Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review

Abstract

The Internet of Things (IoT) is a rapidly advancing area of technology that has quickly become more widespread in recent years. With greater numbers of everyday objects being connected to the internet, many different innovations have been presented to make our everyday lives more straightforward. Pattern recognition is extremely prevalent in IoT devices because of the many applications and benefits that can come from it. A multitude of studies have been conducted with the intention of improving speed and accuracy, decreasing complexity, and reducing the overall required processing power of pattern recognition algorithms in IoT devices. After reviewing the applications of different machine learning algorithms, results vary from case to case but a general conclusion can be drawn that the optimal machine learning based pattern recognition algorithms to be used with IoT devices are K-Nearest Neighbor, Random Forest, and Support Vector Machine.

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

“…The uses of such techniques are presented as follows: The SVM classification approach looks for unusual behavior in IoT devices and malware on Android to assure the dependability of IoT services [ 106 ]. Anomalies, denial-of-service assaults, IoT intrusions, and irregularities in smart cities are all detected using the random forest approach [ 107 ].…”

Machine learning Applications of Data Security and Privacy A Systemic Review

“…In any case, it was once found in this article that these social features did never again work appropriately while clients had been moving, in like manner, diminishing the exactness of the application. This issue with aggregating social records while the customer was once in real life used to be seen to be an issue in various articles as appropriately [14][15][16][17][18][19] and [5]. Likewise, many examination do now not ponder thought on how a client's ways of behaving can substitute over the long haul, for example, the buyer going downhill and composing more slow, the shopper now not being in that frame of mind to utilize their prevailing hand, and different long haul social changes [20].…”

“…As threatening to biometric-based validation, PLA utilizes substantially traits of an obtained sign to verify a purchaser with the exception of the need for a truckload extra hardware. With wired and wi-fi organizations' quickly creating importance because of the extended utilization of the Internet of Things (IoT) gadgets, as appropriately as the ongoing advancements in 5G organizations, the wellbeing of these organizations are vital in ensuring their broad execution and reliability [22][23][24][25]. A regular occurrence of the utilization of substantial layer validation in a Multiple Input Multiple Output (MIMO) net-work is the Bob, Alice, and Eve model.…”

Objects Movement Detection Based on Impulsed Neural Network of the Analogue of the Biological Retinal Model

“…The choice of method greatly affects the efficiency of the entire recognition system [Cheng, Y. , et al, 2015] [Chakrabarti, I., Srinivasarao Batta, K. & Chatterjee, K., 2015] [Beyerer, J., Puente L. & Frese F., 2016] [Sudan, J., Changho, S., Eunmok, Y. & Gyanendra, P., 2021] [Menter, Z., Tee, W. & Dave, R., 2021] [Purohit, S., Singh Jat, D., Poonia, R., Kumar, S. & Hiranwal, S., 2021. And the more efficient the method, the more complex it is, and requires more resources.…”