1. Project Advisor: Dr. Jerry Gao
A Street Cleanliness Assessment System for Smart City using Mobile and Cloud
Project Advisor: Dr. Jerry Gao
Team:
Bharat Bhushan
Mithra Desinguraj
Kavin Pradeep Sriram Kumar
Sonal Gupta.
San Jose State University
Spring 2017

2. Introduction - Problem
Streets are the nerves of any city and society.
Keeping streets clean is a challenge for any city admins.
Street cleanliness assessment is essential but..
Problem:
Manual.
Offline data collection.
Time consuming.
No real-time visibility.
High Cost.

3. Introduction - Solution
Proposed Solution:
Smart City Street Assessment system using Mobile and Cloud.
Automated using mobile and cloud.
Real to near-real time data collection.
Less time.
Real-time visibility with single pane of glass.
Cost Effective.
Integration with other city services.
Public contribution via mobile (crowd sourcing).
Self Learning (Machine Learning)
API driven.
Mobile.

4. Smart City Street Cleaning Infrastructure

5. Cleaning Model - Layers

6. Cleaning Model - Areas San Jose City (95) City 20 24 35 16 Willow Glen
Alum
Rock 35
South
Central 16
Areas with No. of Blocks
123
110 94
109 78
194 89
115 35 78
101 45
Blocks with No. of Streets
Streets with
No. of Grid Pts
Individual
Photo Points.

7. Cleaning Model – Grid

8. Grid Point Model
Picture Point: Multiple Images are captured in each direction (F,B,L,R) on either side of the street and sent to Cloud along with location data.
INode : Represent Intersection. There are several Image points between nodes.
SNode : Represent Sub Intersection. Used to divide large blocks.
Grid Point : Represent logical radius to assess both sides of the street. It can have one or more Pic points. Collectively produces the cleanliness level across the street.
Block with Cleanliness Indicator: Block is collection of several Grid points. Red – Level 4 (very dirty)
Orange – Level 3
Yellow - Level2
Green – Level 1(Not visible, looks clean)

9. Computation – Point Level
Pictures taken every ~20ft., sent to cloud and fed to detection engine and level is generated.
Based on level detected, its marked -Red (4), Orange (3), Yellow (2) and Green (1).
Results are stored in DB with image reference, date time and resulting.
Assessment area is defined by the city admin. Every point is part of one assessment area.
Four images are captured at every point, one in each direction.

10. Computation – Street level
From each point on a street between start (S) and end (E) points, all numbers would be averaged to generate overall assessment of the street.
Assessment would be done for every street generating the aggregate value.
Results are stored in DB with image reference, date time and level.
Each street is a part of one block.
Grid based analysis and part of the block.
(S) (E)

11. Computation – Block level
Grid based analysis.
Aggregate of all the points in the block.
Assessment would be based on every street in the block and the aggregate value.
Results are stored in DB with image reference, date time and level.
Block can have any number of streets, everything is based on each data points.

12. Computation – Area level

13. Assumptions: Fixed image resolution. Vehicle speed is approx.15mph.
Picture set covers 20ft. of distance.
Pictures are collected every ~2-4 sec.
Multiple set of pictures are collected every time.
Stable Network connectivity for real time update.
Offline image transmission (batch transfer option).

14. Infrastructure External Core DB Image Service App (MySQL)
Cloud
Edge
Images sent to
Cloud via Mobile or
City Wi-Fi (batch)
Core DB
(MySQL)
Image Service App
Processing Engine
Residents
Queuing Data
Web App
Reports
Detection
Engine
Storage
Analytics
Mobile App
Admin
Cleaning
Dept.
Edge Device
App
Edge Storage
Cloudlet
Map Service
External

15. Application DB (MySQL)
System Architecture
Mobile Client
(MS)
Street Cleaning UI
Street Cleaning Dashboard
Street Cleaning Reports
Street Cleaning Detection Engine
Controller
Streets
Blocks
Mobile Stations
Street Cleaning Detection Analytics
MS Computing
Street Cleaning DB service
Historical
Engine DB (NoSQL)
Application DB (MySQL)
MS Monitoring
Street Cleaning Service Manager
Street Cleaning Security
MS Repo
Admin
Feedback
Dispatch
ACL/Authentication
MS Security
Role Based Authorization.
Street Cleaning Monitoring
Encryption/Session Mgmt.
Performance
Alerts
Street Cleaning Service Protocols
Mobile Station
Connection Module
ServiceRequest
Module
DB Connection Control
Module
UI Connection
Module

16. Mobile Station (App Simulation)

17. Cloud (Tested with AWS)
Test t2.micro instances.
Running Separate services on different instance.
Mobile web, Apache Tomcat, MySQL, Java.

18. Database

19. UI – Dashboard

20. UI – Map View

21. UI – Analytics - Cleanliness