Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Feb 12;20(4):978.
doi: 10.3390/s20040978.

Heterogeneous Sensing Data Analysis for Commercial Waste Collection

Foued Melakessou  1 , Paul Kugener  1 , Neamah Alnaffakh  1 , Sébastien Faye  1 , Djamel Khadraoui  1
Affiliations

Heterogeneous Sensing Data Analysis for Commercial Waste Collection

Foued Melakessou et al. Sensors (Basel). .

Abstract

Waste collection has become a major issue all over the world, especially when it is offered as a service for businesses; unlike public waste collection where the parameters are relatively homogeneous. This industry can greatly benefit from new sensing technologies and advances in artificial intelligence that have been achieved over the last few years. However, in most situations waste management systems are based on obsolete technologies, with a low level of interoperability and thus offering static processes. The most advanced solutions are generally limited to statistical, non-predictive approaches and have a limited view of reality, making them weakly effective in dealing with day-to-day business issues (overflowing containers, poor quality of service, etc.). This paper presents a case study currently being developed in Luxembourg with a company offering a business waste collection service, which has a significant amount of constraints to consider. Our main objective is to investigate the use of multiple waste data sources to derive useful indicators for improving collection processes. We start with company-owned historical data and then investigate GPS information from tracking devices positioned on collection trucks. Furthermore, we analyze data collected from ultrasonic sensors deployed on almost 50 different containers to measure fill levels. We describe the deployment steps and show that this approach, combined with anomaly detection and prediction techniques, has the potential to change the way this business operates. We also discuss the interest of the different datasets presented and multi-objective optimization issues. To the best of our knowledge, this article is the first major work dedicated to the world of professional waste collection.

Keywords: LPWAN; cluster analysis; data analytics; machine learning; smart waste collection; waste monitoring; wireless sensor networks.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Clients’ spatial distribution on the complete country (Luxembourg) [7].
Figure 2
Figure 2
Distribution of the number of visits by clients in Luxembourg, from February 2018 to May 2019.
Figure 3
Figure 3
Impacts generated by SWAM.
Figure 4
Figure 4
SWAM—High-level architecture.
Figure 5
Figure 5
Bin volume distribution (domestic waste/paper-cardboard and glass) for waste collection processed in 2018.
Figure 6
Figure 6
Deployment of one sensor into a container.
Figure 7
Figure 7
Brighterbins ultrasonic sensor (L=130mm, W=70mm, H=53mm).
Figure 8
Figure 8
Sensor integration under the hinge in a container.
Figure 9
Figure 9
Testing of 4 sensors deployed into a container.
Figure 10
Figure 10
Data collection of 4 sensors placed into a 1100 L test bin [7].
Figure 11
Figure 11
Indoor technical room where Sigfox connectivity was tested.
Figure 12
Figure 12
Example of the collected weight time-series extracted for a selected retail business.
Figure 13
Figure 13
(a) Dispersion plot: weight median vs weight standard deviation. (b) A clustering k-mean algorithm was applied for k=4. (c) A clustering PAM algorithm was applied for k=4.
Figure 14
Figure 14
Variance related to eigen values.
Figure 15
Figure 15
Correlation graph.
Figure 16
Figure 16
Correlation plot between features and principal components (cos2 and contribution).
Figure 17
Figure 17
Dispersion plot in the PCA’s coordinate system (Dim.1,Dim.2).
Figure 18
Figure 18
Example of a waste collection tour of a complete working day.
Figure 19
Figure 19
Successive collection paths on Monday (February 2019) for north and south tours.
Figure 20
Figure 20
Service time profile for a selected shopping center.
Figure 21
Figure 21
Service time profile for a selected retail business.
Figure 22
Figure 22
Traveled distance for the south waste collection tour between January–September 2019 (Average: 220 km, standard deviation: 54 km).
Figure 23
Figure 23
Number of clients served for the south waste collection tour between January-September 2019 (Average: 34 clients, standard deviation: 7 clients).
Figure 24
Figure 24
Dispersion plot (Distance, Client, Service time) for each tour (North and South).
Figure 25
Figure 25
Physical deployment: 47 sensors were integrated in 35 distinct sites.
Figure 26
Figure 26
Raw data on a site composed by 6 bins.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. How the Internet of Things Is Aiding the Garbage Crisis. [(accessed on 6 February 2020)]; Available online: https://newsroom.cisco.com/feature-content?articleId=1757267.
    1. The Impact of Digital Transformation on the Waste Recycling Industry. [(accessed on 6 February 2020)]; Available online: https://store.frost.com/the-impact-of-digital-transformation-on-the-wast....
    1. Sensoneo Smart Waste Management. [(accessed on 6 February 2020)]; Available online: https://sensoneo.com/
    1. EcubeLabs. [(accessed on 6 February 2020)]; Available online: https://www.ecubelabs.com/solar-powered-trash-compactor/
    1. Bigbelly Smart Solutions for Cities. [(accessed on 6 February 2020)]; Available online: https://bigbelly.com/

LinkOut - more resources