## 2 Related Work
The algorithm explores enhancing anomaly detection systems through the use of a neurofuzzy approach. The method in [21] involves the use of an SVM for detecting cloning and jamming attacks in WSNs. The BS categorizes nodes as either cloned or normal by evaluating the distance measurements obtained from the IoT devices. Simulation findings have demonstrated that the approach attains a notable level of detection accuracy while simultaneously reducing the occurrence of false positives and minimizing energy consumption. Research [22] focused on energy and security evaluations of neural networks and machine learning in WSNs. The article began with an energy assessment. The LEACH methodology for CH head selection used the LMNN fast learning method. The Dijkstra, Belman-Ford, and BFS shortest path discovery algorithms were applied one by one to enhance the energy parameter. Using deep learning of recurrent and long-term memory, intrusions in the network were detected. The researchers in [23] aimed to dynamically learn about the network by monitoring nodes, packets, and paths to eliminate intruders and enhance data transmission and energy efficiency. Limited machines were set up to learn from rules and patterns, extend their features, and evaluate all paths by improving them and determining their validity. Intruders were eliminated using network algorithm information and LD2 FA-PSO, which is an ideal efficient, secure, and energy-efficient routing method for WSNs. In [24], a method for clustering heterogeneous sensor nodes using a genetic algorithm to optimize energy consumption was presented. In DCHGA, the network frame was chosen dynamically after each message transmission round. Balanced power usage extended the life of the network and allowed the sensors to drain power gently. As network deployments become large and complex, energy-efficient methods based on intrusion detection are becoming increasingly important. Researchers in [25] used MATLAB Simulink and an artificial neural network to improve energy-based intrusion detection. The findings showed how WSN intrusion detection was increased using the enhanced method based on the biological nervous system and how unsecured nodes impaired network behavior and performance. A handful of well-known techniques can withstand many attacks, but the majority just modified routing algorithms to defend the network against one or two. A Machine Learning-Based Energy-Efficient Weighted Clustering method (EES-WCA) was presented by researchers in [26]. It blends centralized IDS powered by machine learning with EE-WCA. The technique gathered base station traffic samples and created network clusters without interfering with WSN operations. SVM and Multilayer Perceptron (MLP) machine learning algorithms were employed by the base station to classify traffic data and detect hostile nodes within the network. [PERSON] et al. in [27] proposed an efficient data aggregation method for WSNs using node clustering and extreme learning machine (ELM). Their scheme reduced redundant and erroneous data by employing the Mahalanobis distance-based radial basis function in the ELM's projection stage and the Kalman filter at sensor nodes. Through simulations with real datasets, their approach consistently outperformed existing methods in clustering accuracy and energy efficiency. [PERSON] et al. in [28] proposed an energy-efficient IoT health monitoring system using approximate computing for Wireless Body Sensor Nodes (WBSNs). These nodes are crucial for real-time health monitoring outside hospital settings, thereby employing biosensors to capture signals and wireless transmitters to send data to cloud servers. However, energy constraints limit their operational lifespan. To address this, the authors developed a real-time encoding scheme based on iterative thresholding and the approximation of wavelet coefficients to compress biosignals (such as ECG signals) while retaining clinically important features.