The COVID-19 Pandemic affected all sectors worldwide including education sector changing the students learning environment at a large scale. In the education sector, governments had to close schools and universities around the world and turn into online mode of delivery. The sudden transformation to online learning demands educators and institutions to develop more innovative teaching methods in supporting students during this growing crisis. It is pertinent that this is the era of large-scale online learning, where education is delivered remotely utilizing various digital platforms. To this end, this move presented many challenges to students, educators, families, administrators, and government regulations. In this paper, we conduct an oriented and well-rounded survey that captures all aspects of distance learning verticals as perceived by educators in order to study online delivery aspects, including teaching models, communication media, and teaching best practices in distance learning. The paper will answer subtle and comprehensive questions related to student satisfaction and success, technical needs and challenges, teaching models, educator mobility, etc. across different universities in the UAE. In particular, the paper tries to identify best model to use given many key issues related to the adoption of e-learning during COVID-19. Overall, the survey results showed almost all participants agreed that online learning during the COVID-19 is a very good alternative solution to Face-to-Face approach. However, teaching models do vary in promoting students’ participation and students’ engagement. All participants agree that absence of the proper infrastructure at home will hinder the operation especially in courses with hands-on components. Finally, the educators questioned the credibility of online assessments despite the enforced exam integrity tools and methods. The paper concluded with many recommendations for continuous development of the distance learning systems during and after COVID -19.

The internet of things (IoT) represents a rapidly expanding sector within computing, facilitating the interconnection of myriad smart devices autonomously. However, the complex interplay of IoT systems and their interdisciplinary nature has presented novel security concerns (e.g. privacy risks, device vulnerabilities, Botnets). In response, there has been a growing reliance on machine learning and deep learning methodologies to transition from conventional connectivity-centric IoT security paradigms to intelligence-driven security frameworks. This paper undertakes a comprehensive comparative analysis of recent advancements in the creation of IoT botnets. It introduces a novel taxonomy of attacks structured around the attack life-cycle, aiming to enhance the understanding and mitigation of IoT botnet threats. Furthermore, the paper surveys contemporary techniques employed for early-stage detection of IoT botnets, with a primary emphasis on machine learning and deep learning approaches. This elucidates the current landscape of the issue, existing mitigation strategies, and potential avenues for future research.

Since enrgy is a vlaubale resource in Wireless Sensor Networks (WSNs), it is important to continuously monitor the status of this valuable network resource after network deployment. The information about energy status can be used to early notify both sensors and deployers of resource depletion in some parts of the network. It can also be used to perform energy-efficient routing in WSNs. In this paper, we propose a scheme for monitoring residual energy distributions at different parts of the network through a mechanism called Energy Centric scale (ECscale). ECscale is then used to perform optimal as well as approximate Energy-Centric Routing (ECR) in WSNs with the objective of maximizing the network lifetime. The proposed algorithms make use of a fixed virtual wireless backbone that is built on top of the physical topology. Simulation results show that our scheme is scalable and can provide many folds of energy savings when compared to conventional routing schemes.