# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 5 Data Analysis
The authors in [72] addressed the data acquisition of resource-constrained, distributed IoT sensors. Further, the possibility of in situ data analysis was discussed to overcome the network congestion problem, with context-aware and real-time processing, in the context of fog and edge computing. Here, surveillance camera sensors were considered for achieving end-to-end optimization. Multiple feature extraction techniques and various classification algorithms were considered, as were the proposed processing depth and amplification of gain through efficient methods. The experimentation result showed a 4.3-fold reduction in power consumption compared with other designs. It is to be noted that the IoT sensor data consists of complex time series data, and thus requires efficient data analysis mechanisms. The authors in [73; 74] presented a data analytic framework that involved a multi-dimensional feature handling, selection and extraction model. The papers also discussed the dynamic data analytic model for IoT sensor data prediction. The IoT sensor data are represented in time series, and involve complex data analysis mechanisms. The authors proposed a prediction model named the Adaptative Sliding window algorithm, and applied it to the sensor data for feature selection. The experiment was conducted on sensor data obtained from the Intel Berkley Research lab, and results exhibited more than 98% accuracy. The second experiment conducted on Chicago Park Weather Data exhibited more than 92% accuracy. The authors in [75; 76] aimed to reduce the data transfer and processing in a remote cloud by efficient fog-based data analytics for IoT sensor data. The paper argued that uploading sensor data into a remote cloud over the Internet adds no value due to the underlying network complexity. Further, the authors proposed homoscedasticity detection techniques for multi-dimension feature extraction in IoT sensor data. This method, along with neural classifiers, promised to be efficient in extracting important sensor data events in real-time processing. The authors in [77] discussed the pervasive nature of IoT sensor networks and generated a huge volume of sensor data. These sensor data, however, exhibited data redundancy and data outliers that largely degraded the overall performance of the IoT sensor networks. As such, the authors proposed a data analysis framework that aimed at recursively updating and adapting to the dynamic changes in the IoT system's surroundings. In this, the authors proposed data aggregation through principal component analysis, which involved principal component extraction. Further, the squared prediction error score was proposed in order to identify the data outliers. In real-time experimentation, the proposed data analytics framework exhibited efficient data aggregation and data outlier detection with high accuracy. _Fog-Based Data Analysis_: The authors in [78; 79; 80] presented fog-level-based IoT sensor data processing. Here, the senor data features are extracted and processed to classify different signal patterns using a neural network. Consequently, based on the output of neural network classification, event identification and decision-making are performed at the fog level. These sensor signals are classified as normal or abnormal, and accordingly, the alert event is initiated by the fog nodes. The few example applications include smart home systems, fire alarm systems, surveillance systems and air monitoring systems. The objective of fog-based IoT sensor data analysis is to carry out real-time data analytics at the fog device-level, and decision-making as to the alerts used. The authors in [81; 82]introduce a content orchestration mechanism that estimates an opportunistic fog node to offload data. The mechanism considers the available bandwidth delay, the cost and the quality of service metrics to determine the opportunistic fog node.