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The method of charging mobile devices with wireless power transfer (WPT) from the base station (BS) integrated with mobile edge computing (MEC) increases the potential of MEC. The increasing demand for intelligent computation offloading requires effective decisions among local or remote computation specifically in wireless fading channels of the dynamic environment. Our main aim is to generate an effective offloading decision between local and remote computation in a real-time environment for each wireless channel while preserving optimal computation rate. In this article, we consider a wireless powered MEC system that governs a binary offloading decision to execute the task locally at the edge devices or the remote server. We propose a reinforcement learning based intelligent online offloading (RLIO) framework that adopts an optimal offloading action based on reinforcement methods. This framework acquires a worthy decision among local or remote computation for the time varying wireless channel conditions in dense networks. Numerical results show that the proposed framework can achieve optimal performance while preserving the computation time compared with existing optimization methods. Second, the average execution cost of RLIO is less than 0.4 ms per channel, which enables real-time and optimal offloading in dynamic and large-scale networks. [ABSTRACT FROM AUTHOR]

The developments in smartphones, high data rates, and substantial video data traffic have increased the burden on cellular networks. Consequently, this burden significantly affects the Quality of Experience of the cellular users leading to an increased network delay for the diverse video content requests. To accommodate the requests from different users with varying requirements, one of the promising solutions is to cache videos in the near vicinity of users and transcode them online. The online transcoding is performed at the edge level of the cellular network to minimize the network delay and use the bandwidth efficiently. However, the feasibility of online transcoding is significantly affected by various factors, such as the codecs, configurations of virtual machines, the cost incurred, and estimated time to complete the transcoding task, among other parameters. Although online transcoding is discussed in the literature adequately, few studies discuss the feasibility of online transcoding while considering all the aforementioned critical parameters. This study examined the effects of a diverse range of critical parameters on the feasibility of online transcoding. We performed extensive simulations on the local machine environment to study various possible factors affecting online transcoding in detail. We then transcoded the same videos on Amazon Elastic Cloud Computing (EC2) Virtual Machines (VMs) to further study realistic cloud settings with fine-tuned configurations. Our experiments show the superior performance of some codecs and the effects of machine configurations on transcoding tasks duration. We aim to provide a benchmark for practitioners and researchers considering online transcoding in real-time multimedia applications. [ABSTRACT FROM AUTHOR]

The Information and Communication Technology sector is considered to be a major consumer of energy and has become an active participant in Green House Gas emissions. Lots of efforts have been devoted to make network infrastructure and network protocols power-aware and green. Among these efforts, Adaptive Link Rate (ALR) is one of the most widely discussed approaches. This survey highlights the most recent ALR approaches with a brief taxonomy and the state of the art. [ABSTRACT FROM AUTHOR]