Abstract system, where the garbage is collected from the

Abstract
– At present waste
management is a major concern in the metropolitan cities of the developing and
developed countries. As the population is growing, the garbage is also
increasing day by day. Garbage management is
becoming a global problem. Due to the lack of attention took by the authorities
the garbage bins are mostly seem to be overflowing. It has to be taken into
care by corresponding authorities and should think what method can be followed
to overcome this. This
huge unmanaged accumulation of garbage is polluting the environment, spoiling
the area and also leading to the health hazard. To overcome this situation an
efficient smart municipal waste management system has to be developed. In this
era of Internet, Internet of Things (IOT) can be used effectively to manage
this waste as many effective methods can be found out easily. This is the
survey paper which involves the various ideas to solve this problem using some
algorithms that can be easily implemented.

 

 

Key Words: Internet of things (IOT), Smart
Garbage collection.

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1. INTRODUCTION

 

Now-a- days
smart cities represents hot topic in terms of improving living conditions. As one of the application of Smart City, Waste Management
in a city is a big challenge faced by the public administrations. IoT
is a network of sensors where data is exchanged, using different protocols,
within systems.  Waste is defined as any material in which somewhat valuable is not being
used or is not usable and represents no economic value to its owner, the waste
generator. Depending on the state of the waste, they are categorized into dry
waste and wet waste. Waste management includes planning, collection, transport,
recycle and disposal of waste together with monitoring and regulation. The
existing waste management system, where the garbage is collected from the
streets, houses and other establishments on quotidian basis, is not able to
effectively manage the waste generated. Our work focuses on the
optimization algorithms for Smart City management and more specifically this
paper deals with municipal waste collection procedure. Nowadays,
the garbage-truck needs to pick-up all garbage cans even if they are empty. To
avoid such challenges faced we are proposing a system where efficient routes
are defined shortest route to collect the garbage filled bins.

 

2. RECENT RESEARCH IN MUNICIPAL WASTE COLLECTION
OPTIMIZATION

The constant growth of
population urban areas brings increasing municipal solid waste generation with
socio-economic and environmental impact. Municipal solid waste management –
source separation, storage, collection, transfer and transportation, processing
and recovery, and last but not least, disposal, are today current city
challenges. The mathematical programming and processes have been already used
for optimizing the municipal waste management and transfer system. The waste
collection and garbage-truck allocation problem could be solved by traditional
mathematical methods such a linear methods. However, the linear methods show
insufficient efficiency in some more difficult cases of waste collection. Large
amount of variables was the reason for large and hard computation time. The
recent research works use mostly the heuristic solutions and methods dealing
with the municipal waste management as with a Travelling Salesman Problem
(TSP). Dealing with problem formulation, the effectiveness of optimization and
computation is based on input parameters and specific problem implementation. Only
few works tried to use evolutionary algorithm to deal with implementation and
optimization of waste collection problem as the TSP defines. These works use
Ant Colony algorithm. However, the genetic algorithm was also proven as a very effective
tool to deal with TSP of various implementations, but not in the specific
implementation of waste collection 4.

 

2.1 CHALLENGES

 

Challenges
faced while working with wireless sensor networks (WSN)

1.
Energy – Sensors require power for various operations.
Energy is consumed in data collection, data method, and

data communication.

 

2. Self-management – Once
when WSN are deployed it should be capable of working without help of human intervention.

 

3. Security – Confidentiality
is required while data transmission otherwise there is possibility of
eavesdropping attack.

 

4.
Quality of Service – Quality of service is the level of service
provided by the sensor networks to its users. WSN are being used in various
real time applications, so it is mandatory for the network providers to offer sensible
QoS.

 

5. Fault
Tolerance – Sensor network should be able to work even if any node fails whereas the
Network is operational. Network should be in a position to adapt by changing its
property in case of any difficulty.

 

6.
Limited Memory and Storage Space – A sensor is a small
device with low quantity of memory and storage space for the code. In order to
make an effective security mechanism, it is necessary to limit the code size of
the security algorithm.

 

2.2
SOLUTIONS

 

 ·   Data
Freshness – There should be new 
messages even if confidentiality is assured.

·  Secure
Localization – Often, the utility of a device network can trust on its
ability to accurately mechanically find every sensor within the network. A sensor
network designed to find fault scan would like correct location data in order to
purpose the placement of a fault.

· Privacy – The sensor networks have conjointly force privacy issues.
Privacy play an important role.

·   Secure
routing –  Routing and data
forwarding is a crucial service for facultative communication in device
networks.

·  Data
Availability – Availability resolves whether or not a node has the capacity
to use the resources and whether or not the network is obtainable for the
messages to speak. However, failure of the base station or cluster leader’s
availability can eventually threaten the complete sensing element network.

Thus availableness is of primary importance
for maintaining associate degree operational network

 

 

2.3 PATH
OPTIMIZATION TECHNIQUES

 

Optimization
and route planning is a well-researched area and many of the transport systems
have been developed before. There are many projects which provides effective
system for waste management. One of the 
advanced routing model proposed in eastern Finland, they used guided
variable neighbourhood thresholding meta heuristic approach. Garbage truck
scheduling model for solid waste management has been proposed by the city of
Porto Alegre in Brazil. In one of the paper novel cloud based approach is
employed. A new method for optimizing the waste collection routes is developed based
on OSGeo software tools. Some of the path optimization
techniques has been used there are as follows:

 

 

Table
-1: Path
Optimization Techniques

 

Path optimization Techniques

1.  ArcGIS Network Analyst and Ant Colony

Based
on Geo referential spatial
Database.
Facilitate modelling of realistic traffic condition and different scenarios.
 

2. MapInfo

It is 
GIS software used for finding shortest path

3.
OS Geo software tool

Route planning and optimization software.

 

 

 

 

3. DIFFERENT APPROACHES  AND ALGORITHMS

 

 

Fig -1: System Overview

 

There are some different
approaches in paper 9 the proposed system was based on waste data level of
garbage bins in metropolitan areas. The data was sent over the internet for
analyzing and processing. Everyday new data was collected and on that basis the
rate of waste level was calculated so as to predict the overflow of bins
before. Fig 1. Gives the overview of this approach.

 

Algorithms
used in previous papers for research work was done.

 

3.1    XML Parsing used for graph processing –

The XML parsing is used for
the graph (SVG) processing. After XML parsing.

 

3.2    Floyd- Warshall algorithm

 

 The Floyd- Warshall
algorithm is applied to distance recalculation. This algorithm was chosen due
to the fact that we are using metric system and there the negative values of
edges are not used. The algorithm (Floyd-Warshall) also computes straight the
vertices distance, which is less time consuming than i.e. Dijkstra Algorithm
(which computes distances always for each vertex).

 

4. PROPOSED
APPLICATIONS

 

1. Waste Level detection inside the garbage
bins.

Transmission of the information wirelessly to

concerned officials.

2. System can be accessed anytime and from

anywhere.

3. Real-time data transmission and access.

4. Avoids the overflows of garbage bins.

5. This project can only be used by municipal

authorities or other private firms to tackle the

current problem of urban waste collection.

6. This system has no individual use, but can be
used

by a city, state or a country.

7. Using this system, waste collection would
become

efficient and also reduction in transportation
costs

can be witnessed.

 

 

5. CONCLUSIONS

 

 This survey has been performed for collecting
the details of smart garbage management methods and to find out effective
methods which are useful for providing hygiene environment in cities. Our
solution is based on the idea of IoT infrastructure, which should provide
enough information to handle this Smart City issue more efficiently.

 

 

6.
REFERENCES

 

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3     TheodorosAnagnostopoulos ,ArkadyZaslavsky, Alexey
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4     Radek Fujdiak, Pavel Masek,
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Dishant Pandya4, Amol
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