Towards Computational Notebooks for IoT Development

Internet of Things systems are complex to develop. They are required to exhibit various features and run across several environments. Software developers have to deal with this heterogeneity both when configuring the development and execution environments and when writing the code. Meanwhile, computational notebooks have been gaining prominence due to their capability to consolidate text, executable code, and visualizations in a single document. Although they are mainly used in the field of data science, the characteristics of such notebooks could make them suitable to support the development of IoT systems as well. This work proposes an IoT-tailored literate computing approach in the form of a computational notebook. We present a use case of a typical IoT system involving several interconnected components and describe the implementation of a computational notebook as a tool to support its development. Finally, we point out the opportunities and limitations of this approach

Software Engineering in the IoT Context: Characteristics, Challenges, and Enabling Strategies

L'abstract è presente nell'allegato / the abstract is in the attachmen

A mathematica‐based CAL matrix‐theory tutor for scientists and engineers

Under the TLTP initiative, the Mathematics Departments at Imperial College and Leeds University are jointly developing a CAL method directed at supplementing the level of mathematics of students entering science and engineering courses from diverse A‐level (or equivalent) backgrounds. The aim of the joint project is to maintain — even increase ‐ the number of students enrolling on such first‐year courses without lowering the courses’ existing mathematical standards

Development of a learning pilot for the remote teaching of Smart Maintenance using open source tools

[EN] Technology has created a vast array of educational tools readily available to educators, but it also has created a shift in the skills and competences demanded from new graduates. As data science and machine learning are becoming commonplace across all industries, computer programming is emerging as one of the fundamental skills engineers will require to navigate the future and current workplace. It is, thus, the responsibility of educational institutions to rise to this challenge and to provide students with an appropriate training that facilitates the development of these skills. The purpose of this paper is to explore the potential of open source tools to introduce students to the more practical side of Smart Maintenance. By developing a learning pilot based mainly on computational notebooks, students without a programming background are walked through the relevant techniques and algorithms in an experiential format. The pilot highlights the superiority of Colab notebooks for the remote teaching of subjects that deal with data science and programming. The resulting insights from the experience will be used for the development of subsequent iterations during the current year.This project has received funding from the European Union’s “Erasmus+ Capacity Building in the field of Higher Education” programme under grant agreement No 2019-1949 / 001-001 (correspondent to the project shortly entitled “NePRev”, “NExt Production REVolution”).Callupe, M.; Fumagalli, L.; Nucera, DD. (2021). Development of a learning pilot for the remote teaching of Smart Maintenance using open source tools. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 1419-1427. https://doi.org/10.4995/HEAd21.2021.13140OCS1419142

Cloud Energy Micro-Moment Data Classification: A Platform Study

Energy efficiency is a crucial factor in the well-being of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher performance, cloud platforms can assist researchers in conducting classification trials that need high computational power. Under the larger umbrella of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 framework, we aim to influence consumers behavioral change via improving their power consumption consciousness. In this paper, common cloud artificial intelligence platforms are benchmarked and compared for micro-moment classification. The Amazon Web Services, Google Cloud Platform, Google Colab, and Microsoft Azure Machine Learning are employed on simulated and real energy consumption datasets. The KNN, DNN, and SVM classifiers have been employed. Superb performance has been observed in the selected cloud platforms, showing relatively close performance. Yet, the nature of some algorithms limits the training performance.Comment: This paper has been accepted in IEEE RTDPCC 2020: International Symposium on Real-time Data Processing for Cloud Computin

VIPLE Extensions in Robotic Simulation, Quadrotor Control Platform, and Machine Learning for Multirotor Activity Recognition

abstract: Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.Dissertation/ThesisMasters Thesis Computer Science 201

Features-Aware DDoS Detection in Heterogeneous Smart Environments based on Fog and Cloud Computing

Nowadays, urban environments are deploying smart environments (SEs) to evolve infrastructures, resources, and services. SEs are composed of a huge amount of heterogeneous devices, i.e., the SEs have both personal devices (smartphones, notebooks, tablets, etc) and Internet of Things (IoT) devices (sensors, actuators, and others). One of the existing problems of the SEs is the detection of Distributed Denial of Service (DDoS) attacks, due to the vulnerabilities of IoT devices. In this way, it is necessary to deploy solutions that can detect DDoS in SEs, dealing with issues like scalability, adaptability, and heterogeneity (distinct protocols, hardware capacity, and running applications). Within this context, this article presents an Intelligent System for DDoS detection in SEs, applying Machine Learning (ML), Fog, and Cloud computing approaches. Additionally, the article presents a study about the most important traffic features for detecting DDoS in SEs, as well as a traffic segmentation approach to improve the accuracy of the system. The experiments performed, using real network traffic, suggest that the proposed system reaches 99% of accuracy, while reduces the volume of data exchanged and the detection time