Data Serialization Formats for the Internet of Things

IoT devices rely on data exchange with gateways and cloud servers. However, the performance of today's serialization formats and libraries on embedded systems with energy and memory constraints is not well-documented and hard to predict. We evaluate (de)serialization and transmission cost of mqtt.eclipse.org payloads on 8- to 32-bit microcontrollers and find that Protocol Buffers (as implemented by NanoPB) and the XDR format, dating back to 1987, are most efficient

Supporting STEM knowledge and skills in engineering education – PELARS project

In this paper we present our proposal for improving education with hands-on, project-based and experimental scenarios for engineering students with the use of learning analytics. We accomplish this through teacher and learner engagement, user studies and evaluated trials, performed at UCV (University of Craiova, Romania) and DTU (Technical University of Denmark). The PELARS project (Practice-based Experiential Learning Analytics Research And Support) provides technological tools and ICT-based methods for collecting activity data (moving image-based and embedded sensing) for learning analytics (data-mining and reasoning) of practice-based and experiential STEM. This data is used to create analytics support tools for teachers, learners and administrators, providing frameworks for evidence-based curriculum design and learning systems. The PELARS project creates behavioral recording inputs, proving a new learning analytic that is scalable in application, and bridge qualitative and quantitative methods through reasoning and feedback from input data. The project serves to better understand learners' knowledge in physical activities in laboratory and workshop environments, as well as informal learning scenarios. PELARS traces and helps assess learner progress through technology enhancement, in novel ways building upon current research. The project results in learning analytics tools for practice-based STEM learning that are appropriate for real-world learning environments

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Synergistic Ca2+ and Cu2+ requirements of the FGF1-S100A13 interaction measured by quartz crystal microbalance: An initial step in amlexanox-reversible non-classical release of FGF1

It is known that fibroblast growth factor-1 (FGF1) lacking a conventional signal peptide sequence shows non-classical release independent of the endoplasmic reticulum-Golgi system. Recent studies reveal that FGF1 is co-released with S100A13, a Ca2+-binding protein that acts as an extracellular cargo molecule. Although both FGF1 and S100A13 are Cu2+-binding proteins, the role of Cu2+, as well as that of Ca2+, in non-classical release, remains to be clarified. In the present study we examined the requirements of both metal ions for the interaction between these two proteins. The addition of Ca2+ significantly increased the ka value, while decreasing the KD value, for the interaction between Strep-tagII-S100A13 and GST-FGF1; both values were obtained by use of a quartz crystal microbalance, a real-time mass-measuring device. The EC50 of Ca2+ to enhance the interaction was 10.11 μM. Although the addition of Cu2+ alone had no effect, it caused a marked potentiation of the Ca2+-enhanced interaction. The EC50 of Cu2+ for the potentiation was 50.45 nM. On the other hand, the EC50 of Ca2+ and the KD values were decreased from 11.69 to 2.07 μM and 0.75 to 0.38 × 10?7 M, respectively, by the addition of 200 nM Cu2+. The Cu2+-induced potentiation of this interaction was abolished by amlexanox, which inhibits non-classical release of FGF1. All of these findings suggest that synergistic effects of Ca2+ and Cu2+ play a key role in the interaction between FGF1 and S100A13, which is the initial step in non-classical release of FGF1

Prototyping Feedback for Technology Enhanced Learning

The development of new educational technologies, in the area of practical activities is the main aim of the FP7 PELARS project. As part of the constructivist learning scenarios, according to the project proposal, the development and evaluation of technology designs are envisaged, for analytic data generation for Science, Technology, Engineering and Mathematics (STEM) subjects, such as: technology solutions, infrastructure, activities, assessment, curricula, and classroom furniture and environment designs. Inside four EU national settings, three separate learning contexts are being dealt with - from secondary-level high school STEM learning environments to post-secondary level engineering classes and design studios. Given this experience and framework, the present paper provides a perspective on the importance of using such research experience and iterative prototyping in real learning environments for engineering students