Georgios Mylonas

Dimitrios Amaxilatis, Themistoklis Sarantakos, Ioannis Chatzigiannakis et al.

In this work, we explore the application of recent data imputation techniques to enhance monitoring and management of water distribution networks using smart water meters, based on data derived from a real-world IoT water grid monitoring deployment. Despite the detailed data produced by such meters, data gaps due to technical issues can significantly impact operational decisions and efficiency. Our results, by comparing various imputation methods, such as k-Nearest Neighbors, MissForest, Transformers, and Recurrent Neural Networks, indicate that effective data imputation can substantially enhance the quality of the insights derived from water consumption data as we study their effect on accuracy and reliability of water metering data to provide solutions in applications like leak detection and predictive maintenance scheduling.

Vlassis Fotis, Ioannis Romanelis, Georgios Mylonas et al.

In this paper we study the problem of shape part retrieval in the point cloud domain. Shape retrieval methods in the literature rely on the presence of an existing query object, but what if the part we are looking for is not available? We present Part Retrieval Pipeline (PReP), a pipeline that creatively utilizes metric learning techniques along with a trained classification model to measure the suitability of potential replacement parts from a database, as part of an application scenario targeting circular economy. Through an innovative training procedure with increasing difficulty, it is able to learn to recognize suitable parts relying only on shape context. Thanks to its low parameter size and computational requirements, it can be used to sort through a warehouse of potentially tens of thousand of spare parts in just a few seconds. We also establish an alternative baseline approach to compare against, and extensively document the unique challenges associated with this task, as well as identify the design choices to solve them.

Dimitrios Amaxilatis, Georgios Mylonas, Evangelos Theodoridis et al.

Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.

Dimitrios Amaxilatis, Evangelos Lagoudianakis, Georgios Mylonas et al.

We briefly present the design and architecture of a system that aims to simplify the process of organizing, executing and administering crowdsensing campaigns in a smart city context over smartphones volunteered by citizens. We built our system on top of an Android app substrate on the end-user level, which enables us to utilize smartphone resources. Our system allows researchers and other developers to manage and distribute their "mini" smart city applications, gather data and publish their results through the Organicity smart city platform. We believe this is the first time such a tool is paired with a large scale IoT infrastructure, to enable truly city-scale IoT and smart city experimentation.

Georgios Mylonas, Dimitrios Amaxilatis, Lidia Pocero et al.

The use of maker community tools and IoT technologies inside classrooms is spreading in an increasing number of education and science fields. GAIA is a European research project focused on achieving behavior change for sustainability and energy awareness in schools. In this work, we report on how a large IoT deployment in a number of educational buildings and real-world data from this infrastructure, are utilized to support a "maker" lab kit activity inside the classroom, together with a serious game. We also provide some insights to the integration of these activities in the school curriculum, along with a discussion on our feedback so far from a series of workshop activities in a number of schools. Our initial results show strong acceptance by the school community.

Federica Paganelli, Georgios Mylonas, Giovanni Cuffaro et al.

Raising awareness among young people, and especially students, on the relevance of behavior change for achieving energy savings is increasingly being considered as a key enabler towards long-term and cost-effective energy efficiency policies. However, the way to successfully apply educational interventions focused on such targets inside schools is still an open question. In this paper, we present our approach for enabling IoT-based energy savings and sustainability awareness lectures and promoting data-driven energy-saving behaviors focused on a high school audience. We present our experiences toward the successful application of sets of educational tools and software over a real-world Internet of Things (IoT) deployment. We discuss the use of gamification and competition as a very effective end-user engagement mechanism for school audiences. We also present the design of an IoT-based hands-on lab activity, integrated within a high school computer science curricula utilizing IoT devices and data produced inside the school building, along with the Node-RED platform. We describe the tools used, the organization of the educational activities and related goals. We report on the experience carried out in both directions in a high school in Italy and conclude by discussing the results in terms of achieved energy savings within an observation period.

Chrysanthi Tziortzioti, Irene Mavrommati, Georgios Mylonas et al.

Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria.