关键词： 5G mobile communication   Resource management   Quality of service   Wireless networks   NOMA   MIMO communication   Deep learning   5G   B5G   deep learning   machine learning   mobile edge computing   radio resource management  

摘要： In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multiple-input-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

关键词： Mobile edge computing   deep reinforcement learning   computation offloading   resource allocation   task relevance  

摘要： - With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simulation results show that the proposed algorithm has the characteristics of fast convergence and high stability, and it can obtain the optimal offloading decision with the lowest total cost.

关键词： Wireless communication   Cameras   5G mobile communication   Object detection   Wearable computers   Ions   Image edge detection   Mobile edge computing   millimeter-wave   5G wireless   smart wearables   mobile machine vision   deep learning   object detection  

摘要： Advanced wearable devices are increasingly incorporating high-resolution multi-camera systems. As state-of-the-art neural networks for processing the resulting image data are computationally demanding, there has been a growing interest in leveraging fifth generation (5G) wireless connectivity and mobile edge computing for offloading this processing closer to end-users. To assess this possibility, this paper presents a detailed simulation and evaluation of 5G wireless offloading for object detection in the case of a powerful, new smart wearable called (VISION)-I-4, for the Blind-and-Visually Impaired (BVI). The current (VISION)-I-4 system is an instrumented book-bag with high-resolution cameras, vision processing, and haptic and audio feedback. The paper considers uploading the camera data to a mobile edge server to perform real-time object detection and transmitting the detection results back to the wearable. To determine the video requirements, the paper evaluates the impact of video bit rate and resolution on object detection accuracy and range. A new street scene dataset with labeled objects relevant to BVI navigation is leveraged for analysis. The vision evaluation is combined with a full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment. For comparison, the wireless simulation considers both a standard 4G-Long Term Evolution (LTE) sub-6-GHz carrier and high-rate 5G millimeter-wave (mmWave) carrier. The work thus provides a thorough and detailed assessment of edge computing for object detection with mmWave and sub-6-GHz connectivity in an application with both high bandwidth and low latency requirements.

关键词： COVID-19   Blockchains   Security   Pandemics   Medical services   Internet of Things   Industries   Blockchain   deep reinforcement learning   mobile edge computing   energy harvesting   healthcare Internet of Things   COVID-19  

摘要： Recently, the Healthcare Internet of Things (H-IoT) has been widely applied to alleviate the global challenge of the coronavirus disease 2019 (COVID-19) pandemic. However, security and limited energy capacity issues remain the two main factors that prevent the large-scale application of the H-IoT. Therefore, a permissioned blockchain and deep reinforcement learning (DRL)-empowered H-IoT system is presented in this research to address these two issues. The proposed H-IoT system can provide real-time security and energy-efficient healthcare services to control the propagation of the COVID-19 pandemic. To address the security issue, a permissioned blockchain method is adopted to guarantee the security of the proposed H-IoT system. As for handling the limited energy constraint, we employ the mobile edge computing (MEC) method to offload the computing tasks to alleviate the computational burden and energy consumption of the proposed H-IoT system. We also adopt an energy harvesting method to improve performance. In addition, a DRL method is employed to jointly optimize both the security and energy efficiency performance of the proposed system. The simulation results demonstrate that the proposed solution can balance the requirements of security and energy efficiency issues and hence can better respond to the COVID-19 pandemic.

关键词： Cloud computing   Resource management   Servers   Quality of service   5G mobile communication   Virtualization   Real-time systems   Mobile edge computing   MEC infrastructure design and dimensioning   mobile network operator   optimization approaches   proactive   resource management   virtualization   NFV   VNF autoscaling   VNF placement  

摘要： Emerging 5G cellular networks are expected to face a dramatic increase in the volume of mobile traffic and IoT user requests due to the massive growth in mobile devices and the emergence of new compute-intensive applications. Running high-intensive compute applications on resource-constrained mobile devices has recently become a major concern, given the constraints of finite computation and limited storage capacities. Mobile Edge Computing (MEC) has recently become the key technology to overcome these issues by providing cloud computing capabilities and placing IT infrastructures at the mobile network edge. In this survey, we present a list of relevant research papers for the MEC infrastructure implementation phases, including (1) MEC infrastructure designing and dimensioning, (2) MEC infrastructure virtualization using Network Function Virtualization (NFV) concept, and the use of virtualized service placement and auto-scaling methods to deploy an agile system framework, (3) MEC resource management frameworks, and (4) approaches used to optimize the MEC resources on the physical infrastructure. The main focus of this survey is to determine the required aspects to implement an auto-scaled and proactive MEC-NFV infrastructure to support a dynamic and heterogenous mobile users' demand at mobile network operators.

关键词： Task analysis   Servers   Reinforcement learning   Energy consumption   Autonomous aerial vehicles   Multi-access edge computing   Trajectory   Mobile edge computing   unmanned aerial vehicles   user equipments   deep reinforcement learning   path planning   task offloading  

摘要： With the development of Unmanned Aerial Vehicles (UAVs) in recent years, UAV-mounted Mobile Edge Computing (MEC) systems are widely used for broadband connectivity and emergency communications in areas without internet. However, the onboard energy of UAVs is limited, and how to provide long-term computing services for ground User Equipments (UEs) still faces significant challenges. In this paper, UAVs are used as mobile base stations to offer MEC services for UEs. Unlike existing solutions that mainly solve UAV path planning from convex optimization methods, we propose a Multi-Agent Path planning (MAP) scheme based on deep reinforcement learning. We aim to minimize the total energy consumption of UAVs while completing the offloading tasks of UEs. At the same time, fairness is taken into account to ensure UE offloading balance and UAV load balance. To this end, we establish the optimization problem and design the state space, action space, and reward function for modeling each UAV through Deep Neural Networks (DNNs). Extensive simulation experiments show that the proposed scheme outperforms other benchmark schemes.

关键词： mobile edge computing   cloud computing   grey wolf algorithm   quality of services   QoSs   service selection  

摘要： The proliferation of mobile devices has resulted in the tremendous development of edge computing. A common mechanism is that once the requests from the users are too complex to be afforded by a single service, then edge computing services ought to step in. Nevertheless, services on edge servers are most commonly resource-constrained and unstable. To this end, we propose a novel solution for the mobile service selection problem with edge and cloud computing. An extended grey wolf algorithm is presented;specifically, crossover operator and roulette wheel are applied in reproduction and selection operations. Comparative experiments are implemented between our approach and other nature-inspired algorithms to verify the effectiveness and efficiency, which demonstrate our method may find a solution with better QoS values.

关键词： Live streaming   machine learning   mobile edge computing   VoD   video Streaming  

摘要： 5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.

关键词： 6G Network   Actor-Critical learning   Digital Twin Edge Network   Mobile Edge Computing  

摘要： Sixth Generation (6G) network is meant to allow wireless networking and computing by digitalizing and sharing everything, by providing a computer image of the actual network world. Mobile edge computation as one of the key factors in allowing mobile downloads faces unparalleled obstacles because the 6G network environment is incredibly dynamic and unforeseeable. In the latest literature on mobile edge computing, the implications of user mobility and the volatile mobile edge computing world are still ignored. In this paper, we propose a new methodology for the Digital Twin mirror that offers training data to offload decisions for digital edge servers to evaluate the edge servers' status and the Digital Twin for the whole edge computing environment. In the wireless twin edge networks, the proposed system is to reduce the download delay in the face of the cumulative expense of relocation from the accessed service Mobility for consumers. The Lyapunov approach's Optimization is used to simplify the cost constraint of Long-term transformation to an intra-functional enhancement challenge, which is then resolved by profoundly enhanced Actor-Critic (AC) learning. Replications demonstrate that, as opposed to benchmark systems, our proposed arrangements effectively decrease the average offload delay, discharge failure rate and operation migration rate and save device costs with Digital Twin help. © 2022 University of Bahrain. All rights reserved.

关键词： Mobile edge computing   WSN   node deployment   energy consumption of network nodes   trilateral measurement  

摘要： When deploying network nodes, there are many redundant nodes, low network coverage and high energy consumption of network nodes, the node deployment method of wireless sensor networks(WSN) based on mobile edge computing is studied. WSN nodes are divided into anchor nodes and unknown nodes. Taking the location information of anchor nodes as a reference, the specific location of unknown nodes is obtained by trilateral measurement. Minimizing the node distance error is taken as the objective function, and the cuckoo search algorithm is used to solve it to obtain the final location result of the node. The mobile edge computing method is used to design the node deployment method of WSN to complete the node deployment. Simulation results show that the number of redundant nodes in this method is 3, maximum network coverage is 89%, maximum energy consumption of network nodes is 34.3J.