## 1 Introduction  
The number of networks and terminal devices being used worldwide is continually and rapidly increasing. The ability to convey information has kept pace with this proliferation of devices and networks, and the connection between devices has become tighter and more convenient. New sensors are continuously being engineered, and the technologies underlying their operation are constantly improving. Volumes and power losses of devices are decreasing. Sensors can communicate with other devices and upload data to the cloud. They can collect environmental data and make decisions after incorporating the data they receive from other sensors. Internet of Things (IoT) devices can be operated manually but can also be operated using automated systems. Moreover, they can exercise intelligent judgment and take initiative to complete the bulk of the work. IoT devices have created a highly connected world, wherein billions of devices are collecting and communicating data from a range of domains, including home care and health care. Artificial intelligence has become indispensable for interpreting and understanding the enormous volumes of sensor data being generated and for making decisions that yield optimal outcomes [1]. Smart home technologies incorporate IoT devices [2]. Smart home systems feature gateways, which are central hubs that integrate multiple wireless communication technologies and uniformly transmit data to the cloud. To demonstrate the features of a smart home system, a study engineered a smart seawater aquarium system that used artificial intelligence technology specifically to enhance know-how regarding the rearing of seahorses as pets. The smart seawater aquarium system automatically adjusted water quality, water temperature, and oxygen content. Moreover, it changed the water, fed fish, and monitored fish in real time. The system comprised a cloud-based platform and a web-based platform for data transmission and interface control. This approach enabled local as well as remote monitoring of the aquarium, enhancing the convenience of fish rearing [3]. However, the system did not have artificial intelligence technology capable of processing sensor data and improving monitoring, detection, learning, control, and prediction efficiencies. Our study developed an indoor air quality monitoring system based on cloud-side and end-side collaboration and machine learning. The system was implemented on the edge impulse platform using a convolutional neural network (CNN) and long short-term memory (LSTM). Edge impulse is a leading AI platform for collecting data, training models, and deploying them to edge computing devices. It provides an end-to-end framework that seamlessly integrates into the edge MLOps (machine learning operations) workflow. Additionally, edge impulse integrates effortlessly with other machine learning frameworks, allowing you to scale and customize your models or pipelines as needed. The system analyzed and predicted indoor air quality levels, uploaded information to a message queue telemetry transport (MQTT) server using the Category 0 protocol, and sent notifications to subscribers through a mobile application (app), thereby enabling individuals to monitor and adjust indoor air quality as needed [4]. The system was developed using devices from different vendors and different communication protocols [5]. Hardware and cloud services do not need to be purchased to use this system. Furthermore, the system's applications can be expanded to create a more comprehensive IoT ecosystem, including integrating smart home devices, energy-efficient systems, mobile apps, and other machine learning models. The experimental results employ min-max normalization (also known as feature scaling) to reduce the impact of unrelated samples on the measured results. While this approach ensures a bounded range, it also results in smaller standard deviations, which may suppress the effects of outliers.