Efficient Proactive Caching for Supporting Seamless Mobility

We present a distributed proactive caching approach that exploits user mobility information to decide where to proactively cache data to support seamless mobility, while efficiently utilizing cache storage using a congestion pricing scheme. The proposed approach is applicable to the case where objects have different sizes and to a two-level cache hierarchy, for both of which the proactive caching problem is hard. Additionally, our modeling framework considers the case where the delay is independent of the requested data object size and the case where the delay is a function of the object size. Our evaluation results show how various system parameters influence the delay gains of the proposed approach, which achieves robust and good performance relative to an oracle and an optimal scheme for a flat cache structure.Comment: 10 pages, 9 figure

Blended Laboratory Design Using Raspberry Pi Pico for Digital Circuits and Systems

Raspberry Pi Pico, based on chip RP2040, is an easy-to-use development microcontroller board that can provide flexible input/output functions and meets the teaching needs of basic electronics to first-year university undergraduates. This article presents our blended laboratory design using Raspberry Pi Pico for the course unit Digital Circuits and Systems. Considering the impacts of Coronavirus Disease 2019 (COVID-19) and the reduced number of students attending the in-person laboratory, we provide an alternative approach using an online Raspberry Pi Pico simulator produced by Wokwi for those students who cannot attend the physical laboratory. The entire laboratory is designed by design-based learning pedagogical methodology and consists of three dependent sessions. Throughout the three laboratory sessions, first-year undergraduates are expected to understand the basic digital logic and electronic circuits by building a simplified interactive traffic light controller system using Raspberry Pi Pico and Python programming. The intended learning outcomes, full details of the blended laboratory design, and the laboratory design evaluation results are given and discussed in this article to verify the effectiveness of the blended laboratory design using Raspberry Pi Pico. By analyzing the empirical data collected from laboratory participants, the effectiveness of the proposed blended laboratory design can be well supported, and all intended learning outcomes are successfully achieved subject to the impacts of COVID-19

Fog computing for sustainable smart cities: a survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities

School closures and educational attainment in Ethiopia: Can extra classes help children to catch up?

Data availability statement: The data that support the findings of this study are available in Young Lives at https://beta. ukdataservice.ac.uk/datacatalogue/studies/study?id=7823&type=Data%20catalogue.ORCID: Fiona Carmichael https://orcid.org/0000-0002-7932-2410; Christian K. Darko https://orcid.org/0000-0002-1665-2594; Shireen Kanji https://orcid.org/0000-0003-3512-2596; Nicholas Vasilakos https://orcid.org/0000-0003-3279-2885.Copyright © 2022 The Authors. School closures impact children's attainment adversely, but understanding the effects of closures on children's attainment in lower-income countries is still limited. Addressing this deficit, this study examines how past school closures have impacted children's educational attainment in Ethiopia. The study uses individual student-level data from the Young Lives School Survey and standardised test scores in mathematics and language recorded at the start and end of the school year to model children's attainment. Multiple regression with propensity score matching is used to analyse how attainment over the school year is impacted by school closures for a matched sub-sample of 4842 students. The effectiveness of additional classes to make up for lost learning is also evaluated. Past school closures have had a detrimental effect on attainment in mathematics, but not literacy. Extra classes, specifically those that families do not pay for, have helped children in the past to recuperate lost learning and could serve this function post-Covid-19. Inequalities in learning outcomes, measured by Gini coefficients in educational attainment, are widened by school closures. Applying these results to the extensive school closures under Covid-19 furthers our understanding of the likely effects on academic attainment and can inform policy to mitigate the impact.https://beta. ukdataservice.ac.uk/datacatalogue/studies/study?id=7823&type=Data%20catalogu

On the design of a native Zero-touch 6G architecture

The complexity of envisioned 6G telecommunication networks requires an intrinsically intelligent architecture designed to autonomously adapt to dynamics with end-to-end zero-touch service automation operations. Motivated by this vision, this paper tries to formulate concepts and solution aspects towards designing a native Zero-touch 6G architecture. Our discussion concentrates around three main pillars, i.e. (i) introducing Machine Learning (ML) models in the core design of the 6G architecture as native functions rather than add-on model solutions; (ii) distributing 6G functionality to different components up to the extreme edge; to (iii) leverage technology leaps enabling, e.g., the use of multi-access technologies and peer-topeer communications besides the standard cellular connectivity and other centralised functionalit

Modeling a teacher in a tutorial-like system using Learning Automata

The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students