Theodoros Anagnostopoulos

Assessing dynamic models for high priority waste collection in smart cities

We focus on a system that adopts IoT-enabled waste collection in Smart Cities.We propose the adoption of dynamic routing for waste collection.We propose four models for the collection of high priority waste bins.We assess the performance of the proposed models based on real data.We evaluate the models through qualitative and quantitative metrics. 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.

Path Prediction through Data Mining

Context-awareness is viewed as one of the most important aspects in the emerging ubiquitous computing paradigm. However, mobile applications are required to operate in pervasive computing environments of dynamic nature. Such applications predict the appropriate context in their environment in order to act efficiently. A context model, which deals with the location prediction of moving users, is proposed. Such model is used for trajectory classification through machine learning techniques. Hence, spatial and spatiotemporal context prediction is regarded as context classification based on supervised learning. Finally, two classification schemes are presented, evaluated and compared with other ML schemes in order to support location prediction and decision making.

Efficient Location Prediction in Mobile Cellular Networks

Mobile context-aware applications are capable of predicting the context of the user in order to operate pro-actively and provide advanced services. We propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. We introduce different variants of the considered location predictor dealing with location (cell) identifiers and directions. Symbolic location classification is treated as a supervised learning problem. We evaluate the prediction efficiency and accuracy of the proposed predictors through synthetic and real-world traces and compare our solution with existing algorithms for location prediction. Our findings are very promising for the location prediction problem and the adoption of proactive context-aware applications and services.

Challenges and Opportunities of Waste Management in IoT-Enabled Smart Cities: A Survey

The new era of Web and Internet of Things (IoT) paradigm is being enabled by the proliferation of various devices like RFIDs, sensors, and actuators. Smart devices (devices having significant computational capabilities, transforming them to ‘smart things’) are embedded in the environment to monitor and collect ambient information. In a city, this leads to Smart City frameworks. Intelligent services could be offered on top of such information related to any aspect of humans’ activities. A typical example of services offered in the framework of Smart Cities is IoT-enabled waste management. Waste management involves not only the collection of the waste in the field but also the transport and disposal to the appropriate locations. In this paper, we present a comprehensive and thorough survey of ICT-enabled waste management models. Specifically, we focus on the adoption of smart devices as a key enabling technology in contemporary waste management. We report on the strengths and weaknesses of various models to reveal their characteristics. This survey sets up the basis for delivering new models in the domain as it reveals the needs for defining novel frameworks for waste management.

Robust waste collection exploiting cost efficiency of IoT potentiality in Smart Cities

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.

An adaptive location prediction model based on fuzzy control

We focus on the proactivity feature of mobile applications. We propose a short-memory adaptive location predictor that realizes mobility prediction in the absence of extensive historical mobility information. Our predictor is based on a local linear regression model, while its adaptation capability is achieved through a fuzzy controller. Such fuzzy controller capitalizes on an appropriate size of historical mobility information in order to minimize the location prediction error and provide fast adaptation to any detected movement change. Our prediction experiments, performed with real GPS data, show the predictability and adaptability of the proposed location predictor.

Top -- k Query Based Dynamic Scheduling for IoT-enabled Smart City Waste Collection

Smart Cities are being designed and built for comfortable human habitation. Among services that Smart Cities will offer is the environmentally-friendly waste/garbage collection and processing. In this paper, we motivate and propose an Internet of Things (IoT) enabled system architecture to achieve dynamic waste collection and delivery to processing plants or special garbage tips. In the past, waste collection was treated in a rather static manner using classical operations research approach. As proposed in this paper, nowadays, with the proliferation of sensors and actuators, as well as reliable and ubiquitous mobile communications, the Internet of Things (IoT) enables dynamic solutions aimed at optimizing the garbage truck fleet size, collection routes and prioritized waste pick-up. We propose a top -- k query based dynamic scheduling model to address the challenges of near real-time scheduling driven by sensor data streams. An Android app along with a user-friendly GUI is developed and presented in order to prove feasibility and evaluate a waste collection scenario using experimental data. 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.

Mobility Prediction Based on Machine Learning

Mobile applications are required to operate in highly dynamic pervasive computing environments of dynamic nature and predict the location of mobile users in order to act proactively. We focus on the location prediction and propose a new model/framework. Our model is used for the classification of the spatial trajectories through the adoption of Machine Learning (ML) techniques. Predicting location is treated as a classification problem through supervised learning. We perform the performance assessment of our model through synthetic and real-world data. We monitor the important metrics of prediction accuracy and training sample size.

Effective Waste Collection with Shortest Path Semi-Static and Dynamic Routing

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.

An Online Adaptive Model for Location Prediction

Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.