Abstract
Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. However, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. In this paper, we propose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with the shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University (Vietnam) to evaluate the performance and practicability of the system’s implementation. We examine data transfer on the LoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit designed with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management of waste collection.
Highlights
Introduction eInternet of ings (IoT) is a new and promising technology, which has the potential to globally change human life in a positive way, thanks to its diverse connectivity
Energy efficiency was considered in [4, 5], cars were connected by IoT methods in [6,7,8], and water management in smart agriculture was investigated in [9,10,11,12,13], among a long list of other IoT application areas
According to [14], a critical issue for a smart city is the increase of waste generation with accelerated population growth in cities
Summary
Considering the advantages of IoT technologies, many researchers have investigated and developed new applications for smart cities, especially for waste management. An impressive architecture was proposed for a sensor node in [22, 23], which used a microcontroller (ATMega328P), an ultrasonic sensor (SRF05), and a LORA E32 TTL—100 433 MHz module [30,31,32] They only tested the test board as a platform to provide sensor nodes and did not apply any methods for waste management in a smart city, such as optimized waste collection. E predicted state of each trash bin can be examined, based on assigned training data It is, reviewed to refresh the appropriate waste fill level, which is an essential input parameter of the optimal path algorithm. In our proposal, firstly, the IoT node architecture, shown, is composed of three types of components: ATmega328P, LoRa E32 TTL100 433 MHz module, and SRF-05. Amount Modes Current capacity (μA) Operating voltage (V) Power (W) Total power Running time Prices ($)
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