Jamil Abedalrahim Jamil Alsayaydeh*
| Rex Bacarra | Adam Wong Yoon Khang | Noorayisahbe Bt Mohd Yaacob | Safarudin Gazali Herawan
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Environmental waste is still a debacle in our daily lives as well as for the world. Most waste management systems do not have monitoring functionalities, which results in inefficient collection routes, higher processing costs, and environmental damage. These figures only continue to grow. In modern metropolises that are home to most population growth, people are increasingly turning to outdated systems that can no longer handle the amount of waste being produced. These systems then turn out to be too costly, which becomes a major and unsolved problem in the long run. This paper presents a secure, low-cost IoT smart waste system that integrates five sensors, HC-SR04 ultrasonic (fill-level), HX711 load-cell (weight), DHT22 (temperature/-humidity), MQ-135 gas (air quality) and Ublox NEO-6M GPS, around an Arduino-ESP8266 core and Firebase cloud analytics. A 30-day field trial on 12 municipal bins achieved 85% fill-level accuracy, < 3% mean absolute error in weight, and 100% detection of hazardous temperature (≥ 40°C) or humidity (≥ 70% RH). GPS-guided routing cut truck mileage by 20 % and CO₂ emissions by 18% versus fixed schedules (p < 0.01, paired-sample t-test). These results confirm that multi-sensor IoT retrofits can reduce operational costs while improving public-health safeguards, providing a replicable blueprint for sustainable smart-city waste infrastructure.
smart waste management, IoT monitoring, ultrasonic sensors, waste segregation, GPS route optimization, urban sustainability
An effective waste management strategy is essential for urban sustainability and the environment. The rapid growth of populations, urbanization, and industrial activities has significantly strained the existing waste management systems due to an increase in solid waste. Traditional approaches of waste collection did not have real-time monitoring capabilities, which contributed collection inefficiencies, high operational costs, and environmental degradation [1]. Poor waste management practices led to overflowing waste, unclean environments, and increased greenhouse emissions that harm public health, urban aesthetics, and the environment [2].
Smart technology provides a different approach to waste management that is more effective, ecofriendly and cost-effective. IoT-based waste management systems had smart sensors, cloud technology and automated notifications to improve waste collection schedules and routes. By using Arduino based monitoring modules, GPS trackers and ultrasonic sensors, these systems can provided real-time information on environment state and bin fill levels [3]. These systems reduce unnecessary trips for bin collection, improve fuel efficiency, reduce overuse of landfills and are in line with global sustainability goals [4].
Many researches have been done on IoT-based waste management systems. For example, Parkash [5] proposed a innovative and cost-effective device that used IR sensors with microcontrollers to monitor garbage bin levels and notify the collection personnel through internet. In the same way, Kasat et al. [6] developed a system with advanced sensors that not only measured waste levels but also performed composition analysis of waste for better recycling and resource recovery. All these technologies are great for IoT-based waste monitoring for urban environmental sustainability.
In Malaysia managing waste poses a large problem, only 24% of solid waste was recycled with the remainder being disposed in landfills [7]. Landfills not only contribute to the waste management problem worsened but also contributes to other issues such as environmental deterioration, spraying of waste contributes to water pollution, and emissions of carbon dioxide becomes heightened. With IoT’s advanced technology, waste separation, collection, and even eco-friendly disposal of waste can be done efficiently and practiced routinely. Monitoring of both temperature and humidity for bins of waste is essential to reduce decomposition, the spread of disease, and general odor pollution [8].
Thus, implementing a smart waste management system represents a significant advancement in sustainable urban development. By utilizing automated alerts, optimized collection schedules, and AI-driven waste analytics, cities can move towards a data-driven, ecofriendly approach to waste management. This paper presents an IoT-based smart waste management system that combines sensor-based monitoring, GPS-enabled route optimization, and environmental tracking to enhance the urban waste collection process. The system adheres to principles of sustainability, smart city infrastructure, and environmental conservation, aiming for cleaner, healthier, and more efficient urban spaces.
While earlier IoT waste-management studies address either bin-level sensing or collection-route optimisation, few have delivered an end-to-end, field-validated system that quantifies both sensor accuracy and environmental benefits. This work closes that gap and makes four specific contributions:
• Multi-parameter sensing: first low-cost prototype that combines fill level, weight, temperature/humidity, and air-quality monitoring in a single bin unit.
• GPS-assisted dynamic routing: experimentally shown to cut collection mileage by 20% and CO₂ emissions by 18%, outperforming the 12–15% gains reported in recent studies.
• Enhanced sensor accuracy: achieves 85% fill-level accuracy and < 3% MAE in weight estimation, meeting municipal tolerance thresholds and improving on earlier single-sensor designs.
• Southeast-Asian field validation: presents the first month-long deployment of a smart-waste system across 12 municipal bins in Malaysia, demonstrating scalability in a tropical urban setting.
With real-time monitoring, optimized collection routing, and improved sustainability IoT has improved the way we manage waste integrating technology with internet connected devices.Traditional waste management systems worked with irregular schedules, inefficient routing, and waste level tracking, which led to high operational and environmental concerns. Chen et. al suggested that smart sensors integrated with cloud computing and artificial intelligence enabled an intelligent, data-driven waste-collection system. These systems allowed continuous tracking of waste levels and collection route optimization, which reduced fuel consumption, improved operational efficiency, and enhanced sustainability.
Research studies IoT waste management solutions were multifunctional. Different studies suggested sensor based monitoring and AI powered data analytics to enhance the efficiency of waste collection. Kadus et al. [9] underscored the relevance of IoT real-time bin monitoring, where municipalities modified waste collection program by dynamically scheduling the time depending on the fill level of the bin. Meesala et al. [10] created a system of IoT monitored garbage bins that stored data in the cloud and automatically notified the appropriate authorities if the bin requires servicing, thereby eliminating chances of overflowing waste and minimizing unnecessary collection trips.
To improve the economic situation of developing areas, researchers concentrated on cost-effective IoT-based waste management systems. Borojeni and Abdi [11] proposed an IoT-based bin monitoring system with IR sensors and microcontrollers that enabled real-time monitoring of the bin's state. Kasat et al. furthered this with IoT waste classification using machine learning, and increased waste segregation accuracy. Rai et al. [12] emphasized the need for intelligent waste management systems with IoT in India, which requires smart waste classification and route optimization to improve waste disposal efficiency.
Also, Delicato et al. [13] looked into IoT ecosystems and waste management, and showed that real-time data analytics on waste collection is important. Srikanth et al. [14] pointed out that real-time waste classification can improve waste disposal logistics and urban sanitation.
One major inefficiency in traditional waste management is the absence of optimized routes, leading to increased fuel consumption, traffic congestion, and environmental harm. IoT-based waste management systems utilize GPS and AI-driven route optimization algorithms, allowing collection vehicles to visit only the bins that require attention, thereby minimizing unnecessary trips [15]. Chaudhari and Bhole [16] introduced a Solid Waste Collection as a Service (SWCS) model that dynamically adjusts collection frequency based on real-time bin capacity, fuel usage, and traffic conditions, which helps lower carbon emissions and operational costs. In a similar vein, Khan et al. [17] created an IoT-enabled garbage tracking system that combines wireless sensor networks (WSNs) and cloud computing to improve real-time tracking of waste bins and optimize waste collection logistics.
AI in IoT waste management has improved waste classification and collection routes. Abdulla and Sravani [18] created a predictive waste monitoring system that uses historical waste data to predict waste generation trends and schedule collection. Shyam et al. said smart bins powered by AI can automatically classify recyclable and non-recyclable materials, reducing contamination in recycling facilities. Adam et al. [19] looked at IoT waste collection frameworks and showed how AI can help in decision making in waste management, reducing landfills and sustainability.
IoT waste management solutions help with sustainability by reducing landfills, promoting recycling and minimizing environmental risks. Ayilara et al. [20] looked into IoT composting systems and found that sensor driven tracking of organic waste can increase composting efficiency and reduce methane emissions. Mdukaza et al. [21] examined IoT recycling methods and found that automated waste sorting increases recycling rates and diverts waste from landfills.
Recent studies have also looked into the role of IoT in smart city waste management policies. Neema and Gor [22] looked into smart waste management solutions to reduce urban waste accumulation through automated segregation and disposal. Masengo et al. [23] did a comparative study on cybersecurity threats in IoT waste management platforms and found that robust encryption is key to protect sensitive data from cyber-attacks.
Despite promising prototypes, large-scale IoT waste deployments still face intermittent connectivity and sensor drop-outs. Masud and Said [24] showed that packet-loss rates in low-power wide-area networks can exceed 12 % when node density surpasses 60 units km⁻², causing delayed overflow alerts. Vij [25] reported similar disruptions in dense Indian cities where cellular back-haul is congested during peak hours. To mitigate this, Khan et al. embedded adaptive data-rate schemes in LoRa gateways, cutting retransmissions by 38 %. However, no study to date has combined such adaptive networking with multi-parameter sensing in a tropical climate.
Cyber-security is an equally critical bottleneck because smart-bin nodes collect geolocated and potentially sensitive data. Masengo et al. compared five AI-based intrusion-detection systems for software-defined WSNs and found that ensemble classifiers improved attack-detection accuracy to 96% but required 45% more memory—challenging for battery-powered nodes. Qasim et al. [26] added lightweight AES-128 encryption to a home‐security IoT mesh, yet still noted timing overheads of 7–9 ms per packet. These findings underline the need for security mechanisms that balance cryptographic strength with low-power constraints, a trade-off our prototype tackles by offloading heavy analytics to Firebase while using HTTPS-TLS between the ESP8266 and cloud endpoint.
Despite the many benefits of IoT driven waste management it’s facing several key challenges that hinders large scale implementation. Sensor reliability and network stability is one of the major concerns as IoT sensors are prone to environmental factors like temperature, humidity and signal interference. Masud and Said mentioned that inconsistent network connectivity in developing countries can lead to delayed waste collection alerts which reduces the system’s efficiency.
Another big challenge is the high cost of deployment and maintenance. While IoT-based waste management reduces operational cost in long run the initial investment in smart bins, cloud infrastructure and sensor network are too high for many municipalities [27]. Public participation in smart waste management is also a big hurdle as lack of awareness and community engagement affects adoption rates. Kumar and Mallick [28] proposed mobile based IoT applications to encourage public involvement in waste segregation and disposal initiatives but lack of awareness is a barrier for large scale implementation.
Moreover, data security risk in IoT waste management need to be addressed. Masengo et al. mentioned that IoT enabled waste monitoring platforms are vulnerable to cyber threats therefore strong encryption and authentication protocols must be implemented to prevent unauthorized access. Qasim et al. examined IoT integration in home security systems and suggested that similar security frameworks should be applied in waste management applications to protect sensitive data. Sharma et al. [29] discussed smart and cost-effective implementation of waste disposal systems and emphasized that IoT-based networks should be secured against cyber threats to ensure reliable waste monitoring and management. Patil and Gidde [30] provided insights on IoT-based waste management approach for environmental sustainability and stated that energy-efficient and low maintenance smart waste solution is required. Jasim et al. [31] introduced IoT-based smart waste system that enhance waste classification and disposal mechanism to have more effective waste segregation at source [32].
Future work should focus on integrating AI and machine learning to improve waste classification and predict waste generation [33-36]. Solar powered IoT bins will reduce carbon footprint. Cybersecurity should have advanced encryption and intrusion detection systems to prevent cyber-attacks on IoT waste monitoring networks [37-42].
The literature said IoT waste management is the solution to urban waste problems. Several studies have come up with solutions but network reliability, cost and public engagement are Major hurdles to adoption. This research will create a whole waste management system powered by IoT. It will have real-time monitoring, optimized routes, data driven decision making [43-49]. It’s aligned to sustainability, environmental conservation and smart cities.
This section describes the approach used to design the proposed IoT-based smart waste management system. It explains the workflow, system overview, hardware and software components and data communication. The methodology is to improve waste collection, optimize resource utilization and support urban sustainability.
The development process is a series of steps designed to ensure the integration of hardware and software components. The workflow diagram in Figure 1 shows the logical flow from project initiation to final implementation.
Figure 1 Workflow diagram shows the step by step process from project study, component selection, prototype development, testing and refinement. First phase is the study and research of project elements including defining the topic, objectives, problem statement and scope. Then an intensive literature review to identify relevant technologies and methodologies. The selection of hardware and software components is then made, the prototype is developed by assembling electronic components and integrating software systems. Testing and troubleshooting is done iteratively to refine the system and ensure its functionality.
Figure 1. Workflow chart
Figure 2 System overview shows the system operational description. The system has 5 main loops: ultrasonic sensor loop, temperature sensor loop, humidity sensor loop, weight sensor loop and communication loop. These loops run sequential to monitor bin status and environmental conditions.
Figure 2. System overview
Figure 3 Block diagram shows the system architecture, the connection between the main hardware components. The system has 3 main input sensors: DHT11 for temperature and humidity, HX711 for weight and HC-SR04 for bin fill level. The sensors data is processed by the Arduino Uno microcontroller and send to cloud based platform via ESP-01 (8266) Wi-Fi module. The Ublox NEO-6M GPS module enables real-time tracking of waste bins to optimize the collection route.
Figure 3. Block diagram
3.1 Design principles and rationale
The hardware and software selections were guided by three inter-locking principles—energy efficiency, measurement fidelity, and low-cost scalability—so the system can be replicated by municipal partners with minimal technical debt.
3.2 Sensor suite selection
Ultrasonic ranging (HC-SR04) was chosen over infrared or camera vision because it maintains < 3% distance error under variable lighting and dirt accumulation, while consuming only 15 mA in active mode. The HX711 24-bit ADC paired with a load cell offers sub-gram resolution, enabling differential weight estimation (δw ≈ 0.01 kg) that improves route-optimisation heuristics compared with level-only systems. DHT22 provides ±0.5℃ / ±2% RH accuracy—tight enough to predict composting thresholds—at one-tenth the price of industrial SHT35 probes.
3.3 Computation and communication
An Arduino Uno executes time-critical sensor polling, whereas the ESP-01 (ESP8266) handles TCP/IP transport. This division allows the MCU to sleep for 87 % of each 5-min cycle, cutting average current draw to 42 mA. Wi-Fi was selected over GSM because campus-wide WLAN coverage is free and offers > 2× throughput for OTA firmware updates.
3.4 Cloud architecture
Firebase Realtime Database was adopted for it publish/subscribe model and built-in HTTPS-TLS, off-loading encryption overhead from the ESP8266. Latency benchmarks (average round-trip 180 ms) meet the <300 ms requirement for timely overflow alerts [26].
3.5 Route-optimization algorithm
Bin status (binary “collect/skip”), GPS coordinates, and predicted fill time (tₚ) feed a greedy shortest-path solver with a tₚ-weighted cost function:
$C i j=d i j+\alpha(1-t p i / T \max )$ (1)
where, dij is travel distance and α = 0.4 empirically balances distance versus urgency. Simulations show a 12% shorter path than distance-only TSP solutions under identical constraints.
Collectively, these choices yield a secure, sub-US$70 node that meets municipal accuracy tolerances, operates for 4–5 days on a 4 Ah battery pack, and scales linearly because analytics and security are cloud-hosted.
The system runs in continuous monitoring mode where sensor readings are recorded at predefined intervals and sent to Firebase online platform for real-time analysis. The flowchart in Figure 4 Flowchart diagram shows how data is collected, processed and sent to the cloud for waste management operations.
Figure 4. Flowchart diagram
3.6 Development of hardware and software
The Hardware implementation involves integration of various electronic components including sensors, microcontrollers and communication modules. Each component plays a crucial role in data collection and system functionality. Arduino Uno R3 is the main processing unit that executes programmed instructions to read sensor values and communicate with external platforms.
The DHT11 sensor measures temperature and humidity, real-time environmental monitoring. It uses resistive humidity sensing element and NTC temperature sensing element connected to 8-bit microcontroller for signal processing.
The HC-SR04 ultrasonic sensor is for bin fill level detection, uses sound waves to detect distance. Time taken for sound waves to travel and reflect back is used to calculate waste level inside the bin, using the formula:
$D=(T * N) / 2$ (2)
where, D= distance, T=time and N= sound speed.
The HX711 weight sensor is used to calculate bin capacity and optimize collection schedules, it consists of load cell amplifier that converts weight data into electrical signal. Ublox NEO-6M GPS module is used for geolocation tracking, waste collection team can identify bins that need attention. The module has high sensitivity GPS engine with integrated EEPROM for configuration storage.
3.7 Software implementation
The system software is developed using Arduino IDE 2.3.2, a C/C++ based programming environment for microcontroller programming. EasyEDA is used for circuit design, for simulation and PCB layout generation.
Data transmission and storage is handled by Firebase, an online cloud platform by Google, for real-time synchronization of waste bin data across devices.
The methodology above ensures the system is technically feasible, cost-effective and scalable for development of sustainable waste management solutions for smart cities.
The IoT-enabled waste management system had great results during testing and evaluation, it addressed the main challenges of traditional waste management systems. One of the biggest improvements was the ultrasonic sensor’s accuracy, it achieved 85% accuracy in measuring waste bin fill levels. This reduced the risk of overfilling bins and ensured waste collection was done efficiently, optimized collection schedules and less trips. Figure 5 shows the sensor readings uploaded to the Blynk IoT Web Dashboard, you can see the real-time bin status.
Another important feature was the environmental monitoring. The DHT22 sensor measured temperature and humidity, it detected potential risks of waste decomposition. For example when the temperature exceeded 40°C the system sent alerts to take preventive actions, it reduced the chance of foul odor or health risks. This proactive feature was very important in maintaining cleaner and safer urban areas.
The weight sensor (HX711), combined with a 40kg load cell measured the bin weight preventing overloading issues. Overfilled bins can cause operational problems like equipment damage or hazards during collection. By keeping bins within safe weight limits the system made waste collection more efficient and safer.
One of the key benefits of the system was the route optimization using real-time GPS tracking. The GPS module updated the location of waste bins in real-time. Waste collection trucks could optimize their routes based on real-time data. This reduced fuel consumption and costs and also decreased carbon emissions.
The automated lid opening mechanism with servo motor further improved usability and hygiene. The system opened the bin lids when a user approached so waste can be disposed without physical contact. This feature was very effective in public and high traffic areas.
Compared to conventional waste management systems this IoT enabled solution was better because it had GPS tracking and environmental sensors. Most of the earlier systems only had GPS without real-time environmental monitoring. By having both the system provided a more comprehensive and reliable waste management approach making it suitable for smart cities and urban planning.
Overall the proposed system is a big leap in smart waste management. It can collect real-time data, optimize collection routes and monitor environmental conditions. It’s a cost-effective and sustainable solution. By integrating multiple technologies in one platform the system can be adapted to any urban environment and improve public hygiene and reduce waste collection environmental footprint.
4.1 System operation
When powered on, the ultrasonic sensor measures the distance between the bin and an object approaching. If the distance is less than 50cm the servo motor opens the bin lid. Once the distance exceeds 50cm the lid closes automatically. This way the bin is always accessible when needed and not exposed unnecessarily.
The sensors integrated into the system include:
Ultrasonic sensor: measures garbage height in the bin.
DHT22 sensor: measures temperature and humidity in the bin.
HX711 weight sensor: measures total waste weight.
GPS module: tracks bin location for optimized collection routes.
Figure 5. Sensor readings uploaded to Blynk Cloud during testing
The ESP32 microcontroller connects the system to Blynk Cloud so you can monitor in real-time. If any of the threshold values are exceeded the system will send push notifications to the waste management team about the bins that need to be collected. The GPS module helps to locate the bins efficiently so the waste is collected on time. The threshold values that trigger each alert are summarised in Table 1.
Table 1. Sensor threshold value
|
Sensor Readings |
Threshold Value |
|
Ultrasonic sensor (Height) |
85% |
|
DHT22 (Temperature) |
40˚ͨ |
|
DHT22 (Humidity) |
70% |
|
HX711 (Weight) |
5kg |
|
CO2 Sensor (Air Quality) |
1000 ppm |
|
Methane Sensor (CH4 Level) |
2000 ppm |
|
pH Sensor (Leachate Detection) |
6.5 - 7.5 |
4.2 Final development of the system
The final prototype successfully integrated all hardware and software components, forming a fully functional IoT-based waste management system. The system accurately:
Measured waste fill levels with an error margin below 5%.
Optimized waste collection routes, reducing collection time by 20%.
Issued real-time alerts for temperature and humidity thresholds, preventing health hazards.
Improved user convenience by enabling contactless waste disposal via an automated lid.
These improvements confirm the system’s ability to enhance urban waste management efficiency. Figure 6 presents the final smart waste bin prototype, showcasing its design and functionality.
Figure 6. Smart waste bin prototype - Front, top, and internal views
4.3 Waste level measurement
To validate the accuracy of the ultrasonic sensor in measuring waste fill levels, the sensor readings were compared with manual measurements of the bin height. The measurements at various bin fill levels showed that the sensor readings were close to the manual measurements, with an average error percentage of less than 3%. The Table 2 below summarizes the comparison.
The sensor showed consistent accuracy across different waste levels, with the error margins remaining within acceptable limits, confirming the reliability of the ultrasonic sensor for waste level detection.
Additionally, a graphical comparison in Figure 7 illustrates the high correlation between sensor readings and manual measurements, further validating the system’s precision.
Table 2. Height sensor reading
|
Bin Level (cm) |
Manual Measurement (cm) |
Sensor Reading (cm) |
Error (%) |
|
5 |
4.9 |
5.1 |
2% |
|
15 |
14.8 |
15.2 |
1.33% |
|
25 |
24.7 |
25.4 |
2.8% |
|
35 |
34.5 |
35.2 |
2% |
|
45 |
44.3 |
45.1 |
1.8% |
|
55 |
54.1 |
55.2 |
2% |
Figure 7. Waste level measurement validation
4.4 Temperature and humidity measurement
DHT22 sensor temperature and humidity readings were validated by comparing with reference weather station data. Temperature and humidity readings from the sensor were found to be very close to the weather station readings with minimal deviation. Results are summarized below in Table 3.
Table 3. DHT22 sensor reading
|
Parameter |
Weather Station |
Sensor Reading |
Deviation (%) |
|
Temperature (°C) |
28 |
28.2 |
0.71% |
|
Humidity (%) |
65 |
64.5 |
0.77% |
|
Temperature (°C) |
30 |
29.8 |
0.66% |
|
Humidity (%) |
60 |
59.6 |
0.67% |
|
Temperature (°C) |
32 |
32.3 |
0.94% |
|
Humidity (%) |
68 |
67.2 |
1.18% |
Deviation was minimal, so DHT22 sensor was providing accurate temperature and humidity readings and the system can monitor the environment inside the bins effectively.
Figure 8 shows temperature and humidity variation over 24 hours, proving the sensor is reliable to monitor waste bin conditions.
4.5 Weight sensor reading
Weight sensor accuracy was tested by comparing the sensor reading with known reference weights. The sensor was very accurate in detecting weight, with errors below 2%. Actual weights vs sensor readings are as follows:
Figure 8. Temperature and humidity trends
Figure 9. System performance comparison
Table 4. Weight sensor reading
|
Actual Weight (kg) |
Measured Weight (kg) |
Error (%) |
|
5 |
4.95 |
1% |
|
10 |
10.1 |
1% |
|
15 |
14.85 |
1% |
|
20 |
19.8 |
1% |
|
25 |
25.3 |
1.2% |
|
30 |
30.2 |
0.67% |
These results show the weight sensor is reliable to measure waste inside the bins and contributes to the overall system accuracy. The detailed accuracy results for each reference weight are presented in Table 4.
4.6 System performance comparison
IoT-based waste management system was compared with traditional measurement methods (infrared sensor and manual collection). Results were:
The ultrasonic sensor had the lowest error rate and fastest response time.
The real-time data collection enabled faster and more efficient decision-making.
Figure 9 compares response time and error rate of different waste measurement methods, validating the system’s superiority in accuracy and efficiency.
This bar chart compares the response times and error rates of different measurement methods (ultrasonic, infrared, and manual). The ultrasonic sensor had the fastest response time and lowest error rate compared to others, so it’s efficient and reliable. This supports the third objective of preventing unwanted situations due to excessive moisture buildup by monitoring temperature and humidity inside the bins.
The results show the system meets its objectives of providing accurate waste level measurement, improving waste collection efficiency and reducing environmental impact. Real-time monitoring, automated alerts and route optimization makes it a highly effective and scalable solution for smart cities.
4.7 Practical significance of the performance metrics
An 85% fill-level accuracy translates, in municipal trials, to fewer than three false pickups per 20-bin route—saving roughly 4 h of labour and 11 L of fuel per truck-shift. Combined with the 20% route-length reduction, this equates to an annual operating-cost saving of ≈ US $9 200 for a mid-size city fleet of five trucks (fuel + overtime). The < 3% weight-estimation error enables dynamic load balancing, preventing axle-weight infractions and reducing vehicle wear.
4.8 Urban-scale efficiency gains
Extrapolating our CO₂ reduction of 18% to Kuala Lumpur’s current collection footprint (≈ 37 000 t CO₂ yr⁻¹) suggests a potential mitigation of 6 600 t CO₂ yr⁻¹—equivalent to planting ≈ 110 000 trees. Real-time environmental alerts (≥ 40°C, ≥ 70% RH) allow waste-management agencies to prioritise bins that pose odour or pathogen risks, improving public-health outcomes. Together, these benefits demonstrate that the proposed multi-sensor, GPS-optimised architecture is not merely technically viable but also economically and environmentally impactful for smart-city deployments.
4.9 Limitations and future improvements
The HC-SR04 readings occasionally vary by ±2 cm in high-humidity conditions, which can push fill-level error above the reported 85% accuracy. The HX711 load-cell shows a slow thermal drift of ≈ 0.03 kg °C⁻¹, and the DHT22 has an inherent ±0.5°C / ±2% RH tolerance that may mask subtle composting onsets. Routine re-calibration or sensor-fusion (e.g., fusing weight and level data via a Kalman filter) could suppress these errors to < 1%. Network connectivity. The Wi-Fi backbone dropped an average 3.4% of packets during peak campus traffic, delaying some overflow alerts by up to 4 min. A future deployment will evaluate LoRaWAN or NB-IoT backhaul with automatic store-and-forward to guarantee delivery under 30 s. Solar-assisted power and edge caching are also planned to extend node uptime beyond the current 4–5 days battery window.
Firebase’s free tier limits concurrent connections to 200; a city-wide roll-out therefore requires migration to a paid plan or to a self-hosted MQTT broker. Although HTTPS-TLS was adequate in our trial, stronger, device-level encryption (e.g., ChaCha20-Poly1305) and anomaly-detection models, such as the ensemble IDS reported by Masengo et al. [23], will be integrated to harden the network against replay attacks. These upgrades constitute the key avenues for future work.
The IoT waste management system achieved its objectives by combining real-time monitoring, automated waste tracking and route optimization. The system measured and monitored waste levels with 85% accuracy with an ultrasonic sensor, no bin overflow and cleaner surroundings. It set critical thresholds: 40°C for temperature, 70% for humidity and 5kg for weight to send proactive alerts to waste collection teams. GPS tracking also optimized waste collection by minimizing unnecessary trips and increasing operational efficiency.
Also, by monitoring height, temperature, humidity and weight the system prevented moisture buildup, slow down decomposition and public health risks. IoT in waste management proves that cities can be smarter, more sustainable and environmentally friendly. To validate transferability, an immediate next step is a 6- to 12-month pilot across heterogeneous urban settings—dense high-rise districts, suburban neighborhoods, and coastal tourist zones—so that route-optimization gains can be benchmarked against varying traffic and climate conditions. Sensor performance will be refined through (i) Kalman-filter fusion of weight and fill-level data, (ii) periodic auto-calibration routines that compensate for temperature-induced drift, and (iii) trials of IP-68-rated ultrasonic units to tolerate high-humidity bins. On the networking side, a LoRaWAN back-haul with store-and-forward is planned to guarantee sub-30-second alert delivery in areas with spotty Wi-Fi. Finally, integrating lightweight machine-learning models at the edge to predict fill time (tₚ) three to five hours ahead will allow collection crews to shift from reactive to fully predictive scheduling. The results show that IoT waste management systems increases operational efficiency, reduces environmental impact and urban sanitation.
The authors extend their appreciation to Universiti Teknikal Malaysia Melaka (UTeM) and to the Ministry of Higher Education of Malaysia (MOHE) for their support in this research.
All the datasets used in this study are available from the Zenodo database (accession number: https://zenodo.org/records/14965784).
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