## 2 Literature Review  
There are different solutions proposed for existing problems in waste management. A few solutions are described below. The growth of information and communications technology (ICT) and its implementation in smart cities is inevitable. It provides diversified solutions to the existing scenarios with respect to smart cities and the IoT. [PERSON] et al. [14] implemented an effective waste management system using vehicle routing in a two-level approach. The threshold parameter is set at level 1 using real-time data from the IoT devices. The waste in the bins is transferred at level 2, maximizing the recovery value and minimizing the visual pollution. A threshold value of around 70 to 75% provides the best solution. [PERSON] et al. [21] used IoT devices to monitor garbage production and waste conditions, with a CNN classifier together with cloud computing methods, and the best results for waste collection were obtained. The waste is divided into six subcategories. The MobileNetV3 had a minimum running time of 261.7 ms and an accuracy of 94.26%. [PERSON] et al. [22] proposed a multi-level IoT infrastructure for waste management in smart cities. The architecture was built using three protocols: the Constrained Application Protocol (CoAP), the Hyper Text Transfer Protocol Secure (HTTPS), and the Message Queuing Telemetry Transport (MQTT). The MQTT protocol surpasses HTTP and is considered the best protocol, as it provides the maximum throughput with a feasible implementation in the smart city. The infrastructure is capable of managing 3902 bins simultaneously. [PERSON] et al. [23] implemented the smart bin mechanism (SBM) by using the three R concepts, namely reduce, reuse, and recycle, along with the artificial Internet of Things (AIoT). The live data about the bin are collected, significantly reducing the time and energy spent. In addition, the labor cost is also minimized. Fuzzy logic is utilized for decision making. IoT, in conjunction with smart waste management systems, drastically reduces waste generation. Several sensors are used in order to collect the real-time data needed for collecting waste. [PERSON] et al. [24] significantly reduced the waste and the danger of infectious diseases in their proposed model. The uncertainty problems are resolved using fermionic fuzzy sets (FFS). It made a significant impact on the decision-making process and improved accuracy in complex environments. [PERSON] et al. [24] investigated smart waste management at multiple layers using FFSs combined with the Archimedean copula operator, where operations are used. [PERSON] et al. [25] discuss the various methods and the need for real-time data on waste management. Sensors and RFID tags are used to accomplish this. It also provides an overview of the technologies and solutions. [PERSON] et al. [26] proposed a method for clearing garbage by generating an alarm at the municipal office once the basket is filled. This method was successfully implemented by making use of the Arduino UNO board and the sensor. After emptying, the in-charge person confirms that the task is over through an RFID tag. IoT and RFID were embedded to simplify this process. The microcontroller sends an alert message to the urban offices to monitor the workers involved in this cleaning process. [PERSON] et al. [27] discovered an effective system to check the level of waste, which is implemented using sensors, and the monitoring screen helps to display the output in the office. The level of trash is detected by a float level sensor installed internally in the bin. The load sensor is mounted at the bottom of the dust bin and continuously monitors the weight of the garbage inside. By interpolating artificial neural networks, [PERSON] et al. [28]discovered a replacement method for predicting long-term wastes. A comparison between multivariate regression and artificial neural network (ANN) was performed. Statistical datasets of 10 years of generated solid waste from 2000-2010 for the town of Mashhad were analyzed.