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

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.

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.

Smart City Street Cleaning Infrastructure

Cleaning Model - Layers

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.

Cleaning Model – Grid

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)

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.

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)

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.

Computation – Area level

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).

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

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

Mobile Station (App Simulation)

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

Database

UI – Dashboard

UI – Map View

UI – Analytics - Cleanliness