Rahul Gope Abhishek Ekka Yujata IIT-Bombay IIT-Bombay NIT-Jalandhar iWaste ‘Towards a Smart- Cost Effective Techno-managerial Solution’ Rahul Gope Abhishek Ekka Yujata IIT-Bombay IIT-Bombay NIT-Jalandhar

SUMMARY PROBLEM STATEMENT PROPOSED SOLUTION IMPLEMENTATION IMPACT Inadequate Household Waste Management High Quantity of Waste Generated Lack of ICT interventions to handle waste being generated PROPOSED SOLUTION iWaste-Techno-managerial Model Compatible with existing infrastructure Optimized Resource allocation & consumption IMPLEMENTATION Growth Modelling- Stage1: Idea & Launch Stage 2: Infancy & Early Growth Stage 3: Late Growth & Expansion IMPACT Optimizing workload of workers Pre-notification to Factories regarding waste being directed towards them Efficent M&E by health councils

Quantity of MSW generated in Indian Cities Problem Statement Proposed Solution Implementation Impact New Delhi Ahmedabad Surat Pune Chennai Bengaluru Hyderabad Mumbai Kanpur Kolkata Quantity of MSW generated in Indian Cities Urban India generates 188,500 tonnes/day of Municipal Solid Waste (MSW) at a per captia waste generation rate of 500g/person/day Municipal Solid Waste Composition Source: Sustainable Solid Waste Management in India(Jan10,2012) Source: Sustainable Solid Waste Management in India(Jan10,2012)

Implementation Impact Problem Statement Proposed Solution 1A: Waste Generation-Public Area 1A: Waste Generation 2A: Waste Handling, sorting & storage: Street Cleaning 2B: Waste Handling, sorting & storage: By Consumers 3A: Waste Collection 3B: Waste Sorting & Segregation 4A: Recycle 4B: Landfill 4C: Incineration Huge Quantity Waste is being generated in large quantity Heterogeneous Waste All forms of waste comes in mixed composition Lack of Consumer Awareness Consumers do not understand the drawbacks of mixing dry and wet waste Inappropriate Sweeping Methodology Lack of human resource, Strategy and Planning; Manual Scavenging Indiscipline Citizen People throwing waste randomly and being involved in open defection Preliminary Manual Collection Garbage is initially collected by MC workers manually Inappropriate Collection & Transportation Lack of capacity and inadequate access; open transportation with garbage overflowing from them Inadequate Sorting No proper methodology or resources are available for bifurcating waste into dry and waste one Unhygienic Conditions Workers dealing directly with garbage have to undergo unhygienic environment Environmental Issues Emissions from incinerators of toxic substances which pollutes the air Lack of ICT Interventions Conventional approaches are being used because of lack of investment in R&D for waste management

Problem Statement Proposed Solution Impact Implementation Impact Sensor fitted Dustbins Sensors will detect amount & type of garbage and corresponding data will be uploaded to Cloud by means of nearby wifi connectivity 7A: Recycling Factory 3: Community Dustbins 6: Segregation Factory 1A: Household Garbage 2: Garbage Collection by Workers 5: Garbage Trucks GPS fitted Trucks Trucks will directly reach to location where garbage has crossed its threshold limit. Geospatial Optimum Allocation by use of ML Algorithms 1B: Public Area Waste 6B: Garbage Sorting 7B: Compost 7C: Landfill Site 4: Cloud Monitoring that supports sensing 4B: Hospital Council 4A: Municipal Corporation

Clustering Algorithm using Distance Minimization CONTEXT OUTPUT INTERVETION MECHANISM 1: 2: 3: 4: Resource Type Current Location Maximum Travel Distance Characteristics of Road Area Type Amenity Type Clustering Algorithm using Distance Minimization Optimum Resource Allocation Map of sample community Efficient Waste Management Optimized Resource Allocation and Consumption VALUE PROPOSITION Illustration of Clustering Algorithm for Man-Power Workers would be divided into team; each team comprises of Cluster of households. Adaptable to Existing Infrastructure Optimizing Work Load CHALLENGES Awareness among the consumers regarding dry & wet waste Cost effective and implementation ease Dynamic Demographical Data Collection Objective function (Z)=Minimize Overall house to house distance (µdistance); variation in distance travelled (σdistance); amount of waste a particular time in year (µwaste); variation in amount of waste (σ waste)

PHASE 1 PHASE 2 PHASE 3 Problem Statement Proposed Solution Implementation Impact PHASE 1 PHASE 2 PHASE 3 Idea & Launch Stage Late Growth & Expansion Infancy & Early Growth Stage Consumer & Market Insights Business Model Development Pilot Project in 1 / 2 average populated communities of a sub- urban city Implementation of solution in highly populated localities of sub-urban city Collaboration by means of Public Private Partnership (PPP) Models Full Scale up Expand to urban cities to analyse the scope of model and enhance solid waste management efficiently Training & Recruiting Municipal Corporate workers should be trained to deal efficiently with the new model and recruiting skilled labour Monitoring & Evaluation Parameter Cost ( Rs /month) Business Development 50,000 Pilot Project Cloud monitoring system per locality Internet charges*time+ hardware cost ~6150 Parameter Cost (Rs/month) Setting up more Sensor based dustbins Cost per sensor * number of dustbin ~100000 Parameter Cost ( Rs /month) Full scale up ~BDM cost+ impact of scale up Evaluation of growth Market Survey Training & Recruitment Advertisement cost +training cost=time*cost per day*no. of workers ~ 151,000 In 1 locality=4 sensors hardware+ IT guy approx. 10150

1: Municipal Corporation 2: Garbage Collecting Workers Problem Statement Proposed Solution Implementation Impact Fuel Consumption of garbage trucks Developing optimized garbage collection strategies Time-series data is used to optimize workload of workers Team=Cluster of colonies 1: Municipal Corporation 2: Garbage Collecting Workers Pre-notified through cloud data regarding quantity of waste being directed to them Conduct monitoring and evaluation activities related to waste management, without investing significant amount of money for manual monitoring inspections Strategizing to optimize their internal processes. 3: Recycling Factory 4: Hospital Council

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