*Article* **An IoT- and Cloud-Based E-Waste Management System for Resource Reclamation with a Data-Driven Decision-Making Process**

**Mithila Farjana , Abu Bakar Fahad, Syed Eftasum Alam and Md. Motaharul Islam \***

Deptement of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; mfarjana201127@bscse.uiu.ac.bd (M.F.); afahad201119@bscse.uiu.ac.bd (A.B.F.); salam201133@bscse.uiu.ac.bd (S.E.A.)

**\*** Correspondence: motaharul@cse.uiu.ac.bd

**Abstract:** IoT-based smart e-waste management is an emerging field that combines technology and environmental sustainability. E-waste is a growing problem worldwide, as discarded electronics can have negative impacts on the environment and public health. In this paper, we have proposed a smart e-waste management system. This system uses IoT devices and sensors to monitor and manage the collection, sorting, and disposal of e-waste. The IoT devices in this system are typically embedded with sensors that can detect and monitor the amount of e-waste in a given area. These sensors can provide real-time data on e-waste, which can then be used to optimize collection and disposal processes. E-waste is like an asset to us in most cases; as it is recyclable, using it in an efficient manner would be a perk. By employing machine learning to distinguish e-waste, we can contribute to separating metallic and plastic components, the utilization of pyrolysis to transform plastic waste into bio-fuel, coupled with the generation of bio-char as a by-product, and the repurposing of metallic portions for the development of solar batteries. We can optimize its use and also minimize its environmental impact; it presents a promising avenue for sustainable waste management and resource recovery. Our proposed system also uses cloud-based platforms to help analyze patterns and trends in the data. The Autoregressive Integrated Moving Average, a statistical method used in the cloud, can provide insights into future garbage levels, which can be useful for optimizing waste collection schedules and improving the overall process.

**Keywords:** IoT; cloud; e-waste; pyrolysis; Generative Adversarial Networks; bio-fuel; recycling
