Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the deadline.To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time.In order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications.In this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier.The predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the system.Fromresults as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.

Distributed Throughput and Energy Efficient Resource Optimization When D2D and Massive MIMO Coexist

Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources.In this paper,we optimize the throughput and energy efficiency(EE)performance for the coexistence of two technologies that have been identified for the future cellular networks,namely,massive multiple-input multiple-output(MIMO)and network-assisted device-to-device(D2D)communications.In such a hybrid network,the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge.To this end,we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming(MINLP).We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance.It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88%of the average transmission rate and 86%of the energy efficiency performance of the optimal matching with lower complexity.

Water resources optimization and eco-environmental protection in Qaidam Basin

In order to realize sustainable development of the arid area of Northwest China, rational water resources exploitation and optimization are primary prerequisites. Based on the essential principle of sustainable development, this paper puts forward a general idea on water resources optimization and eco-environmental protection in Qaidam Basin, and identifies the competitive multiple targets of water resources optimization. By some qualitative methods such as Input-output Model & AHP Model and some quantitative methods such as System Dynamics Model & Produce Function Model, some standard plans of water resources optimization come into being. According to the Multiple Targets Decision by the Closest Value Model, the best plan of water resources optimization, eco-environmental protection and sustainable development in Qaidam Basin is finally decided.

The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for resource scheduling,however,resource providers have resource utilization requirements for cloud manufacturing platforms.In the process of resource optimization scheduling,the interests of all parties have conflicts of interest,which makes it impossible to obtain better optimization results for resource scheduling.Therefore,amultithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling.The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization,using the negotiation algorithm based on the Stackelberg game,the cloud manufacturing platformnegotiates andmediateswith the participants’agents,to maximize self-interest by constantly changing one’s own plan,iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform.Through multiple rounds of negotiation and calculation,we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task.Finally,through experimental simulation and comparative analysis,the validity and rationality of the model are verified.

Computation offloading and resource allocation for UAV-assisted IoT based on blockchain and mobile edge computing

Recently,Internet of Things(IoT)have been applied widely and improved the quality of the daily life.However,the lightweight IoT devices can hardly implement complicated applications since they usually have limited computing resource and just can execute some simple computation tasks.Moreover,data transmission and interaction in IoT is another crucial issue when the IoT devices are deployed at remote areas without manual operation.Mobile edge computing(MEC)and unmanned aerial vehicle(UAV)provide significant solutions to these problems.In addition,in order to ensure the security and privacy of data,blockchain has been attracted great attention from both academia and industry.Therefore,an UAV-assisted IoT system integrated with MEC and blockchain is pro-posed.The optimization problem in the proposed architecture is formulated to achieve the optimal trade-off between energy consumption and computation latency through jointly considering computa-tion offloading decision,spectrum resource allocation and computing resource allocation.Consider-ing this complicated optimization problem,the non-convex mixed integer problem can be transformed into a convex problem,and a distributed algorithm based on alternating direction multiplier method(ADMM)is proposed.Simulation results demonstrate the validity of this scheme.

Non-Convex Optimization of Resource Allocation in Fog Computing Using Successive Approximation

Fog computing can deliver low delay and advanced IT services to end users with substantially reduced energy consumption.Nevertheless,with soaring demands for resource service and the limited capability of fog nodes,how to allocate and manage fog computing resources properly and stably has become the bottleneck.Therefore,the paper investigates the utility optimization-based resource allocation problem between fog nodes and end users in fog computing.The authors first introduce four types of utility functions due to the diverse tasks executed by end users and build the resource allocation model aiming at utility maximization.Then,for only the elastic tasks,the convex optimization method is applied to obtain the optimal results;for the elastic and inelastic tasks,with the assistance of Jensen’s inequality,the primal non-convex model is approximated to a sequence of equivalent convex optimization problems using successive approximation method.Moreover,a two-layer algorithm is proposed that globally converges to an optimal solution of the original problem.Finally,numerical simulation results demonstrate its superior performance and effectiveness.Comparing with other works,the authors emphasize the analysis for non-convex optimization problems and the diversity of tasks in fog computing resource allocation.

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the control of a base station and does not require a base station to forward it.The advantages of applying D2D communication technology to cellular networks are:It can increase the communication system capacity,improve the system spectrum efficiency,increase the data transmission rate,and reduce the base station load.Aiming at the problem of co-channel interference between the D2D and cellular users,this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning,which creates multi-agent learners from multiple D2D users,and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users.The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted.The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users.Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.

Cross-layer resource allocation based on equivalent bandwidth in OFDMA systems

A quality of service（QoS） guaranteed cross-layer resource allocation algorithm with physical layer, medium access control（MAC） layer and call admission control（CAC） considered simultaneously is proposed for the full IP orthogonal frequency division multiple access（OFDMA） communication system, which can ensure the quality of multimedia services in full IP networks.The algorithm converts the physical layer resources such as subcarriers, transmission power, and the QoS metrics into equivalent bandwidth which can be distributed by the base station in all three layers. By this means, the QoS requirements in terms of bit error rate（BER）, transmission delay and dropping probability can be guaranteed by the cross-layer optimal equivalent bandwidth allocation. The numerical results show that the proposed algorithm has higher spectrum efficiency compared to the existing systems.

Research on the Development Model of University Archives Cultural Products Based on Deep Learning

The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources,and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries.The existing RippleNet model doesn’t consider the influence of key nodes on recommendation results,and the recommendation accuracy is not high.Therefore,based on the RippleNet model,this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model.The performance of the model is verified by experiments,which provide a theoretical basis for the promotion and recommendation of its cultural products of universarchives,solve the problem that RippleNet doesn’t consider the influence of key nodes on recommendation results,and improve the recommendation accuracy.This paper also combs the development course of archival cultural products in detail.Finally,based on the Cn-RippleNet model,the cultural effect of university archives is recommended and popularized.

Distance Matrix and Markov Chain Based Sensor Localization in WSN

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of interest.Relevant geometries of the surrounding environment are essential to establish a successful WSN topology.But it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging task.In this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the node.The method further employs a Markov Chain Model(MCM)for energy optimization and interference reduction.Experiments are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing models.Transition probability is used in the Markova chain to sustain higher energy nodes.Finally,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA methods.The experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows multiple terminals to share the same resource unit at the same time.The receiver usually needs to configure successive interference cancellation(SIC).The receiver eliminates co-channel interference(CCI)between users and it can significantly improve the system throughput.In order to meet the demands of users and improve fairness among them,this paper proposes a new power allocation scheme.The objective is to maximize user fairness by deploying the least fairness in multiplexed users.However,the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker(KKT)constraints.Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.

Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication

With the popularity of green computing and the huge usage of networks,there is an acute need for expansion of the 5G network.5G is used where energy efficiency is the highest priority,and it can play a pinnacle role in helping every industry to hit sustainability.While in the 5G network,conventional performance guides,such as network capacity and coverage are still major issues and need improvements.Device to Device communication(D2D)communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques.The issue of energy utilization in the IoT based system is a significant exploration center.Energy optimizationin D2D communication is an important point.We need to resolve this issue for increasing system performance.Green IoT speaks to the issue of lessening energy utilization of IoT gadgets which accomplishes a supportable climate for IoT systems.In this paper,we improve the capacity and coverage of 5G technology using Multiple Inputs Multiple Outputs(MU-MIMO).MUMIMO increases the capacity of 5G in D2D communication.We also present all the problems faced by 5G technology and proposed architecture to enhance system performance.

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified inter- active honey bee mating optimization （IHBMO） base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources （DERs）. The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting, A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduc- tion in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power orDERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.

Dwell Scheduling Algorithm for Digital Array Radar

Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.

An Adaptive Bandwidth Allocation for Energy Efficient Wireless Communication Systems

In this paper, an energy efficient bandwidth allocation scheme is proposed for wireless communication systems. An optimal bandwidth expansion（OBE） scheme is proposed to assign the available system bandwidth for users. When the system bandwidth does not reach the full load, the remaining bandwidth can be energy-efficiently assigned to the other users. Simulation results show that the energy efficiency of the proposed OBE scheme outperforms the traditional same bandwidth expansion（SBE） scheme. Thus, the proposed OBE can effectively assign the system bandwidth and improve energy efficiency.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Optimal resource allocation with spatiotemporal transmission discovery for efective disease control

Background:The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world,especially in highly populated regions.The healthcare capacity(especially the testing resources,vaccination coverage,and hospital capacity)is becoming extremely insufcient as the demand will far exceed the supply.To address this time-critical issue,we need to answer a key question:How can we efectively infer the daily transmission risks in diferent districts using machine learning methods and thus lay out the corresponding resource prioritization strategies,so as to alleviate the impact of the Omicron outbreaks?Methods:We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant.We collect the publicly available data from the ofcial website of the Hong Kong Special Administrative Region(HKSAR)Government and the study period in this paper is from December 27,2021 to July 17,2022(including a period for future prediction).First,we construct the spatiotemporal transmission intensity matrices across diferent districts based on infection case records.With the constructed cross-district transmission matrices,we forecast the future risks of various locations daily by means of the Gaussian process.Finally,we develop a transmission-guided resource prioritization strategy that enables efective control of Omicron outbreaks under limited capacity.Results:We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong,China.The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks,making it difcult for the public health authority to foresee the outbreaks and plan the responses accordingly.With the guidance of the inferred transmission risks,the developed prioritization strategy enables the optimal testing resource allocation for integrative case management(including case detection,quarantine,and further treatment),i.e.,with the 300,000 testing capacity per day;it could reduce the infection peak by 87.1% compared with the population-based allocation strategy(case number reduces from 20,860 to 2689)and by 24.2% compared with the case-based strategy(case number reduces from 3547 to 2689),signifcantly alleviating the burden of the healthcare system.Conclusions:Computationally characterizing spatiotemporal transmission patterns allows for the efective risk mapping and resource prioritization;such adaptive strategies are of critical importance in achieving timely outbreak control under insufcient capacity.The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants.Moreover,the investigation conducted in Hong Kong,China provides useful suggestions on how to achieve efective disease control with insufcient capacity in other highly populated countries and regions.