Computation Rate Maximization in Multi-User Cooperation-Assisted Wireless-Powered Mobile Edge Computing with OFDMA

In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.

Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement

In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.

Towards sparse matrix operations:graph database approach for power grid computation

The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)technology.The“source-grid-load-storage”architecture of a new power system requires PIoT to have a stronger multi-source heterogeneous data fusion ability.Native graph databases have great advantages in dealing with multi-source heterogeneous data,which make them suitable for an increasing number of analytical computing tasks.However,only few existing graph database products have native support for matrix operation-related interfaces or functions,resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids.In this paper,the matrix computation process is expressed by a strategy called graph description,which relies on the natural connection between the matrix and structure of the graph.Based on that,we implement matrix operations on graph database,including matrix multiplication,matrix decomposition,etc.Specifically,only the nodes relevant to the computation and their neighbors are concerned in the process,which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation.Based on the graph description,a series of power grid computations can be implemented on graph database,which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database.It promotes the efficiency of PIoT when handling multi-source heterogeneous data.An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines.The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.

Online Computation Offloading and Trajectory Scheduling for UAV-Enabled Wireless Powered Mobile Edge Computing

The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

Experimental Study on Cloud-Computing-Based Electric Power SCADA System

With the development of smart grid, the electric power supervisory control and data acquisition （SCADA） system is limited by the traditional IT infrastructure, leading to low resource utilization and poor scalability. Information islands are formed due to poor system interoperability. The development of innovative applications is limited, and the launching period of new businesses is long. Management costs and risks increase, and equipment utilization declines. To address these issues, a professional private cloud solution is introduced to integrate the electric power SCADA system, and conduct experimental study of its applicability, reliability, security, and real time. The experimental results show that the professional private cloud solution is technical and commercial feasible, meeting the requirements of the electric power SCADA system.

Energy-Delay Tradeoff for Online Offloading Based on Deep Reinforcement Learning in Wireless Powered Mobile-Edge Computing Networks

Benefited from wireless power transfer(WPT)and mobile-edge computing(MEC),wireless powered MEC systems have attracted widespread attention.Specifically,we design an online offloading scheme based on deep reinforcement learning that maximizes the computation rate and minimizes the energy consumption of all wireless devices(WDs).Extensive results validate that the proposed scheme can achieve better tradeoff between energy consumption and computation delay.

Low Power Computing Paradigms Based on Emerging Non-Volatile Nanodevices

Traditional digital processing approaches are based on semiconductor transistors, which suffer from high power consumption, aggravating with technology node scaling. To solve definitively this problem, a number of emerging non-volatile nanodevices are under intense investigations. Meanwhile, novel computing circuits are invented to dig the full potential of the nanodevices. The combination of non-volatile nanodevices with suitable computing paradigms have many merits compared with the complementary metal-oxide-semiconductor transistor （CMOS） technology based structures, such as zero standby power, ultra-high density, non-volatility, and acceptable access speed. In this paper, we overview and compare the computing paradigms based on the emerging nanodevices towards ultra-low dissipation.

Energy-efficient and security-optimized AES hardware design for ubiquitous computing

Ubiquitous computing must incorporate a certain level of security. For the severely resource constrained applications, the energy-efficient and small size cryptography algorithm implementation is a critical problem. Hardware implementations of the advanced encryption standard （AES） for authentication and encryption are presented. An energy consumption variable is derived to evaluate low-power design strategies for battery-powered devices. It proves that compact AES architectures fail to optimize the AES hardware energy, whereas reducing invalid switching activities and implementing power-optimized sub-modules are the reasonable methods. Implementations of different substitution box （S-Boxes） structures are presented with 0.25μm 1.8 V CMOS （complementary metal oxide semiconductor） standard cell library. The comparisons and trade-offs among area, security, and power are explored. The experimental results show that Galois field composite S-Boxes have smaller size and highest security but consume considerably more power, whereas decoder-switch-encoder S-Boxes have the best power characteristics with disadvantages in terms of size and security. The combination of these two type S-Boxes instead of homogeneous S-Boxes in AES circuit will lead to optimal schemes. The technique of latch-dividing data path is analyzed, and the quantitative simulation results demonstrate that this approach diminishes the glitches effectively at a very low hardware cost.

Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

SRDAAR－QNPP：a computer code system for the real－time dose assessment of an accident release for Qinshan Nuclear Power Plant

The paper presents a computer code system 'SRDAAR- QNPP' for the real-time dose as-sessment of an accident release for Qinshan Nuclear Power Plant. It includes three parts:thereal-time data acquisition system, assessment computer. and the assessment operating code system. InSRDAAR-QNPP, the wind field of the surface and the lower levels are determined hourly by using amass consistent three-dimension diasnosis model with the topographic following coordinate system.A Lagrangin Puff model under changing meteorological condition is adopted for atmosphericdispersion, the correction for dry and wet depositions. physical decay and partial plume penetrationof the top inversion and the deviation of plume axis caused by complex terrain have been taken in-to account. The calculation domain areas include three square grid areas with the sideline 10 km, 40krn and 160 km and a grid interval 0.5 km, 2.0 km, 8.0 km respectively. Three exposure pathwaysare taken into account:the external exposure from immersion cloud and passing puff, the internalexposure from inhalation and the external exposure from contaminated ground. This system is ableto provide the results of concentration and dose distributions within 10 minutes after the data havebeen inputed.

Modified Packing Algorithm for Dynamic Energy-Saving in Cloud Computing Servers

Since the raising of the cloud computing, the applications of web service have been extended rapidly. However, the data centers of cloud computing also cause the problem of power consumption and the resources usually have not been used effectively. Decreasing the power consumption and enhancing resource utilization become main issues in cloud computing environment. In this paper, we propose a method, called MBFDP （modified best fit decreasing packing）, to decrease power consumption and enhance resource utilization of cloud computing servers. From the results of experiments, the proposed solution can reduce power consumption effectively and enhance the utilization of resources of servers.

Edge Computing

Edge computing refers to the computing paradigm in which the processing power, communication capabilities and intelligence are pushed down to the edge of the networking system like gateways and devices, where the data originates. In doing so, edge computing enables an infrastructure for processing the data directly from devices with low latency, battery consumption and bandwidth cost. With opportunities for research and advanced applications such as augmented reality and wearable cognitive assistance come new challenges. This special issue reports the current re? search on various topics related to edge computing, addressing the challenges in the enabling technologies and practical implementations.

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

三维精确power图的GPU并行计算

power图(加权Voronoi图)的计算是计算机图形学和计算几何等领域的一项基础任务.针对求解三维power图的传统串行方法所需的时间成本较高,且现有并行算法所得结果为近似解,提出一种新颖的GPU并行计算方法.首先给出power图与高一维受限Voronoi图的等价构造方法,将Voronoi图的无网格方法直接推广到power图的计算.因此,给定的加权种子点被置于更高一维空间中的一组方格内,据此快速搜索每个种子点的邻居关系,进而使用各个种子点与其若干个邻居的中垂面对各自的power胞元进行并行裁剪,以快速地获取三维空间中的精确power图.对比不同求解域下和5万个种子点的计算耗时,比现有方法具有超过3倍的加速比.

Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids.Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources.However,there are challenges in building models through centralized shared data due to data privacy concerns and industry competition.Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally.In this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model.We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach.Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

IC Pattern Based Power Factor Maximization Model for Improved Power Stabilization

The voltagefluctuation in electric circuits has been identified as key issue in different electric systems.As the usage of electricity growing in rapid way,there exist higherfluctuations in powerflow.To maintain theflow or stabi-lity of power in any electric circuit,there are many circuit models are discussed in literature.However,they suffer to maintain the output voltage and not capable of maintaining power stability.To improve the performance in power stabilization,an efficient IC pattern based power factor maximization model(ICPFMM)in this article.The model is focused on improving the power stability with the use of IC(Inductor and Conductor)towards identifying most efficient circuit for the current duty cycle according to the input voltage,voltage in capacitor and output voltage required.The model with boost converter diverts the incoming voltage through number of conductors and inductors.By triggering specific inductor,a specific capacitor gets charged and a particular circuit gets on.The model maintains num-ber of IC(Inductor and Conductor)patterns through which the powerflow occurs.According to that,the pattern available,the mofset controls the level of power to be regulated through any circuit.From the pattern,the model computes the Cir-cuits Switching Loss and Circuits Conduction Loss for various circuits.Accord-ing to the input voltage,the model estimates Circuit Power Stabilization Support(CPSS)according to the voltage available in any capacitor and input voltage.Using the value of CPSS,the model trigger optimal number of circuits to maintain voltage stability.In this approach,more than one circuit has been triggered to maintain output voltage and to get charged.The proposed model not only main-tains power stability but also reduces the wastage in voltage which is not utilized.The proposed model improves the performance in voltage stability with less switching loss.

Computer-aided system for hazard zone identification in ship power plants

The most dangerous places in ships are their power plants. Particularly, they are very unsafe for operators carrying out various necessary operation and maintenance activities. For this reason, ship machinery should be designed to ensure the maximum safety for its operators. It is a very difficult task. Therefore, it could not be solved by means of conventional design methods, which are used for design of uncomplicated technical equipment. One of the possible ways of solving this problem is to provide appropriate tools, which allow us to take the operator's safety into account during a design process, especially at its early stages. A computer-aided system supporting design of safe ship power plants could be such a tool. This paper deals with developing process of a prototype of the computer-aided system for hazard zone identification in ship power plants.

大数据背景下的电力营销信息管理平台设计

为了提升电力营销大数据处理和调度效率,设计大数据背景下的电力营销信息管理平台。基于Hadoop和MapReduce设计平台存储和技术框架,利用大数据技术划分电力营销大数据,实现电力营销信息状态安全分析和监控;采用云计算技术分析和处理电力营销数据。测试结果显示,所设计平台的大数据处理吞吐量较高、大数据处理加速比稳定;数据调度计算效率较高、性能稳定;将电力营销数据以对象形式缓存到内存中时,执行时间较短、耗费内存较小。该平台具备较好的数据处理及调度能力。