A systematic literature review on mobile learning in Saudi Arabia

the aim of this paper is to collects, document, examine and critically analyze the current research literature on mobile learning (m-learning) in higher education institutes HEIs in the Kingdom of Saudi Arabia (KSA) published between 2010 and 2017. It explores the acceptance of using the m-learning, the factors that influence the deployment of m-learning. Investigate the trends in m-learning by systematically analyzing the previous studies. explores new emerging practices relating to the use of mobile technologies in nursing education; identify gaps in the research literature of the m-learning. The result shows there is reasonable evidence that the HEIs in Saudi Arabia face considerable factors in implementing m-learning. Also shows that significant studies assessing the effectiveness of m-learning within Saudi Arabia HEIs are lacking and existing studies lacked a theoretical framework. The absence of studies reporting on existing m-learning study reflects the limited penetration of this technology and associated pedagogies and a need to strengthen research in the field of m-learning in the KSA. © 2017 IEEE

Perspectives on resilience in cloud computing: Review and trends

The development of resilient distributed systems is seen as essential to maintaining stable business and state-run processes due to information systems now underpinning most aspects of society. Cloud computing is now one of the most pervasive usage paradigms and due its novelty, research surrounding its resilience is largely lacking and often varied in terms of developed solutions. Therefore this paper provides an up-to-date review of resilience work in cloud computing. This includes methods of measuring and evaluating resilience, solutions for enabling resilience and alternative architectures developed with a focus upon ensuring resilience from the ground up. Firstly, resilience is defined within the context of cloud computing in order to categorise the work appropriately. © 2017 IEEE

Survivability analogy for cloud computing

As cloud computing has become the most popular computing platform, and cloud-based applications a commonplace, the methods and mechanisms used to ensure their survivability is increasingly becoming paramount. One of the prevalent trends in recent times is a turn to nature for inspiration in developing and supporting highly survivable environments. This paper aims to address the problems of survivability in cloud environments through inspiration from nature. In particular, the community metaphor in nature's predator-prey systems where autonomous individuals' local decisions focus on ensuring the global survival of the community. Thus, we develop analogies for survivability in cloud computing based on a range of mechanisms which we view as key determinants of prey's survival against predation. For this purpose we investigate some predator-prey systems that will form the basis for our analogical designs. Furthermore, due to a lack of a standardized definition of survivability, we propose a unified definition for survivability, which emphasizes as imperative, a high level of proactiveness to thwart black swan events, as well as high capacity to respond to insecurity in a timely and appropriate manner, inspired by prey's avoidance and anti-predation approaches. © 2017 IEEE

Resource Allocation for NOMA-based LPWA Networks Powered by Energy Harvesting

In this paper, we explore perpetual, scalable, Low-powered Wide-area networks (LPWA). Specifically we focus on the uplink transmissions of non-orthogonal multiple access (NOMA)-based LPWA networks consisting of multiple self-powered nodes and a NOMA-based single gateway. The self-powered LPWA nodes use the "harvest-then-transmit" protocol where they harvest energy from ambient sources (solar and radio frequency signals), then transmit their signals. The main features of the studied LPWA network are different transmission times-on-air, multiple uplink transmission attempts, and duty cycle restrictions. The aim of this work is to maximize the time-averaged sum of the uplink transmission rates by optimizing the transmission time-on-air allocation, the energy harvesting time allocation and the power allocation; subject to a maximum transmit power and to the availability of the harvested energy. We propose a low complex solution which decouples the optimization problem into three sub-problems: we assign the LPWA node transmission times (using either the fair or unfair approaches), we optimize the energy harvesting (EH) times using a one-dimensional search method, and optimize the transmit powers using a concave-convex (CCCP) procedure. In the simulation results, we focus on Long Range (LoRa) networks as a practical example LPWA network. We validate our proposed solution and we observe a $15\%$ performance improvement when using NOMA