Abstract what method can be followed to overcome this.

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 care and

attention 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 beauty of 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.

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

 

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 formidable challenge faced by the public
administrations.
IoT is a network of sensors where data is
exchanged, using different connectivity protocols, with systems.
 Waste is defined as any material in which something valuable is not
being used or is not usable and represents no economic value to its owner, the
waste generator. Depending on the physical state of the waste, they are
categorized as solid waste and wet waste. With the proliferation of population,
the scenario of cleanliness with respect to waste management has become
crucial. Waste management includes planning, collection, transport, treatment,
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. Literature
Survey

 

The garbage management
in cities has to be effectively and efficiently implemented. The various
proposals were put forward and some of them  are already implemented. But  we cannot considered it as an effective one.
So a survey was done among different proposals and this survey paper includes
survey among different methods for smart garbage management in cities using
IoT.  This section discusses
about the existing approaches in the field of smart waste management.

Insung Hong et.al 1 has suggested that
replacing SGS(Smart Garbage Sensor) instead of RFID garbage collecting system
helps to improve their energy efficiency up to 16% and can reduce the food
waste reduction .Inside the SGS they have installed SGBs (Smart Garbage Bins)
to control the energy efficiency of the system.

Dario Bonino et.al 2 has suggested that it
provides end – to – end security and privacy that is built upon dynamic
federation smart city platform. Its benefits is that it has good dependability
and has resilience on failure of a system over a particular month. It focuses
on the collection of wastages and accomplishment of ontology method.

A lvaro Lozano Murciegoet.al 3 has
suggested that to collect the dustbins that are been filled using a truck. The main
advantage is that it reduces the fuel cost of the trucks rather than travelling
a long distance it makes the path more simpler and easier to reach the dustbin
using route optimization.

TheodorosAnagnostopoulos et.al 4 has
suggested that it first starts with an assumption that the smart city must
include the IoT base. It uses dynamic scheduling. It is based on the fact that
the garbage will be collected only when it is fully filled or the maximum
capacities of the dustbins are filled.

Rachael E. Marshall et.al 5 outlines that
the smart waste management system in the high salaried countries and an
developing countries.

Lilliana Abarca Guerrero et.al 6 outlines
the fact that the developing countries undergo an prominent factor of affecting
the waste management systems due to rising population levels and rapidly
growing urbanization. The collaborator of the waste management are many such as
household, industry sectors, educational and research intuitions etc produces
huge number of wastages. Collecting, transferring, Transportation of the wastages
and they are finally disposed in an open land.

Ala Al – Fuqaha et.al 7 proposed that
sketch of the IoT with a stress on technology, application and protocol
concern. It explains about the differences between IoT and developing
technologies like cloud computing and data analytics.

Jose M. Gutierrez et.al 8 proposed the
functional smart city and the use of an smart waste management .It uses IoT for
sensing the wastage level in the dustbins, processes the data and sends it to
the server for storing and process the data. The process is carried out by the
Geographical Information system.

Vikrant Bhor et.al 9 has suggested that
when the system ensures that the garbage bins are fully filled up to their
maximum it must be cleaned using IR sensor, GSM mode and microcontroller. When
it is not filled it must be reported to the higher authority of a particular contractor.
It concludes that it has a clean environment and it decreases the total number
of trips the garbage collector vehicle rounds.

Fachmin Folianto et.al 10 has suggested
that it uses mesh network. It is used to produce data and deliver it to the
mesh network. Whenever the bins are filled they need to be cleaned. The bin
collector gives the route to collect the bins.

In 11-14, the routing protocols and the
failure detection in sensor nodes are discussed.

 

 

2.1 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. The
large amount of variables was the reason for large computation time. The recent
research works use mostly the heuristic solutions and methods dealing with the
municipal waste collection 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.

 

2.2 CHALLENGES

 

2.2.1 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. Batteries providing power
need to be changed or recharged once they have been consumed. Sometimes it
becomes powerful to recharge or change the batteries as a result of demographic
conditions. The most

critical research challenge for the WSN
analysts is to design,

develop and implement energy adequate hardware
and software protocols for WSNs.

software
protocols.

 

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

 

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 ought to keep functional 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 tiny
device with only a tiny 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.

 

2.2.2 ISSUES

The given below are the list of issues that were represented in the
previous papers.

· Requires
a lot of maintenance price.

· Excess
discharge of garbage within the public areas creates a fragile condition for
the

folks of close to by areas.

· It accomplishes
completely different technologies like Java, relational database…etc.

· The
bins are clean only if it’s totally stuffed.

· During
special days the bins are quickly stuffed and that we ought to increase the
gathering

time periods.

· It is employed to browse, collect, transfer information over the net.

· While not web it’s
impractical. High fuel price once it involves the quantity

Of the gap lined by the bin collector.

· It includes differing
kinds of stake holders with completely different concentration on their
interest.

 

2.2.3 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.

 

2.2.4
SOLUTIONS

 

·   Data
Freshness – Even if knowledge Confidentiality and Data Integrity is
assured, there is a desire to make sure the freshness of every message.
Informally, knowledge freshness suggests that the data is recent, and it
ensures that no old messages have been replayed. To solve this problem another
time – related counter, can be other into the packet to make sure knowledge freshness.

·  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 – Like other ancient networks, the sensor networks have
conjointly force privacy issues. Initially the sensing element networks area
unit deployed for legitimate purpose may later on be used in out of the blue
ways that. Providing awareness of the presence of sensor nodes and knowledge
acquisition is notably vital.

·   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

 

 

2.2.5 PATH
OPTIMIZATION TECHNIQUES

 

The route planning and optimization is a well-researched area and hundreds
of intelligent transport systems have been developed already. There are many
projects aiming to provide an effective system for waste collection purposes.
In 7 an advanced routing and scheduling waste collection model is proposed in
eastern Finland, in which they use the guided variable neighbourhood
thresholding Meta heuristic. A geographical information system transportation
model for the solid waste collection and disposal is another technique. A truck
scheduling model for the solid waste collection has been proposed by the city
of Porto Alegre in Brazil. The aim of the research was to develop an optimal schedule
for the trucks on defined collection routes. In 8 a novel cloud based approach
is being employed. On board surveillance cameras for problem reporting with a
cloud DSS system and dynamic routing models are used and this significantly
increases the cost effectiveness, which is one of the important criteria of the
mart cities. A method is proposed to use the operations research techniques to
optimize the routes of waste collection vehicles servicing dumpster or skip type
container. Here the waste collection problem is reduced to classic TSP then to Concorde
solve the problem. The system aims to minimize the distance travelled and
thereby helps in the reduction of vehicle wear. A new method for optimizing the
waste collection routes is developed based on OSGeo software tools.

 

Table
-1: Path
Optimization Techniques

 

Path optimization Techniques

1.  ArcGIS Network Analyst and Ant Colony

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

2. MapInfo

It is 
GIS software used for finding shortest path

3.
OSGeo software tool

Route planning and optimization software.

 

 

 

3.1     ALGORITHMS USED

 Algorithms used in previous
papers for research work was done.

 

3.1.1    XML Parsing used for graph processing –

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

 

3.1.2    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. SYSTEM ARCHITECTURE

Fig -1: System architecture

 

This is the proposed
design of the system which is to be implemented. In which the system will
perform operation and a proper route will be decided for the garbage truck to
follow which will reduce fuel consumption and multiple times following same
routes will be avoided. After research done in this looking the results in
which algorithms used where less effective so new algorithm will be implemented
which will gives good results and routes will be defined accordingly.

 

 

3. 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.

 

 

4.
REFERENCES

 

1     InsungHong, SunghoiPark,
BeomseokLee, JaekeunLee, Da ebeomJeong, and SehyunPark, “IoT-Based Smart
Garbage System for Efficient Food Waste management” -Scientific World
Journal-Aug 2014.

2     Ala Al – Fuqaha, Mohsen Guizani, Mehdi Mohammadi,
Mohammed Aledhari, Moussa Ayyash, “Internet of Things: A Survey on Enabling
Technologies, Protocols and Applications” –IEEE – 2015.

3     TheodorosAnagnostopoulos ,ArkadyZaslavsky, Alexey
Medvedev , “IRobust Waste Collection exploiting Cost Efficiency of loT
potentiality in Smart Cities” – IEEE – April-2015.

4     Radek Fujdiak, Pavel Masek,
Petr Mlynek, Jiri Misurec, “Using Genetic Algorithm for Advanced Municipal
Waste Collection Management in Smart City”, 2016.

5     Vikrant Bhor1, Pankaj
Morajkar2, Maheshwar Gurav3,

Dishant Pandya4, Amol
Deshpande, “Smart Garbage

Management System” – March
2015.

6     Dario Bonion, Maria
Teresa Delgado Alizo, Alexandre

Alapetite, Thomas
Gilbert, MathaisAxling, HelenUdsen,

Jose Angel Carvajalsoto,
Maurizio Spirito,

“ALMANAC: Internet Of
Things for Smart Cities”  IEEE 2015.

7   FachminFolianto, Yong Sheng Low,Wai Leong
Yeow,

       “Smart
bin: Smart Waste Management System”

       
IEEE – April 2015.

8   KristýnaRybová, Jan Slavík, “Smart cities and
ageing

Population – Implications
for waste management in            the Czech Republic ” – IEEE 2016.

 

9   Jose M. Gutierreza,
Michael Jensenb, Morten Heniusa

      and
Tahir Riazc, “Smart Waste Collection System Based

      on
Location Intelligence” – 2015.

10 Álvaro Lozano Murciego,
Gabriel Villarrubia González,

Alberto LópezBarriuso,
Daniel Hernández de La Iglesia,      Jorge
Revuelta Herrero and Juan Francisco De Paz

Santana, “Smart Waste
Collection Platform Based on

WSN and Route
Optimization ” – 2016.

11 Clarabellejoanna
,Sathiyavathi.R, “Quota based routing

protocol in disruption
tolerant networks”, in International conference on information communication embedded
systems (icices2014)” , Isbn no.978-1-4799-3834-6/14©2014.