Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.INDEX TERMS Fifth generation, Internet of Things, green Internet of Things, fog, machine learning.

No abstract

This research study has investigated the challenges faced due to the pandemic . This paper further provides recommendations that can be adopted by academics, learners, and administrators to make the education system more robust and sustainable. The impact of the COVID-19 pandemic can be felt in various fields across the world including higher education. The closure of face-to-face (FtF) learning in educational institutions worldwide has impacted over 95% of the world's student population. Therefore, in wake of this, many institutions have quickly adopted to offer complete online teaching and learning in a very short period. However, such a quick transition has raised several challenges. 1) What are the challenges encountered by academics and their readiness to adapt to the rapid remote learning transition? 2) What are the challenges encountered by learners (students), and their readiness to adapt to the rapid remote learning transition? 3) What are the recommendations for strategic planners or high-level administrators in institutions to tackle such pandemic risks effectively in the future? To address research questions mixed methods are used. A qualitative questionnaire survey is framed by an extensive literature review to understand the perceptions of academics and learners. A total of (n=525) academician samples and (n= 1460) student samples have been collected. The academic and learner's perceptions are analyzed by estimating the Pearson correlation coefficients. The mean and SD values based on academic rank stood at 3.01±0.96, and by experience stood at 2.96±0.98. Similarly, learner's perceptions stood at 2.67±0.95. Keywords: FtF (Face to Face); problem-oriented and project-based learning (POPBL); LMS (Learning management system); emergency remote teaching (ERT); emergency risk (COVID-19): Standard deviation (SD).Declarations: NA Introduction:Observing the current global crisis turned on due to the pandemic (COVID-19 virus) situation seen around the world has enforced various industries to adopt different methods of work styles to keep pace with the regular life course. The number of COVID-19 affected cases globally has reached near to nineteen million (as of today) [1] and still seems to be increasing exponentially in some countries like the USA, Brazil, India, and most of the European countries including the UK. Although there are concerns that the COVID-19 data by some countries is not accurate never the less the effect is seen to be

No abstract

<abstract> <p>Degradation of PV modules have a severe impact on its power-producing capabilities thus affecting the reliability, performance over the long run. To understand the PV degradation happening under the influence of local environmental conditions a survey was conducted on six polycrystalline silicon-based PV modules over five years. It has been observed that the average degradation rates stood at 1.02%/year at irradiances 800 W/m² and 0.99%/year at irradiances 600 W/m², which are almost double the manufacturer proposed values. Upon further investigations, it has been found that discoloration of encapsulant in modules 3, 5, and 6 have been the main factor causing the reduction of the short circuit current (I_sc) thus affecting the overall power production capacity of the installed PV system. Considering the amount of time, resources and manpower invested to perform this survey an alternate way of estimating the PV degradation rates is also investigated. The exponential decay factor-based model is adopted to correlate the encapsulant discoloration seen on-site in the form of a mathematical equation to predict the current loss. This loss is defined as the visual loss factor in this paper. Further, the output I-V curves are simulated using MATLAB Simulink-based mathematical model which also integrates visual loss factor (VLF) losses into it. Such simulated I-V curves have shown a good match with the measured I-V curves at the same irradiance with an error less than 3%. Authors anticipate that this modelling approach can open the door for further research in developing algorithms that can simulate the PV degradation rates.</p> </abstract>