Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic analysis of using RL or DRL algorithms to address offloadingrelated issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybridbased algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths and drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, the future research directions and open issues are discussed thoroughly.INDEX TERMS Fog computing, Internet of Things (IoT), offloading, reinforcement learning, deep reinforcement learning.
Technology-enhanced learning is utilized to support teaching and learning processes by using technology. ICT has enabled Open Online Learning to become a phenomenon and a prominent feature of higher education in developed countries because it can deliver a wide range of high-quality courses to a massive number of students and increase the collaborative learning experience among learners. It is possible to suggest that the range of pedagogical practices based on open online learning. MOOCs are presently shifting the educational landscape from classical scenarios to digital scenarios where open educational resources are being shared among universities and institutions. In this paper, a framework based on open online learning for serving higher education institutions in the Kurdistan Region of Iraq is proposed. The proposed framework aims to serve teachers and students by providing adapted highquality contents and scaffolding teaching and learning processes, in a cultural-aware context, in order to improve the existing higher education system.
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