A 5G-Enabled Smart Waste Management System for University Campus †
Abstract
:1. Introduction
2. Related Work
3. Building the Smart Bin
3.1. Requirement Analysis
3.2. Prototype Design
3.3. Sensors and Actuators
3.3.1. Waste Sensing Module
- Inductive Sensor: an LJ12A3-4-Z/BX sensor is attached to the bottom of the shelf in the WDU, used for non-contact detection of metallic objects. The detection range limit of this sensor is about 5mm: therefore, it is placed in the center of the WDU, which is curved to facilitate objects to slide towards the sensor.
- Capacitive Proximity Sensor: such type of sensors are generally used for non-contact detection of both metallic and nonmetallic objects. Here, we used an LJC18A3-H-z/BX sensor placed close to the inductive sensor. The detection range of the sensor is about 10 mm.
- Photoelectric Sensors: three couples of photoelectric emitter/receiver are attached at the two opposite sides of the WDU, in through-beam configuration. The emitters are standard LEDs, while we used BPW21 photodiodes as receivers. Such sensors may be used to detect transparent materials such as plastic or glass.
- Camera: on top of the WDU, a Logitech C920 wide-angle camera is placed at 45 cm from the surface, with an inclination of 30 degrees. The camera is configured to acquire images at a resolution of 320 × 240 pixels.
3.3.2. Automatic Waste Segregation
3.3.3. Fill Levels Engine
3.4. Standard Operating Procedure
- 1.
- Start-up routine: during this phase, the firmware executes a series of checks for all the sensors and actuators, as well as for the wireless connectivity with an external server. If all the checks are passed, the SWB can be started; otherwise, the flap door is locked and all LEDs are turned to red color.
- 2.
- Motors synchronization and calibration: after the start-up, the motors that control the automatic waste segregation need to align with the waste disposal unit to ensure correct disposal of trash. Such regulation allows the motors to set their starting position and subsequently compute the positions (in terms of degrees of rotation) of the four trash bags. For this purpose, the motors perform one complete 360 degrees start-up spin: we use a magnet and a Hall effect sensor to mark the starting position of both the shelf and the semicircular structure, thus calibrating the system.
- 3.
- Fill Levels Engine: after the controlling operations, the SWB verify that it has enough room to store new pieces of trash, using the Fill Levels Engine. The current level of each bag is estimated and transmitted via MQTT to an external server. The topic used for such signalling is smartbin/swb_id/fle/material where swb_id and material are the strings controlling the SWB identifier and the waste material corresponding to the sensed bag. If the levels exceed a specified threshold (75% in our case), the waste management administrator is promptly notified, in order to empty the bin before it can saturate the bag size. Moreover, when one or more bags fill levels reach 100% of the capacity, the bin activation is interrupted, the door is locked, and all LEDs are turned red, waiting for an operator to take action.
- 4.
- Waste insertion: when the SWB is active, a user willing to throw a piece of trash can open the lid of the waste disposal unit, insert an object on the shelf as in Figure 5(1) and, finally, close the lid to activate the classification process.
- 5.
- WSM activation: upon the closure of the WDU, the Smart Waste Bin, thanks to the Time-of-Flight sensors, detects that an object is ready to be analysed. At this point, in order to avoid any interference from outside, the SWB securely closes the lid and activates the waste sensing module, gathering measurements from the sensor as well as taking an image with the installed camera.
- 6.
- Waste Classification and Segregation: the sensed data is passed to the waste classification algorithm (Section 4), which returns, as a result, the estimated type of the piece of trash. Finally, the motors are activated and the object is moved in its correspondent bag.
- 7.
- Release: after the object is disposed correctly, the motors come back to the starting position, the fill levels engine is again activated, and the SWB is ready for another operation.
4. Waste Classification Algorithm
4.1. Dataset
4.2. Waste Classification
4.2.1. Classification from Scalar Data
4.2.2. Classification from Images
4.2.3. Hybrid Classification
- 1.
- Integration at prediction time: a first approach consists of running the two classifiers in parallel and then taking a decision considering the lowest (training) classification error (Figure 10). Let y be the output of the two classifiers, taking qualitative value C = [glass, paper, plastic, metal, unsorted], i.e., the output class. For each classifier, we compute the a posteriori misclassification error probability ), being x the true class. To do this, we use the Bayes’ theorem:As an example, let = plastic and be the output of the sensor-based and image-based classifiers, respectively. Assuming the values contained in Table 3 and Table 4 as the learnt probabilities during training we have:The system will therefore select as final class, since its associated error is lower.
- 2.
- Integration at learning time: A second approach is to train a new classifier, where input features come from all available sensors. To do this, we note that the last layer of the fine-tuned CNN consists of 40 nodes, where each node outputs a value between 0 and 1 that represents the probability that the input image belongs to one of the 40 object classes. We treat such values as new features, which are fed to a regularized logistic regression classifier together with the scalar sensor measurements (Figure 11. The classifier is again trained according to k-fold cross-validation using as ground truth labels the waste materials.
4.3. Waste Classifier Location
- 1.
- Local recognition: first, we run the classifier on the Raspberry PI controlling the SWB. In this case, the SWB does not require any connection to an external server as all decisions are taken locally.
- 2.
- Cloud-based recognition: as a second test, we move the classifier on a cloud-based server hosted on Amazon Web Services EC2, located in Ireland. Data gathered from the WSM is transmitted to a listening process on the server: upon reception, the classifier is run and the response is transmitted back to the SWB. We used SCP to transfer data from the SWB to the server, while the MQTT protocol was used to reply from the server to the SWB.
- 3.
- MEC-based recognition: finally, we move the classifier on a multi-access edge computing server, provided by Vodafone Italia S.p.A, located in the core Vodafone network in Milan and running an Ubuntu Server machine with the same characteristics of the AWS EC2 instance. Access to the MEC is enabled by using the 5G connection through the Huawei 5G CPE router, which allows for a low-latency and high-bandwidth connection.
4.3.1. Recognition Time
4.3.2. Energy Consumption
5. Management Application
5.1. Smart Waste Bin Simulator (SWB-Sim)
5.2. Management Server
- 1.
- Data storage: the server runs an MQTT broker that accepts messages from the smart waste bins. Each module publishes messages on specific MQTT topics: for example, in a scenario with two SWBs named swb1 and swb2, the topic smartbin/swb1/fle/glass is used for publishing messages of the glass bag’s filling levels; while the topic smartbin/swb2/daily is used for communicating the daily usage summary of the recycling bin as a Json file. Upon reception of a message, the server reads its content and saves the received information in a local SQL database.
- 2.
- Data visualization: the server also provides a web-based dashboard for data visualization and monitoring purposes. The dashboard is implemented with Node-RED, a framework built on top of Node.js that has recently become very popular in IoT application development. As shown in Figure 13, the dashboard shows aggregated information for each smart waste bin connected to the system: (i) on the top part, the fill levels for the four materials with their daily correspondent trend represented in a chart; and (ii) in the bottom part, a map summarizing the status of all the SWBs present in an area, with different colours according to the overall fill level of each bin, allowing to easily keep track of the status of the bin from the landfill operators.
- 3.
- TSP for waste collection: The management server also allows to calculate an optimized route for the operator in charge of the waste collection. The task is faced as a Travelling Salesman Problem (TSP). In particular, the goal is to minimise the travelling time starting and finishing at a specific node (e.g., the landfill site) after visiting each other node exactly once. In particular, the nodes are represented by the bins and the weight on the links is the travel time of a specific road. Moreover, to avoid useless stops at an empty recycling bin, the SWB with a filling level lower than 75% of the total are automatically excluded by the TSP problem.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Glass | Coke bottle, Beck’s Beer, Aperol Bottle Heineken Beer, Jar, Red Beer, Water Bottle |
Paper | Business Card, Candy Box, Cup, Flyers Paper Bag, Juice Box, Magazine, Paper Napkins, Newspaper |
Plastic | Blue Bottle, White Bottle, Green Bottle Coffee Capsule Packet, Transparent Glass White Dish, Green Dish, Red Dish Cutlery, Tea bottle, Fiesta Snack, Yogurt Cup, Plastic Bag |
Metal | Aluminium can, Metal Box, Aluminium Foil, Jar Lid |
Unsorted | Backing Paper, Bic Pen, CD, Cigarettes, Lighter, Marker, Receipt |
IS | CS | PS | |
---|---|---|---|
Glass | 0 ± 0.0 | 0.96 ± 0.23 | 14.28 ± 8.9 |
Paper | 0 ± 0.0 | 0.12 ± 0.11 | 0.68 ± 0.6 |
Plastic | 0 ± 0.0 | 0.12 ± 0.12 | 17.00 ± 8.7 |
Metal | 0.93 ± 0.25 | 0.98 ± 0.12 | 4.35 ± 2.3 |
Unsorted | 0 ± 0.0 | 0.18 ± 0.13 | 7.12 ± 3.2 |
Predicted Class | ||||||
---|---|---|---|---|---|---|
True Class | Glass | Paper | Plastic | Metal | Unsorted | |
Glass | 530 (97%) | 6 (1%) | 2 (<1%) | 8 (1%) | ||
Paper | 697 (99%) | 5 (1%) | ||||
Plastic | 1 (1%) | 44 (4%) | 846 (83%) | 123 (12%) | ||
Metal | 2 (1%) | 310 (99%) | ||||
Unsorted | 1 (<1%) | 35 (6%) | 96 (18%) | 414 (76%) |
Predicted Class | ||||||
---|---|---|---|---|---|---|
True Class | Glass | Paper | Plastic | Metal | Unsorted | |
Glass | 486 (89%) | 39 (7%) | 21 (4%) | |||
Paper | 21 (3%) | 653 (93%) | 22 (3%) | 6 (1%) | ||
Plastic | 40 (4%) | 963 (95%) | 11 (1%) | |||
Metal | 3 (1%) | 4 (1%) | 283 (91%) | 22 (7%) | ||
Unsorted | 11 (2%) | 4 (1%) | 6 (1%) | 7 (1%) | 518 (95%) |
Predicted Class | ||||||
---|---|---|---|---|---|---|
True Class | Glass | Paper | Plastic | Metal | Unsorted | |
Glass | 535 (98%) | 3 (<1%) | 2 (<1%) | 0 | 6 (<1%) | |
Paper | 2 (<1%) | 700 (99%) | ||||
Plastic | 7 (1%) | 20 (2%) | 951 (94%) | 36 (3%) | ||
Metal | 2 (<1%) | 310 (99%) | ||||
Unsorted | 11 (2%) | 9 (2%) | 11 (2%) | 12 (2%) | 503 (92%) |
Predicted Class | ||||||
---|---|---|---|---|---|---|
True Class | Glass | Paper | Plastic | Metal | Unsorted | |
Glass | 542 (99%) | 2 (<1%) | 2 (<1%) | |||
Paper | 701 (99%) | 1 (<1%) | ||||
Plastic | 1 (<1%) | 14 (1%) | 963 (95%) | 36 (3%) | ||
Metal | 312 (100%) | |||||
Unsorted | 6 (1%) | 7 (1%) | 13 (3%) | 520 (95%) |
Local | Cloud Server | MEC Server | |
---|---|---|---|
Avg. Data Transfer Time (ms) | - | 343.3 | 191.3 |
Avg. Classification Time (ms) | 3159.2 | 123.9 | 123.9 |
Avg. Total Recognition Time (ms) | 3159.2 | 467.1 | 315.2 |
Local | Cloud Server | MEC Server | |
---|---|---|---|
Bin Energy Consumption (J/object) | 11.69 | 1.28 | 0.93 |
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Longo, E.; Sahin, F.A.; Redondi, A.E.C.; Bolzan, P.; Bianchini, M.; Maffei, S. A 5G-Enabled Smart Waste Management System for University Campus. Sensors 2021, 21, 8278. https://doi.org/10.3390/s21248278
Longo E, Sahin FA, Redondi AEC, Bolzan P, Bianchini M, Maffei S. A 5G-Enabled Smart Waste Management System for University Campus. Sensors. 2021; 21(24):8278. https://doi.org/10.3390/s21248278
Chicago/Turabian StyleLongo, Edoardo, Fatih Alperen Sahin, Alessandro E. C. Redondi, Patrizia Bolzan, Massimo Bianchini, and Stefano Maffei. 2021. "A 5G-Enabled Smart Waste Management System for University Campus" Sensors 21, no. 24: 8278. https://doi.org/10.3390/s21248278