In the recent years, population had grown tremendously. As the population keeps on increasing, the amount of wastes generated also increased. The vital issue of waste management to be addressed earlier. Our main concern is to develop a smart bin that will detect the level of the garbage in the bin and the remedy action to clear the bin by sending alert to the Municipal officer by using IoT devices.
In most of the cities, the overflowing of bin causes a bad odor which pollutes the environment and causes several diseases.
IoT components and devices and its performance metrics also compared in this review.
This paper provides a review about the performance analysis of Smart Bin Monitoring System and improvement measures to increase the performance and garbage detecting accuracy. And also reviewed various algorithms used to send notification and update the server. Through this we have provided clean and odorless environment that will safeguard from various diseases.
Waste management is a set of activities that are required to manage the waste from its inception to the disposal of the thrash.
India generates about 62 Million tons of waste per year (according to PIB 2016) and the annual growth rate is 4%. But the proper disposal of the garbage and the periodic removal of the thrash before it overflows is a biggest issue.
Irregular cleaning of the bin causes several issues starting from foul smell to the production of toxic gases which may cause air pollution and diseases. Developing the smart bin causes improper results if appropriate sensor is not used.
Also the power supply for the sensor is a major issue since the sensors need power supply to work. Improper information transfer can also cause bad results which results in poor maintenance.
Internet of Things is the collection of intelligent electronic devices that exchanges the data over the internet. The IoT is mainly used to achieve automation and reducing the human effort. The IoT is growing rapidly as people tend to move to a more modern lifestyle.
It makes use of microcontrollers such as Arduino boards, Raspberry Pi etc along with various sensors such as LDR sensor, Proximity sensor etc. and several actuators to perform the desired operatins and exchange data over the internet. The data from the IoT devices can be stored in the cloud and can be used to predict the unknown future events form the list of available data.Route optimization algorithm to compute the shortest path to collect garbage from all the bins .
The researchers had made a detailed analytics in finding the peak hours of the garbage collected in the bin. GPS tracker is used to share the location of the bin once it reaches the threshold limit. It consists of Ultrasonic sensor which detects the level of the bin. The data collected from the sensors and stored in the form of .csv file.
Then a detailed analytics is made using linear regression Machine learning algorithm to find the peak hours of the thrash-can gets filled on a particular day . The power source is the main issue as it is needed for the operation of all the devices.
Wi-Fi module is used to send notification to the concerned officials about the bin which is filled or not. Once the level of garbage bin reaches the threshold value, the data about the bin is sent to the server using Wi-fi module (ESP – 8286) and the notification about the bin is received by the concerned official’s android mobile device.
The android device is used to stores the data about the bin in the form of notification. This cycle is carried out when each of the bins gets filled . No future prediction is involved as it is needed to plan further actions.
Fig.1 Circuit of the garbage bin .
Here the power supply is given separately to the sensor and the Microcontroller unit (Fig.1). Once the bin becomes full, the data gets collected in the data server and the data is stored in the mobile application. Once the disposal van starts collecting, the nearest bin that is full is notified to the concerned person.
Web camera is used to continuously capture the images or videos of the bin and share over the network that is used to detect the level. Load sensor is used to detect the weight and give output in the form of voltage. Microcontroller is used to give the sensor data as input to the Arduino program and compare it to the threshold limit.
GSM module sends the message to the garbage depot if the input level exceeds the threshold limit. DC motor is needed to drive the application as it is needed in the software. DC motor driver (L293D) is needed to drive the DC motor. The level of each bin is monitored using real time monitoring tool.
Once the garbage van leaves the station, the detail about the completely filled and the almost filled bin is sent along with their location and the garbage collector collects them . Volume of wastes of each category is not known and hence further planning becomes difficult.
Fig.2 Component used in Garbage bin
In the fig.2, the DC motor drives the circuit. The level is monitored continuously with the help of sensor and is stored dynamically in the server. Once the bin is threshold limit, GSM module send SMS to the Municipal officer and the bin data gets updated in the server. It is then used to find the shortest route covering almost all the bin from the starting point.
Here the depth of the bin is collected from the ultrasonic sensor. The threshold limit is when the system is programmed first when the system is developed. Once the bin reaches the threshold limit, alarm and LED is triggered till the bin is emptied.
The information is sent to the cloud along with the date and the time in which the bin is emptied. Here the count is set to 5 as threshold value and note the number of times the bin getting filled. This helps the higher officials to have a note on how properly the system is implemented . Real time analysis is not done which lacks the use of modern systems.
A new model has been proposed this is used to intimate to the municipality office about the immediate cleaning of the dustbin. Here the garbage is compressed inside the bin thus reducing the space occupied by the lightweight particles. A switch is pressed by the garbage in a way, when it reaches the certain limit, the garbage is compressed.
When there is no more space to compress, the municipal office is intimated about the bin through a central hub data in the form of glowing LED . When the garbage bin gets dumped for a longer duration of time, the odor increases which causes smelling environment.
Each bin has an ultrasonic sensor fitted inside the thrash can which is used to monitor the level. The bin is divided into three levels based on the amount of garbage collected in it. The level gets updated continuously with time with the help of real time monitoring system.
Each time when the garbage exceeds the desired level, the data is sent to the server and stored in the cloud. The api shows the real time level to the garbage analyzer and using that it directs the garbage collector to avoid overflow.
The data collected in months are stored in the server and used to predict when the garbage bin gets filled for the next time . The time between the previous collection of the garbage and the next collection should be done in order to check whether effectively the bin is monitored.
Here the level of the garbage that is filled is shown as a graphical view in a web page. This monitors the garbage bins and informs the level of garbage collected via the web page. The web page gives a graphical view of the garbage and highlights the garbage collected in color to show the level of the bin.
The sensors such as IR and ultrasonic sensors are used to detect the levels. IR is mostly used as a proximity sensor and ultrasonic sensor is used to detect the levels ultrasonic sonic sensor can have the measurement accuracy up to 3mm .
IR sensor is used to detect the level of the bin. Power supply is used to operate the circuit. Once the bin reaches the threshold limit, the concerned officer is notified about the level. Arduino board is connected to the microcontroller.
Arduino UNO transfers the data from the sensor to the Wi-fi module. Then using the Wi-fi module, the data about the garbage bin is transferred to the android device of the concerned officer .IR sensor when exposed to external light source such as sunlight may cause improper results.
Performance Analysis of various Sensors used:
Performance analysis of different microcontrollers used:
Characters Arduino Uno Raspberry Pi
This machine learning algorithm is used to predict the peak hours in which the bin gets filled. It uses the list of available data and is used to calculate the statistics, as per the values which are previously derived using Mean, Standard Deviation etc.
Route optimization algorithm:
for i=1 to n do
if Li==3 then
for k=1 to n do
if k!=I then
qk = Tk+(3-Tk)ak1
pk = qk -Ti
if pk0 then
endfor b=0 to length(f(b)) do| Action = create optimized route in the map from the smart-bin ID in f(b) array
if prk>65 then
//The threshold percentage is considered as 65
It is more similar to Dijikstra’s algorithm. It has been used to calculate the shortest path when traversing all the filled bins. It makes use of the graph concept in data structures and calculates the way from source to destination with the more optimal distance  .
Conclusion and Future work:
Based on all the papers surveyed above, we can see that the circuit can inform the official but does not continuously monitor the level. The existing system does not predict the number of days in which the bin gets filled. So it may result in higher transport cost.
And the existing system needs the external power source so requires periodic replacement of battery. We aimed to overcome all the issue in our project. We use solar panel to give the power supply to the circuit which is a renewable power supply.
Also we use prediction to predict the number of days in which the garbage bin gets filled for the next time. This reduces the transportation cost as the bins which are going to be filled in shorter period of time are also collected.
The circuit for garbage bin monitoring system has been developed using renewable source of energy (say Solar Energy). Then continuous monitoring has been done and the peak hours for which the garbage collected is noted.
Then using the list of available data, time needed to fill the garbage bin is predicted using analytics and informed to the concerned officer about the bin data. Then using the image classification algorithm such as Support Vector Machine (SVM), the waste is identified by its type and calculate the total amount of waste collected in each category.