Intergrated Sensing Systems

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INTEGRATED SENSING SYSTEMS ANDALGORITHMS FOR SOLID WASTE BINSTATE MANAGEMENT AUTOMATION

Department of Electronics and Instrumentation Engineering

Faculty of Engineering and Technology

SRM University

By

SMITA KUMARI(1171210258)

JIDEV RANJITH(1171210252)

NITISH DAS(1171210240)

ANUBHAV SAJEEV(1171210231)

Under the guidance of (MRS.INDIRANI)


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ABSTRACT

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Intelligent solid waste bin is essential to develop anefficient and dynamic waste management system. Thisresearch presents the implementation and execution ofan integrated sensing system and algorithm for solidwaste bin to automate the solid waste managementprocess. Several sensing methods have beenintegrated and have combined their verdicts that offerthe detection of bin condition and its parameter

measurement. A number of test runs have beenconducted to assess the functioning of the prototypesystem. The outcomes showed that the sensing systemwith the algorithm is efficient and intelligent and canbe simply used to automate any solid waste binmanagement process.


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*AN INTELLIGENT solid waste bin operates to ensure theefficient measurement of its status while consumingminimum energy. At present, major cities around theworld require challenging solutions for solid waste

management (SWM), as a result of growth inresidential areas and the economy.

*SWM is a costly urban service that consumes around20% - 50% of municipality’s annual budget indeveloping countries. Furthermore, 85% of solid wastemanagement funds are spent on waste collection andtransportation [1]. It becomes an excessive wastage ofresources when bins are collected that are filled up

partially.

INTRODUCTION

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*In waste collection and carrying activities, theoperational cost can be reduced by optimizing thequantity and deployment of collection bins and their

collection rate.

*Estimating the status with waste level and weight ofwaste inside bins help to optimize collection routes andimprove collection efficiency.

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HARDWARE =>Raspberry Pi

IOT and LCD Display

Weight ,Gas and Level Sensors

SOFTWARE => Linux and Python

REQUIREMENTS

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*A SWM system having static scheduling and routing tocollect waste demands more operating costs, longerhauling distances and increased labor hours comparedto a system with dynamic scheduling and routing

attitude [5]–[7]. In [5] and [6], the authors calculateda potential cost savings of 10-20% and transportmileage savings of 26% when dynamic scheduling androuting were used.

*For a truly dynamic and automaticsystem, it isimportant to know the current and actual fill level of abin rather than a prediction relays on historical fill leveldata.

LITERATURE SURVEY

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*So, to implement a SWM system with dynamicscheduling and routing for waste collection, it is veryuseful and important to get real time data about the binstatus.

*Several researches have been done over the last fewdecades concerning solid waste monitoring andmanagement. But a few of them dealt with real timebin status data with a motive to implement dynamic

scheduling and routing approach for an automatic solidwaste management system.

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SMART BIN DIAGRAM

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BLOCK DIAGRAM


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Lid Status Sensing-The functional structure of the lid status sensingsystem is implemented for tracking the initialization of waste loadingand unloading event and perceiving the overflow status of the bin asshown in Fig. 1(a). Accelerometer sensor data are accumulated to

provide the drift and its direction to identify the opening/closing ofthe lid. The magnetic proximity sensor reports whether the lid closedproperly or not by using a reed switch and a permanent magnet. Theswitch can change it’s state due to magnetization or biasing causedby the magnet when a conductor attached in the lid enters into themagnetic field mounted in the upper edge of the bin.

Waste Filling Level Sensing-The sensing of waste filling level insidea bin is based on the measurement of the time-of-flight i.e. thecomplete return trip time, an ultrasonic pulse takes to transmit andreceive its reflected echo between the sensor and the sensedmaterial level.

THE SENSING ELEMENTS

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Weight Sensing-The weight estimation of the waste inside a bin

is based on the principle of an electrical conductor whose

resistance changes when its length changes due to stress and it

is virtually proportional to the applied strain as shown in Fig. 1(b).

A Wheatstone Bridge Network is built by using at least four strain

gauges with four separate resistors. Waste inside the bin causes

a variation in value of one or more resistors due to the generated

strain from the metallic member that contains the strain gauges.

Thus, the bridge output voltage is changed with this variation inresistance that is proportional to the weight of the waste


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IOT-INTERNET OF THINGS

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The Internet of Things (IoT) is the network of physical objects—devices,

vehicles, buildings and other items which are embedded withelectronics,

software, sensors, and network connectivity, which enables these objects to

collect and exchange data.The Internet of Things allows objects to be sensed

and controlled remotely across existing networ k infrastructure,creatingopportunities for more direct integration of the physical world into computer-

based systems, and resulting in improved efficiency, accuracy and economic

benefit when IoT is augmented with sensors and actuators, the technology

becomes an instance of the more general class of cyber-physical systems,

which also encompasses technologies such as smart grids, smart homes,

intelligent transportation and smart cities. Each thing is uniquely identifiablethrough its embedded computing system but is able to interoperate within the

existing Internet infrastructure.

IOT-INTERNET OF THINGS

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https://en.wikipedia.org/wiki/Internethttps://en.wikipedia.org/wiki/Internethttps://en.wikipedia.org/wiki/Smart_gridhttps://en.wikipedia.org/wiki/Smart_homehttps://en.wikipedia.org/wiki/Cyber-physical_systemhttps://en.wikipedia.org/wiki/Embedded_systemhttps://en.wikipedia.org/wiki/Embedded_systemhttps://en.wikipedia.org/wiki/Electronicshttps://en.wikipedia.org/wiki/Internethttps://en.wikipedia.org/wiki/Intelligent_transportationhttps://en.wikipedia.org/wiki/Smart_citieshttps://en.wikipedia.org/wiki/Intelligent_transportationhttps://en.wikipedia.org/wiki/Smart_homehttps://en.wikipedia.org/wiki/Smart_gridhttps://en.wikipedia.org/wiki/Cyber-physical_systemhttps://en.wikipedia.org/wiki/Softwarehttps://en.wikipedia.org/wiki/Internet_accesshttps://en.wikipedia.org/wiki/Sensorhttps://en.wikipedia.org/wiki/Softwarehttps://en.wikipedia.org/wiki/Electronicshttps://en.wikipedia.org/wiki/Embedded_system


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SMART BIN PROTOTYPE

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TABLE FOR LITERATURE SURVEY

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GRAPH

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Sl.No Activities % of

Contribution

Start Date End Date Status

1 Literature Survey 5 15 oct 15 dec Done

2

Productdesigning

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4.01.16 14.1.16 Done

3 Hardware

assembling

25 11.2.16 26.2.16 Ongoing

4 Software testing 25 1.03.16 25.3.16 Pending

5 Hardware

caliberation and

testing

30 27.3.16 15.4.16 Pending

WORK PLAN


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Accelerometer Sensor -> 1055/-

Weight Sensor -> 1599/-

Level Sensor -> 1299/-

Raspberry Pi -> 3599/-

IOT -> 2500 - 4000/-

Total -> 9000/- to 11,000/- (approx.)

BUDGET

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REFERENECES

[1] P. H. Brunner and J. Fellner, “Setting priorities for wastemanagement strategies in developing countries,” Waste Manage.Res., vol. 25, no. 3, pp. 234–240, Jun. 2007.

[2] T. Kulcar, “Optimizing solid waste collection in Brussels,” Eur.J. Oper. Res., vol. 90, no. 1, pp. 71–77, 1996.

[3] M. Faccio, A. Persona, and G. Zanin, “Waste collection multiobjective model with real time traceability data,” Waste Manage.,vol. 31, no. 12, pp. 2391–2405, 2011.

[4] L. A. Guerrero, G. Maas, and W. Hogland, “Solid wastemanagement challenges for cities in developing countries,” WasteManage., vol. 33, no. 1, pp. 220–232, 2013.

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THANK YOU

Intergrated Sensing Systems

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