Waste Collection

Internet of Things (IoT) enables Smart Cities with novel services. Waste collection in Smart Cities becomes a dynamic process with the proliferation of sensors and actuators embedded on real waste bins. Heterogeneous fleets of trucks are used for efficient waste collection exploiting the diverse road network. In this paper we propose a novel approach by incorporating Low Capacity Trucks (LCTs) and High Capacity Trucks (HCTs). However, HCTs are serving as Mobile Depots (MDs) which are cost efficient and decongest traffic in Smart Cities. A detailed system overview illustrates the architecture of the proposed approach. We also propose novel algorithms which support dynamic waste collection with MDs. Scheduling and routing are transformed to dynamic models. Specifically, we propose a novel scheduling algorithm while we customize an existing routing algorithm. The models where experimentally evaluated with real and synthetic data from the city of St. Petersburg, Russia. The results were promising and proved that the incorporation of MDs is efficient for waste collection in IoT-enabled Smart Cities. Finally, we perform an economic analysis in order to define the economic impact of the proposed solution to the municipality budget for an ownership cost for a period of five years; in which the proposed solution proved to be cost efficient.

Intelligent Transportation Systems (ITS) enable new services within Smart Cities. Efficient Waste Collection (WC) is considered a fundamental service for Smart Cities. Internet of Things (IoT) can be applied both in ITS and Smart cities forming an advanced platform for novel applications. Surveillance systems can be used as an as- sistive technology for high Quality of Service (QoS) in waste collection. Specifically, IoT components: (i) RFIDs, (ii) sensors, (iii) cameras, and (iv) actuators are incorpo- rated into ITS and surveillance systems for efficient waste collection. In this paper we propose an advanced Decision Support System (DSS) for efficient waste collection in Smart Cities. The system incorporates a model for data sharing between truck drivers on real time in order to perform waste collection and dynamic route optimization. The system handles the case of ineffective waste collection in inaccessible areas within the Smart City. Surveillance cameras are incorporated for capturing the problematic areas and provide evidence to the authorities. The waste collection system aims to provide high quality of service to the citizens of a Smart City.

In 2050, the amount of waste generated in sub-Saharan Africa is projected to triple current levels. Waste disposal coupled with urbanisation has led to environmental and social issues in rapidly developing African countries. An analysis of production and consumption taking into account the local conditions and cultural context is the first step in developing a suitable waste management strategy for a given region. This study focused on the composition and treatment of household waste in urban areas of Niamey, Niger, and assessed how household waste collection in Niamey City reflects the climate and local consumption. The current method of waste collection was examined with consideration of the quantity and types of household waste generated. The availability of locally made tools and an abundant labour force, as well as the willingness of residents to pay for disposal services, were the key factors influencing waste collection in Niamey. Due to deficiencies in public solid waste management services, and with the increasing number of young seasonal migrants in search of jobs, the importance of private individuals providing door-to-door household waste collection in Niamey has increased.

Smart cities are the next step in human habitation. In this context the proliferation of sensors and actuators within the Internet of Things (IoT) concept creates a real opportunity for increasing information awareness and subsequent efficient resource utilization. IoT-enabled smart cities will generate new services. One such service is the waste collection from the streets of smart cities. In the past, waste collection was treated with static routing models. These models were not flexible in case of segment collapse. In this paper we introduce a semi-static and dynamic shortest path routing model enhanced with sensing capabilities through the Internet connected objects in order to achieve effective waste collection.

During the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present paper, two individual meatheuristic algorithmic solutions, the ArcGIS Network Analyst and the Ant Colony System (ACS) algorithm, are introduced, implemented and discussed for the identification of optimal routes in the case of Municipal Solid Waste (MSW) collection. Both proposed applications are based on a geo-referenced spatial database supported by a Geographic Information System (GIS). GIS are increasingly becoming a central element for coordinating, planning and managing transportation systems, and so in collaboration with combinatorial optimization techniques they can be used to improve aspects of transit planning in urban regions. Here, the GIS takes into account all the required parameters for the MSW collection (i.e. positions of waste bins, road network and the related traffic, truck capacities, etc) and its desktop users are able to model realistic network conditions and scenarios. In this case, the simulation consists of scenarios of visiting varied waste collection spots in the Municipality of Athens (MoA). The user, in both applications, is able to define or modify all the required dynamic factors for the creation of an initial scenario, and by modifying these particular parameters, alternative scenarios can be generated. Finally, the optimal solution is estimated by each routing optimization algorithm, followed by a comparison between these two algorithmic approaches on the newly designed collection routes. Furthermore, the proposed interactive design of both approaches has potential application in many other environmental planning and management problems.

Smart Cities constitute the future of civil habitation. Internet of Things (IoT) enable innovative services exploiting sensor data from sensors embedded in the city. Waste collection is treated as a potential IoT service which exploits robustness and cost efficiency of a heterogeneous fleet. In this paper we propose a dynamic routing algorithm which is robust and copes when a truck is overloaded or damaged and need replacement. We also incorporate a system model which assumes two kinds of trucks for waste collection, the Low Capacity Trucks (LCTs) and the High Capacity Trucks (HCTs). By incorporating HCTs we achieve reduction of the waste collection operational costs because route trips to the dumps are reduced due to high waste storage capacity of these trucks. Finally, the proposed models are evaluated on synthetic and real data from the city municipality of St. Petersburg, Russia. The models demonstrate consistency and correctness.

This paper presents a review of the available literature on solid waste management problems, with a particular focus on vehicle routing problems. The available papers are classified into different catego-ries with the purpose of providing the reader with a guide that facilitates his/her search for papers in his/her field of interest. For each category, a table is presented that gives a summary of how each paper scores from that perspective. Additional explanation is presented about the characteristics of each cate-gory using some key references. Finally, this paper discovers unexplored areas of research and identi-fies trends in the literature.

Internet of  Things(IOT) can plays  a major  role  in  making  the  cities greener and  smart .  It provides a number of services to make the cities smart.  One of the major services offered by Internet of Things (IOT) is smart Management System. Waste Management is one of the threats what we are facing today. This paper provides a solution for this issue based on Internet of Things (IOT) enabled system along with ultrasonic and infrared sensors which will provide intelligence to the waste bins. The collected data will put into a spactio-temporal context and processed by several optimization algorithms to update the status of the wastebin to the authorities and as well as to the waste collectors. Data is transmitted over the internet along with wastebin location. After getting the intimation that is wastebin is true, the wastebin can be cleaned. The vehicle can be tracked and the task completed notification can also send to the authorities using android application.

Waste Management (WM) represents an important part of Smart Cities (SCs) with significant impact on modern societies. WM involves a set of processes ranging from waste collection to the recycling of the collected materials. The proliferation of sensors and actuators enable the new era of Internet of Things (IoT) that can be adopted in SCs and help in WM. Novel approaches that involve dynamic routing models combined with the IoT capabilities could provide solutions that outperform existing models. In this paper, we focus on a SC where a number of collection bins are located in different areas with sensors attached to them. We study a dynamic waste collection architecture, which is based on data retrieved by sensors. We pay special attention to the possibility of immediate WM service in high priority areas, e.g., schools or hospitals where, possibly, the presence of dangerous waste or the negative effects on human quality of living impose the need for immediate collection. This is very crucial when we focus on sensitive groups of citizens like pupils, elderly or people living close to areas where dangerous waste is rejected. We propose novel algorithms aiming at providing efficient and scalable solutions to the dynamic waste collection problem through the management of the trade-off between the immediate collection and its cost. We describe how the proposed system effectively responds to the demand as realized by sensor observations and alerts originated in high priority areas. Our aim is to minimize the time required for serving high priority areas while keeping the average expected performance at high level. Comprehensive simulations on top of the data retrieved by a SC validate the proposed algorithms on both quantitative and qualitative criteria which are adopted to analyze their strengths and weaknesses. We claim that, local authorities could choose the model that best matches their needs and resources of each city.

Smart Cities cater for ever increasing population, which needs sustainable solutions for efficient wellbeing. Waste collection is significant for providing a green ecosystem in such cities. IoT-enabled waste collection solutions assist such a green ecosystem. Waste collection used to be performed by humans or via human intervention.However, contemporary research incorporates robots to perform waste collection. In this paper we describethe real case of a line following robot bin that assists waste collection in the Smart City of Saint Petersburg, Russia. Evaluation is performed through a model combining the distance covered by the actor, the time passed for the collection and the bins emptied. The results show the superiority of robot bins, compared to human workers, highlighting the impact of IoT-enabled robot assisted waste collection as part of a green ecosystem.

In international research projects, partners and data often both come from diverse research backgrounds and adhere to different theoretical perspectives and research paradigms. Researchers have to collaborate as effectively as possible in what is often a temporary research consortium whose composition is based on varying expertise, previous collaboration and occasional fortuitous encounters. Investigating work and its quality in a European context therefore presents a range of interesting methodological, conceptual and organisational or practical challenges that face both the levels of analysis of comparison and the research process as a whole. Are we addressing and comparing individuals and their jobs, sectors and companies, or countries? Or are we aiming to paint a complex picture that explores the interrelationships between all of these analytical levels? And in exploring this complex picture, how do we render our findings useful and relevant for policymakers, social partners, managers and activists? In navigating a research project through these questions, we encounter a number of opportunities and constraints, and also trade-offs between different aims and logics of research. These points are worth reconstructing, to gain some insight into the possibilities and insights and the specific contexts of project-oriented research into the working life of Europeans.