications such as vehicle tracking system, traffic monitoring, medical applications, etc. With built-in detection parts in the nodes, a WSN can monitor and collect information or data in the deployed area. Collected data packets deliver source nodes to the single receiving node, which will cost energy. WSNs give prominence to more simple deploymentand good flexibility of devices. With the fast technological development of sensors, which will greatly improve the efficiency and reliability of infrastructure systems, WSNs will evolve into the key technology for Internet of Things (IoT). Among the main concerns related to applications of IoT is extending the battery life of Machine to Machine (M2M) mobile devices to make the IoT system work for a longer period. As a result, in the vast mass of applications of the Internet of Things, energy-efficient communication and stable energy supply have become the priority objectives in the allocation of resources [12]. Several protocols have been implemented--for example, the cluster-based LEACH (Low Energy Adaptive Clustering Hierarchy) [8], which is based on cluster and Directed Diffusion (DD). ACO (Ant Colony Optimization) is a widespread meta-heuristic approach to solving combinatorial optimization problems such as the asymmetric travelling salesman problem, vehicle routing, WSN routing, etc. This algorithm is inspired by the behaviour of ant colonies. Due to the limited power supply, maximizing the lifetime of a WSN is crucial for the performance of the routing protocol. In this article, ACO is adopted and improved to design an intelligent routing algorithm to maximize the lifetime of a WSN. The remainder of the paper is planned as follows. Firstly, Section 2 presents the ant colony optimization algorithm: the ant system and the ant colony system. Secondly, Section 3 describes the algorithm proposed. Thirdly, the results of the simulation are offered and discussed in Section 4. Finally, Section 5 concludes the paper.