The Drones4STEM initiative marks a pioneering step in enriching STEM education by integrating drone technology into secondary school curriculums. This innovative approach provides educators with easily deployable tools and custom-designed instructional scenarios, significantly enhancing the teaching and learning experience. The initiative is exploits compact, low-cost and user-friendly drones combining their use with a comprehensive ‘Drones4STEM’ floor mat kit, offering a hands-on, interactive learning platform. By simplifying the technological aspects, Drones4STEM allows educators to focus more on educating and inspiring students about the multifaceted applications of drones in various sectors. This paper delves into the specifics of the Drones4STEM kit, its practical applications in educational settings, and the structured Drones4STEM e-course for educators. Through this initiative, educators are equipped with the necessary skills and knowledge to foster an engaging and dynamic STEM learning environment, preparing students for the technological advancements of the future and inspiring them to explore diverse career paths in drone technology and its applications.

In this systematic literature review spanning the last five years (2018–2023), the role of poetry within the ‘A’ for Arts in STEAM (Science, Technology, Engineering, Arts, Mathematics) education is examined. Specifically, the primary purpose of this study is to delve into the integration of poetry and Artificial Intelligence into the STEAM teaching method. The research objectives which guide this study are to understand the methods and depth of including poetry in STEAM and evaluate how Artificial Intelligence (AI) can enhance poetry's role in STEAM. Using the PRISMA methodology, an extensive search was conducted in key databases such as Scopus and Google Scholar. This search identified 26 crucial papers, then analyzed qualitatively and quantitatively. Current STEAM teaching approaches focus on merging literary expressions with essential scientific disciplines. A large part of the findings points to new teaching methods that bring together poetic elements and the usual STEAM subjects. These methods increase student participation, bridge the gap between arts and sciences and encourage creative problem-solving. In the realm of STEAM education, the significance of Artificial Intelligence (AI) is growing. The findings suggest that AI can boost teaching methods centred on poetry in STEAM. AI allows for lesson adjustments based on students' engagement with poetry, creates platforms for more student participation, and offers feedback to help educators improve their teaching methods. However, there are challenges in merging the exactness of AI algorithms with the complexities of poetry, especially when trying to maintain poetry's essence. As STEAM education moves towards a more integrated model, the interplay between AI's technical abilities and the nuances of poetry calls for further scholarly research based on these findings.

In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.

The proliferation of Internet of Things (IoT) devices has created a growing demand for integrating IoT technologies in educational settings. While technical expertise in IoT engineering is essential for developing IoT skills, it is equally crucial for educators to possess pedagogical skills that enable them to effectively teach their students about the diverse range of available IoT systems, hardware, and software components, through properly designed STEM activities early on during secondary education. However, recent studies suggest that most IoT engineering curricula need specifically adapted and properly designed pedagogical frameworks. This paper presents an experimental research study conducted as part of the evaluation stage of the PARADIGM project, a European-funded collaborative initiative under the auspices of the Erasmus+ programme. The project aims to design an e-learning course on pedagogical issues, develop an IoT environmental observatory to provide hands-on experience and establish a citizen science platform as the PARADIGM Virtual Maker Space and Repository. The specific research objective is to assess the impact of PARADIGM tools on educators' pedagogical and technical knowledge of IoT. Fifty educators from secondary schools in Greece, Spain, and Cyprus participated in the study. Pre-and posttest were conducted to measure differences in mean scores, and t-tests were used to analyze these differences. Despite the relatively small sample size, it is noteworthy that this study was part of a larger collaborative initiative involving multiple institutions and stakeholders, which increased the generalizability and external validity of the study and reduced the potential for bias or confounding factors. The results indicate that the use of PARADIGM tools significantly improved educators' knowledge scores in IoT. Furthermore, the findings reveal a statistically significant correlation between educators' knowledge of IoT devices and their hands-on experiences using the PARADIGM observatory IoT devices. These results highlight the need for further research on IoT education programs to enhance educators' skills in IoT devices. In conclusion, this paper contributes to advancing knowledge on IoT education by emphasizing the potential of PARADIGM tools to enhance educators' pedagogical and technical understanding of IoT. The study recommends integrating PARADIGM tools into IoT curricula to equip educators with the necessary skills to teach their students about IoT effectively.

Latest trends show an increasing usage of UAVs in urban and rural areas assigned with various tasks like end-to-end package delivery tools, search and rescue missions or crop disease detection. This subsequently has led to an exponential growth in traffic in the sky above us, accompanied with increased safety-related events like mid-air collisions due to lack of location awareness between users. This problem is addressed in modern manned aviation by using high powered transponders (i.e. ADS-B) for transmitting real time position information and helping air traffic controllers (ATC) raise the level of safety among them. On the other hand, despite using state-of-the-art equipment, most drones running on batteries do not have communication systems that are able to provide long-range coverage. The main concern of this research is to optimize transmitted location awareness payloads so that they can be used in energy efficient/bandwidth IoT networks like LoRaWAn without violating fair usage policy of this network. In this paper, an optimization of position payload, demonstrate that LoRaWAn can be utilized for a real-time location exchange network, sending 4 bytes of payload with 1Hz rate.

Students have always been main targets for museums with clear learning strategies between schools and museums that contribute to a reinforced student learning experience. Although several museums offer virtual experiences (also available for schools), these cannot be easily manipulated or combined with other resources to form curriculum-focused learning activities. This study aims to explore the views and expectations of teachers on the use of Virtual Museum Tours (VMT) in their classrooms. Main findings from an online survey with 101 teachers (with or without VMT experience) shed light on their motivations and challenges when engaging their classrooms in such activities. Statistical analysis and visualisations were used to present teachers’ experiences and expectations, and a binary logistic regression analysis was employed to indicate the factors that associate with teachers’ decision to join VMTs in their classrooms. This study’s observations have implications for the design of customisable VMTs for schools, for how museums develop VMTs that facilitate school participation, and how game designers develop virtual gaming apps used in formal education settings.

HCI researchers and practitioners are increasingly using physiological data to measure User eXperience (UX) parameters. The dynamic nature of physiological data offers a continuous window for an in-depth understanding of users’ interaction experience. However, in order to be truly informative, physiological signals need to be linked to users’ interaction experience aspects, such as their emotional states, in a systematic and efficient way. Studies have shown that skin conductance is a physiological signal highly associated with stress. The main purpose of this paper is to present the validation study of our proposed stress detection mechanism which is integrated into a software named PhysiOBS. PhysiOBS is an observation analysis tool that can be used in the post-study analysis phase. PhysiOBS uses nonspecific skin conductance responses (NS-SCRs) in order to auto-report time periods that are probably associated with a problematic interaction. PhysiOBS can also combine multiple data sources. Hence, UX evaluators are able to further investigate a recorded session in order to reveal additional interaction flaws. The integrated stress assessment mechanism, which uses four trained classifiers, can be applied in the reported periods (auto/expert-reported) in order to classify them as stress or non-stress. For the purpose of the validation study, 24 users were recruited in order to participate in a lab experiment. Results showed that our stress assessment mechanism supports UX evaluators by accurately identifying stressful regions within an interaction scenario.

The present paper describes the design and implementation of a development board, designed in Digital Systems and Media Computing Laboratory of the Hellenic Open University which is very active in the field of digital systems design. The board hosts an MCU and an FPGA on the same PCB, cooperating with tight interconnection between them and supported by a set of basic on-board peripherals that cover some of the most essential educational examples in the field, minimizing the need for external devices. The design is geared i) towards ease of use in order to alleviate any initial setup stress by students or inexperienced designers and ii) towards low-cost fabrication in order to facilitate educational institutions who provide distance education to offer this board to every student for out-of-laboratory usage. The design process, consists of three main stages; the PCB design, the firmware development and the lastly the host computer software development. The architectural and design choices made for each stage are described fully later on the paper with each decision balancing between ease-of-use, cost and functionality, in the form of offered services. The board functions as a very low-cost laboratory educational platform for both low level HDL training as well as higher level MCU Firmware programming, supporting even more complex scenarios of FPGA softcore usage and programming or concurrent usage of FPGA and MCU in complete System-on-Chip (SoC) designs.

The present paper proposes a SIP-based emergency embedded framework, named SEEK, that enables embedded devices to trigger calls to emergency centers and support IP-based real time communication. SEEK framework is consisted of body sensors and a haptic device enabling elderly and disabled people who live independent to perform emergency calls. Sensors embedded in SEEK, aggregate environmental data and vital signs that monitor user's current health status and automatically trigger emergency calls when an emergency occurs, providing continuous real-time sensor data. The haptic device enhances further the communication among the end user (disabled person or elderly), first responders and healthcare providers at emergency centers. A prototype implementation and initial results are presented as a proof of concept for deaf and speech impaired people. The results depict that the proposed framework seems to be an auspicious solution for a wide range of disabled people enabling them to report emergency events and establish real time communication with first responders regardless of their disabilities.

The Internet of Things (IoT) ecosystem, is constantly growing and attracting interest for next generation information and communication infrastructures. In this paper we focus on the exploitation of IoT platforms for remote healthcare status monitoring within the context of the so called connected health applications. In this context users and most importantly healthcare service providers and first responders may have remote access to sensor data collected from devices attached to persons with special needs or under monitoring. The efficient interconnection of medical sensors, aggregation of data towards distant datacenters and backend services requires the implementation of appropriate protocol stacks. In this paper we benchmark different IoT protocol stacks for sensor data aggregation. Specifically, we evaluate IEEE 11073 medical data transport over TCP and UDP based connections exploiting the CoAP and MQTT protocols proposed for IoT data encapsulation and client server interconnection.

The expansion of Internet of Things (IoT) and the evolution in communication technologies have enabled homes, cars even whole cities to be network connected. However, during an emergency incident, IoT devices have not been used to trigger emergency calls directly to healthcare providers mainly due to their constrained capabilities and lack of support session-oriented communications. Moreover, emergency services are currently offered by public safety stakeholders that do not support call triggering by IoT devices. This paper proposes IoTA framework which enables IoT devices to generate automatically emergency calls and support bi-directional communication sessions between healthcare providers and end users. The IoTA framework incorporates intelligent algorithms for processing and evaluating emergency events from various devices and performs emergency calls immediately after the occurrence of an event. The healthcare providers can interact with the IoTA framework requesting continuous real-time sensor data. A prototype implementation and initial evaluation results are presented as a proof of concept for people suffering from diverse chronic diseases. Experimental results have shown that the proposed framework can be considered as a promising solution for detecting, reporting emergency events, eliminating the hoax calls and responding swiftly saving lives.

Ethernet technology is lo longer used only in Local Area Networks (LANs); it is continuously gaining momentum in the Metropolitan Area Networks (MAN s) and Wide Area Networks (WANs). This paper presents a multi-service access concentrator core that has been designed specifically for multi-service, purely Ethernet, access nodes. In particular the presented system is optimised for Ethernet traffic aggregation over MPLS-based optical backbone networks. Moreover, we also demonstrate the bottlenecks that have been identified when such networking applications are executed in a general-purpose network processing device, and the techniques we used in order to bypass them. Our experiments results, executed in a state-of-the-art FPGA-based platform, strongly support that the combination of general purpose processing units with powerful specialized hardware modules, is the most cost-effective approach for designing systems that (i) can support today's and future network speeds and applications, in purely Ethernet networks, (ii) provide the end-user with the required programmability.

One of the main bottlenecks when designing a network processing system is very often its memory subsystem. This is mainly due to the state-of-the-art network links operating at very high speeds and to the fact that in order to support advanced quality of service (QoS), a large number of independent queues is desirable. In this paper we analyze the performance bottlenecks of various data memory managers integrated in typical network processing units (NPU). We expose the performance limitations of software implementations utilizing the RISC processing cores typically found in most NPU architectures and we identify the requirements for hardware assisted memory management in order to achieve wire-speed operation at gigabit per second rates. Furthermore, we describe the architecture and performance of a hardware memory manager that fulfills those requirements. This memory manager, although it is implemented in a reconfigurable technology, can provide up to 6.2 Gbit/s of aggregate throughput, while handling 32 K independent queues.

In order to address the challenge of providing quality of service guarantees in today's network processing systems, the use of efficient scheduling algorithms is required. The efficiency of a scheduler is determined by several factors including its fairness, capability to operate at high speeds, as well as the resources required for its implementation. We present an architecture to support fair scheduling (gigabit FS) considering variable length packets (i. e. for packet forwarding/switching networks) over gigabit links. This high speed scheduler is designed to manage 32 K flows based on an algorithm that yields efficient implementation in hardware by avoiding the complexity of computing the system virtual time function that many packet fair queueing (PFQ) algorithms have proposed. Further, we demonstrate the critical factors in designing an effective scheduling engine at gigabit rates and we present several enhancements together with their associated cost.

Emerging applications for network processors require increased number of processing resources. This paper introduces a novel system to load balance the scheduled traffic over multiple processing cores maintaining in-order service. It is conceived in the framework of today's demanding network processors, but it is obviously applied to every multiprocessor platform. The focus of the paper is on an efficient load-balancer supported by a high speed dual-pipeline engine tailored to operate at line rates over OC-192/10Gbps. Finally, an optimized implementation is presented occupying 1.34 mm/sup 2/ using a standard 0.18 /spl mu/m CMOS technology, while supporting 59.5 Million network packets per second.