Smart Waste Collection System Based on Location Intelligence

Comparison of different waste bin monitoring approaches: An exploratory study

Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins.

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

The Internet of Things (IoT) technology and devices represent an exciting field in computer science that is rapidly emerging worldwide. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. The proliferation of IoT devices coincides with advancements in wireless networking technologies, driven by the enhanced connectivity of the internet. Today, nearly any everyday object can be connected to the network, reflecting the growing demand for automation and efficiency. This paper reviews the emergence of IoT devices, analyzed their common applications, and explored the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Given the rapid pace at which IoT technology is advancing, this paper provides researchers with a deeper understanding of the factors that have brought us to this point and the ongoing efforts that are actively shaping the future of IoT. By offering a comprehensive analysis of the current landscape and potential future developments, this paper serves as a valuable resource to researchers seeking to contribute to and navigate the ever-evolving IoT ecosystem.

Swarm Intelligence Internet of Vehicles Approaches for Opportunistic Data Collection and Traffic Engineering in Smart City Waste Management

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics.

Recent advances in applications of artificial intelligence in solid waste management: A review

Efficient management of solid waste is essential to lessen its potential health and environmental impacts. However, the current solid waste management practices encounter several challenges. The development of effective waste management systems using advanced technologies is vital to overcome the challenges faced by the current approaches. Artificial Intelligence (AI) has emerged as a powerful tool for applications in various fields. Several studies also reported the applications of AI techniques in the management of solid waste. This article critically reviews the recent advancements in the applications of AI techniques for the management of solid waste. Various AI and hybrid techniques have been successfully employed to predict the performance of various methods used for the generation, segregation, storage, and treatment of solid waste. The key challenges that limit the applications of AI in solid waste are highlighted. These include the availability and selection of applicable data, poor reproducibility, and less evidence of applications in real solid waste. Based on identified gaps and challenges, recommendations for future work are provided. This review is beneficial for all stakeholders in the field of solid waste management, including policy-makers, governments, waste management organizations, municipalities, and researchers.

Towards the smart and sustainable transformation of Reverse Logistics 4.0: a conceptualization and research agenda

The recent advancement of digitalization and information and communication technology (ICT) has not only shifted the manufacturing paradigm towards the Fourth Industrial Revolution, namely Industry 4.0, but also provided opportunities for a smart logistics transformation. Despite studies have focused on improving the smartness, connectivity, and autonomy of isolated logistics operations with a primary focus on the forward channels, there is still a lack of a systematic conceptualization to guide the coming paradigm shift of reverse logistics, for instance, how "individualization" and "service innovation" should be interpreted in a smart reverse logistics context? To fill this gap, Reverse logistics 4.0 is defined, from a holistic perspective, in this paper to offer a systematic analysis of the technological impact of Industry 4.0 on reverse logistics. Based on the reported research and case studies from the literature, the conceptual framework of smart reverse logistics transformation is proposed to link Industry 4.0 enablers, smart service and operation transformation, and targeted sustainability goals. A smart reverse logistics architecture is also given to allow a high level of system integration enabled by intelligent devices and smart portals, autonomous robots, and advanced analytical tools, where the value of technological innovations can be exploited to solve various reverse logistics problems. Thus, the contribution of this research lies, through conceptual development, in presenting a clear roadmap and research agenda for the reverse logistics transformation in Industry 4.0.

Indicators and ICTs application for municipal waste management

The worldwide populace is rising steadily. Urbanization is likewise expanding quickly with the rising populace. Fast urbanization has considerably increased the generation of municipal solid waste (MSW). The MSW management issues have recently been analyzed through various assessment indicators and information and communication technologies (ICTs). This article provides an overview of applications of assessment indicators and ICTs for addressing the environmental issues of waste disposal and management in municipalities. The selection of indicators mainly depends on the stakeholders' specific requirements, such as waste management strategies, urban planning and development, human health, and energy generation. The literature analysis revealed that collection, sorting, recycling, cost efficiency, and environmental aspect were the leading indicators used in waste management studies. And these indicators reduce the complexity of systems and formulate evaluations easier for the decision-maker. Moreover, these are also helpful in assessing the improvement and reporting the waste condition to the expert. These analysis further revealed that information and communication technology is a requirement in the planning and managing of current solid waste disposal problems. The use of ICTs in waste management systems mitigates possible constraints regarding spot selection, inept waste disposal, waste collection monitoring, and proper recycling.

A deep learning approach based hardware solution to categorise garbage in environment

Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

Internet of Things Applications: Opportunities and Threats

In the century of automation, which is digitized, and more and more technology is used, automatic systems' replacement of old manual systems makes people's lives easier. Nowadays, people have made the Internet an integral part of humans' daily lives unless they are insecure. The Internet of Things (IoT) secures a platform that authorizes devices and sensors to be remotely detected, connected, and controlled over the Internet. Due to the developments in sensor technologies, the production of tiny and low-cost sensors has increased. Many sensors, such as temperature, pressure, vibration, sound, light, can be used in the IoT. As a result of the development of these sensors with new generations, the power of the IoT technology increases, and accordingly, the revolution of IoT applications are developing rapidly. Therefore, their security issues and threats are challenging topics. In this paper, the benefits and open issues, threats, limitations of IoT applications are presented. The assessment shows that the most influential factor for evaluating IoT applications is the cost that is used in 79% of all articles, then the real-time-ness that is used in 64%, and security and error are used in 57% of all reviewed articles.

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

LoRaWAN and Urban Waste Management-A Trial

The city of Lisbon, as any other capital of a European country, has a large number of issues regarding managing waste and recycling containers spread throughout the city. This document presents the results of a study promoted by the Lisbon City Council for trialing LPWAN (Low-Power Wide-Area Network) technology for the waste management vertical under the Lisbon Smart City initiative. Current waste management is done using GSM (Global System for Mobile communications) sensors, and the municipality aims to use LPWAN in order to improve range and reduce costs and provisioning times when changing the communications provider. After an initial study, LoRa (Long Range) and LoRAWAN (LoRa Wide Area Network) as its network counterpart, were selected as the LPWAN technology for trials considering several use cases, exploring multiple distances, types of recycling waste containers, placements (underground or surface) and kinds of commercially available waste level measurement LoRa sensors. The results showed that the underground waste containers proved to be, as expected, the most difficult to operate correctly, where the container itself imposed attenuation levels of 26 dB on the LoRa link budget. The successful results were used to promote the deployment of a city-wide LoRa network, available to all the departments inside the Lisbon City Council. Considering the network capacity, the municipality also decided to make the network freely available to citizens.

Methodological Proposals for the Development of Services in a Smart City: A Literature Review

This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions.

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

Simulation and optimization of dynamic waste collection routes

Smart waste collection strategies have been developed to replace conventional fixed routes with dynamic systems that respond to the actual fill-level of waste bins. The variation in waste generation patterns, which is the main driver for the profit of smart systems, is exacerbated in the United Arab Emirates (UAE) due to a high expatriate ratio. This leads to significant changes in waste generation during breaks and seasonal occasions. The present study aimed to evaluate a geographic information system (GIS)-based smart collection system (SCS) compared to conventional practices in terms of time, pollution, and cost. Different scenarios were tested on a local residential district based on variable bin filling rates. The input data were obtained from a field survey on different types of households. A knowledge-based decision-making algorithm was developed to select the bins that require collection based on historical data. The simulation included a regular SCS scenario based on actual filling rates, as well as sub-scenarios to study the impact of reducing the waste generation rates. An operation cost reduction of 19% was achieved with SCS compared to the conventional scenario. Moreover, SCS outperformed the conventional system by lowering carbon-dioxide emissions by between 5 and 22% for various scenarios. The operation costs were non-linearly reduced with the incremental drops in waste generation. Furthermore, the smart system was validated using actual waste generation data of the study area, and it lowered collection trip times by 18 to 42% compared to the conventional service. The present study proposes an integrated SCS architecture, and explores critical considerations of smart systems.

Proposal for Planning an Integrated Management of Hazardous Waste: Chemical Park, Jiangsu Province, China

This study elucidates the current state of hazardous waste management in industrial parks with a particular focus on Jiangsu Province. The development of the Internet of Things and big data technology has encouraged the application of intelligent technologies for hazardous waste management. However, the concept of considering each chemical park as a large enterprise have been suggested to reduce potential risks from the transportation of hazardous waste, help local environmental protection departments intuitively understand the position of park administrators, and to realize cyclic utilization of hazardous waste resources. We propose integrated management of hazardous waste at the chemical park level. In this study, principles such as integrated closed management, data unification, clustering management, and intelligent technologies were introduced for hazardous waste management in chemical parks. Additionally, potential approaches, such as a unified environmental butler service, an intelligent standard box system, and system optimization scenarios, are proposed and comprehensively explained. These approaches are likely to enable informed, structured, comprehensive, and interdisciplinary management of hazardous waste not only in chemical parks but also in industrial parks in general in the future.

Designing a sustainable dynamic collection service for WEEE: an economic and environmental analysis through simulation

The integration of economic and environmental objectives is crucial in the waste collection sector, especially for flows characterized by a high economic value like waste from electric and electronic equipment (WEEE). WEEE needs a complex and flexible reverse logistics system to face high uncertainty and variability of waste flows, while keeping a high efficiency. A few efforts in the literature have focused on planning an efficient collection service on a local scale. In this paper, a simulation-based methodology is adopted to compare different alternatives for a WEEE collection service in Italy. A dynamic collection scheme (i.e. with variable collection frequencies based on the actual level of waste flow) is simulated in two different logistics configurations, i.e. one based on direct connection and one based on an "hub-and-spoke" network. The impact of adopting electric vehicles is also evaluated. Alternatives are compared through economic and environmental key performance indicators to assess the level of sustainability. Results show the advantages of a "hub-and-spoke" configuration with the use of electric vehicles.

The future of waste management in smart and sustainable cities: A review and concept paper

The potential of smart cities in remediating environmental problems in general and waste management, in particular, is an important question that needs to be investigated in academic research. Built on an integrative review of the literature, this study offers insights into the potential of smart cities and connected communities in facilitating waste management efforts. Shortcomings of existing waste management practices are highlighted and a conceptual framework for a centralized waste management system is proposed, where three interconnected elements are discussed: (1) an infrastructure for proper collection of product lifecycle data to facilitate full visibility throughout the entire lifespan of a product, (2) a set of new business models relied on product lifecycle data to prevent waste generation, and (3) an intelligent sensor-based infrastructure for proper upstream waste separation and on-time collection. The proposed framework highlights the value of product lifecycle data in reducing waste and enhancing waste recovery and the need for connecting waste management practices to the whole product life-cycle. An example of the use of tracking and data sharing technologies for investigating the waste management issues has been discussed. Finally, the success factors for implementing the proposed framework and some thoughts on future research directions have been discussed.

Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization

New solutions for managing waste have emerged due to the rise of Smart Cities and the Internet of Things. These solutions can also be applied in rural environments, but they require the deployment of a low cost and low consumption sensor network which can be used by different applications. Wireless technologies such as LoRa and low consumption microcontrollers, such as the SAM L21 family make the implementation and deployment of this kind of sensor network possible. This paper introduces a waste monitoring and management platform used in rural environments. A prototype of a low consumption wireless node is developed to obtain measurements of the weight, filling volume and temperature of a waste container. This monitoring allows the progressive filling data of every town container to be gathered and analysed as well as creating alerts in case of incidence. The platform features a module for optimising waste collection routes. This module dynamically generates routes from data obtained through the deployed nodes to save energy, time and consequently, costs. It also features a mobile application for the collection fleet which guides every driver through the best route—previously calculated for each journey. This paper presents a case study performed in the region of Salamanca to evaluate the efficiency and the viability of the system’s implementation. Data used for this case study come from open data sources, the report of the Castilla y León waste management plan and data from public tender procedures in the region of Salamanca. The results of the case study show a developed node with a great lifetime of operation, a large coverage with small deployment of antennas in the region, and a route optimization system which uses weight and volume measured by the node, and provides savings in cost, time and workforce compared to a static collection route approach.

Improving logistic efficiency of WEEE collection through dynamic scheduling using simulation modeling

The complexity of collection systems for Waste from Electric and Electronic Equipment (WEEE) in the EU is increasing, due to the latest directive that sets new collection targets and modes. The high variability and the uncertainty of reverse flows require innovative logistic approaches. One recent option for increasing efficiency and responsiveness in waste collection services, boosted by new technological solutions for waste level monitoring, is to adopt a dynamic collection scheme, where the collection frequency is not established a priori (based on a fixed plan), but it is based on the actual filling levels of waste bins. This option can allow the service provider to plan the collection service following the actual demand, resulting in a more responsive service, while improving the logistic efficiency. This paper evaluates the implementation of dynamic scheduling schemes for the collection of WEEE. A hybrid simulation model has been developed in order to support researchers and practitioners in assessing quantitative impacts of adopting dynamic scheduling in WEEE collection. Three logistic alternatives (a fixed collection schedule scheme, a pure dynamic scheme and a mixed one) have been compared in a test case based on data of an Italian municipality; collection services for different types of WEEE (i.e. large appliances and small items) have been analyzed. Results show a promising performance of dynamic schedules compared to the fixed one, revealing, for the specific test case, how a mixed solution can combine the advantages of dynamic and fixed scheduling, gaining flexibility towards customer demand while improving truck resource utilization.