IoT based Waste Management for Smart Cities

Rao, Padmakshi Venkateswara; Azeez, Pathan Mahammed Abdul; Peri, Sai Sasank; Kumar, Vaishnavi; Devi, R. Santhiya; Amirtharajan, Rengarajan; Thenmozhi, K.; Praveenkumar, Padmapriya

doi:10.1109/iccci48352.2020.9104069

Cited by 20 publications

(11 citation statements)

References 6 publications

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“…(7) Waste management: At a different level, DTs offer novel approaches to waste management. Several studies have exploited new technologies such as IoT and computer vision to monitor the level of waste in garbage cans, aka smart bins, which can then notify the relevant teams when waste should be collected, and possibly suggest optimum driving routes to be followed during the process of waste collection from a myriad of bins across the city (Rao et al, 2020;Aktemur et al, 2020;Jadli & Hain, 2020). (8) Resource management: This involves identifying the best use of available scarce resources, whether monetary or physical, to realize greatest value.…”

Section: D1: Application Areamentioning

confidence: 99%

“…Depending on the purpose of a use case, in less common cases, a DT may need not involve more than one city layer, similar to how Rao et al (2020) and Aktemur et al (2020) were only concerned with infrastructure layer only. Yet, more frequently a DT may penetrate through multiple relevant city layers.…”

Section: D3: Layeringmentioning

confidence: 99%

“…For instance, Barmpounakis and Geroliminis (2020) and Nallaperuma et al (2019) produced comprehensive information with details about individual vehicles. Some studies introduced DTs to provide information about every single smart bin across the city (Rao et al, 2020;Aktemur et al, 2020;Jadli & Hain, 2020), while others have rather captured city buildings separately (Francisco et al, 2020) and individual 50m x 50m grids (Kim, 2020). When it comes to national scale, Pang et al (2020) worked on integrating city DTs across the nation to support better prediction of pandemic infection spreading patterns.…”

Section: D4: Spatial Scale and Resolutionmentioning

confidence: 99%

“…This type is tied-up in an enduring connection with the twinned real entity and constantly receiving up-to-date influx of data; thus, can never go obsolete as long as this live digital thread persists. To illustrate, Francisco et al (2020) relied on IoT technology and smart meters to monitor the energy consumption of buildings in real-time (Rao et al, 2020;Aktemur et al, 2020;Jadli & Hain, 2020;Nallaperuma et al, 2019).…”

Section: D5: Temporality and Resolutionmentioning

confidence: 99%

See 3 more Smart Citations

A multi-dimensional digital twin use cases classification framework

Al-Sehrawy¹,

Kumar²,

Watson³

2021

Proceedings of the 2021 European Conference on Computing in Construction

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The concept of a Digital Twin [DT] has been gaining increasing attention in the realm of urban planning and city infrastructure management. In support of this movement, DT advocates have been consistently casting light on the possible DT use cases to better manifest its potential and the enormous value it promises to unlock. However, these attempts are arguably limited by the lack of a formal and standard DT use cases classification framework. Hence, this paper puts forward a multidimensional DT use cases classification framework, based on published key DT case studies and a framework development methodology, to address this limitation. It concludes with insights on further possible implications of, and enhancements to this framework.

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Section: D1: Application Areamentioning

confidence: 99%

Section: D3: Layeringmentioning

confidence: 99%

Section: D4: Spatial Scale and Resolutionmentioning

confidence: 99%

Section: D5: Temporality and Resolutionmentioning

confidence: 99%

See 2 more Smart Citations

A multi-dimensional digital twin use cases classification framework

Al-Sehrawy¹,

Kumar²,

Watson³

2021

Proceedings of the 2021 European Conference on Computing in Construction

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“…Some studies suggested that waste governance needs to identify waste stakeholders and their collaboration with the national authorities to achieve the goals for public health, resource recovery, environment and waste prevention (Guerrero et al 2013). Rao et al (2020) has also concluded that smart dustbins can improve the cleanliness of the cities via using ultrasonic sensors for automatic dustbin status monitoring.…”

Section: Kulkarni and Anantharama 2020mentioning

confidence: 99%

Analysing Challenges to Smart Waste Management for a Sustainable Circular Economy in Developing countries: A fuzzy DEMATEL study

Yadav¹,

Soni²,

Kumar³

2021

Preprint

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To improve the quality of life and achieve sustainability Internet of Things (IoT) plays a significant role. Waste can be converted to high value asset if treated properly with smart solutions. This study aims to develop an exploratory framework in the adoption of smart waste management in developing economies such as India analysing the various deferring parameters. To accomplish this aim the present study consists of two phases: In the first phase barriers in the adoption of IoT for smart waste management were identified after an extensive literature review and discussion with selected experts in this field. In the second phase the quantitative analysis using Fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method was performed on the selected barriers. The fuzzy DEMATEL methodology helps in prioritizing the most significant barrier by separating them into cause-effect group. The data were analysed on comparative basis based on two different perception to provide more detailed insight on the problems faced while smart waste management implementation. The results discloses that “Lack of government strict regulatory policies”, “Lack of proper Financial planning” and “Lack of benchmarking processes” are among the most critical causal barriers towards IoT based smart waste management that are hindering the vision of efficient waste management system. Further “Difficulty in implementing innovative technologies” and “Absence of Dynamic Scheduling and Routing” fall under significant potential causal effect category. The most prominent effect barriers are “Lack of awareness among community”, “Lack of source segregation and recycling commitment” and “Lack of service provider’s” which was concluded in results considering the comparison parameters from different perspective. The study also suggest that the effect barriers can be overcome automatically with mitigating the causal barriers in the long run. The comparative details of the study will further help the policy makers, stakeholders and government experts to frame policies and interventions to eradicate the identified barriers towards an efficient and innovative system for waste handling.

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A survey of smart dustbin systems using the IoT and deep learning

Arthur,

Shoba,

Pandey

2024

Artif Intell Rev

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With massive population growth and a shift in the urban culture in smart cities, the constant generation of waste continues to create unsanitary living conditions for city dwellers. Overflowing solid waste in the garbage and the rapid generation of non-degradable solid waste produce a slew of infectious illnesses that proliferate throughout the ecosystem. Conventional solid waste management systems have proved to be increasingly harmful in densely populated areas like smart cities. Also, such systems require real-time manual monitoring of garbage, high labor costs, and constant maintenance. Monitoring waste management on a timely basis and reducing labor costs is scarcely possible, realistically, for a municipal corporation. A Smart Dustbin System (SDS) is proposed that is to be implemented in densely populated urban areas to ensure hygiene. This paper undertakes a comprehensive analysis of the application of smart dustbin systems, following an extensive literature review and a discussion of recent research that is expected to help improve waste management systems. A current SDS used in real-time is implemented with the most recent advances from deep learning, computer vision, and the Internet of Things. The smart dustbin system used in day-to-day life minimizes the overloading of bins, lowers labor costs, and saves energy and time. It also helps keep cities clean, lowering the risk of disease transmission. The primary users of the SDS are universities, malls, and high-rise buildings. The evolution of the SDS over the years with various features and technologies is well analyzed. The datasets used for Smart Waste Management and benchmark garbage image datasets are presented under AI perception. The results of the existing works are compared to highlight the potential limitations of these works.

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IoT based Waste Management for Smart Cities

Cited by 20 publications

References 6 publications

A multi-dimensional digital twin use cases classification framework

A multi-dimensional digital twin use cases classification framework

Analysing Challenges to Smart Waste Management for a Sustainable Circular Economy in Developing countries: A fuzzy DEMATEL study

A survey of smart dustbin systems using the IoT and deep learning

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