Smart city solutions: Comparative analysis of waste management models in IoT-enabled environments using multiagent simulation

Effective waste management arises as a crucial challenge for smart city development in the current era of rapid urbanization, shifting towards sustainability and public health. Harnessing modern technologies, especially the integration of the Internet of Things (IoT) with intelligent waste bins, can revolutionize urban waste collection, optimizing efficiency and reducing costs. This paper delves into a multiagent simulation-based framework for understanding and assessing the dynamics of an IoT-enabled smart waste management system. Initiating with the intricate process of garbage generation, we shift our focus to the real-time monitoring capabilities of IoTconnected waste bins. The study further explores protocols to regulate bin status, along with timing mechanisms to trigger garbage collection rounds. Subsequently, a predictive routing system is introduced to determine the most efficient garbage collection routes. For the bins' filled level tracking, the ultrasonic sensors are commonly used that send out sound waves and track their echo return time, whereas weight sensors measure the garbage load in the bin, providing insights into waste production trends. For data transmission from bins to the central system, various communication technologies such as Wi-Fi, cellular networks, and long-distance networks are considered. Through a simulation, we contrast the innovative IoT-enabled sensor-based collection mechanism against the conventional periodic review strategy. Field experiments at the Al Rayyan locale, proximate to Doha, Qatar, facilitate the model demonstration. By leveraging region-specific data, we simulated various aspects including economic factors, environmental impact, public satisfaction, and operational efficiencies. The findings indicate that with an average daily garbage generation of 1.3 kg per individual, the sensordriven mechanism remarkably outperforms the periodic review approach by covering fewer distances with fewer trucks, while concurrently achieving the key objectives of cost-efficiency, environmental preservation, public satisfaction, and reduced employee workload. This research contributes to the developing field of smart city technology by providing critical insights for urban planners, policymakers, and technologists attempting to build more sustainable, efficient, and livable cities.