Design and implementation of a platform for smart connected school buildings

We have designed and implemented a platform that enables monitoring and actuation in multiple buildings, that has been utilised in the context of a research project in Greece, focusing on public school buildings. The Green Mindset project has installed IoT devices in 12 Greek public schools to monitor energy consumption, along with indoor and outdoor environmental parameters. We present the architecture and actual deployment of our system, along with a first set of findings

Raising awareness for water polution based on game activities using internet of things

Awareness among young people regarding the environment and its resources and comprehension of the various factors that interplay, is key to changing human behaviour towards achieving a sustainable planet. In this paper IoT equipment, utilizing sensors for measuring various parameters of water quality, is used in an educational context targeting at a deeper understanding of the use of natural resources towards the adoption of environmentally friendly behaviours. We here note that the use of water sensors in STEM gameful learning is an area which has not received a lot of attention in the previous years. The IoT water sensing and related scenaria and practices, addressing children via discovery, gamification, and educational activities, are discussed in detail

IoT sensors in sea water environment: Ahoy! Experiences from a short summer trial

IoT sensors for measuring various sea water parameters, are explored here, aiming towards an educational context, in order to lead to a deeper understanding of the use of aquatic environments as natural resources, and towards the adoption of environmentally friendly behaviors. Sea-water sensing via IoT has not been extensively explored, due to practical difficulties in deployment, and the same applies to devising appropriate scenaria for understanding aquatic parameters in STEM education. A short hands-on IoT sensing trial, that has been conducted in various location of the Aegean sea, is reported in this paper. This research set out to gain insight into real data sets on which to base observations for devising realistic educational scenaria pertaining aquatic parameters. The results of this experiment are meant to guide research further, by shedding light into the IoT sensing issues that are involved in an educational scientific context. The goal is conducting broader research in the area of IoT water sensing towards its further utilization in STEM education

A smartwater metering deployment based on the fog computing paradigm

In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system's design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system

On the design of a fog computing-based, driving behaviour monitoring framework

Recent technological improvements in vehicle manufacturing may greatly improve safety however, the individuals' driving behaviour still remains a factor of paramount importance with aggressiveness, lack of focus and carelessness being the main cause of the majority of traffic incidents. The imminent deployment of 5G networking infrastructure, paired with the advent of Fog computing and the establishment of the Internet of Things (IoT) as a reliable and cost-effective service delivery framework may provide the means for the deployment of an accurate driving monitoring solution which could be utilized to further understand the underlying reasons of peculiar road behaviour, as well as its correlation to the driver's physiological state, the vehicle condition and certain environmental parameters. This paper presents some of the fundamental attributes of Fog computing along with the functional requirements of a driving behaviour monitoring framework, followed by its high level architecture blueprint and the description of the prototype implementation process

Utilising fog computing for developing a person-centric heart monitoring system

Heart disease and stroke are becoming the leading causes of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECGs have limited accuracy and rely on external resources to analyze the signals and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behaviour. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud, (c) the overall power consumption and (d) the confidentiality of private data. Based on this concept, the HEART system presented in this work combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate, and instant heart monitoring. The wearable device is remotely trained by a physician to learn to accurately identify critical events related to each particular patient. Following this training session, the wearable device becomes capable of interpreting a large number of heart abnormalities without relying on cloud services and edge resources, when the medical doctor is not present. The Fog computing approach extends the cloud computing paradigm by migrating data-processing closer to the production site, thus accelerating the system's responsiveness to events. The HEART system's performance concerning the accuracy of detecting abnormal events and the power consumption of the wearable device is evaluated. Results indicate that a very high success rate can be achieved in terms of event detection ratio and the battery is able to sustain operation up to a full week without the need for a recharge

Utilising Fog Computing for Developing a Person-Centric Heart Monitoring System

Heart disease and stroke are becoming the leading causes of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that help physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECGs have limited accuracy and rely on external resources to analyse the signals and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyse the signal and identify abnormal behaviour. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud, (c) the overall power consumption and (d) the confidentiality of private data. Based on this concept, the HEART system presented in this work, combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate, and instant heart monitoring. The wearable device is remotely trained by a physician to learn to accurately identify critical events related to each particular patient. Following this, the wearable device becomes capable of interpreting a large number of heart abnormalities without relying on cloud services and edge resources, when the medical doctor is not present. The Fog computing approach extends the cloud computing paradigm by migrating data-processing closer to production site, thus accelerating the system's responsiveness to events. TheHEART system's performance concerning the accuracy of detecting abnormal events and the power consumption of the wearable device is evaluated. Results indicate that a very high success rate can be achieved in terms of event detection ratio and the battery is able to sustain operation up to a full week without the need for recharge

Sparks-Edge: Analytics for intelligent city water metering

Smart Meter infrastructures are emerging systems that measure, col- lect, and analyze utility data and communicate with the network’s backbone on a fixed schedule. Such infrastructures are a vital part towards real Intelligent Cities. In this article we propose an edge- processing oriented Internet of Things architecture for smart meter networks that helps reduce data communication while keeping the sys- tem secure, reliable and responsive. We discuss our system architecture based on a real-world water metering deployment of 48 water meters inside a University Campus, using off-the-shelf wM-Bus water meters. We also provide a study of how our solution can face the same problems regardless of the size of the water meter network, scaling up to cities of millions of citizens and measuring points, reducing traffic and data sizes event by 80%