IoT-based waste management: hybrid optimal routing and waste classification model

Area of Science:

• Computer Science
• Environmental Science
• Engineering

Background:

• The Internet of Things (IoT) enables connectivity for physical devices, facilitating data collection and transfer for automated systems.
• Efficient waste management is crucial for minimizing environmental and human health risks associated with waste.
• Optimizing waste management operations through data insights and efficient routing is a key challenge for municipalities.

Purpose of the Study:

• To propose an efficient Internet of Things (IoT)-based waste management system.
• To develop an optimized routing mechanism for data transmission in IoT networks.
• To enhance waste image classification for improved waste management processes.

Main Methods:

• An IoT routing process utilizing a hybrid Snake Optimization Updated Beluga Whale Optimization algorithm (SOUBWO) considering distance, energy, link quality, delay, and trust.
• Waste image processing involving Wiener filtering for pre-processing and a proposed Balanced Iterative Reducing and Clustering Using Hierarchies-Altered Distance Metrics (BIRCH-ADM) algorithm for segmentation.
• Feature extraction using multi-text on histogram, Local Gabor XOR Pattern (LGXP), and statistical features, followed by classification using a hybrid deep maxout and Bidirectional-Long Short Term Memory (Bi-LSTM) model.

Main Results:

• The proposed IoT routing algorithm (SOUBWO) effectively manages data transmission under various constraints.
• The waste image classification model achieved high accuracy through the integration of deep maxout and Bi-LSTM networks.
• The developed system demonstrated significantly lower energy consumption (0.123) compared to conventional methods.

Conclusions:

• The proposed IoT-based waste management system offers an efficient solution for optimizing waste collection and processing.
• The hybrid optimization and classification approaches contribute to reduced energy consumption and improved system performance.
• This research provides a foundation for developing smarter, more sustainable waste management strategies leveraging IoT technologies.