Study on smart garbage bin system for understanding household garbage disposal andcontents estimation
Name: Eunice Likotiko
Laboratory’ｓ name: Ubiquitous Computing Systems
Supervisor’ｓ name: Prof. Keiichi Yasumoto
Abstract
Daily in homes, there will always be a lot of garbage produced. However, the type of garbage
contents and the amount varies with the family structure, including eating and cooking
behaviours. Again, garbage separation by the person who disposes of garbage has been widely
accepted as ethical behaviour. Although, the existing IoT-based smart garbage systems and theclassification of garbage from images by artificial intelligence have gained positive achievements.
Yet, most current IoT garbage systems are unfriendly in learning garbage growth behaviour andidentifying the type of garbage contents disposed of daily by households. Moreover, they provide
unsatisfactory garbage categories for reasonable practices of household garbage separation.
Therefore, an enhanced IoT-based garbage management system and substitute automation tools
for classification are necessary to improve existing garbage management systems. In thisdissertation, we present a smart garbage bin system, SGBS in short, to realize its benefits on
three challenges: (1) How to predict garbage growth behaviour for a single house?; (2) How tounderstand household garbage disposal behaviour and identify the type of garbage contents?; and(3) How to substantially improve the automation of garbage classification? Regarding the threechallenges, first, we designed and developed a smart garbage bin system, SGBS, embedded withToF (time of flight) and load cell sensors to track the amount of garbage, and DHT22 (temperatureand humidity) and MQ135 gas sensors to monitor the running state of the smart garbage bin.
Using a Wi-Fi gateway, data are stored in the cloud. Then, to evaluate the first challenge, weexperimented with the SGBS in a student laboratory. An autoregressive integrated moving
average (ARIMA) model was applied, providing MAE of 5.17 cm and an SD of 0.33 cm, thus
considered satisfactory accuracy on the garbage growth prediction. To examine the second
challenge, we later extended our SGBS to identify the disposed of garbage contents through asmartphone installed with a garbage annotation application comprised of 4 categories of garbage
contents and 10 garbage content identities, thus guiding users during garbage disposal.
Afterwards, we conducted an initial experiment in three households to evaluate our approach.
Our findings show that households’ garbage disposal behaviour depends on the amount andcontents of garbage and the routine of disposing of such garbage content. In connection with thethird challenge, we first extended the initial interface of the garbage annotation application tohave 8 garbage categories and 25 garbage content identities for more logical household garbage
separation. Then, we deployed the designed SGBS with the extended annotation interface in fivehouseholds. Eventually, we constructed a garbage content estimation model, and we confirmedthat the overall result models with leave-one-house cross-validation can achieve an accuracy of
91% in kitchen waste contents, 89% in paper/softbox contents and 85% in the garbage categories
for the classification tasks.