# 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
One thing to note here is that this work does not focus on anomaly detection, but rather the general usefulness of the AoT data, as well as how to correlate the data with themselves and ground-truth measurements from outside the set. The work in [12] utilizes time-sliced anomaly detection (TSAD) to discover spatial, temporal, and spatial-temporal anomalies in segments of a large-scale sensing system. This work utilized the AirBox dataset (out of Taiwan) to determine anomaly detection for air pollutants. This work defines spatial, temporal, and spatio-temporal anomalies. TSAD determines spatial anomalies using [PERSON]'s test, a temporal anomaly if data fall out of a reasonable measurement drift. Spatial-temporal anomalies are determined if both occur. Once this is completed, the results are fed into three other components: ranking, emission detection, and malfunction detection. The ranking is used to determine how well the device is performing, emission detection is used to determine, in real-time, the air quality based on the anomalies seen, and malfunctioning devices if extreme values are seen very often. This is essentially a set of heuristic or meta-heuristic tests, rules that may or may not assume certain criteria must be followed. In addition, no comparison against other methods was performed. While this work references Chicago AoT, it does not perform AD work with it. The driving factor for [14] is the danger of and necessity to eliminate urban heat islands, a human-made condition where areas of urban development experience extreme heat events. To facilitate this, this paper employs tensor factorization to impute missing values and correct errors in sensor network data. This study uses the Chicago AoT dataset, and NOAA data are used to compare and determine the ground truth. To do this, Tucker ranking, nuclear and L2 norms, singular-value thresholding, and the alternating direction method of multipliers framework were used in the factorization. The result was an output free of outliers and contained complete (though imputed in places) data. This work focuses on cleaning the data for analysis rather than generating a model that finds anomalies, though it does seem to produce an anomaly tensor (\(\epsilon\)). Exactly how these tensors are created is not detailed in this paper. In addition, the focus was on removing the outliers, not determining whether they provided anything of import. The work in [26] utilized satellite data and the Chicago AoT to predict urban heat islands, where the temperature is significantly hotter than other portions of the city due to lack of green space, shade, or less circulation. This work performed linear interpolation on the bi-weekly satellite data to obtain daily data. For ML algorithms, random forest regression (RFR), artificial neural network/multi-layer perceptron (ANN/MLP), support vector machine (SVM), and polynomial regression were used. Mean squared error (MSE) and mean absolute error (MAE) were used to evaluate model performance. This paper also details how the data were processed, an important step in beginning an ML analysis. The data were filtered by location and time; then, the data were collected in 10 m time-series windows, reduced to an average value over that window, and anomalies were removed based on temperature values that exceeded some sensor boundary or predetermined cutoff value (rules-based AD). Other AD methods are performed on the same sensors on the same node to find outlier cutoff values. To do this, quantiles determine an upper and lower bounded \"fence\". Again, this is a rules-based AD. The valid data from similar sensors are then averaged again to produce a single attribute value to be used. To deal with missing values in the data, multivariate imputation by chained equations was used [27]. For convenience, a summary of these approaches can be seen in Table 1.