A Reverse Auction Mechanism for Time-Varying Multidimensional Resource Allocation in Vehicular Fog Computing with Cloud and Edge Collaboration

It is a hot issue to allocate resources using auction mechanisms in vehicular fog computing(VFC)with cloud and edge collaboration.However,most current research faces the limitation of only considering single type resource allocation,which cannot satisfy the resource requirements of users.In addition,the resource requirements of users are satisfied with a fixed amount of resources during the usage time,which may result in high cost of users and even cause a waste of resources.In fact,the actual resource requirements of users may change with time.Besides,existing allocation algorithms in the VFC of cloud and edge collaboration cannot be directly applied to time-varying multidimensional resource allocation.Therefore,in order to minimize the cost of users,we propose a reverse auction mechanism for the time-varying multidimensional resource allocation problem(TMRAP)in VFC with cloud and edge collaboration based on VFC parking assistance and transform the resource allocation problem into an integer programming(IP)model.And we also design a heuristic resource allocation algorithm to approximate the solution of the model.We apply a dominant-resource-based strategy for resource allocation to improve resource utilization and obtain the lowest cost of users for resource pricing.Furthermore,we prove that the algorithm satisfies individual rationality and truthfulness,and can minimize the cost of users and improve resource utilization through comparison with other similar methods.Above all,we combine VFC smart parking assistance with reverse auction mechanisms to encourage resource providers to offer resources,so that more vehicle users can obtain services at lower prices and relieve traffic pressure.

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.

Intelligent Task Offloading and Collaborative Computation in Multi-UAV-Enabled Mobile Edge Computing

This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.

A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

ECC:Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

Due to the explosion of network data traffic and IoT devices,edge servers are overloaded and slow to respond to the massive volume of online requests.A large number of studies have shown that edge caching can solve this problem effectively.This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario.It solves the problem of large average access delay caused by unbalanced load of edge servers,meets users’differentiated service demands and improves user experience.In particular,the edge cache node selection algorithm is optimized,and a novel edge cache replacement strategy considering the differentiated user requests is proposed.This mechanism can shorten the response time to a large number of user requests.Experimental results show that,compared with the current advanced online edge caching algorithm,the proposed edge collaborative caching strategy in this paper can reduce the average response delay by 9%.It also increases the user utility by 4.5 times in differentiated service scenarios,and significantly reduces the time complexity of the edge caching algorithm.

Cloud-Assisted Distributed Edge Brains for Multi-Cell Joint Beamforming Optimization for 6G

In 5G networks,optimization of antenna beam weights of base stations has become the key application of AI for network optimization.For 6G,higher frequency bands and much denser cells are expected,and the importance of automatic and accurate beamforming assisted by AI will become more prominent.In existing network,servers are“patched”to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead,large inference latency and potential risks of data security.Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response,but it is inefficient in collecting and sharing global information among base stations.In this paper,we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization.We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation.Compared with the centralized solution,our solution improves prediction accuracy by 24.66%,and reduces storage cost by 83.82%.Compared with the decentralized solution,our solution improves prediction accuracy by 68.26%,and improves coverage performance by 0.4 dB.At last,the future research work is prospected.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

Blockchain-based collaborative edge caching scheme for trustworthy content sharing

Moving data from cloud to the edge network can effectively reduce traffic burden on the core network,and edge collaboration can further improve the edge caching capacity and the quality of service(QoS).However,it is difficult for various edge caching devices to cooperate due to the lack of trust and the existence of malicious nodes.In this paper,blockchain which has the distributed and immutable characteristics is utilized to build a trustworthy collaborative edge caching scheme to make full use of the storage resources of various edge devices.The collaboration process is described in this paper,and a proof of credit(PoC)protocol is proposed,in which credit and tokens are used to encourage nodes to cache and transmit more content in honest behavior.Untrusted nodes will pay for their malicious actions such as tampering or deleting cached data.Since each node chooses strategy independently to maximize its benefits in an environment of mutual influence,a non-cooperative game model is designed to study the caching behavior among edge nodes.The existence of Nash equilibrium(NE)is proved in this game,so the edge server(ES)can choose the optimal caching strategy for all collaborative devices,including itself,to obtain the maximum rewards.Simulation results show that the system can save mining overhead as well as organize a trusted collaborative edge caching effectively.