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The stream data acquired by heterogeneous Internet of Things (IoT) sensors are seldom perfect. Most of the collected data streams include either missing or abnormal values caused by various factors such as failure, malfunction, or integrity attacks. Such unreliable data affect the real-time monitoring and compromise the quality of data analysis. By simply analyzing the sensor data via anomaly detection, applications may still be unreliable over the incomplete sensor data streams. Therefore, a reliable method for recovering the missing data and detecting the abnormal ones is indispensable in the IoT environment. This paper presents FuzHD++, a new method to recover missing sensor data and detect abnormal nodes jointly rather than independently. Both elements, data recovery and abnormal node detection, rely on the observed temporal and spatial correlation of sensor data to effectively achieve reliable recovery estimation and detection performance. In the data recovery process, the system adopts a matrix profile to extract the top- $k$ repeated patterns from different sensor nodes. Furthermore, it utilizes the $k$ -nearest neighbor estimator to recover the missing data based on the extracted pattern information of multiple neighbor nodes. During the abnormal node detection process, the system adopts a refined fuzzy rule-based detection method. The refined fuzzy rule-based inference system integrates the expert rules and the rules obtained from sensor data analysis to treat the ambiguity in the decision-making process. We validated the performance of FuzHD++ by comparing it with existing methods using two real-world datasets. Our results showed that the proposed missing sensor data recovery method achieves more than 20% improved root mean square error results than most existing methods. Furthermore, FuzHD++ achieved an average accuracy of 92% for analyzing the sensor readings and detecting the abnormal ones. According to the results, the proposed mechanisms based on the observed temporal and spatial correlation analysis improve the robustness of IoT against data loss and integrity attacks.

Many heterogeneous sensors exhibit strong spatio-temporal correlations that can be used to enhance the abnormal node detection problem in a wireless sensor network (WSN). Corruption in these correlations has been shown effective in detecting false data injection attacks. In this paper, we adopt a new cross-correlation-based method to extract the sensor relationships. It utilizes the observed spatiotemporal (ST) and multivariate-attribute (MVA) sensor correlations to decide whether the sensor is subject to abnormalities or represents actual events. Based on the ST correlations, the cross-correlation is extracted in both space and time by conducting shape-based logical subclustering and two-phase analysis methods. In the first analysis phase, the system uses a variable-size sliding window and a median absolute deviation (MAD) measure. If the collected sensor data streams output a certain percentage of anomalous points, the MAD will flag these points as anomalous measurements, and all the sensor data and the window size will be passed to the second analysis phase. The latter performs both tumbling-window and sliding-window analyses to extract multicriteria cross-correlation measures. Finally, all the extracted sensor time-series features will be fed to the shape-based clustering to generate a sensor similarity-like graph. The latter reflects the similarity degree of the sensor with the other nodes. The nodes with a low similarity degree below the threshold will be identified as candidate abnormal nodes. Based on the observed MVA correlations, a set consisting of a few rules is introduced to check whether the detected candidate abnormal nodes represent actual events. Finally, if abnormal nodes exist, then such nodes are reported. Our experiments using two real-world datasets demonstrate that our proposed approach detects abnormal nodes with an average accuracy of 96.50%, an average precision of 88.69%, and a recall rate of 93.00%.