Smart Waste Management for a Sustainable Future - Kumar, Jogendra
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Machine Learning (ML), the Internet of Things (IoT), and Wireless Sensor Networks (WSNs) are transforming waste management, making it more efficient and sustainable. ML analyzes data from waste systems to predict trends and optimize decision-making. For instance, ML algorithms forecast waste generation patterns, helping authorities adjust collection schedules and lower operational costs. Predictive analytics also identify inefficiencies, enabling targeted interventions in underperforming areas.IoT enhances waste management by allowing real-time monitoring of waste bins and containers via…mehr

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Produktbeschreibung
Machine Learning (ML), the Internet of Things (IoT), and Wireless Sensor Networks (WSNs) are transforming waste management, making it more efficient and sustainable. ML analyzes data from waste systems to predict trends and optimize decision-making. For instance, ML algorithms forecast waste generation patterns, helping authorities adjust collection schedules and lower operational costs. Predictive analytics also identify inefficiencies, enabling targeted interventions in underperforming areas.IoT enhances waste management by allowing real-time monitoring of waste bins and containers via sensors that measure fill levels, temperature, and other environmental factors. Paired with WSNs, these sensors provide continuous data streams that help trigger immediate responses to overflowing bins or hazardous materials. IoT-based systems can also track waste vehicle movements, optimizing routes and reducing fuel consumption.WSNs further integrate these sensors and devices into a connected network, improving communication across the waste management infrastructure. This enables a faster, dynamic response to issues such as bin overflows and system malfunctions.
Autorenporträt
Dr. Jogendra Kumar is an Assistant Professor in the Computer Science and Engineering Department at G.B. Pant Institute of Engineering and Technology, Uttarakhand. With 15 years of teaching experience, his research interests include Wireless Networks, IoT, Blockchain, Big Data Analytics, Machine Learning, and WSN.