## 7 Classification of the Surveyed AI Techniques
### Machine Learning Techniques
#### 7.1.1 Supervised Learning Techniques
It is given as  
\[\mathbf{MSE}=\frac{1}{\mathbf{n}}\sum\
olimits_{\mathbf{i}=1}^{\mathbf{n}}( \mathbf{predicted_{i}}-\mathbf{y_{i}})^{2} \tag{11}\]  
A gradient descent approach is then systematically used to update the values of \(\mathbf{b_{0}}\) and \(\mathbf{b_{1}}\) to reduce the MSE. Convolutional Neural Networks (CNN) are a class of deep neural networks commonly applied to vision-based analysis, image and video recognition systems, recommender systems, and image classification. CNN consists of an input, output and a hidden layer that comprises a series of convolutional layers. Some of the obstacles of using CNN in IoT nodes include high computational complexity, lack of sufficient data sets, high energy and memory usage [125]. However, these obstacles can be mitigated by scaling a large model down or by using a simplified model designed for resource-constrained environments. For example, authors in ref. [126] proposed a simple but efficient CNN model suitable for IoT devices. The simplified model achieves its state-of-the-art performance by factorizing standard \(3\times 3\) convolutions into pairs of \(1\times 3\) and \(3\times 1\) standard convolutions, instead of performing depth-wise convolutions. Authors in ref. [127] proposed a streaming hardware accelerator for achieving image detection using CNN in IoT nodes. To promote energy efficiency, the accelerator avoids unnecessary data movement and uses a unique filter decomposition technique to support arbitrary convolution window size. Also, to improve throughput, the accelerator uses an external pooling module to provide a pooling function. The validity testing of the accelerator showed that it can support popular CNNs and it is suitable to be integrated with the IoT devices. Authors in ref. [128] present a CNN and RNN based network traffic classifier for classifying IoT traffic. The proposed method provided a better detection result than alternative algorithms without the added feature engineering technique common in other models. In a related study, authors in ref. [129] applied the compressed sensing scheme at the input layer of a CNN model for image classification to reduce the resources consumption and the required number of training samples. The proposed technique was then evaluated using the public data sets, MINST and CIFAR-10, with results showing reduced training and inference time. Further, the model achieved a higher classification accuracy when compared with the traditional large CNN models. In ref. [130], a CNN indoor localization framework based on RSSI measurements was developed using a 3D radio image-based region recognition process. It aims to localize a sensor node accurately by determining its location region. To achieve this, 3D radio images are constructed based on the Received Signal Strength Indicator (RSSI) fingerprints. The RSSI measurements and the kurtosis values are then used to provide new information to the network. The proposed method solved the problem of the high computational complexity of the traditional methods and ensured a good localization accuracy. Authors in ref. [131] developed a general-purpose CNN for image and video classification in IoT systems. To overcome the high computational cost of CNNs, the developed system distributes their computation onto the units of the IoT system which is then formalized as an optimization problem of minimizing the latency between the data-gathering and the decision-making phase. The strength of the proposed CNN lies in its ability to supports multiple IoT sources of data as well as parallel execution on the same IoT system.