Aims: IoT-based trash collection system is a system that can automatically detect obstacles, which can be simplified as trash, and opens the Meta Bin lid to receive the trash. The main goal of the system is to make an environment where to find a digital and automatic way to the trash collection system. Study Design: The existing research aims to design, simulate, and implement a new system that can play a vital role in terms of making the environment clean, in a large sense the world clean. Along with that a system that can carry not only a trash bin but also any portable devices as it can be operated automatically. Place and Duration of Study: Department of Computer Science and Engineering and Department of Electrical and Electronic Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh between October 2022 and February 2023. Methodology: In this work, we have designed a new model using Arduino, an IoT device, a servo motor, an ESP8266 Wi-Fi microchip, several DC motors, several IR Sensors, LEDs, etc. to build a system like Meta-Bin. Arduino IDE is used for program development. Results: This is an automated trash bin, which has a different level of trash collection capacity with proper identifications and LED light indications. Also, a continuous notification system is enabled here. After testing the implemented system, the system gives an accurate result in every possible way and has an accuracy rate of more than 95%. Conclusion: After the successful implementation of this research, we hope that there will be an autonomous, well-decorated, digital, and user-friendly system available for the citizens of Bangladesh. This will be immensely helpful for all kinds of people and to ensure a clean environment, this research might play a vital role soon.

This research paper focuses on current and previous efforts to detect traffic rule violations. So far, some remarkable works have been discovered, and many approaches for detecting traffic rule violations have been introduced from the current situation. Hence, machine learning has been the main target to detect traffic rule violations. A summary of the frameworks and methods that have been used to solve this problem so far is also provided in this study. This study has been divided into two parts. In the first part, the recent works on traffic rule violations have been portrayed. Moreover, the algorithms and frameworks that have been used so far and major works on violation detection using machine learning can be found in this section. In the second part, this study summarizes a brief discussion based on the image quality, camera resolution, device performance, and accuracy level of the works, as well as the algorithms and frameworks that have been used to conduct the detection of traffic rule violation problems using machine learning.