Scenarios for Educational and Game Activities using Internet of Things Data

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

Environmental educational activities using data from the Internet of Things

Η ευαισθητοποίηση των νέων και η αλλαγή των συνηθειών τους όσον αφορά στην προστασία του περιβάλλοντος και την εξοικονόμηση ενέργειας, θα είναι καθοριστικής σημασίας για την διατήρηση ενός βιώσιμου πλανήτη στο εγγύς μέλλον. Σε αυτό το πλαίσιο, η αντιμετώπιση της κλιματικής αλλαγής απαιτεί αρχικά την ενημέρωση και κατόπιν την αλλαγή νοοτροπίας και εφαρμογή συμπεριφορών φιλικότερων προς το περιβάλλον. Σε αυτό το κείμενο, παρουσιάζουμε μερικές εκπαιδευτικές πρακτικές STEM που αξιοποιούν συστήματα Διαδικτύου των Αντικειμένων με αισθητήρες (Internet of Things, IoT), σε συνδυασμό με κατάλληλα σχεδιασμένες εκπαιδευτικές δραστηριότητες διερευνητικής μάθησης. Η ενίσχυση της εκπαιδευτικής κοινότητας στην περιβαλλοντική εκπαίδευση των νέων έχει πολλαπλασιαστικό αποτέλεσμα, καθώς η υιοθέτηση ενεργειακά αποδοτικών συνηθειών επηρεάζει και το άμεσο οικογενειακό τους περιβάλλον. Ως ένα τέτοιο παράδειγμα, η πλατφόρμα του έργου GAIA αξιοποιεί πραγματικά δεδομένα από σχολικά κτίρια, και μπορεί να χρησιμοποιηθεί ως βάση για τη σχεδίαση εκπαιδευτικών δραστηριοτήτων STEM, αντλώντας δεδομένα από διάφορα περιβάλλοντα. Επιπλέον, προτείνεται η χρήση αισθητήρων για τη μέτρηση βασικών ωκεανογραφικών παραμέτρων όπως η θερμοκρασία, η αλατότητα, το διαλυμένο οξυγόνο, η αλκαλικότητα και η θολερότητα σε παράκτια περιβάλλοντα, η οποία δεν έχει εξεταστεί  στο παρελθόν, ως ένα επόμενο βήμα για το σχεδιασμό εκπαιδευτικών σεναρίων αυτού του τύπου.Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. Promoting sustainable behavior at school impacts the home behavior, as children communicate their newly acquired knowledge to parents.  In this context, reinforcing the educational community on educating new generations potentially has a multiplier effect for reducing our environmental footprint.  IoT sensing and related scenario and practices, which engage school children via discovery and educational activities, focusing on encouraging sustainability of energy and natural resources, are examined in this paper. As an example of such an approach, the GAIA platform can act as the basis for scenarios utilizing real world data for educational activities that encourage energy efficient behavior. In addition, the use of seawater sensors in STEM education, that has been realized in very few cases, is proposed as educational scenarios utilizing real-world data that are worth exploring.  The GAIA platform (Mylonas et al, Amaxilatis et al, 2017) is one of a number of recent IoT systems that focus on the educational community. A real-world IoT deployment is spread in 3 countries (Greece, Italy, Sweden), monitoring in real time 18 school buildings in terms of electricity consumption and indoor/outdoor environmental conditions. The data collected is used as input in educational scenarios, whose goal is to educate and attempt to transform the behavior of students through a series of trials conducted in the educational environment. Feedback mechanisms inform the students and teachers on current energy consumption at school; in this way, they assist towards raising awareness regarding environmental effects of energy spending and promote energy literacy among students.  GAIA (Mylonas et al, Nov. 2017) is based on the assumption that continuous monitoring of the power consumption-related behavior of students can positively contribute towards energy savings. Since the IoT deployment is multi-site and multicountry can motivate, e.g., to identify energy consumption patterns in different countries and across different climate zones. This can be used to draw comparisons or kickstart competitions; for instance, students of school A can compete with students of school B in terms of energy efficiency. This could also help to better understand cultural differences with respect to energy efficiency awareness and sustainability. The devices deployed provide 880 sensing points organized in four main categories: (1) classroom environmental comfort sensors (devices within classrooms); (2) atmospheric sensors (devices positioned outdoors); (3) weather stations (devices positioned on rooftops); and (4) power consumption meters (devices attached to the main breakout box of the buildings, measuring energy consumption). The IoT deployments vary significantly from school to school (e.g., in number of sensors, hardware manufacturer, networking technology, communication protocols for delivering sensor data, etc.). The IoT devices used are either open-source hardware IoT nodes (based on the Arduino popular electronics prototyping platform, Pocero et al, 2017) or off-the-shelf products, acquired from IoT device manufacturers.  The platform also incorporates participatory sensing technologies for periodical collection of energy usage to acquire information in buildings where no IoT sensing elements are available, e.g., utilizing web/smartphone/social networking applications for acquiring information on room occupancy, usage of conditioning or special machinery, opening of windows, etc. The goal of GAIA is to include the users in the loop of monitoring the energy consumption in the buildings they use daily, thus making the first steps towards raising awareness, connecting the educational activities carried out at schools with their activities at their home environment and also engaging the parents and relatives at home. The teachers can initiate participatory sensing sessions during the courses, so that students can use phones and tablets to gather data in real time and then review them in class, as part of an educational activity. Bringing IoT into the sea: Most IoT related research focuses on terrestrial applications. Even when offshore infrastructures or vessels are considered, IoT devices are mostly deployed in “dry” surfaces and only some specific transducers are actually deployed into the water. The underwater environment is hostile, and consequently underwater IoT devices are very expensive. If you only consider a reliable water-proof housing for shallow water, it costs at least 2 orders of magnitude more than the respective terrestrial solutions, or even more in the case of deep water scenarios. Underwater operations are complex and challenging. As an example, the fast growth of algae or microorganisms can rapidly affect the quality of sensors readings that have to be often cleaned up. In addition, underwater communications are still extremely difficult and energy hungry; RF propagates only at a few centimeters and only acoustic or optical communications can be used for longer distances. The energy cost of underwater communications strongly limits the device lifetime that is usually in the order of few months at best and requires frequent replacements of the batteries, a time and resource-consuming task. Finally, communication standards are emerging only in the last years. Due to these reasons, the availability of underwater IoT data is still very limited. One of the few attempts to provide a federation of underwater testbeds for the Internet of Underwater Things is the EU project SUNRISE (SUNRISE Project website). While SUNRISE clearly showed us the potential of exploring underwater data, it was not originally conceived for STEM educational activities, and both the complexity of the tools and the costs of the equipment are not yet suitable to be operated by students. Despite these difficulties, there are already some efforts for more affordable tools for underwater investigations (Baichtal, 2015) (OpenROV website) (The Cave Pearl Project website) and is, however, possible to design significant STEM activities that focus on shallow water and/or surface sampling that significantly lower the above discussed difficulties. Indeed, the focus on the shallow water and/or the sea surface allow us to a) engage students in participatory sampling (i.e., they are directly involved in the sampling procedure at sea), b) deploy relatively simple networking infrastructures capable to deliver the data acquired by possible underwater transducers.  In the latter case, the transducers can be placed underwater and the collected data are delivered by a cable to a wireless device on the surface that makes them available on the cloud. In order to achieve better use of the potential of the sea and protect it at the same time, more detailed studies are still required (Green Paper Marine Knowledge 2020, 2012). In this paper, we propose a set of educational scenarios, whereby sensors are used to measure physical and chemical marine parameters. Bringing the IoT into the sea is still very difficult, therefore the focus of these scenarios is on surface sampling activities that are more affordable in the context of STEM educational activities.  The steps of the pedagogical activities followed are: awareness, observation, experimentation and action. School students located in Europe's coastal areas use portable equipment to carry out relevant measurements and submit them to a database they have access to. Depending on the teaching needs and priorities, students can collect and analyze the following: real-time values and any fluctuations of them during the observation period of the activity,  changing values for longer periods of time, e.g., making comparisons between different times of the day, between months, seasons, or years, variance of the phenomena between different areas. The mathematical and scientific thinking developed in the above process can be exploited in various ways by tutors, in the context of teaching mathematical and other science skills, not only during science courses but also in cross-thematic approaches that combine such observations and analyze the economic, social and other aspects of our efforts for clean seas. Discussion: During spring 2017, a set of preliminary GAIA testing was conducted over several weeks to get feedback regarding the educational scenarios that promote energy efficiency and sustainability. Several hundreds of students and teachers had a first interaction with the GAIA platform, while a form-based survey was conducted focusing on the gamification component. 78% of the students found the content of its gamification component interesting, and an 89% found the activity user-friendly. Regarding the acceptance of the tools from educators, the direct response gathered through a set of workshops, addressed specifically at educators, has been positive and several schools have provided their own proposals for schedules to integrating GAIA tools in their curriculum. Thus, in terms of overall acceptance of both the tools and the infrastructure inside buildings and the schools’ curricula, these results indicate that GAIA’s educational scenarios had a quite positive initial response. In addition to the GAIA platform that facilitates educational activities and scenarios for energy awareness and environmental sustainability utilizing real data from IoT sensors, we present here a number of alternative scenarios utilizing different data sets. Marine water scenarios remain an undiscovered but challenging territory that remains unexplored in STEM education. IoT platforms such as GAIA can facilitate educational scenarios towards the sustainability of the environment, based on understanding the implications of real world data

Towards characterization of edge-cloud continuum

Internet of Things and cloud computing are two technological paradigms that reached widespread adoption in recent years. These paradigms are complementary: IoT applications often rely on the computational resources of the cloud to process the data generated by IoT devices. The highly distributed nature of IoT applications and the giant amounts of data involved led to significant parts of computation being moved from the centralized cloud to the edge of the network. This gave rise to new hybrid paradigms, such as edge-cloud computing and fog computing. Recent advances in IoT hardware, combined with the continued increase in complexity and variability of the edge-cloud environment, led to an emergence of a new vision of edge-cloud continuum: the next step of integration between the IoT and the cloud, where software components can seamlessly move between the levels of computational hierarchy. However, as this concept is very new, there is still no established view of what exactly it entails. Several views on the future edge-cloud continuum have been proposed, each with its own set of requirements and expected characteristics. In order to move the discussion of this concept forward, these views need to be put into a coherent picture. In this paper, we provide a review and generalization of the existing literature on edge-cloud continuum, point out its expected features, and discuss the challenges that need to be addressed in order to bring about this envisioned environment for the next generation of smart distributed applications

A study of the LoRa signal propagation in forest, urban, and suburban environments

International audienceSensing is an activity of paramount importance for smart cities. The coverage of large areas based on reduced infrastructure and low energy consumption is desirable. In this context, Low Power Wide Area Network (LPWAN) plays an important role. In this paper, we investigate LoRa, a low-power technology offering large coverage, but low transmission rates. Radio range and data rate are tunable by using different spreading factors and coding rates, which are configuration parameters of the LoRa physical layer. LoRa can cover large areas but variations in the environment affect link quality. This work studies the propagation of LoRa signals in forest, urban, and suburban vehicular environments. Besides being environments with variable propagation conditions, we evaluate scenarios with node mobility. To characterize the communication link, we mainly use the Received Signal Strength Indicator (RSSI), Signal to Noise Ratio (SNR), and Packet Delivery Ratio (PDR). As for node mobility, speeds are chosen according to prospective applications. Our results show that the link reaches up to 250 m in the forest scenario, while in the vehicular scenario it reaches up to 2 km. In contrast, in scenarios with high-density buildings and human activity, the maximum range of the link reaches up to 200 m in the urban scenario

Urban IoT ontologies for sharing and electric mobility

Cities worldwide are facing the challenge of digital information governance: different and competing service providers operating Internet of Things (IoT) devices often produce and maintain large amounts of data related to the urban environment. As a consequence, the need for interoperability arises between heterogeneous and distributed information, to enable city councils to make data-driven decisions and to provide new and effective added value services to their citizens. In this paper, we present the Urban IoT suite of ontologies, a common conceptual model to harmonise the data exchanges between municipalities and service providers, with specific focus on the sharing mobility and electric mobility domains

Dynamic Content-based Indexing in Mobile Edge Networks

As the number of technological devices continues to grow exponentially, the demand for edge computing has become increasingly urgent. This is due to the need for efficient and rapid data processing, especially in scenarios where latency and bandwidth constraints prevent the use of cloud-based solutions. In this context, we present in this work a study above face recognition and clustering algorithms to sustain the reliability to introduce them in a Face-Based Indexing system, built on top of an ecosystem for sharing data and computation named EdgeGarden. Our study relies on the future implementation of a Face-Based Indexing system with an efficient and scalable solution for face recognition and indexing at the edge, enabling real-time processing of facial data and reducing the burden on centralized systems. We believe that this system has the potential to significantly improve the performance and efficiency of edge computing systems, paving the way for the development of more so- phisticated applications in the future. A study among several face detection and extraction models led to the implementa- tion of the HaarCascade face detection model and the ArcFace with 50-Residual Neural Network, a Convolutional Neural Network (CNN)-based model for face feature extrac- tion. For the indexing phase, we compare and analyze the performance of two clustering density-based algorithms, DBSCAN and Denstream. This study shows that the Den- stream algorithm performs better in terms of computational efficiency, however, in terms of accuracy it did not remark as good as DBSCAN. Therefore this study essentially highlights the selection of face detection and feature extraction models focusing on the creation of a Face-Based Indexing system and the evidence that it works using a static data clustering algorithm as DBSCAN.Com o número de dispositivos tecnológicos a crescer exponencialmente, a procura por computação de ponta tornou-se cada vez mais urgente. Isso se deve à necessidade de processamento eficiente e rápido de dados, especialmente em cenários onde as restrições de latência e largura de banda impedem o uso de soluções baseadas em cloud. Neste contexto, apresentamos neste trabalho um estudo sobre algoritmos de reconhecimento e agrupamento de faces para sustentar a confiabilidade de introduzi-los num sistema de indexação baseado em caras, construído sobre um ecossistema de partilha de dados e computação denominado EdgeGarden. O nosso estudo põe em perspectiva uma futura implementação de um sistema de indexação baseado em caras com uma solução eficiente e escalável para reconhecimento facial e indexação na periferia, permitindo o processamento em tempo real de dados faciais e reduzindo a carga em sistemas centralizados. Acreditamos que este sistema tem potencial para melhorar significativamente o desempenho e a eficiência dos sistemas de computação na periferia, abrindo caminho para o desenvolvimento de aplicações mais sofisticadas no futuro. Um estudo entre vários modelos de detecção e extração de caras levou à implemen- tação do modelo de detecção de caras HaarCascade e o ArcFace com Rede Residual Neuronal-50, um modelo baseado em Rede Neuronal Convolucional para extração de características de uma cara. Para a fase de indexação, comparamos e analisamos o desem- penho de dois algoritmos baseados em densidade, DBSCAN e Denstream. Este estudo mostra que o algoritmo Denstream tem um desempenho melhor em termos de eficiência computacional, no entanto, em termos de precisão, não foi tão bom quanto o DBSCAN. Deste modo, este estudo destaca essencialmente a seleção de modelos de detecção e extração de caras com o foco na criação de um sistema de indexação baseado em caras e a evidência de que este funciona usando um algoritmo de agrupamento de dados estáticos como DBSCAN

Dynamic Content-based Indexing in Mobile edge Networks

Recently, we have seen a huge growth in the usage of mobile devices, and with this growth, the data generated has also increased, being in a huge scale, user generated, e.g, photos, books, texts or messages/e-mails. Usually this data requires a permanent storage and its respective indexing for users to efficiently access it however, due to the unpredictability of this data, a concern regarding its indexing starts to raise as it can be hard to predict labels and indexes capable of representing every possible set of data. For instance, during a birthday party, users may want to share photos and videos of this event which can be seen as uploading streams of data to a content sharing system. This content stream will most likely have no index, unless it is explicitly generated, making its retrieval difficult. However, when clustering this stream, as data keeps increasing, we might, somewhere in the future, be capable of detecting similarities between each photo (e.g. a guest’s face) and might want to index them. Indices can directly impact a system’s performance however, there is a drawback from having either too many or too few indices, posing a challenge when it comes to evolving content. We propose Chives, a Content-Based Indexing framework, built on top of a content sharing publish/subscribe system at the edge named Thyme, where we evaluate unsupervised learning in data stream techniques to generate indices. It also offers a content-based query to automatically subscribe to indices containing similar content, e.g images. After evaluating our proposal in a simulated environment, we can see that our framework offers a great abstraction, allowing an easy extension, furthermore our implementation can generate indices from data streams and the indexing follows a clustering criteria, generating the indices as conditions are met. Furthermore, results show that our clustering quality and consequently its generated indices rely strongly on the quality of the image discrimination and its ability to extract features representing its face. In Conclusion, more studies should be done regarding this framework as such, our solution is built in a way where we can exclusively study each component and upgrade it in future work.Recentemente, tem-se observado um enorme crescimento na adesão a dispositivos móveis e com este crescimento, tem também aumentado a quantidade de dados partilhados, sendo em grande escala, gerado pelos utilizadores, por exemplo, fotos, livros, textos ou até mensagens/e-mails. Normalmente estes dados necessitam de um local de armazenamento permanente e a sua respectiva indexação de modo a poderem ser acedidos de forma eficiente por parte dos utilizadores no entanto, dada a imprevisibilidade destes dados, pode surgir um problema relativamente à indexação dado que poderá ser difícil prever etiquetas e índices capazes de representar qualquer conjunto de dados. Por exemplo, durante uma festa de anos, utilizadores poderão partilhar fotografias e vídeos deste evento que poderá ser também interpretado como um upload de dados em stream para um sistema de partilha de conteúdo. Esta stream de dados, muito provavelmente não terá nenhum índice capaz de o descrever, tornando difícil a obtenção deste visto que não existe representação semântica desta. No entanto, ao agrupar esta stream, à medida que os dados vão crescendo, poderemos, algures no tempo ser capaz de detectar semelhanças entre cada fotografia (por exemplo. a cara de um convidado) e podemos querer indexar. Índices podem causar um impacto directo sobre o sistema, no entanto o inverso pode acontecer quando existe índices em défice ou em excesso, apresentando um desafio acerca de dados evolutivos. Nós propomos uma framework de indexação baseada em conteúdo, construído por cima de um sistema de partilha de conteúdo que usa um sistema de Publish/Subscribe na edge denominado Thyme, onde avaliamos técnicas de aprendizagem não supervisionada em data streams para gerar dinamicamente índices. Depois de avaliar a nossa framework, conseguimos concluir que esta oferece uma boa abstração, facilitando a sua extensão, para além disso a nossa proposta permite gerar índices quando as condições definidas para o clustering são respeitadas. Para além disso, os resultados demonstram que o clustering realizado pelo nosso algoritmo dependem fortemente da qualidade de discriminação de imagens e das características obtidas por este discriminador em relação às faces. Concluindo, mais estudos devem feitos em relação à framework, como tal esta foi construída de modo a permitir uma rápida e fácil extensão para futuros melhoramentos

Reaaliaikainen tilojen käytön seuranta: langaton sensoriverkko implementaatio

This paper discusses solutions and technologies for automatically assessing the state of space reservation in a building, e.g., a university or an office building. Improving space utilization can benefit an organization or a company in multiple aspects. These aspects include enhanced user experience and improved workflow, and minimized losses from maintaining unused space. Additionally, the space utilization information can be utilized lower electricity usage, by optimizing HVAC (Heating, ventilation, and air conditioning) and lighting systems. The goal of this study is to asses and compare different methods of gaining the real-time reservation status of a space. Both hardware and software factors are taken into account. The methods are first studied in the form of a literature review. This part of the study includes comparisons between relevant technologis. The most suitable solution is also inspected in practice. Furthermore, as part of the study a real-time reservation status monitoring system was developed utilizing Philips hardware and recommendable software technologies. This experimental phase of the study also includes an overview of testing and installing such a system. A system built by an outside supplier was also installed for reference. Both systems employ PIR (Passive Infrared) sensors. The sensors are not designed for people counting purposes, which was not a core focus of this study. Gathering space utilization data can have further, far stretching benefits. Therefore, this paper also discusses different use cases for the occupancy data. The value of such data can be significant, due to property costs taking up a large portion of companies’ expenses. Effectively utilizing this data can therefore prove to be remarkably advantageous.Tämä tutkimuspaperi käsittelee ratkaisuja ja teknologioita automaattiseen tilojen varausasteen määrittämiseen julkisissa rakennuksissa, kuten yliopistoissa tai toimistoissa. Tilojen käytön tehostaminen voi hyödyttää organisaatiota tai yritystä monilla tavoin. Näihin tapoihin kuuluvat muun muassa parantunut tilojen käyttökokemus ja työn sujuvuus, sekä minimoidut käyttämättömien tilojen ylläpitokustannukset. Lisäksi tilojen käyttösteinformaatiota voidaan hyödyntää sähkönkulutuksen alentamiseksi, optimoimalla LVI- ja valaistusjärjestelmiä. Tämän tutkimuksen tavoite on arvioida ja vertailla eri tapoja hankkia tieto tilojen reaaliaikaisesta varaustilanteesta. Sekä laitteisto, että ohjelmisto tekijät on otettu huomioon. Tapoja tutkitaan aluksi kirjallisuuskatsauksella. Tässä osassa tutkimusta vertaillaan eri teknologioita. Sopivimman ratkaisun toimintaa tutkittiin myös käytännössä Lisäksi osana tutkimusta kehitettiin reaaliaikaisen varaustilanteen seurantajärjestelmä hyödyntäen Philips Hue -laitteita ja suositeltavia ohjelmistoteknologioita. Tämä kokeellinen tutkimusvaihe sisältää myös yleiskuvan vastaavien järjestelmien testaamisesta ja asentamisesta. Myös kolmannen osapuolen toimittama järjestelmä asennettiin tiloihin vertailukohteeksi. Molemmat järjestelmät käyttävät PIR (Passive Infrared) sensoreita. Sensorit eivät ole tarkoitettu henkilölaskentaa varten, koska se ei ollut tämän tutkimuksen ydintavoite. Tilojen käyttödatan keräämisellä voi lisäksi olla muita, kauaskantoisia hyötyjä. Siksi tutkimuksessa käsitellään myös erilaisia tapoja hyötykäyttää sitä. Datan arvo voi olla merkittävä tilakustannusten ollessa merkittävä osa yritysten kuluja. Datan tehokas käyttö voi osoittautua huomattavan suotuisaksi

Applications of ontology in the internet of things: A systematic analysis

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions