Multi-Agent Based IoT Smart Waste Monitoring and Collection Architecture

Likotiko, Eunice David; Nyambo, Devotha G.; Mwangoka, Joseph

doi:10.5121/ijcseit.2017.7501

Cited by 15 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The authors' model verified the optimal waste collection route, aiding the development of smart and innovative waste management systems and modeling for real life scenarios. Continuous empirical data and Geographical Information Systems (GIS) are proposed to be used for further model extensions [10].…”

Section: Simulation Games and Gamified Toolsmentioning

confidence: 99%

“…The minimum range of waste bin capacity must be the minimum 0.5-1.5 L/capita in line with 3(C) and maximum/over 1.5-2.5 L/capita as mentioned in level 1(A) in Table 6 [12,15]. CI rates how clean the selected area is using Equation (10), where N I col is the number of items collected (i.e., cigarette butts, PMD, papers, other plastics, etc.) and Sur is the surface in m 2 of the specific selected area.…”

Section: Waste Infrastructure (Wi)mentioning

confidence: 99%

See 1 more Smart Citation

Modelling Key Performance Indicators in a Gamified Waste Management Tool

et al. 2021

View full text Add to dashboard Cite

Broader understanding of waste management has the potential to bring about broad societal change impacting the climate crisis and public health. We present existing waste management tools and commercially-available games involving waste management, highlighting the strengths and opportunities left unaddressed by these tools in educational contexts and planning use cases. A survey motivates the need for enhanced interactive tools providing clear feedback through quick-visibility performance indicators. After identifying an opportunity to build upon highly-detailed multi-criteria simulation tools, we explore the need for easy-to-read performance metrics that will bring to the field of waste management easily identifiable and measurable key performance indicators (KPIs) that vary alongside factors affecting waste management policies. Such metrics are introduced and detailed as part of a unified waste management model. We then develop a representative gamified educational tool based upon this model to be used by students, decision makers planning real-world policies, and the public. This simulator is built upon the Unity Game Engine and emulates waste management techniques and resulting KPIs within the context of a virtual city.

show abstract

Section: Simulation Games and Gamified Toolsmentioning

confidence: 99%

Section: Waste Infrastructure (Wi)mentioning

confidence: 99%

Modelling Key Performance Indicators in a Gamified Waste Management Tool

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Hong et al [6][7][8][9][10] designed a smart garbage system that collects food from houses, and users pay regularly according to their waste materials. They used RFID to track the garbage collectors and weight sensors to weight the waste material.…”

Section: State Of the Artmentioning

confidence: 99%

Smart eNose Food Waste Management System

et al. 2021

View full text Add to dashboard Cite

The modern age is an era of fast-growing technology, all thanks to the Internet of Things. The IoT becomes a prime factor of human life. As in this running world, no one cares about the wastage of food. However, this causes environment pollution as well as loss of many lives. A lot of researchers help in this era by introducing some great and beneficial projects. Our work is introducing a new approach by utilizing some low-cost sensors. In this work, Arduino UNO is used as a microcontroller. We use the eNose system that comprises MQ4 and MQ135 to detect gas emission from different food items, i.e., meat, rice, rice and meat, and bread. We collect our data from these food items. The MQ4 sensor detects the CH4 gas while the MQ135 sensor detects CO2 and NH3 in this system. We use a 5 kg strain gauge load cell sensor and HX711 A/D converter as a weight sensor to measure the weight of food being wasted. To ensure the accuracy and efficiency of our system, we first calibrate our sensors as per recommendations to run in the environment with the flow. We collect our data using cooked, uncooked, and rotten food items. To make this system a smart system, we use a machine learning algorithm to predict the food items on the basis of gas emission. The decision tree algorithm was used for training and testing purposes. We use 70 instances of each food item in the dataset. On the rule set, we implement this system working to measure the weight of food wastage and to predict the food item. The Arduino UNO board fetches the sensor data and sends it to the computer system for interpretation and analysis. Then, the machine learning algorithm works to predict the food item. At the end, we get our data of which food item is wasted in what amount in one day. We found 92.65% accuracy in our system. This system helps in reducing the amount of food wastage at home and restaurants as well by the daily report of food wastage in their computer system.

show abstract

“…Please refer figure 4 which has shown high level building blocks of iSmartWMS. Let us walk through the major components of iSmartWMS [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Smart Waste Management System "Ismartwms"mentioning

confidence: 99%

Smart City Waste Management through ICT and IoT driven Solution

Gade

Aithal

2021

IJAEML

View full text Add to dashboard Cite

The growing population and mass relocation of citizens from urban and semi-urban areas to Smart Cities have resulted in exponential growth in Smart Cities and thereby certain challenges. One of the major challenges Smart Cities are facing is to control, manage and process waste generation on daily basis. Waste collection and processing at a wider scale is not an easy job. Growing population and resource constraints in waste management activities are the primary reasons which have made waste management a tough job. To deal with this challenging process, Smart Cities uses Smart Waste Management System. This paper has provided an overview of a typical Smart Waste Management system and a review of selected research papers on Smart Waste Management. We tried to identify areas of improvement with the existing Smart Waste Management Solutions and proposed an innovative solution called "iSmartWMS" for carrying out waste management specifically for Smart Cities. The paper has discussed in detail the architecture and building blocks of the proposed Smart Waste Management System, along with the details of software tools, sensors, and technologies proposed in iSmartWMS. The Paper has finally discussed results with respect to prototype implementation of iSmartWMS and also future plans to further improve the iSmartWMS smart waste management system.

show abstract

Multi-Agent Based IoT Smart Waste Monitoring and Collection Architecture

Abstract: Solid waste management is one of the existing challenges in urban areas

Cited by 15 publications

References 13 publications

Modelling Key Performance Indicators in a Gamified Waste Management Tool

Modelling Key Performance Indicators in a Gamified Waste Management Tool

Smart eNose Food Waste Management System

Smart City Waste Management through ICT and IoT driven Solution

Contact Info

Product

Resources

About