Abstract: 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%.

Key words: Adaptive PSO, Computing offload, Intelligent algorithm, IoT, MEC, Resource allocation, User association