Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste\nmanagement is a daily task in urban areas, which requires a large amount of labour resources and affects natural,\nbudgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as\nusing the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods.\nHowever, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently,\nthere has been a trend of combining optimal waste management strategies with low-cost IoTarchitectures. In this paper, we\npropose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the\nwaste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with\nthe shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University\n(Vietnam) to evaluate the performance and practicability of the systemâ??s implementation. We examine data transfer on the\nLoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit\ndesigned with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management\nof waste collection.
Loading....