# Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records
## 2 Related Work
### Recent Advances in Anomaly Detection (AD) on IoT and AD Using Chicago AoT  
In [24], [PERSON] et al. focus on utilizing multi-agent systems (MAS) and time-series AD techniques to detect unusual human movements. The dataset utilized involved mobile phone data. The time-series data were determined to be non-stationary using the Dickey-Fuller technique, then converted to stationary using the min-max technique. Part of their approach involved filling gaps in missing time-series data. They completed this by utilizing a simple rule (fill in 0 s if there are 0 s previously and ahead) and regression using root mean squared error. Boosted decision tree (BDT) regression was chosen for outlier detection. Seasonal trend decomposition was used. In [25], the focus is on finding faults in etching equipment used in Tool-to-Tool Matching (TTTM) in semiconductor manufacturing. Their approach focuses on rapid gradient shifting and deviations from the normal trend range. The first algorithm is called the gradient drift detection algorithm (GDD). The approach relies on the interpolation of missing data and relies on a median value for determining the sensor range using bins of data. The gradient of the data is found by differencing these median values. Rapid change detection is performed using algorithms like Isolation Forest. The second algorithm is called trend zone detection (TZD). A trend line is calculated between the data, smoothed, and adjusted. A plane model like an SVM is used to detect deviations. In [13], [PERSON] et al. focus on how the environment, particularly air quality, affects personal health. It utilizes two datasets: the Chicago Health and Real-Time (CHART) project, which contains health and daily activity impact on elderly Chicagoans, and the Chicago AoT's air sensor data. CHART collects GPS data and surveys on willing participants to determine activity and health. This work points out that 80% of CHART participants lived within at least one AoT node, so the overlap between datasets is viable. Previous studies were used to determine the ground truth of the AoT air pollutant data to determine the minimum and maximum levels for certain gasses by checking if the AoT node exceeded the highest or lowest values or where humidity was lower than 0 or higher than 100%. In addition, any data points with less than 10 months of temp and humidity data available were excluded from annual averages. To generate averages over the area, inverse distance weighting over a 200 m \(\times\) 200 m grid was utilized, and mean values within 250 m of a CHART participant's address were used to determine exposure. Correlation between gasses was performed, and any highly correlated gasses were removed in favor of the known \"criteria air pollutants\" they were correlated with. Logistic regression models were fit based on the binary surveys performed by CHART respondents using the Hosmer-Lemeshow test. To test the improvement of the fit using the AoT data, likelihood ratio tests and [PERSON]'s and Bayesian information criteria were used. Three models were used. The first model utilized air quality and weather conditions. The second model was an individualized model based on surveys of health, demographics, and length of time in the area. The last model combined the two to determine short and long-term effects. Notable results were that the air quality measurements in the area only affected respiratory health in long-term residents, particularly due to higher levels of \(NO_{2}\), and that while air pollutants do negatively affect overall health, due to the correlation between the different pollutants, it is unclear which specific pollutants are the most egregious. In addition, this work points out that when AoT data are compared to EPA data on air pollutants for the area, an unknown amount of measurement error may have been introduced due to the low-cost nature of the sensors in the AoT nodes.