Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm

Alqahtani, Fayez; Al-Makhadmeh, Zafer; Tolba, Amr; Said, Wael

doi:10.1007/s10586-020-03126-x

Cited by 65 publications

(12 citation statements)

References 26 publications

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“…However, they define the necessary changes needed to optimize the IoT-based waste management system. The researchers in [209] proposed a waste management approach using Cuckoo Search Algorithm to monitor the smart waste bins. The researchers in [210] proposed an algorithm named as two-step heuristic algorithm multiple trucks routing algorithm (MITRA) for identifying the optimal ways for the waste-collecting vehicles in the smart cities.…”

Section: Fig 20: Vehicle To Everything (V2x)mentioning

confidence: 99%

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Javed

Shahzad

Rehman

et al. 2022

Cities

314

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Section: Fig 20: Vehicle To Everything (V2x)mentioning

confidence: 99%

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Javed

Shahzad

Rehman

et al. 2022

Cities

314

View full text Add to dashboard Cite

“…Alqahtani et al, [35] have presented an Internet of Things-Based Urban Waste Management method. IoT devices have been utilized for monitoring human activities and maintain waste management.…”

Section: Literature Surveymentioning

confidence: 99%

An IoT aware nature inspired Multilayer Hybrid Dropout Deep-learning paradigm for waste image classification and management

Kumar¹,

Kumar²,

Surender

et al. 2023

IRASE

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In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

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“…Using the IoT technology to achieve the scheduling of garbage vehicles requires high coverage of urban infrastructure, which largely tests the level of urban infrastructure. Alqahtani et al [ 17 ] proposed a cuckoo search optimized long short-term recurrent neural network (CLSTRNN), which supports the decision-making of vehicle transportation scheduling. Using this model to analyze data to provide decision-making faces the problems of reliable data sources and data acquisition to some extent.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

Wei

Liang

Tang

2022

Computational Intelligence and Neuroscience

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The disposal of municipal solid waste (MSW) is based on the divide-regional operation model at present, which brings management convenience. However, due to the constraints of the division between the operating regions, there are problems such as inflexible vehicle scheduling and low efficiency. Changing this mode will put pressure on management. It is necessary to break the constraint of the region and form a fully automatic operating scheme without too much manual management. First, the initial clusters are formed by considering the distance in the initial allocation module. Secondly, through the type labeling and reallocation module, the single big data set is transformed into multiple small data sets by considering the allocated amount of garbage and the carrying capacity of garbage vehicles. Then, this work proposes the improved hierarchical agglomerative clustering (IHAC) algorithm and the garbage collecting path planning (GCPP) algorithm to realize the intelligent allocation of waste and scheduling route planning of garbage vehicles. Finally, through simulation by real example and comparative analysis, the advantages of the proposed scheme are discussed. The results show that the proposed scheme is more effective than the original scheme and other advanced methods, which can provide decision support for the scientific and intelligent collection and transportation of MSW.

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Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm

Cited by 65 publications

References 26 publications

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

An IoT aware nature inspired Multilayer Hybrid Dropout Deep-learning paradigm for waste image classification and management

A Cross-Regional Scheduling Strategy of Waste Collection and Transportation Based on an Improved Hierarchical Agglomerative Clustering Algorithm

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