article · Neural Computing and Applications
Rapid urban population growth has led to increased municipal waste generation, creating a clear need for smarter public utility planning. An intelligent waste management system addresses this challenge through three distinct operational phases powered by Internet of Things devices, including sensors, detectors, and actuators. The initial phase optimises energy consumption across smart waste bins using an adapted clustering hierarchy to lengthen network lifespan. The second phase handles missing sensor data from bins by pairing a nearest-neighbour algorithm with artificial hummingbird optimisation. Finally, an energy-efficient routing mechanism calculates paths for collection trucks to lower fuel usage and cut transit times to target bins. Experimental evaluations confirm that the architecture achieves a 34 percent reduction in energy consumption for the bin network, alongside lower error rates and faster algorithmic run times during data retrieval.
Expanding cities place intense strain on municipal waste services, resulting in higher fuel costs and collection delays. By lengthening the battery life of smart bin networks and calculating optimal collection journeys, urban authorities can significantly decrease transport emissions, manage missing sensor signals reliably, and maintain cleaner, more livable city environments at a lower operational cost.
This technology could support municipal authorities, smart city software providers, and contracted fleet operators seeking to reduce fuel expenditure and monitor distributed waste networks. Given that the abstract details experimental results and algorithm performance comparisons rather than pilot deployment across a live city fleet, the system currently appears to be at an applied research stage awaiting operational field trials.
AI-generated from the published abstract. Always read the original work before citing.
Abstract Recently, the population density in cities has increased at a higher pace, so waste generation is on the rise in most societies due to population growth. Given this concern, it would be highly important to manage waste generation. Intelligent city planning is necessary to improve the quality of city life and make cities more livable. This paper presents an intelligent waste management system (IWMS) in smart cities based on Internet of Things components like sensors, detectors, and actuators. IWMS contains three main phases. The first phase of the system is to adapt the low energy adaptive clustering hierarchy approach as an optimization process to better balance the energy consumption of smart waste bins (SBs), thus leading to extending the life of the smart waste network. The second phase is handling the missing values which are retrieved from SBs using an improved version of the k-nearest neighbor algorithm based on artificial hummingbird optimization (AHA), while the third phase presents an optimal energy-efficient route process for the routing of waste trucks that improves fuel efficiency and reduces the time to get an appropriate SB. According to the experimental results, the proposed system has achieved energy savings of 34% for the smart waste bin network. Moreover, compared to other systems, it has a lower mean error rate when generating missing values, and the results related to convergence and running time validate its superiority compared with other metaheuristic algorithms.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s00521-023-08970-7
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
