## 2 Related Works  
Increasing emphasis is being placed on anomaly detection in the Internet of Things arena, particularly regarding healthcare systems that create vast amounts of data through WBANs. In the existing studies, numerous models for detecting anomalies in WBAN-based systems using various methods have been suggested, and these will be analyzed in the following paragraphs. The authors of Ref. [4] assessed the WBAN's dependability in terms of identifying anomalies, assessing human health conditions, hardware failure rates, and transient fault correction mechanisms. The study proposed a measure using the mean time to failure (MTTF), which provided improved performance when assessing the dependability of WBANs in terms of specification and detection of anomalies. The findings indicated a detection rate of 95% and an MTTF of 43.01 s, which is considered to be short. Even though the study indicated a high level of detection as a measure of dependability, the metric must be altered to account for some irregularities. Another study [5] proposed a methodology for detecting anomalous sensor data. Their approach was built on concurrent fuzzy clustering and data compression using the Hadoop MapReduce model. The experimental results demonstrated that the suggested framework attained better accuracy with the fewest false alarms, achieving between 97% and 98% accuracy. This study employed a parametric statistical method that is computationally intensive. A different method for recognizing changes in WBAN-collected data was proposed in [7] using the Kalman filter approach. The researchers claimed that this approach can identify any physiological reaction in WBANs. However, the Kalman filter approach has considerable disadvantages, including a greater computational cost to achieve optimal results. The authors of [8] proposed a unique method for detecting WBAN anomalies, which was based on Gaussian regression and majority voting. Using a real dataset, the proposed method presented a strategy that was capable of differentiating between true medical emergency situations and false alarms. This technique is successful in terms of detection and the false-positive rate, as was demonstrated by the findings. However, this method was hindered by its computing complexity, high false-alarm rate, and sample size. Elsewhere, the authors of [9] proposed enhancing the performance of anomaly detection by using a correlation method for multiple body sensors. The proposed method employed thresholds to detect anomalies. However, in this study, only one kind of correlation that utilized the spatial relationship among sensors was examined, while disregarding the temporal correlation for each sensor reading. Two additional studies [10; 11] examined the unreliability of certain sensors that are responsible for generating many false alarms in medical-based systems. Both investigations used a dynamic sliding window and a weighted moving average to identify abnormally flawed sensor values. However, researchers using the weighted moving average method need to pay more attention to the complex relationships within the data. The authors of [12] proposed an anomaly detection algorithm for WBANs to eliminate the false alarms resulting from faulty measurements. The strategy utilized both spatial-temporal correlation and a game theory approach. Additionally, the local processing unit of the proposed design utilized the Mahalanobis distance for multivariate analysis. The suggested method demonstrated improved efficacy in producing a reduced false alarm rate and excellent detection precision. One potential flaw of the game-theoretic method is that it is necessary to account for novel anomalies. The experimental findings revealed that the suggested technique offered the quickest execution time and greatest energy efficiency among the sensors. However, this technique is incapable of detecting random changes in various physiological signals.