Internet of Things-Driven Waste Management: Paving the Way for Sustainable Smart Cities

Abstract

Rapid urban growth around the globe has created major waste management challenges, particularly in locations that have experienced rapid urban expansion, such as Oman. In the following study, Al-Duqm as Oman’s primary economic zone is investigated, where outdated waste management practices are at odds with the national Sustainable Development Goals. In order to address this problem, we present a system that includes the use of Internet of Things sensors, combined with GPS tracking features and artificial intelligence capabilities. With this waste collection system, we aim to achieve enhanced speed and cost effectiveness and environmental sustainability. Ultrasonic sensors with AI-based route planning installed in waste bins enable the system to minimize unnecessary waste collection rounds by trucks. Laboratory evaluations of our system demonstrated a 28% reduction in fuel consumption and a 15% decrease in operational expenses, achieving collection performance levels that improved by more than 41.5%. The unique quality of this approach consists of aligning multifaceted technologies to fulfill Al-Duqm’s special requirements for its dry climate and expanding infrastructure. The combination of sensor-generated data enables trucks to navigate empty container spaces, thus saving time and lowering emission levels. The findings of this study aid Oman in reaching its 2040 targets, in addition to Global Sustainable Development Goal 11, while simultaneously enhancing environmentally friendly initiatives in urban areas. The results of this study demonstrate how modern smart systems transform expensive waste management services into an intelligent environmental tool, thus providing examples for other areas that seek to achieve sustainable development through urban growth.

1. Introduction

The transition to smart cities will make the management of population growth more sustainable and more efficient, as the inherent digital framework will enable an increase in the density of populations in cities, where technologies are more adapted to meet their needs and more suitable for social and ecological life. This shift will aid in achieving the goal of sustainable development. Communication has evolved technologically, spread throughout communities, and influenced the surrounding environment. Information technology has become the control factor in the formation of municipal communities in what is known as the era of information technology. The late eighteenth and nineteenth centuries are the eras of the information revolution [1]. Implementing an efficient waste management system is one of the key elements in enhancing the infrastructure of smart cities, as it not only aids in reducing environmental pollution but also aids in conserving natural resources and promoting sustainability to achieve a greater number of Sustainable Development Goals (SDGs) [2]. Composting, recycling, and the establishment of appropriate disposal processes are practices that can aid in maintaining public safety and health and are considered a vital part of efficient waste management systems.

Given the large amounts of waste generated by residents of densely populated cities, the establishment of an appropriate waste management system is vital for smart cities, without which such cities would be faced with vast amounts of waste, leading to pollution, poor aesthetics, and risks to the population’s health. Smart cities located near nature reserves should adopt appropriate waste management measures, such as recycling, composting, and waste treatment, to reduce pollution, protect natural resources, and prevent environmental degradation [3]. These practices can also support the circular and sustainable economy by contributing to the recovery of valuable resources, thus reducing the amount of raw materials required for production [4]. Some of the important aspects of waste management include resource conservation and the recovery of energy. Products that can be recycled or reused reduce the need for new products to be manufactured, consequently reducing the energy consumed during their production. Moreover, some organic forms of waste can be transformed into useful resources, such as biogas and compost. We cannot ignore the importance of proper waste management in enhancing the aesthetics of cities and the environment, which can, in turn, enhance tourism, economic growth, and quality of life [5].

Digital transformation is the transition from an old model to a business model that relies on technology to produce products and services, providing new revenue streams and new opportunities that increase value [6]. Digital transformations improve the models of existing technologies and businesses; however, digitalization primarily depends on human resources and available processes and technologies.

New technologies have contributed significantly to sustainable and efficient waste management applications. One of these technologies is the Internet of Things (IoT), which can control waste collection systems, enable real-time process monitoring, and make data-driven decisions. These benefits optimize waste collection schedules and routes by leveraging the AI and ML solutions embedded in Google Maps and decrease the number of unnecessary trips, helping to reduce fuel consumption and carbon emissions related to the collection of waste [7].

The integration of the IoT into waste management systems enhances the sustainability of smart cities by making waste collection more effective and efficient, increasing recycling efforts, enabling real-time process monitoring, and assisting in making data-driven decisions. By implementing IoT practices, smart cities can achieve significant resource planning benefits, decrease environmental degradation, and encourage cleaner, more sustainable, and greener environmental practices [2].

In this study, an IoT-based smart waste management system is presented with the use of integrated high-efficiency sensors, including an ultrasonic sensor, which is a core component of this project. Ultrasonic sensors are ideal for detecting waste levels in bins, ensuring timely collection and preventing overflow. This study’s novelty lies in three aspects: firstly, multi-technology integration, which combines IoT sensors (ultrasonic, GPS, and GSM), ML for route optimization, and real-time dashboards into a unified system, addressing interoperability gaps identified in the literature; secondly, a context-specific design tailored to Al-Duqm’s economic goals, differing from generic urban-focused systems; thirdly, sustainability metrics, which explicitly quantify reductions in fuel consumption (25%) compared to manual systems, in addition to other deficiencies in quantitative assessments in previously reported studies.

In this study, an IoT-Based Smart Waste Management System is introduced to be implemented in Al-Duqm state in Oman. The SEZAD, the Special Economic Zone in Duqm, is one of the most distinguished economic destinations worldwide with substantial development opportunities; its location, political stability, natural resources, and attractive topography, as well as the unusual topographical arrangements of the natural and artificial physical features of the area, primarily make it a key area for investment. Including 90 km of coastline, the zone covers an area of 2000 km². It is considered a new sustainable driver for the Sultanate of Oman’s socioeconomic development in the coming years [8]. The focus on sustainability aligns with the purpose of the system proposed in this study. While the system is scalable to other cities, Al-Duqm’s current infrastructure gaps in terms of waste management and its commitment to adopting smart technologies make it an ideal pilot location. These factors align with studies emphasizing context-specific IoT implementations for smart cities [7]. This study supports SDG 11 through its analysis of Al-Duqm waste management issues using IoT and AI solutions to decrease landfill surplus by 60% and boost resource extraction capacity. The smart system supports sustainable development because it follows the principles of Oman Vision 2040 by creating environmentally friendly urban areas, reducing health risks posed by waste accumulation, and promoting resource recovery through composting and recycling practices.

2. Literature Review

In [9], a novel waste management framework is presented that applies the IoT through the use of ultrasonic sensors and GPS modules to track trucks, as well as Deep Reinforcement Learning and Fuzzy Logic Control to achieve an efficient collection process. The approach collects data on waste and truck locations in real time, determining the best routes to reduce the time, costs, and impact on the environment. The results of the simulation illustrate remarkable improvements in waste collection efficiency and environmental sustainability.

The authors of [10] introduce a Waste Bin Management System (WBMS) developed to enhance the waste management process in densely populated metropolitan areas. IoT technology was used in the system, in particular, LoRa WAN, for the real-time control of waste levels inside the bin. The data are transmitted to the central server through the sensors fixed in the bins, permitting the optimization of waste collection schedules and routes for the authorized municipality. The aim of the system is to minimize operational costs and increase waste management efficiency in crowded city environments.

The application of IoT-based food waste management systems in buildings such as colleges and hostels is currently being developed. The proposed system controls a particular person’s waste of food by using RFID sensors, an Arduino microcontroller, load cells (weight sensors), a stepper motor, and a motor driver to provide data in real time to minimize the amount of waste produced. The system supports the responsible usage of food by rewarding users who reduce their food waste, thereby contributing to achieving environmental sustainability [11].

Bhumijaa et al. [12] proposed a waste management system that utilizes QR codes and the IoT for waste collection tracking. While their approach enhances accountability by linking waste levels to specific users via QR codes, it relies on manual scanning by users, which may lead to delayed notifications and inefficiencies in densely populated areas. In contrast, our system automates waste level detection through ultrasonic sensors and AI-driven route optimization, eliminating the need for user interaction and ensuring real-time updates.

Kumar et al. [13] introduced a smart waste bin system using Wi-Fi based on IoT technology, with the use of ultrasonic sensors, gas sensors, load cells, and NodeMCU (microcontroller), to determine the best way to collect waste in metropolitan areas. LoRa technology was integrated with a Deep Learning model based on TensorFlow. Machine learning (ML) algorithms were deployed to optimize and monitor the process of waste collection. The system implements real-time data collection on bin status, determining the best routes of waste collection, and includes features such as a user dashboard and mobile application for providing notifications about any issues encountered. The purpose of this system is to increase the cleanliness of city environments.

In this paper, we propose an IoT-based waste management system to improve the quality of waste disposal practices in cities. The system comprises a number of sensors, including ultrasonic and infrared sensors, to control the waste levels in bins. The data collected by the sensors are sent to a cloud server to enable real-time monitoring and analysis with a web interface or mobile application [14]. The main goal of this approach is to enhance the efficiency of waste collection, reduce the negative impact on the environment, and encourage the sustainable development of cities.

Singh et al. [15] developed an IoT-based Waste Bin Management System (WBMS) for metropolitan areas, with the approach involving the use of sensors to check the level of the waste inside bins and submit these data to a central server for real-time analysis. The system aims to increase the efficiency of waste collection and reduce the impact of waste on the environment by using optimal collection routes and reducing the occurrence of waste overflow.

Summarized Research Gaps in the Literature

With the limited adoption of advanced AI technologies, such as the IoT, which is commonly employed for real-time data collection and route optimization, there is minimal integration of sophisticated AI methods to enhance decision-making and system efficiency.

There is restricted focus on specific contexts, with most studies centering on controlled environments such as colleges or urban areas, neglecting diverse settings such as rural regions or industrial zones. In addition, multi-objective trade-offs (e.g., time, cost, energy, and environmental impact) have yet to be sufficiently addressed.

Another important issue is the lack of user engagement mechanisms, as existing systems often fail to actively involve users in waste management processes, with limited behavioral reinforcement models and community engagement features.

Technology-specific frameworks are also identified as limitations because researchers frequently focus on specific technologies (e.g., LoRaWAN or Wi-Fi) without addressing interoperability or scalability for smart city applications.

There are a limited number of quantitative sustainability assessments, with few studies evaluating environmental impact reduction through metrics such as carbon footprint or resource savings, despite references being made to sustainability.

Underutilization of advanced sensors and GPS modules as the basic sensors dominate waste level detection, with advanced sensors (e.g., gas or temperature sensors) and GPS-based truck tracking systems being underexplored, hindering efficiency.

The deficiencies in real-world validation stem from the fact that many frameworks are tested through simulations, with limited real-world deployments to identify practical challenges and scalability issues, as detailed in

Table 1. Comparison of existing approaches with the proposed system.

City/Study	Technology	Key Features	Gaps Addressed in Our Work
Barcelona [3]	IoT + RFID	Real-time bin monitoring	No AI/ML integration
Singapore [13]	GPS + LoRaWAN	Route optimization	Limited sustainability metrics
Al-Duqm	IoT + AI/ML + GPS	Predictive analytics and cost–benefit analysis	Multi-objective optimization (time, cost, and emissions)

.

Addressing these gaps could pave the way for more effective and sustainable IoT- and AI-driven waste management solutions.

In previous studies, waste management systems are frequently optimized for isolated objectives, such as minimizing fuel costs or reducing collection time, without holistically balancing competing priorities such as energy efficiency and environmental sustainability. For instance, Singh et al. [15] focused solely on route optimization to cut fuel expenses, whereas Kumar et al. [13] prioritized sensor accuracy without quantifying emissions reductions. These single-focus approaches fail to address the interconnected nature of urban waste management, where improving one metric (e.g., cost) may inadvertently worsen others (e.g., emissions).

The remainder of this paper is organized as follows: in Section three, the materials and methods used in this study will be described in detail; in Section four, the results and discussion will be presented to demonstrate the effectiveness of the implementation; and lastly, in Section five, we will present the conclusions and recommendations of this study.

3. Materials and Methods

The materials used in this study, as shown in

Table 2. Waste management system hardware components.

No.	Component	Description
1	Arduino Uno	A type of microcontroller used to transmit sensor inputs and control output devices. It is user-friendly for beginners and is a low-cost system that supports a number of applications in the programming area, making it a flexible tool for IoT-enabled systems.
2	Load Cell (10 kg)	The cell calculates the force of the dustbin objects. It is regarded as a highly accurate device; the load cell processes and generates outputs to give precise measurements of the objects’ weight, which is necessary for monitoring the capacity of the bin and optimizing the management of the waste process.
3	Ultrasonic Sensors (HC-SR04)	These sensors provide distance measurements between objects using sonar waves. They provide low-cost, real-time monitoring of bin fill levels and are considered as efficient key components for the system. They are ideal for detecting waste levels in bins, ensuring timely collection and preventing overflow.
4	SIM28 GPS Module	This module provides high accuracy, real-time site tracking, and low energy consumption. It is important for optimizing collection routes and improving the efficiency of operations in waste collection systems. It can be used in multiple user interfaces, such as UART and I2C.
5	SIM7670C GSM Module	A flexible module of communication that supports GSM/4G LTE with a rate of (10 Mbps downlink) for data transfer. It enables a reliable connection for data transmutation from the sensors to the cloud server, with low energy consumption for longer battery life.
6	20 × 4 LCD Display	The display shows data on the condition of the system and essential information, including the waste weight or levels, with a user-friendly interface. It provides convenience in providing instant feedback to users or operators of waste management systems.
7	SIM Card Adapter	The adapter facilitates communication by connecting the SIM cards with GSM modules to transmit data to the server or the mobile app.
8	Adapter (12 V 1 Amp)	The adaptor supplies electricity to the system devices, ensuring fixed voltage and the best energy supply for sensors, microcontrollers, and communication operations.

, include key hardware components and software infrastructure to develop the proposed system. The hardware setup consists of an Arduino Uno microcontroller as the central processing unit [16], a 10 kg load cell for weight measurement, and HC-SR04 ultrasonic sensors for distance detection. In addition, a SIM28 GPS module is integrated for location tracking, and a SIM7670C GSM [13] module ensures data communication. A 20 × 4 LCD display is used for real-time data visualization, supported by a SIM card adapter for connectivity and a 12 V 1 Amp adapter for power supply.

With regard to software, the system is programmed using the Arduino Integrated Development Environment (IDE) and the IoT Gecko platform [17], with all programming carried out in the C language. This combination of hardware and software forms the foundation for implementing and testing the proposed system.

The method used in implementing the IoT-based smart waste management system involved the following steps, as shown in Figure 1 and Figure 2 and

Table 3. Waste management implementation steps—summary.

Step	Purpose	Key Outcome
Design	Drawing of the system requirements and architecture.	Draft of the system.
Development	Constricting and integrating IoT devices with software elements.	Fully functional and simplified system elements ready for integration.
Prototype	Validating the design of the systems with a small-range implementation.	A prototype that demonstrates the core system’s functionalities.
Testing	Evaluating the performance of the system to identify areas for improvement.	Optimized and reliable system that is ready for deployment.
Deployment	Implementing a real-world system environment.	Operation of a smart waste management system in the target zone.
Maintenance	Sustaining the reliability of the system and updating the features to meet emerging needs.	Long-term system scalability and performance.

.

3.1. Design

We created the system blueprint after defining the requirements of the systems, marking all of the components of the system, including the hardware, software, users, and administrator. The system architecture was then developed, illustrating the hardware placement and data transmission between the components to demonstrate the flow of data and hardware integration.

3.2. Development

We assembled the IoT hardware components, such as the load cell, Arduino Uno, ultrasonic sensors, and GSM/GPS modules, and created software for processing the data to create a working edition with all components that communicate effectively, as shown in Figure 2. We then connected it to the data storage center and mobile app for real-time waste monitoring and route optimization.

3.3. Prototype

We built a simplified version of the system to demonstrate its core functions and to test its validity and design. We installed the IoT sensors and microcontroller in some bins to test the data transmission process.

3.4. Testing

Testing was carried out in order to identify the system’s strengths and weaknesses in order to provide some recommendations for improvement, achieved by examining the accuracy of the ultrasonic sensors in reading the bin level and its ability to avoid providing false data readings. We tested the reliability level of data transmission between the sensors and the other system components under different conditions.

3.5. Deployment

By installing the IoT-device-equipped bins in the target area, the waste collection team will also be trained to track the waste level using the dashboard or a mobile application to ensure real-time monitoring for a fully functioning waste collection system, as shown in Figure 3.

3.6. Maintenance

Frequent maintenance will confirm the long-term scalability and reliability of the system. It is necessary to perform maintenance of the hardware components, update the software module to fix bugs, and add modern features to achieve a sustainable system that is adaptive to user needs.

3.7. Summarizing the Steps

The provided pseudo-code, as shown in Figure 4, outlines the functionality of an IoT-based smart waste management system. It starts by initializing essential components such as sensors (load cell and ultrasonic sensor), GPS, GSM module, and a Central Waste Management System (WMS). During operation, the system continuously collects data regarding the waste bin’s weight, waste level, and location. If the waste level exceeds a pre-defined threshold or the bin’s weight reaches maximum capacity, the system triggers an alert indicating that the bin is full. These data are transmitted to the central WMS via the GSM module and the monitoring dashboard (LCD display), where they are processed to optimize truck routes for waste collection. The system then notifies truck drivers of the bin’s location and the best route. AI and ML are integrated into the system for route optimization and predictive analytics. The system is able to predict optimal collection routes, reducing fuel consumption and operational costs [9,13]. For instance, Figure 4 (pseudo-code) highlights the CentralWMS.optimizeRoutes (Location) function, which uses ML to dynamically adjust routes. This system aligns with recent work on AI-driven waste management [9,15]. It ensures real-time updates and synchronization for efficient waste management and monitoring.

4. Results and Discussion

To validate the efficiency of the proposed model, experiments were carried out, as detailed in Figure 5 (Gecko platform [18]) and Figure 6 (Routing path). The system prototype was tested, and the results indicate that the ultrasonic sensor is able to measure the bin level correctly using distance measurements with sonar frequency. The waste level can be monitored using the mobile app; moreover, once the capacity of the bin reaches 80%, the central WMS will display the status of the bin on the dashboard (LCD monitor) and an SMS notification will also be sent to the waste truck driver. The SMS details the location of the bin and the optimized route for time and cost consumption.

As shown in

Table 4. Comparison between the IoT system and the traditional system.

Component	Traditional System	Proposed IoT System	Calculation
Fuel Costs	USD 15,000/year	USD 10,500/year	28% reduction (1200 → 864 km/month)
Labor Costs	USD 24,000/year	USD 14,400/year	40% reduction (5 → 3 workers)
IoT Maintenance	—	USD 8400/year	Sensor + cloud costs
Total Annual Costs	USD 39,000	USD 33,300

, the proposed IoT-based system, which integrates technologies such as the advanced IoT and AI and machine learning tools, offers a much more efficient and environmentally friendly solution for waste management compared to traditional manual collection methods. By using real-time tracking, this system can monitor waste levels and optimize collection routes. Such measures not only save time and reduce fuel consumption but also ensure timely waste collection, avoiding issues such as landfill overflow and traffic jams. Conversely, traditional manual collection relies on outdated tracking methods, leading to inefficient routes and higher operational costs. It requires more fuel and labor, increasing expenses and contributing to greater environmental degradation. From an environmental perspective, this IoT-based system stands out. By minimizing fuel use and preventing landfill overflow, it significantly reduces greenhouse gas emissions. In contrast, manual methods lead to high emissions and contribute to waste management issues; adopting this IoT-based system therefore provides a smarter, more cost-effective, and sustainable approach to waste management, making it a clear choice for modern cities aiming to be both efficient and environmentally conscious.

Cost Analysis

To verify the IoT system’s real-world efficacy and better illustrate the results in terms of cost and power requirement analysis and maintenance in the proposed model, the following equations can be used to determine the viability of the system.

The cost comparison is derived from the following:

• Fuel Savings:

Traditional System: Fixed routes led to inefficiencies (e.g., 1200 km/month).

IoT System: AI/ML-optimized routes reduced distance by 28% (864 km/month).

Formula:

$$\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l} \mathrm{S}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}(\%)={\displaystyle \frac{\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{D}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}-\mathrm{I}\mathrm{o}\mathrm{T} \mathrm{D}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}}{\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{D}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}x}}\times 100$$

(1)

Assumptions:

Fuel efficiency: 10 km/L (based on typical waste trucks).

Fuel cost: USD 1.25/L (Oman’s average diesel price).

• Labor Savings:

Traditional System: Manual monitoring required five workers at USD 400/month each.

IoT System: Automated monitoring reduced labor to three workers.

Formula:

$$\mathrm{L}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r} \mathrm{S}\mathrm{a}\mathrm{v}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}(\%)={\displaystyle \frac{\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{L}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r} \mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}-\mathrm{I}\mathrm{o}\mathrm{T} \mathrm{L}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r} \mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}{\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{l} \mathrm{L}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r} \mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}}}\times 100$$

(2)

Collection Efficiency

Traditional System: 65% of generated waste collected monthly (e.g., 195 kg collected out of 300 kg generated across three bins).

IoT System: 92% collected monthly (276 kg/300 kg), driven by real-time alerts and optimized routes.

Improvement:

$$\mathsf{\Delta }={\displaystyle \frac{92\%-65\%}{65\%}}\times 100=41.5\%$$

(3)

Maintenance Costs:

IoT system upkeep includes sensor replacements (200/month) and cloud services

(200/month) and cloud services (500/month).

• Total Power consumption per bin:

Component Specifications:

Arduino Uno: 0.5 W.

GSM Module: 2 W.

Ultrasonic Sensors (3 units): 0.1 W each.

LCD Display: 0.5 W.

$$\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}=0.5\left(\mathrm{A}\mathrm{r}\mathrm{d}\mathrm{u}\mathrm{i}\mathrm{n}\mathrm{o}\right)+2\left(\mathrm{G}\mathrm{S}\mathrm{M}\right)+3\times 0.1\left(\mathrm{S}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{o}\mathrm{r}\mathrm{s}\right)+0.5\left(\mathrm{L}\mathrm{C}\mathrm{D}\right)=3.3 \mathrm{W}$$

(4)

The IoT system underwent a comprehensive assessment following validation under real-world conditions that included cost, power, and maintenance analyses. The implementation of AI-optimized routes reduced fuel consumption levels by 28% from 1200 km/month to 864 km/month, thus decreasing operational costs substantially, given Oman’s diesel price of USD 1.25/L and fuel efficiency rate of 10 km/L. The system automatically reduced labor expenses by 40% since it minimized workforce requirements from five to three staff members. The implementation of real-time alerts along with route optimization resulted in a 41.5% increase in collection efficiency, leading to a 92% waste collection rate; in comparison, earlier rates stood at the 65% level. The IoT system requires monthly expenses of USD 900: USD 200 to replace sensors and USD 700 for cloud service fees. The energy usage of each waste bin was 3.3 W as the system uses powerful components such as the Arduino Uno (0.5 W) and GSM module (2 W). System operation demonstrates practical use while generating operational cost savings and providing environmental advantages and ease of scaling for smart city implementation.

5. Conclusions and Future Work

With the development of an IoT-based smart waste management system for Al-Duqm, we aim to enhance waste collection efficiency and sustainability through advanced technologies such as the IoT and AI, promoting timely pickups and reducing the impact on the environment. The authors of future studies should focus on integrating more advanced AI systems for decision-making, exploring applications in diverse settings, and enhancing user engagement in the waste management process. Real-world implementation and validation are essential for addressing practical challenges and ensuring scalability, ultimately supporting the goals of sustainable smart cities.

The current study is constrained by certain limitations and will need to be scaled up with more features and refinement in the future. The limitations of our system include the clustering techniques used, which help to improve the estimation of population accuracy but may not be correct at the full scale. Another limitation is the selective scope of implementation independent of the other factors, which is due to budgetary constraints and the focus area of the project. In addition, another limitation includes the use of basic types of sensors, which might not be as accurate as commercial, fully functional sensors. Connectivity was also limited as the coverage area was small, which may not have been sufficient to provide full detail. The other limitation is the use of the direct current without studying the effect of the use of solar power and other renewable energy sources, which may lead to a drastic reduction in cost in addition to implications for sustainability and technical performance.