Cloud-Based Waste Management System using Genetic Algorithm
Since the population is increasing day by day, the environment must be neat and clean. In many cities the overﬂowed garbage bins are creating an unhygienic environment. Recently, it is seen that dustbins placed at various places like public places such as hospitals, educational Institutes, and Industries are overﬂowing. This overﬂowing of garbage bins create an unhygienic condition which can spread the diseases. Also, the rapid increase in population waste gives rise to improper waste management. To avoid this situation, we proposed a new system “Smart City Garbage Collection Monitoring System”. In recent decades, Urbanization has increased tremendously. At the same time there is an increase in waste production. Waste management has been a crucial issue to be considered. This project is a way to achieve this good cause. In this project, smart bins are built on a microcontroller-based platform Arduino board which is interfaced with GSM modem and Ultrasonic sensor. The Ultrasonic sensor is placed at the top of the dustbin which will measure the status of the dustbin. Arduino will be programmed in such a way that when the dustbin is being ﬁlled, the remaining height from the threshold height will be displayed. Once the garbage reaches the threshold level ultrasonic sensor will trigger the GSM modem which will continuously alert the required authority until the garbage in the dustbin is squashed. According to the location, the authority will send the message to the respective operator
Why garbage collection management is important?
One of the main concerns with our environment has been solid waste management which in addition to disturbing the balance of the environment also has adverse eﬀects on the health of the society. Considering the need for modern technology the smart garbage bin can expensive but considering the amount of dustbin needed in India, expensive garbage bin would not be a prior experiment that is why we have decided to use based sensors to reduce its cost and also make iteﬃcient in applications. This project work is the implementation of a smart garbage management system using Ultrasonic/Weight sensor, microcontroller, and Communication Module. This system assures the cleaning of dustbins soon when the garbage level reaches its maximum. If the dustbin is not cleaned at a speciﬁc time, then the record is sent to the higher authority who can take appropriate action against the concerned contractor/collector. This system also helps to monitor the fake reports and hence can reduce corruption in the overall management system. This reduces the total number of trips of garbage collection vehicles and hence reduces the overall expenditure associated with the garbage collection. It ultimately helps to keep cleanliness in society. Therefore, the intelligent garbage management system makes the garbage collection more eﬃcient. Such systems are vulnerable to the plundering of components in the system in diﬀerent ways that need to be worked on.
Problem Deﬁnition :
In the proposed work to design an architecture that works on optimization algorithms for Smart City management and more speciﬁcally this system deals with municipal waste collection procedure. consider existing IoT infrastructure and sensor networks for proposed execution. We need to achieve all the parameters using the IoT environment.
- Implement a waste collection system for smart cities.
- Successfully implementation of Genetic Algorithm (GA) with proposed simulation and ﬁnd the best path for collection.
- Recommend the feasibility of this system for real-time implementation for smart cities.
- Compare the system results for existing systems.
- Reduce the execution time with real-time parameters (e.g. capacity of garbage, current ﬁll ration, etc.)
What concepts we are using :
Internet of Things
Database management system
System Architecture :
The system architecture which works on optimization algorithms for Smart City administration also more especially this method deals with the public waste gathering method. Each container has a different storage space capacity, base on that we randomly ﬁll the containers. In second phase before ﬁnding the vehicle root, we ﬁrst collect all the readings of every container of ﬁlling ratio, and give input to the genetic algorithm. In the third phase Genetic will execute all the input population and once GA will terminate it will ﬁnd the vehicle root base on container ﬁlling probability. We give around 4 to 5 parameters of every container as chromosomes, like container id, Location, capacity, current ﬁlling ratio, etc
About Genetic Algorithm and its working principle:
The workability of genetic algorithms (GAs) is based on Darwinian’s theory of survival of the fittest. Genetic algorithms (GAs) may contain a chromosome, a gene, a set of population, fitness, fitness function, breeding, mutation, and selection. Genetic algorithms (GAs) begin with a set of solutions represented by chromosomes, called population. Solutions from one population are taken and used to form a new population, which is motivated by the possibility that the new population will be better than the old one. Further, solutions are selected according to their fitness to form new solutions, that is, offsprings. The above process is repeated until some condition is satisfied. Algorithmically, the basic genetic algorithm (GAs) is outlined below:
Step I : [Start] Generate a random population of chromosomes, that is, suitable solutions for the problem.
Step II :[Fitness] Evaluate the fitness of each chromosome in the population.
Step III :[New population] Create a new population by repeating the following steps until the new population is complete. a) [Selection] Select two parent chromosomes from a population according to their fitness. Better the fitness, the bigger chance to be selected to be the parent. b) [Crossover] With a crossover probability, cross over the parents to form new offspring, that is, children. If no crossover was performed, offspring is the exact copy of parents. c) [Mutation] With a mutation probability, mutate new offspring at each locus. d) [Accepting] Place new offspring in the new population.
Step IV :[Replace] Use the newly generated population for a further run of the algorithm.
Step V :[Test] If the end condition is satisfied, stop, and return the best solution in the current population.
Step VI: [Loop] Go to step 2. The genetic algorithm’s performance is largely influenced by crossover and mutation operators
User Interface :
Web Application: The main objective of a web application is to provide Registration and Login facilities. Web application shows information about dustbins like dustbin id, local area, latitude, longitude, weight, etc. We can also add a new dustbin in the web application.
Android Application: Android app will be used to set up the page layout and add minimal styling to make the interface user friendly. Android app will show an optimized and the best path to the vehicle depending upon storage capacity for the collection of dustbins.
Monitoring the fullness of bins during the utilization of sensors, it is probable to obtain a more eﬃcient system than the currently existing. Our plan of Smart waste administration system mostly concentrates on Monitoring the waste administration, given a smart technology used for waste system, avoiding human interference, tumbling human time as well as eﬀort also which outcome in healthy and waste ridden surroundings.
The scope of this application is limited as a project for BE students. All information and images provided in this application/article are represented as per our knowledge and information gathering.