Wireless sensor networks have limitations such as processing power, storage resources, and time delay in data transfer to the cloud. The cloud computing by the development of cloud-based services to the edge of the network reduces traffic and delays, so these types of n
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Wireless sensor networks have limitations such as processing power, storage resources, and time delay in data transfer to the cloud. The cloud computing by the development of cloud-based services to the edge of the network reduces traffic and delays, so these types of networks are used in many systems, such as medical care, wearable devices, transportation systems and smart cities. Task scheduling techniques in fog computing are considered to be NP-hard issues. Applications require resources to run. Network fog devices are close to the sensors and the cloud and have the required processing power to run the applications. Each fog device can be used to run resource allocation policies. In this paper, we present an optimized Knapsack-based method optimized by symbiotic organism search to allocate resources appropriately to tasks in fog network. The proposed method is simulated in the iFogsim as a developed library from Cloudsim for fog computing. The results indicate improvement in energy consumption, resource consumption, and execution cost of the network. The proposed method is better than FCFS and Knapsack methods.
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