Riemann garbage bin: a self-sustained waste management system
Date Issued
2019
Author(s)
Zelenkovski, Kiril
Karafiloski, Filip
Abstract
This paper elaborates the idea for building a selfsustained sensor system, a network composed of nodes
implementing the Riemann Garbage Bin (RGB) model. Hence,
the case study explores the potential of employing sensor enabled
systems to improve on waste monitoring and management in
public waste bins. The network consists of wireless nodes that use
ultrasonic sensors to measure the empty space in the bins. The
sensors periodically report the fill rate of the waste bins to a
sensor gateway that is based on Long Rage Wide Area Network
(LoRaWAN) protocol. For our LoRaWAN server and network
cover we choose to use The Things Network (TTN). These fill
rates would be monitored by a mobile or web application
connected to the network server. The goal of this project is
through the Internet of Things (IoT) to monitor all the waste bins
in one city, to improve the garbage management by relocating
resources and by giving insight to the public about this global
health threating problem.
implementing the Riemann Garbage Bin (RGB) model. Hence,
the case study explores the potential of employing sensor enabled
systems to improve on waste monitoring and management in
public waste bins. The network consists of wireless nodes that use
ultrasonic sensors to measure the empty space in the bins. The
sensors periodically report the fill rate of the waste bins to a
sensor gateway that is based on Long Rage Wide Area Network
(LoRaWAN) protocol. For our LoRaWAN server and network
cover we choose to use The Things Network (TTN). These fill
rates would be monitored by a mobile or web application
connected to the network server. The goal of this project is
through the Internet of Things (IoT) to monitor all the waste bins
in one city, to improve the garbage management by relocating
resources and by giving insight to the public about this global
health threating problem.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
CIIT_2019_paper_36.pdf
Size
525.04 KB
Format
Adobe PDF
Checksum
(MD5):76b3ead729d208b55a4766ce337d9859