作者：Cuiqin; Dai; Qingyang; Songinternetofeverythingheuristiccomputingcontactplandesignparticleswarmoptimizationgreedyalgorithms
摘要：To satisfy the increasing demands of high-speed transmission,high-efficiency computing,and real-time communications in the high-dynamic and heterogeneous networks,the Contact Plan Design(CPD)has attracted continuous attention in recent years,especially for the spatial-node-based Internet of Everything(IoE).In this paper,we study the NP-hardness of contact scheduling and the attenuation of atmospheric precipitation in the spatial-node-based IoE.Two heuristic computing methods for contact plan design are proposed by comprehensively considering the time-varying topology,the intermittent connectivity,and the adaptive transmission in different weather conditions,which are named Contact Plan Design-Particle Swarm Optimization(CPD-PSO)and Contact Plan Design-Greedy algorithm with the Minimum Delivery Time(CPD-GMDT)separately.For the population-based algorithm,CPD-PSO not only solves the CPD problem with a limited-resource condition,but also dynamically adjusts the search scope to ensure the continuous searching capability of the algorithm.For the CPD-GMDT that makes CP decisions based on the current state,the algorithm uses the idea of greedy algorithm to schedule Satellite-Platform Links(SPLs)and Inter Satellite Links(ISLs)respectively using the strategies of optimal matching and load balancing.The simulation results show that the proposed CPD-PSO outperforms Contact Plan Design-Genetic Algorithm(CPD-GA)in terms of fitness and delivery time,and CPD-GMDT presents better overall delay than Fair Contact Plan(FCP).
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