关键词： Task analysis   Edge computing   Processor scheduling   Servers   Wireless communication   Delays   Dynamic scheduling   Mobile edge computing   task offloading   Internet-of-Things   LTE over unlicensed   constrained deep reinforcement learning  

摘要： To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralized control. In this paper, we investigate the computing offloading problem for LTE-U-enabled IoT, where computing tasks on an IoT device are either executed locally or offloaded to the edge server on an LTE-U base station. Considering a constrained edge computing cost (e.g., operation power consumption) for offloaded tasks, the task scheduling problem is formulated as a constrained Markov decision process (CMDP) to maximize the long-term average reward, which integrates both task completion profit and task completion delay. In order to address the uncertainty of task arrivals and channel availability, a constrained deep Q-learning-based task scheduling algorithm with provable convergence is proposed, where an adaptive reward function can appropriately bound the average edge computing cost. Extensive simulation results show that the proposed scheme considerably enhances the system performance.

关键词： Task analysis   Data mining   Data processing   Servers   Privacy   Mobile handsets   Blockchain   mobile edge computing   mining   task offloading   privacy   deep reinforcement learning  

摘要： Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user's power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.

关键词： Mobile edge computing   Dynamic service migration   Reinforcement learning   Q-learning   Software-defined networking  

摘要： Service migration in mobile edge computing is a promising approach to improving the quality of service (QoS) for mobile users and reducing the network operational cost for service providers as well. However, these benefits are not free, coming at costs of bulk-data transfer, and likely service disruption, which could consequently increase the overall service costs. To gain the benefits of service migration while minimizing its cost across the edge nodes, in this paper, we leverage reinforcement learning (RL) method to design a cost-effective framework, called Mig-RL, for the service migration with a reduction of total service costs as a goal in a mobile edge environment. The Mig-RL leverages the infrastructure of edge network and deploys a migration agent through Q-leaming to learn the optimal policy with respect to the service migration status. We distinguish the Mig-RL from other existing works in several major aspects. First, we fully exploit the nature of this problem in a modest migration space, which allows us to constrain the number of service replicas whereby a defined state-action space could be effectively handled, as opposed to those methods that need to always approximate a huge state-action space for policy optimality. Second, we advocate a migration policy-base as a cache to save the learning process by retrieving the most effective policy whenever a similar migration pattern is encountered as time goes on. Finally, by exploiting the idea of software defined network, we also investigate the efficient implementation of Mig-RL in mobile edge network. Experimental results based on some real and synthesized access sequences show that Mig-RL, compared with the selected existing algorithms, can substantially minimize the service costs, and in the meantime, efficiently improve the QoS by adapting to the changes of mobile access patterns. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Mobile edge computing  

摘要： Social cognition requires neural processing, yet a unifying method linking particular brain activities and social behaviors is lacking. Here, we embedded mobile edge computing (MEC) and light emitting diodes (LEDs) on a neurotelemetry headstage, such that a particular neural event of interest is processed by the MEC and subsequently an LED is illuminated, allowing simultaneous temporospatial visualization of that neural event in multiple, socially interacting mice. As a proof of concept, we configured our system to illuminate an LED in response to gamma oscillations in the basolateral amygdala (BLA gamma) in freely moving mice. We identified (i) BLA gamma responses to a spider robot, (ii) affect-related BLA gamma during conflict, and (iii) formation of defensive aggregation under a threat by the robot, and reduction of BLA gamma responses in the inner-located mice. Our system can provide an intuitive framework for examining brain-behavior connections in various ecological situations and population structures.

关键词： Mobile edge computing   Game theory   Computing offloading   Resource allocation   IoT device   Nash equilibrium  

摘要： With the explosive growth of Internet of Things (IoT) devices and the popularization of intelligent terminals, the computing resources of various new applications are intensive, and existing work offloads them to mobile edge centers for computing. However, due to limitations in the coverage and resource capabilities of edge devices, there is still a problem of unbalanced load in the network. Therefore, in order to balance the load reasonably, this paper proposes a computing offloading and resource allocation algorithm for IoT devices using game theory in Mobile Edge Computing (MEC). Firstly, a multi-user multi-MEC computing offloading and resource allocation model is designed, which manages a large number of devices by multiple base stations and multiple MEC partitions, and models the system including computing model and task model. Then, a prediction method of MEC server waiting time is proposed according to the task queue in base stations. The task processing sequence, time and frequency are arranged to improve the calculation efficiency of this algorithm, which makes rational use of computing resources for devices and MEC. Finally, the problem of multi-user and multi-MEC computing offloading and resource allocation is transformed into a multi-user game problem, and it is concluded that there is always a Nash equilibrium in this game problem. Furthermore, the optimal solution of this problem, that is, the optimal device computing offloading and resource allocation scheme, is obtained when the system is in Nash equilibrium state. The simulation results based on MATLAB platform show that our proposed algorithm is effective compared with other algorithms. It can converge reliably and has obvious optimization in energy consumption and time delay;besides the performance of this algorithm is improved.

关键词： Deep reinforcement learning   Intelligent IoT   Mobile edge computing  

摘要： In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet of things (IoT), where multiple users have some computational tasks assisted by multiple computational access points (CAPs). By offloading some tasks to the CAPs, the system performance can be improved through reducing the latency and energy consumption, which are the two important metrics of interest in the MEC networks. We devise the system by proposing the offloading strategy intelligently through the deep reinforcement learning algorithm. In this algorithm, Deep Q-Network is used to automatically learn the offloading decision in order to optimize the system performance, and a neural network (NN) is trained to predict the offloading action, where the training data is generated from the environmental system. Moreover, we employ the bandwidth allocation in order to optimize the wireless spectrum for the links between the users and CAPs, where several bandwidth allocation schemes are proposed. In further, we use the CAP selection in order to choose one best CAP to assist the computational tasks from the users. Simulation results are finally presented to show the effectiveness of the proposed reinforcement learning offloading strategy. In particular, the system cost of latency and energy consumption can be reduced significantly by the proposed deep reinforcement learning based algorithm. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is a new technology that provides an IT service environment and cloud-computing resource at the edge of the mobile network, aiming to improve the user experience by reducing latency and delivering highly efficient services. A mobile health care system is a typical case of MEC. Recent advances in mobile health technology enable us to automatically record the states and behaviors of patients in hospitals by identifying their faces. Instead of using a centralized server or cloud to process these heavy loading analyses, MEC analyzes the patients' behavior locally in their rooms to enable quick response to their accidents and reduce the storage size of large-scale patient information. By using MEC, only one frontal face for each patient is available in the gallery to reduce the local computational power. Although a patient's face can be captured from any other pose, doing so challenges the face-identification algorithm. Several studies have been published to solve this problem, but most of them rely on pose estimation and landmark detection. In this paper, we propose a deeper convolutional neural network to extract the pose-invariant face features and synthesize the virtual poses simultaneously, aiming to develop a landmark-free and pose-estimation-free frontal-face synthesis system. The deeper network was divided into multiple overlapped local networks, each one trained to synthesize a small pose change, such as one larger than45 degrees to30 degrees. The local networks were jointly trained to synthesize the frontal face from a nonfrontal pose in a progressive manner. By stacking multiple local networks, we were able to extract more robust pose-invariant features, to generate multiple virtual poses before the frontal face was synthesized. The pose-invariant features and virtual poses were incorporated to identify the face across poses. The proposed method was evaluated on the CAS-PEAL dataset with 16 layers and fine-tuned on the FERET dataset

关键词： Bi-objective Optimization   Computation Offloading   Energy   Heuristic   Mobile Edge Computing   Multi-task   Processing time  

摘要： This article describes a processing time, energy and computing resources optimization in a Mobile Edge Computing (MEC). We consider a mobile user MEC system, where a smart mobile device (SMD) demands computation offloading to a MEC server. For that, we consider a SMD contains a set of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the processing times. We proposed a heuristic solution schema. To evaluate our solution, we realized a range of simulation experiments. The results obtained in terms of treatment time and energy consumption are very. © 2020. All Rights Reserved.

关键词： Task analysis   Trajectory   Unmanned aerial vehicles   Resource management   Servers   Computer architecture   Relays   Mobile edge computing   UAV   wireless power transfer   resource allocation   trajectory design  

摘要： A wireless-powered mobile edge computing (MEC) architecture with the cooperation between an access point (AP) and an unmanned aerial vehicle (UAV) is studied in this article. The AP, powered by the grid, is integrated with a high-performance processing server to help compute the user equipment's (UEs') offloaded tasks while also performing high-power laser-like energy charging for the UAV. The UAV serves as (1) an information relay to help the UEs offload/download their computation tasks/results, (2) an energy relay to broadcast energy from the AP to the UEs, as well as (3) an MEC server to help the UEs compute their tasks. We aim at maximizing the weighted sum completed task-input bits (WSCTB) of UEs under the task and time allocation, information-causality, energy-causality, and the UAV's trajectory constraints, by jointly optimizing the task and time allocation as well as the UAV's energy transmit power and trajectory. The formulated WSCTB maximization problem is non-convex, and we propose a three-step block coordinate descending algorithm to address three sub-problems iteratively for obtaining a proper solution. Simulation results show that the UAV's trajectories highly depend on the AP's location and the UEs' weight values. In addition, significant performance improvement is achieved by the proposed algorithm compared to some practical benchmarks.

关键词： Mobile edge computing  

摘要： This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-constrained wireless edge network. The optimization is done such that the portion of the task allotted to each learner is completed within a given global delay constraint and a local maximum energy consumption limit. The time and energy consumed are related directly to the heterogeneous communication and computational capabilities of the learners;i.e. the proposed model is heterogeneity aware (HA). Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed based on using the solution of the relaxed synchronous problem to obtain the solution to the asynchronous problem. The proposed HA asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) equal batch allocation scheme. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU synchronous/asynchronous (HU-Sync/Asyn) approach and can provide gains of up-to 25% compared to the HA-Sync scheme. © 2020, CC BY.