# Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
## 2 Literature Review  
In this section, some related papers from the field of the detection of outlier sensors and the positioning of sensors in the field of the Internet of things have been studied [16].  
[PERSON] et al. [17] presented a tensor Tucker-based OCSTuM with a GA for the intellectual outlier detection of big sensor data. This technique takes one-class SVMs and expands them to tensor space. The OCSTuM and GA-OCSTuM were autonomous outlier detection methods for large amounts of sensor data. They preserved data structure while boosting the effectiveness and precision of outlier detection. Experiments on real-world datasets showed that their suggested strategy enhanced the accurateness of anomaly identification while preserving the inherent structure of massive sensor data.  
[PERSON] et al. proposed a distributed outlier detection system for WSNs [18]. They introduced the DODS (distributed outlier detection scheme), where various types of data were examined and outliers were recognized locally by all nodes by a collection of classifiers, such that neither knowledge of nearby neighbors nor connections between nodes were needed. These features made the suggested approach effective and scalable in terms of both energy usage and communication costs. The suggested scheme's functions were evaluated by extensive simulations utilizing real-world data gathered from the Intel Berkeley Research Lab. The collected findings proved the suggested scheme's efficacy in comparison to the examined algorithms.  
[PERSON] et al. proposed outlier detection based on the Volterra graph method for air pollution sensor networks [19]. To properly assess the outliers of the sensors that comprise a network, we suggest the VGOD (Volterra graph-based outlier detection) method, which detects and localizes anomalous indicators in air pollution sensor networks using a graph educated from data and a Volterra-like graph signal reforming model. The suggested unsupervised process was compared to some other published works, including graph-based and non-graph-based ones, and shows advancements in both the recognition and placement of outlier measured data, allowing irregular measurements to be corrected and misbehaving sensors to be supplanted.  
[PERSON] et al. [20] presented a study on machine learning methods for outlier detection in WSNs. In this study, machine-learning-based strategies for outlier detection were examined, with a Bayesian network appearing to be superior to other methods. In a WSN, the Bayesian classification technique may be utilized to calculate the conditional reliance of the accessible nodes. Based on the explanations in the paper, the technique can also compute the value of missing data.  
[PERSON] et al. introduced outlier sensor detection in WSNs [21]. They attempted to close this gap by providing an experimental assessment of two cutting-edge online detection algorithms. The first technique depends on LS-SVM and a sliding-window-based learning algorithm, whereas the next technique is based on PCA and robust orthonormal projection estimation subspace tracking with rank-1 adjustment. The efficiency and applicability of the approaches were assessed by a testbed and produced nonstationary time series comprising a benchmark three-tank system and a WSN in which organized algorithms were applied by a multiagent outline. In the following Table 1, a brief explanation of the literature is given.  
Recently, utilizing optimization algorithms, especially metaheuristics, for the purpose of the detection of IoT outlier sensors has been enhanced. The results show that these techniques have substantially better outcomes than the classic optimization algorithm. However, because of the no free lunch theory, there is no optimization algorithm can be the best when compared to others. This being the case, in this paper we utilized an algorithm to solve this issue.