## 1 Introduction
### Research Contributions  
The main contributions of this paper are as follows:  
1. The introduction of a novel Artificial Intelligence of Things (AIoT) Infrastructure. This paper proposes a novel hardware infrastructure that has not been previously documented in the literature. The prototype is designed to revolutionize the collection of PET bottles at the residential level. In contrast to conventional waste collection systems, the standard PET collection bin will be replaced by the PET Recycling System (PRS) introduced in this paper. This advanced system has significantly improved functionality and storage capacity, making it a highly efficient solution for domestic waste management. 2. The development of a software application. The proposed system comprises an AIoT-based software application designed to monitor the entire collection process across multiple points, utilizing Microsoft Azure cloud hosting capabilities. 3. The design of the PET Bottle Identification Algorithm (PBIA). An advanced algorithm for identifying plastic bottles has been developed, capable of recognizing bottles regardless of color or condition and distinguishing them from other types of waste. The PBIA ensures that only valid PET bottles are accepted by the system. It helps to improve the residential recycling process. 4. The presentation of an Azure Custom Vision case study. A detailed analysis of Azure Custom Vision services for object recognition in the field of PET identification is conducted. This examination focuses on its capabilities, performance metrics, and potential use within intelligent PET waste management systems. 5. The development of the Algorithm for Citizens' Trust Level by Recycling (ACTLR). An algorithm is introduced to calculate a trust level associated with each citizen's recycling activities, reflecting their engagement and reliability regarding the recycling process. To encourage proper recycling, the authors have proposed the ACTLR for municipalities, aiming to stimulate recycling by offering citizens points that can be redeemed for goods from the municipality's partner stores. If citizens try to defraud the system, they will be penalized. 6. The introduction of the Citizens' Trust Level through Recycling Activities (_CTLRA_). This proposed indicator incorporates an exponential penalty as the number of non-PET items increases, discouraging citizens from engaging in fraudulent behavior. 7. The conducting of prototype cost analysis. A cost analysis of the prototype's production reveals that the system has low implementation costs, highlighting the economic feasibility of deploying the proposed intelligent waste management solution. Figure 1: Original PET bottle and volume reduction of PET bottle post-flattening. A key advantage of the PRS is its improved storage capacity. Unlike traditional bins that are used to collect PET bottles in their uncompressed form, this system is specifically designed to accept PET bottles that have been crushed or compressed. The volume is reduced by removing the air from the bottles, allowing more PET bottles to be stored in the same physical space. One of the major benefits of this system is its space-saving feature. PET bottles, when left uncompressed, tend to occupy a substantial amount of space in households, making it inconvenient for users to accumulate large quantities before disposal. The PRS represents a significant step in modernizing the handling of PET waste in residential areas. It offers an intelligent, space-efficient, and user-friendly solution that benefits households and waste management services. The paper is structured in six sections. Section 2 presents the related works. Section 3 outlines the proposed AIoT hardware infrastructure for implementing the PET recycling system, detailing the PET bottle identification process that incorporates AI components and references the Azure Custom Vision case study. Section 4 describes the experimental results. Section 5 presents a discussion of the study's findings and limitations.