# An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques
## 5 Data Analysis
This structure can be expressed so that each neuron in an RNN has an internal memory that preserves the computational information from the previous input. Long short-term memory (LSTM) is a variant of RNN that can look back for a long time before predicting. LSTM can efficiently retrieve and identify features for the prediction from the sensor data. Autoencoders (AE) is a neural network that consists of the same number of input and output layers connected through a series of hidden layers [64]. The autoencoders can construct an input at the output layer. Due to this feature, the autoencoders are used in industrial IoT for many applications, such as fault detection in hardware devices, anomaly detection in the performance of assembly lines, etc. Generative adversarial networks (GANs) contain two neural networks, namely the generative and discriminative networks, which mainly aim to produce synthetic and high-quality data [7]. They work on the principle of min-max games, where one network tries to maximize the value function, and the other network wants to minimize it. Applying GAN for IoT applications involves generating entirely different data based on the available original data set. The general applications of GAN include localization formulation and wayfinding in graph-based networks [65; 66]. In these applications, the generator network module of GAN estimates all the possible feasible paths that exist between two nodes. The discriminator module of GAN performs optimal path identification between the source and the target node. GANs are widely used in assistant systems for disabled persons. Further, the authors discuss GAN's implementation in converting the image to audio applications for visual impaired humans. Another example is the generation of descriptive information in text form out of a given original image. The authors applied GAN for image processing applications, where the objective is to identify the forged celebrity consisting of different pictures of the authentic celebrity. The IoT sensor network-based applications involve dynamic factors, distributed services, and real-time responsive mechanisms [67]. Hence, there is a middleware layer requirement between IoT-based applications and the underlying IoT sensor data. Further, the scalability addresses huge volumes of data that are obtained from the IoT sensor network. To tackle dynamic, real-time processing and scalability issues, solutions through the integration of IoT sensor networks with other emerging technologies, such as cloud computing, fog computing and edge-based data analysis, are required. _Cloud-Based Data Analysis_: The authors in [68; 69] proposed cloud-based data analytics for the Internet of Things. To perform efficient data analysis requires a hierarchical and distributed model for the data processing systems. This method is replicated in several virtual machines that re associwith a remote cloud data center. Then, without any prior data knowledge, the cloud-based analytics model can handle dynamic data processing and scalability issues. The cluster-based operations involve breaking more complex mathematical processes into simpler small tasks, and executing them in different clusters, thus reducing computation cost. Further, to handle the huge volume of data generated by the IoT sensors, efficient solutions based on cloud computing necessitate the application of big data and massively parallel distributed system technologies. The authors in [70] address the storage of massive quantities of high-velocity and complex sensing data generated by IoT sensor systems. These papers presented data fusion through efficient curation techniques that integrate IoT and the huge sensor data on the remote cloud server, which thus enables the system to provide efficient services for IoT applications. Various issues, such as sensor network scalability, data cleaning, data compression, data storage, data analysis and data visualization, were addressed through the data curation of IoT sensor data into the cloud [71].