A 5G-Enabled Smart Waste Management System for University Campus

Longo, Edoardo; Sahin, Fatih Alperen; Redondi, Alessandro E. C.; Bolzan, Patrizia; Bianchini, Massimo; Maffei, Stefano

doi:10.3390/s21248278

Cited by 22 publications

(18 citation statements)

References 37 publications

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“…It is also worth noting that it exists a variety of sensors for the determination of physical-chemical parameters that could complement the sensor network considered in this case study, such as a variation of the ones presented in [47]. These sensors could provide additional insight, especially if combined with GIS and process optimisation, and also facilitate the real-time implementation of the presented approach.…”

Section: Discussionmentioning

confidence: 99%

“…Very specific works can be found about optimisation of supply chain networks in the field of waste valorisation, such as in [45], where an integrated geographical information system (GIS)-based optimisation is performed, but it requires highly detailed and tailored data, so its implementation becomes time-consuming and highly dependent on data availability; furthermore, it does not tackle process optimisation regarding waste processing facilities. Regarding logistics, other works can be found for path planning optimisation such as in [46], where truck routes are traced based on GIS-oriented algorithms, or in [47], where a smart waste bin prototype is developed for sensor-based waste classification. As it can be seen, there is a gap in the literature regarding network optimisation of existing waste management facilities (such as AnD plants) that would include both logistics (i.e., minimising route impact and length) and quality (i.e., improving process performance) optimisation.…”

Section: Introductionmentioning

confidence: 99%

“…This is a necessary gap to fulfil, since, as stated before, AnD networks are currently managed in an ad-hoc, manual fashion by the practitioners, which is dramatically time-consuming and needs highly qualified personnel. Although there are currently different approaches that could help overcoming specific parts of this challenge (i.e., those observed in [44][45][46][47]), none of them can currently successfully accomplish the overall task.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Palma-Heredia

Verdaguer

Puig

et al. 2022

Sensors

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Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Palma-Heredia

Verdaguer

Puig

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…A smart campus is a small-scale version of a smart city with advanced capabilities that facilitate creativity, social interaction, and intellectual exploration [18] [19]. The similarities between a smart city and a smart campus anchor on many aspects: They cover large urban areas with many different buildings (administrative buildings, residential halls, research laboratories, lecture halls, bars, and cafés) and are inhibited by a variety of people including university staff and students [20]. Additionally, Chagnon-Lessard et al [6] shared an organizational structure that encompasses seven smart areas such as "smart building, smart economy, smart environment, smart governance, smart living, smart mobility, and smart people".…”

Section: Smart Campusmentioning

confidence: 99%

Internet of Things and Its Applications to Smart Campus: A Systematic Literature Review

Çavuş

Mrwebi

Ibrahim

et al. 2022

Int. J. Interact. Mob. Technol.

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A smart campus is an emerging trend that will revolutionize the education system by enabling universities to improve services, and processes as well as achieve sustainability goals. With the proliferation of advanced technologies, a smart campus has emerged as an important concept that integrates technology into higher education. A smart campus takes advantage of IoT technologies to facilitate teaching and research activities. The purpose of this study is to identify the IoT technologies that are required in the development of a smart campus. This study uses a Systematic Literature Review (SLR) methodology and PRISMA processes to analyze high-quality articles on the IoT-based smart campuses from the last five years (2017-2022) as extracted from three databases like Scopus, ScienceDirect, and IEEE. The findings of the study reveal that the implementation of an IoT-based smart campus offers many advantages and benefits but also presents challenges requiring further exploration. Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further. The paper explores the many benefits and advantages that are brought by the implementation of IoT-based smart campuses. It also identifies key challenges that are presented by such implementations. Researchers, policymakers, teachers, and students can benefit from this study by gaining insights into the IoT-based smart campus. Keywords—smart campus, smart university, Internet of Things, IoT applications, big data

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“…Currently, the state considers essential urban services, such as water, sanitation, and solid waste management, to be the responsibility of local or national governments [ 1 , 2 , 3 , 4 ]. Research on the identification and classification of trash has been performed [ 5 , 6 , 7 ]. However, it was not optimal regarding the amount of trash detected and accurate trash detection.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System

Naf’an

Sulaiman

Ali

2023

Sensors

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This study aims to optimize the object identification process, especially identifying trash in the house compound. Most object identification methods cannot distinguish whether the object is a real image (3D) or a photographic image on paper (2D). This is a problem if the detected object is moved from one place to another. If the object is 2D, the robot gripper only clamps empty objects. In this study, the Sequential_Camera_LiDAR (SCL) method is proposed. This method combines a Convolutional Neural Network (CNN) with LiDAR (Light Detection and Ranging), with an accuracy of ±2 mm. After testing 11 types of trash on four CNN architectures (AlexNet, VGG16, GoogleNet, and ResNet18), the accuracy results are 80.5%, 95.6%, 98.3%, and 97.5%. This result is perfect for object identification. However, it needs to be optimized using a LiDAR sensor to determine the object in 3D or 2D. Trash will be ignored if the fast scanning process with the LiDAR sensor detects non-real (2D) trash. If Real (3D), the trash object will be scanned in detail to determine the robot gripper position in lifting the trash object. The time efficiency generated by fast scanning is between 13.33% to 59.26% depending on the object's size. The larger the object, the greater the time efficiency. In conclusion, optimization using the combination of a CNN and a LiDAR sensor can identify trash objects correctly and determine whether the object is real (3D) or not (2D), so a decision may be made to move the trash object from the detection location.

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A 5G-Enabled Smart Waste Management System for University Campus

Cited by 22 publications

References 37 publications

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Comparison of Optimisation Algorithms for Centralised Anaerobic Co-Digestion in a Real River Basin Case Study in Catalonia

Internet of Things and Its Applications to Smart Campus: A Systematic Literature Review

Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System

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