AN IoT Based Waste Segreggator for Recycling Biodegradable and Non-Biodegradable Waste

Raj, Jeberson Retna; Rajula, B.Infant Philo; Tamilbharathi, R.; Srinivasulu, Senduru

doi:10.1109/icaccs48705.2020.9074251

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…A load cell is also used to detect the weight of the bin [124,127]. In the literature, various sensors are used to detect harmful gases [126], movements near the container [120], and metal sensors to separate metallic waste [128], and to measure humidity [126,128], as well as temperature [43,123]. The My Waste Bin IoT container presented in [43] is shown in Figure 8.…”

Section: Sensors For Waste Monitoringmentioning

confidence: 99%

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Sensors for Sustainable Smart Cities: A Review

et al. 2021

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Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.

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Section: Sensors For Waste Monitoringmentioning

confidence: 99%

“…For this reason, new sensors oriented to waste segregation need to be developed and implemented. Segregation is a key component in the waste management system, as it allows much of what is discarded to be recycled or reused, resulting in a reduction in the amount of garbage that reaches landfills [128,172].…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Sensors for Sustainable Smart Cities: A Review

et al. 2021

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“…Authors in [20] implemented a waste segregation model with Internet of Things (IoT) and deep learning. Machine learning and IoT were used in [21,22] to isolate the biodegradable and non-biodegradable waste and also to detect the presence of harmful gases in the bin. To segregate waste, the model should be trained on the dataset collected from the area in which the segregator needed to be placed.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based approach to Segregate Solid Waste Generated in Residential Areas

Sundaralingam

Ramanathan

2023

Eng. Technol. Appl. Sci. Res.

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Residential waste is a substantial contributor to solid waste generation, which is approximately around 36.5 million tons annually in India. The waste created in households is not separated at the source. All waste is accumulated in a single waste bin and stashed in a nearby public waste bin, resulting in a massive amount of waste being dumped in landfills and also infused with other types of waste, causing environmental pollution. The core objective of this research is to develop a household waste segregator using the TensorFlow object detection model and Arduino microcontroller. The SSD MobileNet V2 model has been trained with a household dataset consisting of paper, plastic, metal, organic waste, glass, and one more additional empty class to detect whether waste is placed for detection or not. This proposed system can predict the waste class and segregate it into their specific dustbin with mean Average Precision (mAP) and recall of 86.5% and 88.3%, respectively. Waste segregation and recycling can reduce landfills, lower carbon footprints, increase recycling, recover value from garbage, and lower greenhouse gases emitted from waste. Segregation at the source will reduce the cost of the segregation process carried by the municipal solid waste management.

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