Sensor-Based Solid Waste Handling Systems: A Survey
Abstract
:1. Introduction
- A comprehensive analysis of RFID, WSN, and IoT-based approaches towards the automation of solid waste handling systems.
- Each category is analyzed with a typical system architecture. Significance and limitations for the same are discussed.
- Recommends apt communication technology for IoT-based solid waste handling systems.
- The critical open research issues on state-of-the-art solid waste handling systems are concluded from the review and recommendations for future directions are presented.
2. Methodology
- Similar approaches published in different years are excluded from the survey.
- Conference papers with proof of concept are only included for the survey.
3. RFID Based Solid Waste Management Systems
- To obtain information on the waste collection area and the respective collecting time;
- To develop a system for monitoring and tracking of waste collection trucks and waste bins;
- To obtain information on the quantity of solid waste inside the bin and the surroundings.
3.1. System Architecture
RFID-Enabled Trash Bin Level Monitoring Systems
- RFID-based systems are unable to provide continuous real-time monitoring of the bin’s filled level;
- Trash bins are monitored only when the truck is within the range of RFID tags;
- RFID tags are using to identify the bins uniquely and they do not provide any data on the filled levels of the bins;
- Additional infrastructure is required to obtain the filled levels of the trash bins.
4. Wireless Sensor Networks Based Solid Waste Management Systems
- Capacity for dealing with node failures;
- Optimal for nodes equipped with batteries;
- Nodes mobility and heterogeneity;
- Scalability to a vast distribution scale.
4.1. Network Architecture
WSN-Based Trash Bin Level Monitoring Systems
5. IoT-Enabled Solid Waste Management Systems
- No missed pickups: The data recorded from the smart bins assist in reducing missed pickups. The authorities will be automatically notified if the sensors detect that the garbage container is full. Then, the IoT waste management system enables the scheduling of next pickup for this location. This simplifies the process of waste management and reduces overflowing garbage cans.
- Waste production analysis: Throughout the day, the connected devices keep track of how quickly the bins fill up and how often they empty in different locations. The analysis of these data opens the possibilities of better trash bin distribution, elimination of improper disposal techniques, and even waste reduction at the landfill.
- Route Optimization: The real-time data provided by the smart trash bins can be used to determine the best paths for garbage collection by prioritizing the most required regions.
5.1. Network Architecture
- IoT end device: It consists of sensors to measure the unfilled level or weight of the trash bins, a microcontroller to perform local processing, and a radio device to establish wireless connectivity with gateways or base stations.
- Gateway or Base stations: Several communication technologies are available to establish connectivity between the end devices and the cloud server. According to the adopted wireless technology, the gateway or base station will act as a bridge between the end devices and the cloud server. For instance, a gateway for LoRaWAN devices, a base station for Sigfox and NB IoT devices, and a wireless router for Wi-Fi devices.
- Cloud Server: For IoT applications, cloud servers are preferred due to their flexibility, scalability, and secure authentication process.
- End-user: Remotely monitoring a system can be performed by the end-user. The end-user may be an employee of the solid waste management company or a person in charge of the solid waste collection department in a municipality or corporation. A hierarchical view of trash bins’ filled level can be obtained through a web page or application software.
IoT Embedded Trash Bin Level Monitoring Systems
- LoRaWAN is an open specification, whereas NB-IoT and Sigfox are proprietary network protocols.
- LoRaWAN allows the establishment of private networks in which sensor nodes, gateways, and backhaul can be deployed by the user for a specific application. This is in contrast to NB-IoT and Sigfox, where a user needs to pay for connecting their sensor to the networks.
- LoRaWAN supports firmware upgrade over the air (FUOTA) that enables the remote firmware update of the multiple devices deployed over the network.
- LoRaWAN supports the Adaptive Data Rate mechanism for the optimization of power consumption, airtime, and data rates, whereas NB-IoT and Sigfox do not support this feature.
6. Comparison and Discussion
7. Research Gaps and Future Direction
- Self-powered end nodes: IoT systems comprise a large number of end nodes and gateways or base stations; while the end nodes are deployed at various locations, the gateways or base stations gather the data from the end nodes and send it to the internet for further analysis. The end nodes are preferably powered with batteries, and the frequent replacement of batteries is impractical when considering the wide range deployment of nodes. Some works in the literature proposed energy harvesting by using solar panels to address this issue. Still, there is a large scope of systems that need to adopt advanced methodologies to enhance the design of IoT-enabled solid waste handling systems with self-powered end nodes:
- Edge computing enabled waste segregation: Currently, waste segregation is implemented by placing multi-colored or labeled bins for the easy classification of the waste material. Even if it is an easy method of collecting a particular kind of waste, this approach cannot always be successful because, by mistake, people may place waste in the wrong bins and it will disturb proper segregation. Nevertheless, waste segregation by the bin itself would be a solution. There is a solid research scope in the aforementioned scenario to design edge computing-enabled smart nodes incorporated with image processing techniques to implement waste segregation.
- Hybrid network architecture: Waste generation is dynamic and varies concerning the environment. For instance, the frequency of filling of a trash bin installed at public places will be dynamic and the filling of trash bins installed at houses or residential areas will be probably in a uniform manner. From the survey performed on existing approaches, it is evident that LPWAN communication technologies are best suited for monitoring bins at public places since bins are deployed at distant locations. Similarly, short-range communication technologies such as Wi-Fi are best suited for monitoring trash bins located at houses since most houses will be equipped with a Wi-Fi router for internet connectivity. Nevertheless, none of the existing systems describe solid waste management models or network architectures for waste management in flats or apartments. Moreover, waste management in urban areas is incomplete without addressing the aforementioned scenario. As a result, a lot of research attention is required in developing hybrid network architectures that are capable to address the solid waste management requirements in public areas and residential areas differently.
- Smart Transportation: Transportation plays a crucial role in an efficient waste management system. Most of the existing approaches send the trash bin status to the central monitoring station or cloud servers for performing data analysis. On the basis of this analysis, waste collection routes will be scheduled and a truck or waste collection vehicle shall follow this scheduled path for optimized waste collection. Meanwhile, an unscheduled trash bin may become completely filled and miss the pick up. Thus, there is a need for dynamic path optimization centered on the waste collection vehicles’ live location and the real-time status of the trash bins.
- Customized node design: Based on the study performed on the various solid waste management systems, it is inferred that most of the end nodes are employed with development or evaluation boards. Consequently, the performance metrics obtained for those nodes will have some tolerance levels when compared with dedicated nodes designed for trash bin monitoring. Even though some works in the literature have designed customized nodes for compactness and power efficiency, there is still a scope for further enhancement. For instance, the majority of works considered in this study follow some hypothetical conditions, such as the waste-filled level being uniform and a single ultrasonic sensor can detect the unfilled level of the trash bins. However, in real scenarios, an end node with a single sensor will not be sufficient for detecting the unfilled level of larger trash bins. Therefore, there is a need for customized end nodes with multiple sensors for detecting the unfilled levels of larger trash bins efficiently.
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref. | Bin Deployment | Sensors | Camera | GPS | GIS | Routing |
---|---|---|---|---|---|---|
[27] | exterior | none | Yes | Yes | Yes | undefined |
[28] | exterior | none | Yes | Yes | Yes | undefined |
[29] | not specified | infrared sensor | No | Yes | No | undefined |
[30] | exterior | ultrasonic sensor, pressure sensor, | Yes | Yes | Yes | undefined |
[31] | exterior | load cell sensor | No | No | No | undefined |
[32] | interior/exterior | photoelectric, image sensor | No | Yes | No | defined |
[33] | exterior | digital weight scale | No | No | No | undefined |
Wireless Technology | Wireless Range | Power Consumption | Operating Frequency | Data Rate |
---|---|---|---|---|
Zigbee | 10–100 m | Low | 2.4 GHz | 20–250 kbps |
Wi-Fi | 100 m | Medium | 2.4 GHz, 5 GHz | 10–100 Mbps |
Bluetooth LE | >100 m | Low | 2.4 GHz | 125 kbps–2 Mbps |
Z-Wave | 15–150 m | Low | sub-GHz | 9.6–40 kbps |
IEEE 802.15.4 | 10–20 m | Low | 2.4 GHz | 250 kbps |
SimpliciTi | 50 m | Low | 2.4 GHz | 250 kbps |
Ref. | Sensor | Microcontroller | Wireless Technology | Communication Network | GPS | Visualization | Energy Harvesting |
---|---|---|---|---|---|---|---|
[34] | Ultrasonic sensor | Arduino Uno | Zigbee | Mesh | Yes | No | No |
[35] | Ultrasonic sensor | Arduino Pro Mini | Zigbee | Star | Yes | No | Yes |
[36] | ArgosD sensor | MSP430F1611 | IEEE 802.15.4 | LoWPAN | No | Yes | No |
[37] | Ultrasonic sensor | MSP430F2274 | SimpliciTi | WLAN | No | Yes | No |
[38] | Ultrasonic sensor | MSP430F2274 | SimpliciTi | WLAN | No | Yes | Yes |
[39] | Ultrasonic sensor | ATSAMW25H18 | Wi-Fi | WLAN | No | Yes | Yes |
Ref. | Sensors | Radio Technology | Wireless Range | GPS | Energy Harvesting |
---|---|---|---|---|---|
[41] | weight sensor, proximity sensor | Zigbee | Short | No | No |
[42] | level sensor | Not Specified | Not Specified | No | No |
[43] | Not Specified | Wi-Fi | Short | No | Yes |
[44] | Ultrasonic sensor, load cell | GSM | long | Yes | No |
[45] | Ultrasonic sensor | Wi-Fi | short | Yes | No |
[46] | Ultrasonic sensor | GSM | long | No | No |
[47] | IR sensor | RF | Short | No | No |
Communication Technology | Wireless Range | Power Consumption | Operating Frequency | Data Rate | Modulation Technique |
---|---|---|---|---|---|
LoRaWAN | <15 km | Low | Sub-GHz | 0.3–50 kbps | SS Chirp |
SigFox | 50 km | Low | Sub-GHz | 100 bps | DBPSK |
NB-IoT | <35 km | Low | Cellular Bands | 200 kbps | QPSK |
Ref. | Sensor | Microcontroller | Radio Device (LoRa) | Custom Node Design | GPS | Energy Harvesting | Real Time Deployment |
---|---|---|---|---|---|---|---|
[51] | Camera, Ultrasonic Sensor | Arduino Uno | SX 1272 | Yes | Yes | Yes | Yes |
[52] | Ultrasonic Sensor | Atmega328P | SX 1272 | Yes | No | No | Yes |
[53] | Ultrasonic Sensor | Atmega328P | SX 1278 | Yes | No | No | Yes |
[54] | Ultrasonic Sensor, Load cell, Temperature sensor | ATSAML21 | SX 1276 | Yes | No | No | Not Specified |
[55] | Ultrasonic Sensor | Raspberry Pi3 | IP67 LoRa gateway | Yes | No | No | Not Specified |
[56] | Ultrasonic Sensor | ATmega 2560 | RN2903 | Yes | Yes | Yes | Yes |
[57] | Ultrasonic Sensor | Atmega328P | SX 1278 | Yes | No | No | Yes |
[58] | Ultrasonic Sensor | ATmega 2560 | RN2903 | Yes | Yes | Yes | Yes |
Ref. | Title of the Project | Estimated Prototype Cost in USD |
---|---|---|
[39] | An IoT-based bin level monitoring system for solid waste management | 107 |
[51] | An internet of things based smart waste management system using LoRa and tensorflow deep learning model | 180 |
[53] | A low power IoT sensor node architecture for waste management within smart cities context | 57 |
[56] | A LoRaWAN IoT enabled Trash Bin Level Monitoring System | 161 |
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Vishnu, S.; Ramson, S.R.J.; Rukmini, M.S.S.; Abu-Mahfouz, A.M. Sensor-Based Solid Waste Handling Systems: A Survey. Sensors 2022, 22, 2340. https://doi.org/10.3390/s22062340
Vishnu S, Ramson SRJ, Rukmini MSS, Abu-Mahfouz AM. Sensor-Based Solid Waste Handling Systems: A Survey. Sensors. 2022; 22(6):2340. https://doi.org/10.3390/s22062340
Chicago/Turabian StyleVishnu, S., S. R. Jino Ramson, M. S. S. Rukmini, and Adnan M. Abu-Mahfouz. 2022. "Sensor-Based Solid Waste Handling Systems: A Survey" Sensors 22, no. 6: 2340. https://doi.org/10.3390/s22062340
APA StyleVishnu, S., Ramson, S. R. J., Rukmini, M. S. S., & Abu-Mahfouz, A. M. (2022). Sensor-Based Solid Waste Handling Systems: A Survey. Sensors, 22(6), 2340. https://doi.org/10.3390/s22062340