Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

/pdf/a-survey-of-multi-access-edge-computing-in-5g-and-beyond-25j4pqqhet.pdf

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art.

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

pdf/energy-efficient-mobile-cloud-computing-powered-by-wireless-j0rgeg9f46.pdf

Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

The promising technique of Wireless Power Transfer (WPT) to end devices and sensors has gained the attention of researchers recently. Mobile edge computing (MEC) is also succeeding from Cloud Computing due to its minimum latency constraints. In MEC, smart devices offload computation intensive tasks to the MEC server which achieves low latency. However, limitations exist for smart device battery lifetime and task execution delay because of an effective decision in the offloading scenario necessitating joint WPT and MEC offloading. The joint WPT and MEC offloading decisions are based on real time application requirements, placement of Base Station (BS) with power transfer capabilities for smart devices, and offloading opportunities in the MEC. To meet the energy consumption requirement, a BS integrated with MEC server and power transfer capability transfers wireless power to end devices as an incentive and offers opportunities for offloading. Transferring wireless power to end devices effectively meets the requirement of smart devices while extending battery lifetime. This article encapsulates the state of art work in methodologies of offloading in MEC and WPT to end nodes. We consider MEC offloading techniques with WPT and real time application requirements while summarizing related studies. We formulate a taxonomy of joint WPT and offloading in MEC. We compare the state-of-the-art studies based on parameters identified from taxonomy. Finally, we provide the challenges and debate future research directions relevant to the domain of joint MEC-WPT.

Joint wireless power transfer and task offloading in mobile edge computing: a survey

A cognitive radio network (CRN) is integrated with the Internet of Connected Vehicles (IoCV) in order to address spectrum scarcity and communication reliability issues. However, it is limited, possessing less throughput, a low packet delivery ratio, high latency, and high mobility in the spectrum. In this research study, the existing issues are addressed by proposing a 6G cognitive radio network–Internet of connected vehicles (6GCRN–IoCV) approach. Initially, all the entities such as secondary users (SUs), primary users (PUs), and pedestrians are authenticated in blockchain to ensure security. The edge-assisted roadside units (ERSU) initiate clustering only for authenticated SUs using the improved DBSCAN algorithm in consideration of several metrics. The ERSU then generates an intersection-aware map using the spatial and temporal-based logistic regression algorithm (STLR) to reduce collisions in the intersection. The spectrum utilization is improved by performing spectrum sensing in which all the SUs involved in spectrum sensing use lightweight convolutional neural networks (Lite-CNN) in consideration of several metrics and provide the sensing report to the fusion center (FC) in an encrypted manner to reduce the spectrum scarcity and security issues. The communications between the SUs are necessary to avoid risks in the IoCV environment. Hence, optimal routing is performed using the Dingo Optimization Algorithm (DOA), which increases throughput and packet delivery ratio. Finally, communication reliability is enhanced by performing hybrid beamforming, and this exploits the multi-agent-based categorical Deep-Q Network (categorical DQN), which increases spectral efficiency based on its adaptive intelligent behavior. The proposed study is simulated using the SUMO and OMNeT++ simulation tools and the performances are validated with existing works using several performance metrics. The result of the simulation shows that the proposed work performs better than the existing approaches.

/pdf/secure-spectrum-access-routing-and-hybrid-beamforming-in-an-1w00idhh.pdf

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments

Most mobile edge computing (MEC) works assume that the mobile devices (MDs) can offload their tasks to the MEC-severs at anytime, which may not be a practical assumption due to the tension between a large number of MDs and the scarce spectrum resources. In this paper, a framework for wireless powered cognitive radio (CR)-based MEC-enabled networks is proposed, which integrates three technologies: MEC, CR, and wireless power transfer (WPT). CR technology with imperfect spectrum sensing is adopted by the MD to find the spectrum access opportunities. An optimization problem is formulated to maximize the average calculated number of bits (CNoB) of the MD, which is non-convex and hard to solve. A two-loop procedure using a one-dimensional line search method is proposed. The time for spectrum sensing, WPT, energy harvesting (EH) and offloading, the central processing unit (CPU) frequency, and the transmit power of the MD are jointly optimized. Some semi-closed form solutions are obtained through Lagrangian dual decomposition and successive pseudo-convex approximation (SPCA) methods. Simulation results are presented to show the effectiveness of the proposed CR-based MEC scheme with different parameters.

/pdf/wireless-powered-cognitive-based-mobile-edge-computing-with-pz2i9nmy33.pdf

Wireless Powered Cognitive-Based Mobile Edge Computing With Imperfect Spectrum Sensing

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Wireless Powered Cognitive-Based Mobile Edge Computing With Imperfect Spectrum Sensing