Smart Bins, Smarter Cities: IoT-Driven Waste Collection with Real-Time Sensing and Methane Mapping

Urban waste management is a prominent challenge in the fast-developing urban areas of the world, with India being a prime example. Urbanization is speeding forward at a remarkable speed, creating an amount of daily generation of trash at appalling levels. India, in 2021, was producing over 160,000 metric tons of trash daily, an amount that increases in direct proportion with the size of the population and the changing lifestyles. Such a high amount of trash generation creates immense challenges before the municipal bodies, as they are finding it extremely tough to carry out collection, transport, and disposal in an optimized manner. Traditionally employed collection methods, based on predetermined routes and time schedules, are often found lacking in serving the needs of modern urban environments. Inadequate collection, over-packed bins, and poor routing are surfacing from such conventional methods. Municipal vehicles are presently picking up household trash and taking it to the sites of disposal in the nearest location; however, the process is plagued with inefficiencies. Route optimization is not taking place, thereby increasing the travel distance of vehicles, further increasing the consumption of fuel and operating costs. Irregular collection schedules result in filled bins, creating not only obnoxious odors but also nuisance-causing rodents that are vector-borne and carry disease, thus compromising public health. In addition, no real-time monitoring is restricting the government’s ability to measure the fill levels of bins correctly, thereby leading to sustainable and technology-driven approach to urban waste management. To meet these challenges, route optimization is essential for the effective and orderly collection of waste. By utilizing real-time information from Internet of Things (IoT) sensors, municipalities can optimize collection routes according to the real fill levels of the waste bins, minimizing the number of trucks required and lowering travel distances. This strategy not only leads to lower operational costs but also to lower carbon emissions, thus making urban areas cleaner and more sustainable. In addition, regular bin emptying and sanitation require the monitoring of odor levels continuously so that the authorities can identify locations with high odor concentrations and mark them as odor-prone areas. Such a proactive approach can help mitigate public health threats and improve the overall urban quality of life. Another important aspect of waste management is the monitoring of methane emissions from waste bins. Flammable methane is produced during organic waste decomposition processes. High concentrations of methane in trash cans create serious fire risks, especially for city centers with high concentrations of people in urban areas. Real-time IoT sensor-based tracking of methane concentrations, as conceived by Ahmed et al. [1], can serve as early signals of rising concentrations of methane and allow agencies to take remedial action against possible catastrophes. Moreover, monitoring of other obnoxious emissions like hydrogen sulphide and ammonia can provide assistance for city-level odor pollution and public sanitation. IoT integration with waste management systems has proved effective in reducing the said problems. IoT-powered sensors in intelligent trash cans can potentially provide immediate information regarding fill levels, gas emissions, and geographic positions and thereby allow agencies to make well-judged decisions and optimize waste collection processes. IoT and cloud analytics have opened up possibilities of bringing traditional waste management systems into scalable and economical models of city adoption. For example, research in Henaien et al. [2] shows that IoT solution strategies have the capacity for increasing the efficiency of the waste collection process up to 30% and thereby bring considerable cost and environmental benefits. Similarly, Lakhouit [3] explained the effectiveness of artificial intelligence strategies for improving the efficiency of the collection process with implications for cost-saving and resource efficiency. In spite of all these advancements, it should be observed that most of the existing IoT-powered waste management systems focus only on fill levels of trash cans as their only most serious issue to the virtual exclusion of other serious ones like emissions of methane and odor control, and thus lose their usefulness for coping with the entire spectrum of waste management system problems of cities.

References In order to bridge this gap, we introduce Trash Tech, IoT waste management system with real-time monitoring, dynamic route optimizer, and odorous and methane emission mapping to provide end-to-end solution for the intricacies of city waste management. With several innovative features for city trash management, the Trash Tech system includes additional IoT sensors that provide real-time monitoring of bin fill levels, gas emissions, and odors, thereby providing the municipality observational insight information. The multi-sensory nature of the system provides deeper insight into waste management-related concerns, thereby allowing focused intervention on specific issue like fire risk control and odor control. Aside from all that, the system includes dynamic route optimizer, thereby reducing garbage trucks by approximately 20%, thereby resulting in considerable savings on operating costs as well as carbon footprint. Through garbage bin routing via optimized routes through information in real-time, Trash Tech prevents garbage bins from overfilling, thus reducing the occurrence of unsanitary conditions as well as public health complaints. Aside from that, Trash Tech utilizes very accurate sensors, the ADS1115 analog-to-digital converter of 16-bit-resolution, thereby accurate measurements of gas levels and bin fill levels are taken. High accuracy in measurements ensures more accurate decisions, thereby improving the effectiveness as well as efficiency of the process of collections. Aside from all that, the system utilizes cloud-based analytics for information storage and processing garnered from several sensors, thereby providing the governance with a bird's eye view for information gathering process. Such data-oriented approach, in addition to improving effectiveness in information gathering, further aids in planning for the future and resource provisioning through predictive analysis. The application of IoT and cloud analytics for Trash Tech provides a giant jump from traditional approaches towards the process of collections. By providing actionable learning and defining the need for raising the garbage collection frequency, the system has the significant role of facilitating the achievement of clean, secure, and sustainable city environments. Its application in Malad, Mumbai, brought about significant improvement in terms of efficiency of collection, as evidenced through the number of garbage trucks required that decreased by 20%, as well as through the savings in terms of fuel and maintenance cost. Such evidence is reflective of the ability of Trash Tech in minimizing breakdowns for traditional collecting systems and thus facilitating the achievement of more intelligent and more sustainable city environments.

One of the things that would be used in solving our community issue of random waste disposal resulting in full bins and unhygienic surroundings is a smart system Trash Tech based on IoT technology. The solution involves equipping each bin with ESP8266 microcontrollers. The microcontrollers will be connected with special sensors that will be employed for monitoring the status of each bin. The sensor input will be used by a specific app for optimisation and scheduling of the area's garbage disposal process. Figure 1 shows information flow in garbage monitoring systems.

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Figure 1. Flow of data through the garbage monitoring system

3.1 Data collection

An assortment of specialized sensors, coupled with a microcontroller, is used for gathering significant information from the bins. The heart of the system is the ESP8266 microcontroller, which is valued for its small size and affordability, utilizing Wi-Fi capabilities for seamless communication. The microcontroller is compatible with programming languages like Arduino C++, Lua, and MicroPython, and is supplemented with specialized tools like the Arduino IDE, thus fostering the design of new solutions.

The Ultrasonic Sensor is founded on the operation of producing high sound waves and determining the length of the sound waves reflected. The sensor is capable of detecting pulses that are inaudible and understanding reflected pulses off surfaces in order to calculate distances based on the velocity of sound. The Methane Sensor detects levels of methane gas in the atmosphere and functions like a specialized sense of smell. The MQ4 detector is capable of detecting even low levels of the flammable gas, which is helpful in case of concerns regarding security. The GPS Sensor hastens the process of obtaining information through the determination of accurate points on surfaces of the Earth based on signals from satellites orbiting the Earth. The devices note accurate places and times from satellites in order to provide accurate information about several places. The H2S Sensor is important for security as it detects levels of the hydrogen sulfide gas and triggers the early alarm system in order to prevent possible danger. The conductivity of the MQ136 increases with increased levels of the H2S gas, allowing for detection of even low odors from garbage bins. The Ammonia Sensor is capable of detecting putrid odors, thus facilitating attempts at monitoring the environment. The MQ137, which is economical and sensitive, is the first line of defense against hidden danger of ammonia-contaminated environments, thus facilitating security in the environment and improving efficiency of processes. The ADS1115 module has a bit resolution of 16 and is capable of recording up to a sample of 860 per second via I2C interfacing. The module is helpful in achieving accurate conversion of the digital from the analog, enabled through four input pins for providing support of the analog for effective acquisition of information. GPIO16 is also coupled with the RST of the ESP8266 for power-saving sleep mode functionality. All of these put together form advanced circuitry for efficient information gathering. The Trash Tech system utilizes the ESP8266 microcontroller based on its effectiveness costwise and presence of integrated Wi-Fi capabilities without the need for external communication modules like Zigbee or WeMos. The system utilizes the ADS1115 analog-to-digital converter for more accurate measurements with the provision of a 16-bit resolution far better than utilizing standard Arduino boards with 10-bit resolution. This enhances the ability for more precise detection of bin fill levels and gas concentration, thus allowing for more accurate decision-making concerning waste and hazard mitigation. The overall architecture of the IoT-enabled smart bin setup is illustrated in Figure 2.

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Figure 2. Schematic diagram with IoT components

3.2 Data pre-processing

The initial step of data preprocessing in intelligent garbage monitoring systems is pivotal in purifying the raw data gathered from a number of garbage bin sensors. The gathered information has several features, from which the main feature is selected. The application of the GPS sensor delivers features comprised of latitude, longitude, altitude, as well as number of satellites in view, from which the derived primary features are the longitude and latitude of garbage bins. Data integration is necessitated as a result of convergence of information from multiple diverse sensors such as depth, odor, and flammability sensors. The main features from diverse sensors are consolidated in homogenized fashion, thereby allowing the garbage collecting agency to derive actionable information and establish evolving patterns. Data processing is subsequent to integration, where diverse types of information from diverse sensors are standardized. Processing delivers consistency, thereby allowing comparison and analysis in non-ambiguity. The ultrasonic sensor reading is measured in terms of percentages for the purpose of rendering consistency of bin fill levels, while information from odor and methane sensors is measured in terms of parts per million (ppm). Data aggregation involves summarization of purified information through calculations of averages or totals over defined time periods. Averages of bin fill levels, for example, are computed on daily basis. The summarized information delivers valuable information in garbage bin garbage collecting routes as well as number of garbage collecting trucks to be used, thus enhancing the efficiency of the system. A data processing algorithm is used in the purification of information gathered from the sensors, integration, aggregation, and processing, with the processed information being transmitted through Firebase. Here, Arduino's Integrated Development Environment (IDE) is used as the compiler with which the microcontroller boards are programmed and designed. The Adafruit ADS1X15.h and TinyGPS++.h libraries are also being used for digitizing the analog signals and for retrieving GPS coordinates, respectively. For processing the data, several libraries are used.

3.3 Data storage

The smart trash monitoring system depends on the ESP8266 microcontroller to send real-time sensor feedback from trash bins to firebase, a cloud database. Firebase securely hosts various types of sensor data like ultrasonic, methane, GPS, hydrogen sulfide, and ammonia sensors. NoSQL databases provide schema flexibility, which is a requirement for IoT applications, with support for various sensor types without pre-defined schemas for the data. Firebase's real-time database supports the enforcement of rules for data integrity maintenance and access control. Combine this with Firebase's authentication and encryption capabilities for improved security. The design offers real-time trash bin and ambient condition statuses with scalability, security, and proper handling of the data.

3.4 Pick-up optimization

Based on the data gathered from the smart bins, we formulate adaptable routes for each garbage truck.

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Figure 3. Firebase data layout

Here we explain the steps of Algorithm 2 in detail:

1. Initialization: The algorithm begins by initializing an array C which contains the coordinates and volume of each filled dustbin. This is represented as C = {(2, 3, volume), (5, 7, volume), (8, 1, volume), ...}.

2. Sorting: The coordinates in C are sorted based on Euclidean distance or another relevant criterion to deter mine the optimal pickup sequence.

3. Truck Initialization: An empty garbage truck T is initialized to start the collection process.

4. Collection Process: While Loop is used to ensure the process continues until all dustbins are emptied. Inside the loop, a for loop iterates over each dustbin represented by (x, y, volume) in C.

5. Filling the Truck: The algorithm checks if the current truck T has enough space to accommodate the volume of the current dustbin.

If yes, the truck T is filled with the volume from the current dustbin.

If no (i.e., the truck is filled to 90% capacity), a new garbage truck T′ is assigned.

The remaining coordinates in C are then sorted again, and the new truck T′ is filled until it reaches 90% capacity. The current truck T is then updated to T′.

A. Privacy and network infrastructure solutions

The system has robust privacy protection through several mechanisms of anonymizing the data, aimed at maintaining users' anonymity while abiding by demanding legislation. Data anonymization includes GPS coordinates rounded for accuracy of 100 meters in grid formation, the utilization of Universally Unique Identifiers (UUIDs) as stand-ins for precise location names in bin names, avoiding the gathering of information pertaining to persons, and publishing only aggregations of information based on zones in order not to track persons.

Firebase Security Compliance provides further security for the information as per businesses utilizing ISO 27001-compliant environments. Firebase is GDPR-compliant for processing, has AES-256 encryption for all transfers, and implements role-based security for controlling the accessibility of information based on the hierarchical company structure and user identity.

The hybrid communication approach successfully overcomes connectivity problems in diversified city and rural installation environments with the help of a multi-layer network solution. In the said solution, Wi-Fi has been used as the prime communication channel in city installations with 90% coverage, while it utilizes Long Range Wide Area Network (LoRaWAN) for communication in remote and rural installations with poor internet infrastructure. The fallback channel is the GSM/3G for emergency notification messages and automatic synchronization upon regaining connectivity.

Its phased deployment approach is starting with deploying Wi-Fi infrastructure citywide and growing the footprint further through deploying LoRaWAN gateways and finally deploying hybrid network for the end-to-end communication in all the target areas with the aim of improving system reliability and coverage areas.

3.5 Visual representation

Data visualization is one of the most significant elements of the Trash Tech system in understanding and communicating the intelligence of gathered data. Data information is rendered into easy-to-understand visual representations with the help of tools such as React Native and Google Maps. React Native, upon which applications are created with the help of Visual Studio Code and hosted with Expo Go, creates apps for the waste management wing. The apps enable fill levels and trend monitoring of bins in real-time. react-native maps and react-native-google-maps-directions libraries are used for displaying bin location on Google Maps and providing directions for the garbage vans from one dustbin to another. Garbage Pickup Vans allocation algorithm creates the garbage vans' route planning. Garbage collection route optimization is enabled through it with trucks being able to be aptly allocated to which bins they should service. The tools provide an overall overview of system performance with the ability to make decisions based on it for garbage management efficiency improvements. The firebase-based data structure used to manage these dynamic routes and bin statuses is shown in Figure 3.

3.6 Formulation

A. Capacitated Vehicle Routing Problem (CVRP) Formulation.

Let G = (V, E) be a complete graph, where V = {0, 1, 2, ..., n} represents bins (0 is depot), E represents edges between locations, d_ij= Euclidean distance between bins i and j.

Objective Function:

$\min \sum k \in k \sum i \in v \sum j \in V c_{i j} \cdot x_{i j k}$

Subject to Constraints:

Vehicle capacity:

$\sum i \in V q_i \cdot \sum j \in V x_{i j k} \leq 0.9 \cdot Q_k \forall k \in K$

Each bin visited once:

$\sum k \in k \sum i \in V x_{i j k}=1 \, \forall j \in V, \forall k \in K$,

Flow conservation:

$\sum i \in V x_{i j k}-\sum i \in V x_{i j k}=0, \forall j \in V, \forall k \in K$

where, $x_{i j k}=1$ if vehicle k travels from bin i to $\mathrm{j}, 0$ otherwise, $q_i=$ waste volume at bin I, $Q_k=$ capacity of vehicle $\mathrm{k}, c_{i j}=$ cost of traveling from i to j.

B. Dynamic Threshold Algorithm.

Pseudocode:

A comparative summary of truck count, collection time, and distance between the traditional and Trash Tech systems is provided in Table 7.

Figure 12 shows data for the sample of 28 bins, and Figure 13 shows synthetic data for 100 bins. Figure 12 and Figure 13 reveal that there are fewer trucks required for garbage disposal as compared with the traditional system utilized and the amount of saving is of the order of 20%. The saving decreases the travel distance of the trucks and decreases disposal time for the waste. Odor from the bins is detected and monitored, and odor-emitting zones are detected as odor-prone zones and treated with special treatment. Methane gas is detected continuously for mitigating health hazard in the region. Trash Tech is also economical in terms of cost through its application of ESP8266 module, as from Table 1, and the cost of the overall system comes out to be 5000 INR per bin. Evidently, the present study discusses the great prospect of smart bin technology for improving city waste disposal systems.

Table 7. Comparison of distances, times, and number of trucks

5.1 Capacity threshold analysis

The 90% truck capacity threshold is based on:

Industry Standards:

Recommendations for municipal solid waste collection provide for 85-95% capacity usage, and sensitivity analysis shows that more than 90%, although there is marginally improved efficiency, excess capacity is significant. The detailed results of the sensitivity analysis for different truck capacity thresholds are summarized in Table 8. The table shows the variation of trucks required, distance (km), and collection time (hrs) with capacity usage showing declining returns after 90%.

Analysis:

Beyond 90%, there is minimal efficiency improvement with greatly heightened risk of overflow.

This study confirms the selection of 90% as the ideal truck capacity limit for effectiveness in waste gathering. The levels of thresholds (80-100%) were juxtaposed with the aim of identifying the ideal level that will maximize the use of trucks while there is allowance for margin against overloading during transport.

Table 8. Sensitivity analysis results

5.2 Sensor accuracy validation

Reference instrument: Testo 340 industrial flue gas analyzer

The gas sensors (MQ4, MQ136, MQ137) were validated against professional-grade reference instruments to ensure reliable odor and harmful gas detection. The Root Mean Square Error (RMSE) measures prediction accuracy, while Accuracy (%) shows how close the readings are to reference values, with the sensors achieving 96-97% accuracy sufficient for real-time waste monitoring. Table 9 provides accuracy comparisons between Trash Tech gas sensor readings and a professional-grade reference instrument. Table 10 compares the performance metrics of the ADS1115 and 10-bit ADC modules.

This comparison justifies the decision to use the 16-bit ADS1115 ADC over standard 10-bit converters for higher accuracy of measurements. Resolution is the smallest detectable change, Fill Level Error is measurement deviation in centimeters, and Temperature Drift is the impact of temperature on accuracy - the ADS1115 is 64× better in resolution and 75% lower in error.

Table 9. Gas sensor accuracy comparison

Table 10. ADS1115 vs. 10-bit ADC comparison

5.3 Statistical analysis

Paired t-test results (n = 9 samples):

H₀: μ_traditional = μ_Trash Tech

H₁: μ_traditional > μ_Trash Tech

t-statistic = 4.73

p-value = 0.0012

α = 0.05

Result: p < 0.05, reject H₀. The reduction is statistically significant.

Table 11. ANOVA analysis

Effect Size (Cohen's d): 1.58 (Large effect)

Paired t-tests and ANOVA analysis were employed statistically to determine the Trash Tech system has a considerable impact of reducing use of trucks by a considerable 20% compared to conventional methods. The t-statistic (4.73) and p-value (0.0012 < 0.05) were employed to determine statistical significance, whereas the large effect size as determined using Cohen's d (1.58) demonstrated the system's real effect in the field is considerable and not an artefact of random chance. A detailed breakdown of the ANOVA test results is presented in Table 11.

ANOVA Terminology: df (degrees of freedom) reflects sample size restrictions, Sum of Squares (SS) quantifies total variation, Mean Square (MS) is the variance estimate, and F-statistic determines if group means are significantly different.

A. User role definition

The stakeholder platform uses a hierarchical user role structure to support efficient system operation and responsibility assignments clearly defined at municipality-wide collection teams. It identifies four user roles according to the App User Hierarchy: The System Administrator with full access to the system and user administration authority, Municipal Manager with control over resource allocations and route optimization, Collection Supervisor with control over real-time monitoring and alert management, and Truck Driver with control over the routes and bin status reporting.

Summary of 15 Municipal Worker feedbacks 87% of respondents reported system ease of use through finding the interface easy to use and routes improved with 93% of respondents, while 73% requested offline map views in poor connectivity zones. 2.3 hours average time to learn is a reflection of the usability of the system and rapid adoption at both levels of Municipal Staff.

B. Policy integration framework

The policy integration module allows Data-Driven Decision Making through the creation of practical municipal policies and strategic initiatives from the processing of operating data. Levels of gas emissions provide input into public health policy development, trends in waste generation provide insight into bin position best practice, collection effectiveness ratings provide insight into municipal budgetary allocations, and seasonal trend analysis feeds long-term resource plans. The policy integration module ensures that the Trash Tech system optimizes operating effectiveness but also allows for evidence-based municipal governance and sustainable urban refuse policy development.

Though the Trash Tech system is quite efficient in managing trash through the reduction of 20% of the number of trucks and immense cost-saving, a few of the limitations need consideration. First is the connectivity limitation in that the use of Wi-Fi by the system to carry information is a problem in underdeveloped regions, something that can lower the effectiveness of city-wide implementation, while real-time data processing is dependent on a secure internet that may be lacking in underdeveloped or rural regions. Scalability concerns and environmental constraints are related to geographical limitation of study to Malad, Mumbai, and possible limitation of results generalizability to other urban settings, and possible deviations in sensing accuracy due to moisture and temperature variations despite the use of high precision ADS1115 sensors. Economic constraints are related to setup expenses in IoT sensors and cloud infrastructure, possibly discouraging adoption by under-funded municipalities despite long-term operating cost benefits offsetting such expenses. Subsequent editions will require the inclusion of self-calibration sensors, hybrid communication networks (LoRaWAN/NB-IoT), and thorough cross-city validation research in an effort to mitigate such constraints and facilitate wider adoption of the system and success.