# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 4 The Proposed Method  
The proposed method for routing in the IoT is performed to reduce energy consumption in two phases. The steps of the proposed algorithm are depicted in Algorithm 1. The first phase is related to clustering, and the second phase is related to steady state for routing. For optimizing energy consumption in sensors in the Internet of Things, the clustering method is an effective way to reduce the energy consumption of sensors and raise their lifespan and thus increase the overall lifespan of the network. Clustering is used to reduce energy consumption in the grid, and dynamic cycles are used to prevent re-clustering. ```
1:Phase one
2:Sensors clustering (inputs: energy and distances)
3:Cluster heads \(\leftarrow\) higher-energy nodes
4:Clinging other nodes to cluster heads based on their distance
6:Phase two
7:Steady-state for routing
8:Send data to cluster head
9:Selecting best route neighbor nodes (output: best node for transferring)
```  
**Algorithm 1** proposed algorithm steps. Several higher-energy nodes will be selected for network clustering as cluster heads based on the amount of residual energy in the sensor nodes. The other available sensor nodes will be attached to each selected cluster head based on the selected cluster heads' distance and then become cluster members. Eventually, clusters will be generated in the network. Accordingly, member nodes sense data from the environment and send it to their cluster head. The cluster heads are then created based on routing, and the distance to the central station sends the aggregated data received from the nodes of their cluster to another cluster head as the next step in routing or directly to the base station. Hierarchical clustering on the Internet of Things can significantly affect global scalability, lifespan, energy efficiency, and latency. Hierarchical routing is an efficient way to consume less energy within a cluster, aggregation, and data combination to reduce the number of sent messages to the base station. A single-level network may overload the cluster heads with increased sensor density. Such an overload may lead to the unsuccessful proceeding of events. In addition, a single-level architecture is not scalable for a broad set of nodes that cover a large area because sensors cannot usually communicate over long distances. Hierarchical clustering is significant, especially in applications that require scalability. Scalability in this area requires load balancing and the proper use of resources and applications that require effective data aggregation. Clustering is part of routing protocols. Clustering has numerous other benefits with different goals, such as supporting network scalability and reducing energy consumption (through data aggregation). In this paper, through the application of the clustering method based on the ant colony algorithm and the mentioned advantages, we also reduce the collisions, which are discussed as the newest challenges in wireless networks. On the other hand, clustering can stabilize the network topology at the sensor level and reduce the overhead and overall maintenance costs of the topology; this means that the sensors are maintained only when they are connected to their cluster heads and are not affected when there are changes in the levels between the cluster heads. The cluster head can also implement optimized management strategies, which will improve network performance and increase the battery life of nodes, thereby increasing network life. Clustering-based routing protocols are one of the most important ways to reduce power consumption in wireless sensor networks. This new clustering protocol, called the ant colony algorithm based on clustering algorithm, clusters the network nodes based on the criteria including the energy level, collision reduction, and distance from the cluster node to the destination and neighboring energy, and tries to balance the energy in the cluster better and ultimately increase network life and maintain network coverage.