# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 1 Introduction  
The Internet connects all users, but this network's structure is changing, and a new member is being added to the accumulation. This new member is not a user and is referred to as (Things) [1, 2]. The word is derived from the phrase \"of the Things.\" A Thing may be addressed to any device that has a sensor for exchanging information [3]. In general, the Internet of Things is a network of physical objects embedded with electronic components [4], software [5], sensors [6], and connections [7]. Therefore, they can provide more value and services by exchanging information (at unprecedented speeds and scales) with manufacturers, operators, or other devices. The Internet of Things can improve the quality of life in various areas, including medical services, smart homes, smart cities, industry, environmental and water protection, energy management and consumption, and more [8]. Data transfer is significant for communication and in human-to-human or human-machine or machine interaction, since time plays a vital role in data transfer, so that a message will reach its destination within a permitted period. Therefore, the data must use the minimum or shortest route to reach the destination in such a way. Routing is creating and maintaining paths between nodesin wireless networks and is an essential issue in Internet of Things (IoT) networks [9]. A significant challenge for researchers is to develop routing algorithms in IoT networks. Firstly, in IoT networks, the paths are continually changing due to the dynamic nature of the nodes in these networks, which causes frequent changes in network connectivity and segmentation and new routes need to be discovered for data transformation. There are two types of routing protocols based on topology and clustering criteria [10; 11]. The topology criterion refers to nodes and objects orders in the network (based on the maintenance of routing information), and in the clustering technique, the nodes are grouped within a cluster [12]. In each cluster, a cluster head is selected, and the cluster head communicates with the other cluster heads to transfer the packet, which ultimately saves the node energy by communicating through the cluster head. Numerous studies were conducted on energy saving issues in wireless sensor networks [13]. The IoT system needs a robust network infrastructure and a proper routing structure. Energy consumption issues must be considered to increase the lifespan of the node in grid systems. There are essential features such as energy-saving and the life extension of wireless networks for nodes. A node spends most of its energy on transmitting and receiving packets. In the IoT, the primary source of power for the node is the battery. However, in most cases, it is difficult for users to reach the location of the nodes. Battery replacement is often impossible due to a large number of nodes. However, the battery power of a node is also limited. Therefore, energy conservation has become a significant concern in wireless networks. It is necessary to develop new and efficient energy-saving schemes to reduce energy consumption and extend the grid life. This paper aims to present a routing method based on machine learning algorithms to reduce energy consumption and increase network life in the IoT system. In these networks, it is possible to determine the best path to the optimal points (destination) using machine learning algorithms [12; 13; 14; 15]. This paper aims to use the efficient, ultra-innovative, and nature-inspired optimization algorithm called the ant colony optimization algorithm to route IoT and optimize energy consumption and network life. This method is inspired by the routing of leading ants and placing a substance called a pheromone. This paper is organized into five sections. The second section reviews the previous work, and the third section describes the proposed algorithm for optimization.