Predictive Waste-to-Resource Ecosystem Using Big Data & IoT

Abstract

The move towards a green globalization has increased the rate at which waste is turned into wealth using smart systems. The paper presents an idea of Predictive Waste-to-Resource Ecosystem (PWRE) using the Internet of Things (IoT) and Big Data Analytics to streamline the process of waste conversions into other resources in urban, industrial, and agricultural settings. The model combines the information that is acquired in real time with machine learning and sensor networks to control, process, and forecast waste production/conversion routes, which improves the performance of the circular economy and environmental sustainability. The model will allow effective restoration of the burden imposed by the climate and help to reduce its consequences at the same time through the harmonization of smart urban ecosystems with intelligent supply chains and environmental surveillance. Empirical literature indicates predictive performance in composting, concrete reuse, CO2 adsorption systems, and solutions using micro algae organisms. In addition, the model highlights the interdependence between the circular economy design, machine learning, and bioengineering innovation. Such an ecosystem facilitates the design of policies, resilience in operations, and its involvement with stakeholders in early and developed economies. The paper is a work set up on a scalable, intelligent, and inclusive waste to resource transition plan that operates through the new technologies.