Smart City Platform Development for an Automated Waste Collection System

Popa, Cosmin; Căruţaşu, George; Cotet, Costel Emil; Căruțașu, Nicoleta Luminița; Dobrescu, Tiberiu

doi:10.3390/su9112064

Cited by 64 publications

(30 citation statements)

References 17 publications

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“…However, the aim of this paper was to present an IoT cloud solution combining device connection, data processing, and control, rather than the design and optimization of waste collection. A food waste collection method was presented in [21], where the information was collected using radio frequency identification (RFID) technology and transmitted using a wireless mesh network. However, the disadvantages of this technology were severe in the long range, especially considering the aim of the smart city is management with a large area.…”

Section: Related Workmentioning

confidence: 99%

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Waste Management System Using IoT-Based Machine Learning in University

Khoa

Phuc

Lam

et al. 2020

Wireless Communications and Mobile Computing

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Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. However, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. In this paper, we propose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with the shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University (Vietnam) to evaluate the performance and practicability of the system’s implementation. We examine data transfer on the LoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit designed with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management of waste collection.

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Section: Related Workmentioning

confidence: 99%

“…Second, the path length should be shortened, in order to avoid high fuel costs and reduce the work amount, by applying graph theory [14][15][16][17]. Some solutions have introduced the use of IoT devices to estimate the fill level of inboxes and send this data over the Internet to a server for decision-making [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Waste Management System Using IoT-Based Machine Learning in University

Khoa

Phuc

Lam

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…The ultrasonic sensors notices when a container is full and the conveyer will transport the waste to the next available container. At the end, the client receives the voucher [25].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Industry 4.0 Cloud Data Transfer Method for an Automated Waste Collection System

2020

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Moving to Industry 4.0 involves the collection of massive amounts of data and the development of big data applications that can ensure a quick data flow between different systems, including massive amounts of data and information collection from smart sensors, and sending them to cloud applications that allow real-time data monitoring and processing. Securing and protecting the transmitted data represents a big issue to be discussed and resolved. In the paper, we propose a new method of data encoding and encryption for cloud applications using PNG format images. The proposed method is described in comparison with one of the classical methods of data encoding and transmission used currently. The paper includes a case study in which the proposed method was used to collect and transmit data from an automated waste collection system. The results show that the proposed method represents a secure, fast and efficient way to send and store the data in the cloud compared to the methods currently used. The proposed method is not limited to being used only in waste management but can be used successfully for any type of manufacturing system from smart factories.Sustainability 2020, 12, 1839 2 of 15 the production of the physical object, because operating a smart, connected manufacturing device requires a supporting cloud-based system [6]. The digital twin of the manufacturing architecture is related with all the above-mentioned technologies used in this paradigm.Big data is one of the nine pillars involved in achieving Industry 4.0. A huge amount of data are available in manufacturing systems, waste management systems, etc., but the data and information should be properly collected, encoded and transmitted to the cloud application in order to be processed and to allow a real-time monitoring of equipment and systems. The major challenges are represented by: data acquisition from smart sensors, actuators and PLCs; data conversion; data security and privacy; data encoding, encryption and decryption [7][8][9][10].Software architectures of the big data processing platforms are analyzed in the literature, including solutions for job scheduling for big data applications [11], solutions which offer a flexible co-programming architecture able to support the life cycle of time-critical cloud native applications [12], mobility-driven cloud-fog-edge collaborative real-time framework solution, which has IoT, Edge, Fog and Cloud layers and which exploits the mobility dynamics of the moving agent [13].Several data encoding and encryption solutions for cloud applications are presented in the literature [14][15][16][17][18][19][20][21][22], but without including detailed case studies to present identified problems, propose solutions, and implement results.The main problem is the identification of the right solution that will ensure the data transmission and processing in a secure way, quickly, efficiently and with low costs. The proposed solution presented in Chapter 2 can be used both in smart factories and for smart city applications incl...

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“…Virtual reality and speech recognition, as well as the use of augmented reality, will change how work is done. For example, displayed information or graphical instructions allow the user to see the work instructions during the critical activities [22][23][24].…”

mentioning

confidence: 99%

Industry 4.0 Diagnosis from an iMillennial Educational Perspective

2020

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Although the new economic paradigm is based on the rapid evolution of technology, it is not clear if this evolution is only dependent on a spectacular transformation of human resources or if the evolution of human resources has imposed major changes at a technical level as well. The main focus of this paper is to identify how to cope with these new technologies as educational actors, using a diagnosis of contemporary generation characteristics. The fourth industrial revolution (Industry 4.0) imposes a rapid evolution (or revolution) of the human resources paradigm in engineering: iMillennials should adapt to that paradigm, and the paradigm should be adapted to them. The research objectives were to identify some relevant characteristics of iMillennials’ technological background and to create a map of the abilities of this generation as required by the evolution of new technologies. For a batch of students with a technical background, two psychological inventories that describe emotional intelligence and motivation acquisition were applied. Each inventory used focuses on certain features that describe motivational achievement (AMI) or emotional intelligence (EQ-I). Besides the motivational features, the AMI questionnaire also refers to socio-emotional abilities. A correlation between the parameters of the two inventories occurred. Three correlated parameters (assertiveness, reality testing, and commitment) were identified. Based on these results, a constellation map of soft skills was designed to match characteristic features of iMillennials with necessary competencies for an Industry 4.0 environment. Furthermore, this paper proposes a tool for educational actors to cope with these transformations based on the new technologies of Industry 4.0 and the characteristics of the iMillennials generation.

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Smart City Platform Development for an Automated Waste Collection System

Cited by 64 publications

References 17 publications

Waste Management System Using IoT-Based Machine Learning in University

Waste Management System Using IoT-Based Machine Learning in University

An Innovative Industry 4.0 Cloud Data Transfer Method for an Automated Waste Collection System

Industry 4.0 Diagnosis from an iMillennial Educational Perspective

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