关键词： deep reinforcement learning   wireless power transfer   mobile edge computing   time division multiple access  

摘要： Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multiaccess edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.

关键词： Deep belief network   Markov approximation   Mobile edge computing   Energy efficiency and entropy encoding  

摘要： The rising popularity of health-related wearables has increased the demand for computational offloading. Latency, battery life, and compute capabilities are all areas that could use significant improvement despite the massive expansion of wearable devices and offloading techniques. There are a number of issues caused by the limited battery life of mobile devices, so it is essential to conserve power by moving responsibilities for running applications to the cloud. This is especially crucial in situations where scheduling is being done in a dynamic mobile cloud computing environment. In this piece, we highlight the fact that most smart wearable devices can pair with smartphones via Bluetooth, and that this connection is significantly more power-efficient than 3G/LTE or Wi-Fi communication. To be more precise, we investigate the origins of this phenomenon. New computing technology, Mobile Edge Computing (MEC), may be the answer to capacity and performance problems in older systems like Mobile Cloud Computing (MCC). Some examples of these problems include excessive latency, a clogged core network, a lack of quality of experience (QoE), and the wasteful consumption of expensive resources like power and data transfer. To address these issues in the MEC setting, we introduced a Deep Belief Network (DBN) equipped with a Markov Approximation Algorithm (MAA). Because of this, a choice could be made that takes into account energy use, time constraints, and system load. AFCFS, MINET, and trade-off judgments for code offloading are all examples of state-of-the-art techniques that can be outperformed by this approach (TRADEOFF). Using entropy encoding as a post-processing step after quantization allows for lossless compression of an image. It allows for a picture to be shown in a way that is both more effective and requires less space for storage or transmission. Our proposed DBN-MAA reduces energy consumption while simultaneously increasing the number of completed jobs, as show

关键词： MBMS   multimedia services   computation offloading   mobile edge computing   resource allocation  

摘要： With the advancement of multimedia technology, 5G MBMS has become a promising technology that makes full and effective use of network resources to provide services. Mobile edge computing (MEC) is a key technology that brings important improvements to MBMS by utilizing contextual information such as multimedia, virtual reality, etc. However, the deployment of MEC may bring the latency to the network, which requires the deployment consideration of computing nodes. Moreover, mobile network operators need to consider the number of base stations required to accommodate the upcoming MBMS data streams. In this paper, we propose a computational offloading strategy and a co-optimization mechanism of video resource assignment in an edge cloud server enabled MBMS service system. Our approach aims to maximize computation speed while satisfying limited resource constraints, constrained energy consumption, and minimum latency time requirements, in order to provide better MBMS services. Additionally, our approach offers an advanced solution for achieving optimal computational offloading and resource allocation in edge cloud server enabled MBMS service systems. Extensive simulation results demonstrate that the proposed mechanism outperforms current state-of-the-art schemes. Specifically, the total calculation rate of the large-scale cloud server is significantly reduced under energy constraints, total delay requirements, and different network parameters.

关键词： Vehicular networks   mobile edge computing   task offloading   resource allocation   joint optimization  

摘要： In this article, we study the task offloading problem on mobile edge in vehicular networks. Specifically, we take computational resource constraints into consideration, and aim to simultaneously reduce latency and energy consumption. For this purpose, we establish an offloading model that consists of local edge computing resources, edge server resources of both macro and subsidiary base stations, as well as cloud computing server resources. Each task can be offloaded through one of five strategies, and is evaluated via a loss function determined by its latency and energy consumption. Based on this model, our goal is to solve a mixed-integer non-linear optimization problem (MINLP) whose objective function is the weighted sum of the task-specific loss functions. To address this optimization problem, we split it into two sub-problems, referred to as resource allocation and offloading strategy. We develop a method based on Block Coordinate Descent technique combining convex optimization and Gray Wolf algorithm (BCD-CONGW) that alternatively solves the two sub-problems, until convergence. The former sub-problem is convex and can be solved in polynomial time, whereas the latter is non-convex and hence NP-hard. For the latter, we relax discrete variables and employ Gray Wolf algorithm with elite strategy to approximate its optimal point. By numerical evaluations, we show that our method outperforms existent methods in terms of latency and energy consumption.

关键词： Healthcare monitoring   Software-defined networks   Mobile edge computing   Priority-aware semi-greedy   Task scheduling  

摘要： Health monitoring systems (HMS) with wearable IoT devices are constantly being developed and improved. But most of these gadgets have limited energy and processing power due to resource constraints. Mobile edge computing (MEC) must be used to analyze the HMS information to decrease bandwidth usage and increase reaction times for applications that depend on latency and require intense computation. To achieve these needs while considering emergencies under HMS, this work offers an effective task planning and allocation of resources mechanism in MEC. Utilizing the Software Denied Network (SDN) framework;we provide a priority-aware semi-greedy with genetic algorithm (PSG-GA) method. It prioritizes tasks differently by considering their emergencies, calculated concerning the data collected from a patient's smart wearable devices. The process can determine whether a job must be completed domestically at the hospital workstations (HW) or in the cloud. The goal is to minimize both the bandwidth cost and the overall task processing time. Existing techniques were compared to the proposed SD-PSGA regarding average latency, job scheduling effectiveness, execution duration, bandwidth consumption, CPU utilization, and power usage. The testing results are encouraging since SD-PSGA can handle emergencies and fulfill the task's latency-sensitive requirements at a lower bandwidth cost. The accuracy of testing model achieves 97 to 98% for nearly 200 tasks.

关键词： Computation offloading   Mobile edge computing   Deep reinforcement learning   Adaptive regulator  

摘要： Computation offloading, as an efficient and promising computing paradigm for mobile edge computing (MEC), provides optimal computation allocation between MEC servers and Internet of Things (IoT) devices. However, in modern wireless communications, most of the computational tasks are latency-sensitive, presenting an efficiency challenge for offloading services. Moreover, to make the best use of computation resources, it remains an issue to ensure stable optimal offloading for the allocation of computation resources. In this paper, a novel adaptive deep reinforcement learning-based offloading (ADRLO) framework is developed to achieve a stable optimal computation revenue with low execution latency for MEC offloading. Structurally, we implement a residual connected deep neural network (DNN) to generate a series of offloading strategies and use them to obtain the global system computation rate. Especially, the strategy that achieves the maximum computation rate is then selected as experience to support the DNN training, forming a closed-loop deep reinforcement learning (DRL) mechanism. To further enhance the efficiency and stability of the MEC offloading, an adaptive regulator is presented for the framework parameters including the size of action space and the DNN learning rate. Numerical results show that, compared to existing state-of-the-art approaches, our proposal achieves optimal computation revenue, exhibits better convergence performance, and significantly reduces the execution latency by almost 50%.

关键词： Task analysis   Resource management   Computational modeling   Wireless communication   Servers   Machine learning   Analytical models   Mobile edge learning   dynamic task allocation   distributed machine learning   mobile edge computing   federated learning   parallelized learning  

摘要： This paper introduces the new paradigm of Mobile Edge Learning "MEL" that enables the implementation of realistic distributed machine learning (DML) tasks on wireless edge nodes while taking into consideration the heterogeneous computing and networking environments. Therefore, a heterogeneity aware (HA) scheme is designed to solve the problem of dynamic task allocation for MEL in a way that maximizes the DML accuracy over wireless heterogeneous nodes or 'learners' while respecting the time constraints. The problem is first formulated as a quadratically-constrained integer linear program (QCILP). Being NP-hard, it is relaxed into a non-convex problem over real variables which can be solved using commercially available numerical solvers. The relaxation also allows us to propose a solution based on deriving the analytical upper bounds of the optimal solution using Lagrangian analysis and Karush-Kuhn-Tucker (KKT) conditions. The merits of the proposed analytical solution are demonstrated by comparing its performance to the numerical approaches and comparing the validation accuracy of the proposed HA scheme to the baseline heterogeneity unaware (HU) equal task allocation approach. Simulation results show that the HA schemes decrease convergence time up-to 56% and increase the final validation accuracy up-to 8%.

关键词： Servers   Reinforcement learning   Deep learning   Correlation   Training   Multi-access edge computing   IEEE transactions   Mobile edge computing   file caching strategy   multi-agent deep reinforcement learning  

摘要： Mobile edge computing (MEC) reduces data service latency by pushing data to the network edge. However, due to the dynamic and diverse requests of mobile users, the problem of mobile edge caching is more complex than cloud caching. Therefore, the existing model-based caching strategies cannot be directly used in the mobile edge caching environment. Besides, when taking the cooperative storage relationship between neighbor edge servers into consideration, the caching problem becomes more difficult. To this end, we formulate an mobile edge caching problem to minimize the total latency in mobile edge computing. Firstly, a heuristic caching strategy is proposed to solve the mobile edge caching problem in the single-time-slot scenario. Then, with the consideration of users' mobility and the correlation of files, we propose a caching strategy for the multiple-time-slot scenario based on multi-agent deep reinforcement learning. To address the cold start problem in deep reinforcement learning, we adopt the proposed heuristic caching strategy used in the single-time-slot scenario to further optimize the training results. Extensive experiments on generated data and real-world datasets are conducted to verify that the proposed edge caching strategies can achieve the minimum latency compared with the state-of-the-art strategies.

关键词： Risk prediction   Mobile edge computing   Intelligent system   Financial risk prediction  

摘要： The financial system has reached its pinnacle because of economic and social growth, which has propelled the financial sector into another era. Public and corporate financial investment operations have significantly risen in this climate, and they now play a significant part in and impact the efficient use of market money. This finance sector will be affected by high-risk occurrences because of the cohabitation of dangers and passions, which will cause order to become unstable and definite financial losses. An organization's operational risk is a significant barrier to its growth. A bit of negligence could cause the business's standing to erode rapidly. Increasing funding management and forecasting risks is essential for the successful development of companies, enhancing their competitiveness in the marketplace and minimizing negative effects. As a result, this study takes the idea of mobile edge computing. It creates an intelligent system that can forecast different risks throughout the financial investment process based on the operational knowledge of important investment platforms. The CNN-LSTM approach, based on knowledge graphs, is then used to forecast financial risks. The results are then thoroughly examined through tests, demonstrating that the methodology can accurately estimate the risk associated with financial investments. Finally, a plan for improving the system for predicting financial risk is put out.

关键词： mobile edge computing   MEC   vehicular networks   intelligent transportation systems   vehicle-to-everything  

摘要： In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading);(ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.