Pattern Recognition by IoT Systems of Machine Learning

Introduction

The term “internet of things” refers to a network of things with embedded software, sensors, transmitters, receivers, and other technologies. All IoT devices are capable of carrying out a variety of complex calculations with the help of ML algorithms. Selecting the appropriate ML algorithms is essential for pattern recognition in IoT devices (Gupta & Quamara, 2020). Faster processing, less space and power consumption would be possible with a more efficient ML algorithm. This can be supplemented by ongoing research into biometric security and human motion detection (Kim et al., 2016; Sihare, 2017a; Chen et al., 2022).

Choosing the right ML algorithm for IoT devices is a very challenging process. An IoT device is a hardware machine that follows a computer-like architecture that includes memory, processing units, input and output units, and a data transfer bus, whereas ML involves building algorithms according to the architecture and organization of IoT devices (Burd et al., 2018). Combining pattern recognition and human activity with IoT devices has become very important today for security and safety purposes (Merenda et al., 2020). IoT devices are such cutting-edge technology that every piece of hardware we own must be controlled remotely by connecting it to the Internet. If IoT devices are combined with ML, then hardware machines will work intelligently; there is no doubt about it. Today's era is one of technologies, and the size of each technology is also getting smaller (Ferozkhan & Anandharaj, 2021). Hardware, ML, and algorithm development are going through a new phase of change. The integration of ML algorithms for pattern recognition with IoT devices has grown in popularity as the demand for these algorithms has increased (Batra et al., 2019). Due to the compactness of different types of sensors and their size, monitoring human activity can be made more robust and efficient. Wearable sensors can monitor human activity and body changes (Mukhopadhyay, 2014).

ML algorithms built on the IoT are widely used to track and manage human activity. To select the optimal algorithm from a collection of ML algorithms, the processing efficiency, memory needs, and energy consumption criteria are given significant weight (Mahdavinejad et al., 2018; Aloraini et al., 2022). Wearable sensors are installed in the IoT device to detect various human activities using predetermined datasets, and human activity is then tracked in accordance with the entered dataset (Larranaga et al., 1996). A pre-existing dataset is fed into the IoT machine, and the computing performance of each is assessed in order to choose the optimal ML algorithm (Singh et al., 2013).

The dataset used for all ML algorithms is identical, and the algorithm with the highest computing speed and accuracy is chosen. Among the algorithms, gradient boosting, random forest, SVM, and K-NN have the maximum processing capacity and efficiency (Cho et al., 2019; Chkirbene et al., 2020). Accuracy and zero percent error rates are crucial in the field of security. IoT and ML algorithms are therefore employed to identify various patterns. Security systems can be strengthened and made more potent by integrating ML algorithms with IoT devices to recognize various human actions, like a biometric gadget that can identify a person by their finger print or face recognition (Boutaba et al., 2018). IoT systems are employed in smart homes so that the owner can control and monitor security from anywhere. In a smart home, the security of the property can be effectively maintained by connecting every electronic device to the Internet (Abdulla et al., 2020).