# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 4 The Proposed Method
### Clustering  
This step of the proposed method's primary purpose was to divide the sensor network nodes into clusters. The sensor node clustering phase consists of two stages. In the first stage, based on validation indicators, the optimal number of clusters is determined. The clustering algorithm depends on several factors, such as the number of clusters and the distance between clusters. One of the most critical issues in clustering is selecting the right number of clusters.  
In general, the number of clusters is appropriate when:  
- The samples in a cluster are as similar as possible. A common criterion for determining data density is data variance.  
- Samples of different clusters should be as separate as possible.  
The above conditions are also expressed so that the clusters should have maximum compaction, and their separation should be as high as possible. Clustering can be defined as an unsupervised classification method in which there is no prior knowledge of categories. The minimum default is to provide a suitable solution for clustering in all available methods to determine the number of clusters. It contradicts the basic assumption of unsupervised classification in the clustering definition. Traditional clustering algorithms are suitable methods for clustering that require the number of clusters at the beginning of the algorithm. The purpose of cluster validation is to find clusters that best fit the data. In general, it can be said that three methods are used to measure and calculate the separation of clusters, which are:  
* The distance between the nearest data from two clusters;
* The distance between the farthest data from two clusters;
* The distance between the centers of the clusters.  
The primary purpose of sensor clustering is to divide the sensors into several different clusters based on their similarity. Therefore, each cluster's sensors are more similar to each other, and the features in different clusters are less similar to each other. Most of the proposed methods for sensor clustering have drawbacks.  
Among such shortcomings, one can mention the following cases:  
- In most clustering methods, the number of clusters must be determined before performing the clustering algorithm. In other words, in most of these methods, parameter \(k\), which specifies the number of clusters, must be specified by the user. In general, it is difficult to determine the number of clusters for the initial characteristics, and only the number of optimal clusters can be determined by trial and error.  
- Data distribution in a cluster is one of the essential criteria in clustering, which is not considered in most previously proposed methods for clustering sensors. The degree of scattering of features in a cluster can significantly enhance the performance of the clustering algorithm.  
- In most existing methods for clustering sensors, all features are considered the same during the clustering process and will have an equal effect on clustering. While in some cases, it is better to have a more significant impact on the clustering process for many features that are more similar to each other.  
In this paper, to address these problems, a specific algorithm called the ant colony optimization algorithm was used to cluster sensor nodes.