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Scheduling of fog networks with optimized knapsack by symbiotic organisms search

机译:共生生物搜索优化背包的雾网络调度

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Internet of things as a concept uses wireless sensor networks that have limitations in power, storage, and delay when processing and sending data to the cloud. Fog computing as an extension of cloud services to the edge of the network reduces latency and traffic, so it is very useful in healthcare, wearables, intelligent transportation systems and smart cities. Scheduling is the NP-hard issues in fog computing. Edge devices due to proximity to sensors and clouds are capable of processing power and are beneficial for resource management algorithms. We present a knapsack-based scheduling optimized by symbiotic organisms search that is simulated in iFogsim as a standard simulator for fog computing. The results show improvements in the energy consumption by 18%, total network usage by 1.17%, execution cost by 15%, and sensor lifetime by 5% in our scheduling method are better than the FCFS (First Come First Served) and knapsack algorithms.
机译:作为概念的事物互联网使用在处理和向云发送数据时具有电源,存储和延迟的局限性的无线传感器网络。雾计算作为云服务的扩展到网络边缘降低延迟和流量,因此在医疗保健,可穿戴性,智能交通系统和智能城市中非常有用。调度是雾计算中的NP难题。由于对传感器和云附近的边缘设备能够处理电力并且有利于资源管理算法。我们介绍了基于Knapsach的调度,由Symbiotic生物搜索优化,该搜索在IfoGsim中被模拟为用于雾计算的标准模拟器。结果表明,通过18 %的能量消耗,通过1.17 %的总网络使用情况,执行成本为15 %,并且在我们的调度方法中,传感器寿命为5 %,比FCF更好(首先第一次服务)和背包算法。

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