IoT-Enabled Solid Waste Management in Smart Cities

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

Analysing the factors influencing groundwater quality with different pollution indices and PLS-SEM approach in the vicinity of an open dumping yard in Saduperi, Vellore, Tamil Nadu, India

Open dumping is the prevailing municipal solid waste (MSW) disposal technique in India. Unsanitary landfill releases leachate that contaminates valuable groundwater. Hence, the present study was carried out in the vicinity of the Saduperi open dumpsite, Vellore, Tamil Nadu, India, to explore the key factors that influences groundwater contamination. A total of 216 groundwater samples were collected between May 2021 and April 2022. These samples were categorised into four different seasons such as summer, southwest monsoon (SWM), northeast monsoon (NEM), and winter. Pollution indices such as the Leachate Pollution Index (LPI) and the Heavy Metal Pollution Index (HPI) were used to evaluate the contamination potential. The calculated LPI > 35 in all seasons indicates the prevailing poor environmental condition. It was observed that about 56% of the sampling site was affected by heavy metal concentrations such as Cd, Cr, and Ni. The HPI value was found to be more than the critical value of 100 in the 10 sampling wells for all seasons. Partial least squares-structural equation modelling (PLS-SEM) has also been carried out in this study to create a link between latent variables such as 'IOT Parameters', 'Leachate Parameters', 'Heavy Metal', and 'Groundwater Quality' which were quantified by the yield of R2 value. The R2 value of the sampling well ahead of the dumpsite and along the direction of the groundwater flow values ranges from 24.7 to 86.5% in comparison to the wells located behind the dumpsite, which are prone to more contamination due to migration of leachate. Hence, this present study shows various influencing factors that affect the groundwater quality.

Evaluation of landfill leachate biodegradability using IOT through geotracking sensor based drone surveying

In specific this work focuses on designing and developing a trial model first of its kind which is chemically non reactive using Urea Formaldehyde (UF) and a value added flat board that is to be prepared from solid waste. The display setup is to be monitored by Arduino Controller which incorporates the assessment of numerical factors and measurable compositions in the board. The compilation of manufactured goods from organic waste is a course of development network that is centrally operated by Radio Frequency Identification (RFID) tools. Preservation of database from the organic fraction is managed through software guidelines, the fifth generation radio technology with lower economic can cover communication over a larger radio spectrum with the support of ultra high frequency RFID Tags and systems, which assure the most favourable management of valuable products from the storage unit. Sensors attached with board and cloud platform supports collection of data from the huge solid waste yard. The current leachate management has a desire to suggest a strong resolution in handling solid waste that is elaborated with a novel methodology, where we suggest the radiofrequency identification of leachate monitoring by the use of modern versions of unmanned aerial vehicle the drones. At some stage in neutralizing the toxic waste the risk of life may be eliminated and accumulated waste were identified and with the support of advanced techniques and instruments like drones are used in managing wastes.

Smart waste bin monitoring using IoT for sustainable biomedical waste management

Suboptimal management of healthcare waste poses a significant concern that can be effectively tackled by implementing Internet of Things (IoT) solutions to enhance trash monitoring and disposal processes. The potential utilisation of the Internet of Things (IoT) in addressing the requirements associated with biomedical waste management within the Kaduna area was examined. The study included a selection of ten hospitals, chosen based on the criterion of having access to wireless Internet connectivity. The issue of biomedical waste is significant within the healthcare sector since it accounts for a considerable amount of overall waste generation, with estimates ranging from 43.62 to 52.47% across various facilities. Utilisation of (IoT) sensors resulted in the activation of alarms and messages to facilitate the prompt collection of waste. Data collected from these sensors was subjected to analysis to discover patterns and enhance the overall efficiency of waste management practices. The study revealed a positive correlation between the quantity of hospital beds and the daily garbage generated. Notably, hospitals with a higher number of beds were observed to generate a much greater amount of waste per bed. Hazardous waste generated varies by hospital, with one hospital leading in sharps waste (10.98 kgd-1) and chemical waste (21.06 kgd-1). Other hospitals generate considerable amounts of radioactive waste (0.60 kgd-1 and 0.50 kgd-1), pharmaceuticals, and genotoxic waste (16.19 kgd-1), indicating the need for specialised waste management approaches. The study sheds light on the significance of IoT in efficient waste collection and the need for tailored management of hazardous waste.

Use of Internet of Things in the context of execution of smart city applications: a review

The Internet of Things (IoT) is rapidly becoming one of the most talked-about and essential components of any digitization process. The IoT is comprised of several key necessary components, the most important of which are sensors, communication (the internet), and user interfaces for data processing. IoTs are currently finding applications in virtually every industry, including healthcare, where they are known as the internet of medical things (IoMT), industry, where they are known as the industrial internet of things (IIoT), and interconnection between people, where they are known as the internet of everything (IoE). The challenge is to leverage the Internet of Things (IoT), technology, and data to create smarter and more sustainable cities that enhance the quality of life for residents. Therefore, in this article; we have demonstrated the use of the IoT in a variety of applications for smart communities. These applications include smart transportation, smart water management, smart garbage management, smart house illumination, smart parking, smart infrastructure, etc. This research also includes an explanation of the flow process of implementing the IoT in different applications of smart communities, as well as their characteristics and particular applications. Along with their flow illustration, the stages involved in the implementation of smart city applications and the components they consist of are also displayed here. We have also taken into consideration the instances of particular cases and their implementation utilizing IoT. Some of these cases include the automated water collection methods of smart water management systems as well as the condition of the water. Based on the findings of the research, we came to the conclusion that IoT devices play an essential role in each and every one of the smart city project implementations.

IoT based smart waste management system in aspect of COVID-19

The rapid evolution of the IoT has led to various research challenges for improving smart city applications. Owing to the characteristics and virtues of IoT services, waste management has emerged as a prominent issue in today's society. An undiscerning illegal eviction of waste, lack of waste disposal and management systems, and inept waste management policies have resulted in severe health and environmental challenges. Based on an integrative review, the proposed technique provides insight into the potential of smart cities and associated communities in assisting waste management initiatives. This study has referred to the existing waste management issues in urban areas and proposed an IoT-based smart waste management system of India in aspects of COVID-19 afflicted houses. Our system intends to improve waste management by making regular environmental sterility and making COVID situations more convenient. The proposed framework ensures a solution for efficiently handling waste generated in urban areas, focusing on the interaction among concessioners and waste generators to monitor the unfilled level of bins. This proposal offers dynamic waste collection scheduling and route optimization while achieving quality of service.

Technical solutions for waste classification and management: A mini-review

The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about segregation of waste into different categories and proper disposal techniques in a country like India with an accelerated population growth is a critical issue. Since trash has different disposal techniques, according to its type, segregating waste through an automated process at the point of collection will streamline the process and result in effective waste management and utilization. The mini-review article evaluates the recent literature on technologies used for municipal waste segregation and management, with the motive of providing critical information for advancement in current research. This article reviews the use of various convolutional neural network architectures for waste classification and describes in detail as to why image processing methods are preferred over sensors for segregation into respective categories. It is also important to have an efficient waste monitoring and management system for proper disposal. A comprehensive mini-review was undertaken to understand internet of things-based models proposing efficient waste handling, from the perspective of reduced costs, collection time and optimized routes. The proposed systems were compared and evaluated based on the sensors used monitoring, microcontrollers and communication protocols such as Long Range, Global System for Mobile Communication, Zigbee and Wi-Fi, which are employed for the secure and efficient data transmission.

Building the Bridge to a Participatory Citizenship: Curricular Integration of Communal Environmental Issues in School Projects Supported by the Internet of Things

Generally, there is much to praise about the rise in acknowledging the need for young citizens to exercise their rights and duties, but the belief remains that this is not yet entrenched in young citizens' overall democratic involvement. A lack of citizenship and engagement in community issues was revealed by a recent study conducted by the authors in a secondary school from the outskirts of Aveiro, Portugal, during the 2019/2020 school year. Under the umbrella of a Design-Based Research methodological framework, citizen science strategies were implemented in the context of teaching, learning, and assessment, and at the service of the educational project of the target school, in a STEAM approach, and under Domains of Curricular Autonomy activities. The study's findings suggest that to build the bridge for participatory citizenship, teachers should engage students in collecting and analyzing data regarding communal environmental issues in a Citizen Science approach supported by the Internet of Things. The new pedagogies addressing the lack of citizenship and engagement in community issues promoted students' involvement at school and in the community, contributed to inform municipal education policies, and promoted dialogue and communication between local actors.

Smart Rainwater Harvesting for Sustainable Potable Water Supply in Arid and Semi-Arid Areas

This paper presents a smart rainwater harvesting (RWH) system to address water scarcity in Palestine. This system aims to improve the water harvesting capacity by using a shared harvesting system at the neighborhood level and digital technology. The presentation of this system is organized as follows: (i) identification of the challenges of the rainwater harvesting at the neighborhood level, (ii) design of the smart RWH system architecture that addresses the challenges identified in the first phase, (iii) realization of a simulation-based reliability analysis for the smart system performance. This methodology was applied to a residential neighborhood in the city of Jenin, Palestine. The main challenges of smart water harvesting included optimizing the shared tank capacity, and the smart control of the water quality and leakage. The smart RWH system architecture design is proposed to imply the crowdsourcing-based and automated-based smart chlorination unit to control and monitor fecal coliform and residual chlorine: screens, filters, and the first flush diverter address RWH turbidity. Water level sensors/meters, water flow sensors/meters, and water leak sensors help detect a water leak and water allocation. The potential time-based reliability (Re) and volumetric reliability (Rv) for the smart RWH system can reach 38% and 41%, respectively. The implication of the smart RWH system with a dual water supply results in full reliability indices (100%). As a result, a zero potable water shortage could be reached for the dual water supply system, compared to 36% for the municipal water supply and 59% for the smart RWH system. Results show that the smart RWH system is efficient in addressing potable water security, especially when combined with a dual water supply system.

An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.

Sensor-Based Solid Waste Handling Systems: A Survey

As a consequence of swiftly growing populations in the urban areas, larger quantities of solid waste also form rapidly. Since urban local bodies are found to be unable to manage this perilous situation effectively, there is a high probability of risks relative to the environment and public health. A sudden change is indispensable in the existing systems that are developed for the collection, transportation, and disposal of solid waste, which are entangled in turmoil. However, Smart sensors and wireless technology enable cyber-physical systems to automate solid waste management, which will revolutionize the industry. This work presents a comprehensive study on the evolution of automation approaches in solid waste management systems. This study is enhanced by dissecting the available literature in solid waste management with Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), and Internet of Things (IoT)-based approaches and analyzing each category with a typical architecture, respectively. In addition, various communication technologies adopted in the aforementioned categories are critically analyzed to identify the best choice for the deployment of trash bins. From the survey, it is inferred that IoT-based systems are superior to other design approaches, and LoRaWAN is identified as the preferred communication protocol for the automation of solid waste handling systems in urban areas. Furthermore, the critical open research issues on state-of-the-art solid waste handling systems are identified and future directions to address the same topic are suggested.

IoT Analytics and Agile Optimization for Solving Dynamic Team Orienteering Problems with Mandatory Visits

Transport activities and citizen mobility have a deep impact on enlarged smart cities. By analyzing Big Data streams generated through Internet of Things (IoT) devices, this paper aims to show the efficiency of using IoT analytics, as an agile optimization input for solving real-time problems in smart cities. IoT analytics has become the main core of large-scale Internet applications, however, its utilization in optimization approaches for real-time configuration and dynamic conditions of a smart city has been less discussed. The challenging research topic is how to reach real-time IoT analytics for use in optimization approaches. In this paper, we consider integrating IoT analytics into agile optimization problems. A realistic waste collection problem is modeled as a dynamic team orienteering problem with mandatory visits. Open data repositories from smart cities are used for extracting the IoT analytics to achieve maximum advantage under the city environment condition. Our developed methodology allows us to process real-time information gathered from IoT systems in order to optimize the vehicle routing decision under dynamic changes of the traffic environments. A series of computational experiments is provided in order to illustrate our approach and discuss its effectiveness. In these experiments, a traditional static approach is compared against a dynamic one. In the former, the solution is calculated only once at the beginning, while in the latter, the solution is re-calculated periodically as new data are obtained. The results of the experiments clearly show that our proposed dynamic approach outperforms the static one in terms of rewards.

The Scandinavian Third Way as a Proposal for Sustainable Smart City Development—A Case Study of Aarhus City

The practical implementation of the goals of smart sustainable cities has different forms. This paper explores an example of the Danish smart city Aarhus, through which the so-called ‘Scandinavian third way’ of smart city development is being proposed. The foundations of the ‘third way’ are directly derived from the Scandinavian tradition of cooperation; it is supposed to be an alternative to the more commercial American model and the more centrally-controlled Asian tradition. The paper aims to identify how the Scandinavian collaborative model has influenced the process of developing the smart city Aarhus, to analyse the proposed ‘Scandinavian third way’ of smart city development, and finally to assess its applicability in other urban centres. To achieve these goals, the method of literature analysis and a case study along with qualitative analysis of existing data and individual in-depth interviews with decision makers and observers of political life were applied. As the results show, the Scandinavian tradition of governance and political decision-making present in Denmark is not without significance for the functioning form of the smart city of Aarhus. Its foundations have been adopted by the Aarhus municipality in the implementation of its smart city activities, creating a unique modern city management model.

A Flexible, Wearable, and Wireless Biosensor Patch with Internet of Medical Things Applications

Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.

Digital Twins in Civil Infrastructure Systems

This research explores the existing definitions, concepts and applications surrounding the efficient implementation and use of digital twins (DTs) within civil infrastructure systems (CISs). The CISs within the scope of this research are as follows: transportation, energy, telecommunications, water and waste, as well as Smart Cities, which encompasses all of the previous. The research methodology consists of a review of current literature, a series of semi-structured interviews and a detailed survey. The outcome of this work is a refined definition of DTs within CISs, in addition to a set of recommendations for both future academic research and industry best practice.