超密集物联网络中多任务多步计算卸载算法研究

Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks

随着物联网(Internet of Things,IoT)的迅速发展,各种物联网移动设备(IoT Mobile Device,IMD)需要处理越来越多的计算密集型和延迟敏感型任务,这给移动边缘计算网络带来了新的挑战。为了应对这些挑战,装备移动边缘计算(Mobile Edge Computing,MEC)的超密集物联网应运而生。在该网络中,IMD可将计算密集型任务卸载至边缘计算服务器上进行处理,从而节省自己的计算资源并降低能耗。然而,这样会造成额外的传输时间,进而导致更高的延迟。为了均衡能耗与时延,针对多用户多任务的超密集物联网络,提出了一个最小化能耗和时延的均衡问题,以联合优化用户(IMD)关联、计算卸载和资源分配。为了进一步平衡网络负载,充分利用计算资源,在问题建模时采用多步计算卸载。最后,利用智能算法——自适应粒子群算法(Particle Swarm Optimization,PSO)对所提问题进行求解。相比传统粒子群算法,自适应粒子群算法能降低20%~65%的总开销。

With the rapid development of Internet of Things(IoT),various IoT mobile devices(IMDs)need to process more and more computing-intensive and delay-sensitive tasks,which puts forward new challenges for the mobile edge networks.To address these challenges,the MEC-equipped ultra-dense IoT has emerged.In such networks,IMDs can save their computation resources and reduce their energy consumption by offloading computing-intensive tasks to edge computing servers for processing.However,it will result in additional transmission time and higher delay.In view of this,an optimization problem is formulated for finding the trade-off between energy consumption and delay,which jointly considers the user(IMD)association,computation offloading and resource allocation for ultra-dense MEC-enabled IoT.To further balance the network load and fully utilize the computation resources,the optimization problem is finally modeled as multi-step computation offloading one.At last,an intelligent algorithm,adaptive particle swarm optimization(PSO),is utilized to solve the proposed problem.Compared with traditional PSO,the total cost of adaptive PSO reduces by 20%~65%.