1. Street Cleanliness Assessment System for Smart City using Mobile and Cloud
Bharat Bhushan, Kavin Pradeep Sriram Kumar, Mithra Desinguraj, Sonal Gupta
Project Advisor: Prof. Jerry Gao.
Department of Computer Engineering, Charles W. Davidson College of Engineering, San Jose State University, San Jose, CA
Abstract
Infrastructure
Calculation Reference
Dashboard Screenshots
Street cleanness is an important aspect of any city as it helps to improve the environment of the city. It is difficult to detect and maintain street cleanliness of any big city like San Jose. Millions of dollars spent every year to remove litter and garbage from streets, alley, and freeways. Road debris is one of the reasons behind many fatal accidents. Garbage and road debris also diminish the beauty of city environment and can cause health hazards for the residents of the city. There is clearly a need for an efficient system to help city to become more clean, green and smart city.
This smart automated system uses multiple high definition cameras mounted on the vehicle also known as “mobile stations” along with GPS-enabled devices which move around and the city periodically and collect data in the form of pictures and the location coordinates. This data is then transmitted to cloud-based detection engine for analysis to assess the cleanliness levels on each picture and the result is then stored in the database along with the location. Big data analytics is performed on the data collected and then fed to the next layer of the database which is the back-end for the web and mobile interface to city and residents reflecting the current cleanliness level and where the attention and action are needed. Reports are generated to view the current and past status and help proactively plan for future maintenance and planning
Database
Architecture
Problem
Several issues and challenges faced by city:
Manual.
Offline data collection.
Time consuming.
No real-time visibility.
High Cost.
Mobile Station (Mobile app)
Data Collection Modes.
Factors
Batch
Real Time
Performance
Offers better performance as no overhead at mobile station but data is not available in real-time.
High overhead as backend operation working continuously for real-time data updates.
Dependency
In batch, photos can be collected offline with local storage and not necessarily need to be connected with city network.
City network connectivity required throughout the data collection.
Consistency
Lower chances of inconsistency as batches are updated asynchronously.
Higher chance on inconsistency, as multiple streams updating data synchronously.
Cost
Cheaper as does not require high bandwidth wireless connectivity on each mobile station. It can use Wi-Fi to transfer the data. Low compute on mobile station.
Expensive as it would require high performing network on each mobile station. Highly optimized compute on mobile station is required.
Storage
More storage is required on each mobile station to store local data until synced with central database or cloud.
Less storage required as data is being synced continuously and not much data is stored on mobile station.
Control
More manual control
Less Manual control
Requirements
Less optimized mobile system design for network and compute.
High optimized mobile system design for network and compute.
Monitoring
Less monitoring required on network but need close checks on storage consumptions on mobile stations.
Close monitoring is required on network connectivity but less monitoring for storage. Need to monitor local compute and memory.
Availability
Data is available after a period.
In near real-time.
On-Demand
Being delayed operation, would be not a good fit for on-demand cleaning service.
Good fit for on-demand cleaning service.
Solution
Cleanliness Model
Smart City Street Assessment system using Mobile and Cloud with
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.
Conclusions
This project is an attempt to create a smart automated street cleanliness assessment system with enhanced visualization reporting and management using state of the art.
A cloud-based model connecting street views using the mobile station with camera collecting images and real-time analysis of images to generate the city street cleanliness to plan and manage the street cleaning.
During our study, design, and development of this project, we have worked with San Jose city and tried to create an automated street cleaning system which contributes to turning a city into Smart City.
Technologies Stack
Grid Analysis
Acknowledgements
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).
We like to express our sincere gratitude to our Project Advisor Dr. Jerry Gao for the continuous guidance and support throughout the project since the beginning. His vision and recommendations kept us going and without his guidance, it was impossible to complete the project.
We would also like to thank San Jose City team for providing valuable insights about the street cleaning system. San Jose city gave us the opportunity to understand the problems and building a solution and be part of Smart City initiative. A B