WasteApp: Smarter waste recycling for smart citizens

Bonino, Dario; Alizo, Maria Teresa Delgado; Pastrone, Claudio; Spirito, Maurizio A.

doi:10.1109/splitech.2016.7555951

Cited by 26 publications

(20 citation statements)

References 12 publications

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“…As the bins become full, the drivers nearest to the bins are assigned to pick up the waste. The authors in [22] discuss that change in recycling habits might highly impact the ecological and sustainability of a city. The use of a recycling app on a smartphone may be the solution to increase recycling awareness and behaviour as people tend to use smartphone in their daily life.…”

Section: Journal Of Engineering Technology and Applied Physicsmentioning

confidence: 99%

I-Bin: Weight Based IoT Smart Recycling Scheduler for Guarded Neighbourhood

Ramasamy

Thiagarajah

Pillay

2020

JETAP

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Overpopulation and lack of awareness are the main causes of poor waste management. While existing research in waste management employs current technology such as the Internet of Things, it lacks emphasis on residential centric type of waste management system. This project designs a weight based scheduling system for a guarded neighbourhood using Arduino Uno, load cells and plastic bins which are then incorporated with WiFi to send waste weight information in real time to cloud for monitoring.Design verification tests such as the linearity test and non-repeatability test showed less than 1 percent standard deviation error. A Proof of Concept test was conducted to test the system's performance at a guarded residential area.Analysis of test showed that an average of 0.0966kg of recyclable waste was collected per house. Based on theresults also, it is predicted that approximately 483kg of waste can be effectively collected from 10 residential areas using the I-Bin system. Residents tend to dispose waste after office hours and scheduling more waste collection frequency after office hours will lead to increased revenue for the recycling company.

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Section: Journal Of Engineering Technology and Applied Physicsmentioning

confidence: 99%

I-Bin: Weight Based IoT Smart Recycling Scheduler for Guarded Neighbourhood

Ramasamy

Thiagarajah

Pillay

2020

JETAP

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“…AI, machine learning and analytics are not integrated to the platform. In [42], Bonino et al provide an application example of ALMANAC to waste recycling. Cloud APIs are employed to deliver the location of waste bins and their garbage level to an Android OS smartphone application.…”

Section: ) Services Life-cycling and Analytics/aimentioning

confidence: 99%

Platforms for Smart Environments and Future Internet Design: A Survey

Alberti

Santos²,

Souza³

et al. 2019

IEEE Access

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Internet of things (IoT) is pushing the integration of physical and virtual worlds. Sensor devices provide rich sensory information to context-aware services. Actuators implement in the physical world application needs. The role of software is increasing as more and more resources become virtualized and software-defined. Smart environment is an emerging concept that has the potential to address many of the humanity problems. In this context, what are the promising tools for building the smart environments of the future? This paper provides a review of platforms, middleware, and frameworks that can help in this big challenge, discussing their architectures, service life-cycling, digital twins, cloud-based operation, virtualization, security, privacy, communication model, support for AI and machine learning, among other aspects. The proposed revision innovates by employing previous work on future Internet key enablers as parameters for qualitative comparisons. The idea is to determine the degree of alignment among current initiatives for smart environments and the ones emerging from future Internet research. Among the main conclusions are: (i) heterogeneity come to stay; (ii) many contemporary proposals do not cover important aspects raised in future Internet research; (iii) publish/subscribe model is largely employed; (iv) many proposals are stuck to the limitations of current Internet model (another reason to explore the relationship among current platforms and previous future Internet research for IoT); (v) devices interoperability is a problem solved; (vi) ingredients from future Internet research, such as SDN/NFV, ICN and SCN are systematically being adopted for smart environments design. Many others are to come; (vii) AI, machine learning, and big data support are missing not only in TCP/IP-based approaches, but also in future Internet-based.

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“…Authors designed Mobibee to realize indoor localization by inspiring citizens to contribute their data [19] and proposed a truth discovery algorithm to detect falsified data [20]. Besides, there are also other services, e.g., recycling waste with WasteApp [22] and measuring wireless performance with MCNet [23]. • Disaster Prediction.…”

Section: Introductionmentioning

confidence: 99%

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision

Sun

Ding

Shu

et al. 2022

IEEE Systems Journal

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Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges that are required to be considered. Mobile Crowd Sensing (MCS) embeds three beneficial characteristics: a) cost-effectiveness, b) scalability, and c) mobility and robustness. With the Internet of Things (IoT) becoming a reality, the smart phones are widely becoming available even in remote areas. Hence, both the MCSs characteristics and the plug and play widely available infrastructure provides huge opportunities for the MCS-enabled smart agriculture.opening up several new opportunities at the application level. In this paper, we extensively evaluate the Agriculture Mobile Crowd Sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we provide a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low cost requirements. However, we note that AMCSs may still posses limitations in regard to data integrity and quality to be considered as a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns the MCS-enabled agriculture by putting forward six potential applications of AMCS-enabled agriculture. Finally, we propose future research and focus on agricultural characteristics, e.g., seasonality and regionality.

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WasteApp: Smarter waste recycling for smart citizens

Cited by 26 publications

References 12 publications

I-Bin: Weight Based IoT Smart Recycling Scheduler for Guarded Neighbourhood

I-Bin: Weight Based IoT Smart Recycling Scheduler for Guarded Neighbourhood

Platforms for Smart Environments and Future Internet Design: A Survey

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision

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