Task Offloading and Resource Allocation for Edge-Enabled Mobile Learning

Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile devices.However,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a challenge.Therefore,for mobile learning,we propose a"Cloud,Edge and End"fusion system architecture.Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment.Then,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem respectively.Finally,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users.

Ontology and metadata for online learning resource repository management based on semantic web

An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.

Usability and Effectiveness of Mobile Learning Course Content Application as a Revision Tool

The use of mobile phone technologies in the education sector is getting more attention nowadays. This is due to the advancement of technologies equipped in majority of the mobile phones which makes the devices become more capable of supporting the learning and teaching activities. Mobile learning （m-learning） is a learning tool which can be run on mobile devices. It can be considered as an enhancement to the electronic learning （e-learning）. M-learning overcomes several limitations of e-learning especially in term of mobility. It provides more independent way of learning whereby learners can use the application to do the learning activities at anytime and any place. However, as with other learning and teaching applications, applications to be developed for mobile learning must also be developed based on certain learning theories and guidelines in order for them to be effective as well as usable. Therefore, in this paper, the development process of a mobile learning course content application called Mobile System Analysis and Design （MOSAD） as a revision tool will be shared and its testing＇s conduct and results will also be presented and discussed. MOSAD was developed with the content of a topic from the System Analysis and Design （SAD） course conducted at Universiti Teknologi PETRONAS （UTP）. A heuristic test involving 5 experts in the area of Human Computer Interaction （HCI） were conducted after the first version of MOSAD was completed to strengthen its functionality and usability, followed by a Post Test Quasi Experimental Design which was conducted to 116 UTP second year students who took the SAD course to test the effectiveness and usability of MOSAD after it was revised. As a result from the post test, the students who had used MOSAD （66 out of the 116 students） as their revision tool for answering ten quiz questions obtained a mean score of 7.7576 as compared to 5.160 obtained by the other group of students （50 out of the 116 students） who used traditional methods of revision. Besides, usability test which tested on consistency, leamability, flexibility, minimal action and minimal memory load of MOSAD gave results above 3.5 for each metric based on the rating of 1 to 5. Thus, both results indicate that MOSAD is effective and usable as a revision tool for the higher education students.

A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning

Federated Edge Learning(FEL),an emerging distributed Machine Learning(ML)paradigm,enables model training in a distributed environment while ensuring user privacy by using physical separation for each user’s data.However,with the development of complex application scenarios such as the Internet of Things(IoT)and Smart Earth,the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands.Therefore,joint resource optimization may be the key solution to the scaling problem.This paper simultaneously addresses the multifaceted challenges of computation and communication,with the growing multiple resource demands.We systematically review the joint allocation strategies for different resources(computation,data,communication,and network topology)in FEL,and summarize the advantages in improving system efficiency,reducing latency,enhancing resource utilization,and enhancing robustness.In addition,we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements,indirectly.This work not only provides theoretical support for resource management in federated learning(FL)systems,but also provides ideas for potential optimal deployment in multiple real-world scenarios.By thoroughly discussing the current challenges and future research directions,it also provides some important insights into multi-resource optimization in complex application environments.

Resource management at the network edge for federated learning

Federated learning has been explored as a promising solution for training machine learning models at the network edge,without sharing private user data.With limited resources at the edge,new solutions must be developed to leverage the software and hardware resources as the existing solutions did not focus on resource management for network edge,specially for federated learning.In this paper,we describe the recent work on resource manage-ment at the edge and explore the challenges and future directions to allow the execution of federated learning at the edge.Problems such as the discovery of resources,deployment,load balancing,migration,and energy effi-ciency are discussed in the paper.

Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

Resource Allocation for Cognitive Network Slicing in PD-SCMA System Based on Two-Way Deep Reinforcement Learning

In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy source.Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants.This study proposes an integrated deep learning-based photovoltaic resource assessment method.Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time.The proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment.The proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and landscape.The experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.

A learning automata based edge resource allocation approach for IoT-enabled smart cities

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart homes.Moreover,these applications act as the building blocks of IoT-enabled smart cities.The high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for processing.However,there is a high computation latency due to the presence of a remote cloud server.Edge computing,which brings the computation close to the data source is introduced to overcome this problem.In an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay constraint.An efficient resource allocation at the edge is helpful to address this issue.In this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation problem.First,we presented a three-layer network architecture for IoT-enabled smart cities.Then,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization problem.Learning Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource mapping.An extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

Enhancing Learning Outcomes in Vocational Nursing Education Through Low-cost Digital Resources

With the deepening of educational reform,clinical nursing faces increasingly higher requirements in response to societal developments.Vocational nursing students primarily study humanities and social sciences,medical foundations,preventive health care theories,nursing basics,and clinical nursing skills.These subjects are broad and abstract,necessitating the integration of theory with practice to enhance understanding and mastery.In the digital era,numerous resources,such as smartphones,the Superstar Learning Platform,WeChat communication tools,and artificial intelligence,can be utilized in teaching.This study aims to employ low-cost online resources to implement blended teaching methods in nursing education at higher vocational colleges,enriching the classroom experience,stimulating student enthusiasm,improving learning outcomes,and meeting clinical needs.

Efficient Virtual Resource Allocation in Mobile Edge Networks Based on Machine Learning

The rapid growth of Internet content,applications and services require more computing and storage capacity and higher bandwidth.Traditionally,internet services are provided from the cloud(i.e.,from far away)and consumed on increasingly smart devices.Edge computing and caching provides these services from nearby smart devices.Blending both approaches should combine the power of cloud services and the responsiveness of edge networks.This paper investigates how to intelligently use the caching and computing capabilities of edge nodes/cloudlets through the use of artificial intelligence-based policies.We first analyze the scenarios of mobile edge networks with edge computing and caching abilities,then design a paradigm of virtualized edge network which includes an efficient way of isolating traffic flow in physical network layer.We develop the caching and communicating resource virtualization in virtual layer,and formulate the dynamic resource allocation problem into a reinforcement learning model,with the proposed self-adaptive and self-learning management,more flexible,better performance and more secure network services with lower cost will be obtained.Simulation results and analyzes show that addressing cached contents in proper edge nodes through a trained model is more efficient than requiring them from the cloud.

Adjustment to affective factors in English learning by using Internet English Curriculum Resource

The new curriculum standard points out that affection is one of the most important goals of fundamental education. The non-target language environment is easier to cause the affective change of middle school students who are changeable in their affective state. Based on the affective filter hypothesis, this paper deals with the adjustment to affective factors in English learning by using Internet English Curriculum Resource, such as attitude and motivation, anxiety and inhibition, self-esteem and self-confidence. At last, some suggestions are offered to judge Internet English Curriculum Resource.

An Exploratory Study of the Application of Modern Distance Learning Resources for Rural Elementary and Middle Schools in Western China:The Case of Three Cities in Gansu Province

Based on a specially adapted questionnaire,research was carried out to explore how resources in modern distance learning were being applied in rural elementary and middle schools in Western China,in order to enhance the usefulness of the allocated resources.One particular distance learning project was used as the subject of the study,and teachers at 22 rural elementary and middle schools in 3 prefecture-level cities in Gansu Province,Western China,acted as the sample group.Analysis of the teachers’questionnaire responses showed that,(1)there is a comparatively high number of modern distance learning resources in use in rural elementary and middle schools in Western China,and they are being effectively used in the classroom to assist in everyday teaching;(2)teachers of rural elementary and middle schools in Western China have the ability to make use of these modern resources,and can obtain them by way of multi-channel inquiry;(3)modern distance learning resources are already becoming important components of everyday teaching resources in Western China;and(4)teachers are generally able to apply these resources well,despite some problems remaining.In the case of problems in application,most teachers are able to find adequate solutions.

A Machine-Learning Based Time Constrained Resource Allocation Scheme for Vehicular Fog Computing

Through integrating advanced communication and data processing technologies into smart vehicles and roadside infrastructures,the Intelligent Transportation System(ITS)has evolved as a promising paradigm for improving safety,efficiency of the transportation system.However,the strict delay requirement of the safety-related applications is still a great challenge for the ITS,especially in dense traffic environment.In this paper,we introduce the metric called Perception-Reaction Time(PRT),which reflects the time consumption of safety-related applications and is closely related to road efficiency and security.With the integration of the incorporating information-centric networking technology and the fog virtualization approach,we propose a novel fog resource scheduling mechanism to minimize the PRT.Furthermore,we adopt a deep reinforcement learning approach to design an on-line optimal resource allocation scheme.Numerical results demonstrate that our proposed schemes is able to reduce about 70%of the RPT compared with the traditional approach.

Machine Learning Based Resource Allocation of Cloud Computing in Auction

Resource allocation in auctions is a challenging problem for cloud computing.However,the resource allocation problem is NP-hard and cannot be solved in polynomial time.The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution;however,these algorithms have the disadvantages of low computational efficiency or low allocate accuracy.In this paper,we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions.By learning a small-scale training set,the prediction model can guarantee that the social welfare,allocation accuracy,and resource utilization in the feasible solution are very close to those of the optimal allocation solution.The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.

Deep Reinforcement Learning Based Joint Partial Computation Offloading and Resource Allocation in Mobility-Aware MEC System

Mobile edge computing(MEC)emerges as a paradigm to free mobile devices(MDs)from increasingly dense computing workloads in 6G networks.The quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC servers.Renewable energy harvested by energy harvesting equipments(EHQs)is considered as a promising power supply for users to process and offload tasks.In this paper,we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage energy.We investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC system.We propose an effective joint partial computation offloading and resource allocation(CORA)algorithm which is based on deep reinforcement learning(DRL)to obtain the optimal scheduling without prior knowledge of task arrival,renewable energy arrival as well as channel condition.The simulation results verify the efficiency of the proposed algorithm,which undoubtedly minimizes the cost of MDs compared with other benchmarks.

Joint Scheduling and Resource Allocation for Federated Learning in SWIPT-Enabled Micro UAV Swarm Networks

Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.

Multi-Objective Deep Reinforcement Learning Based Time-Frequency Resource Allocation for Multi-Beam Satellite Communications

Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).