Hybrid Approach for Enabling Hierarchical Fog Networks in an IoT Deployment

Jayawardene, Nuwan; Fernando, Pumudu

doi:10.1109/compsac.2019.10258

Cited by 2 publications

(1 citation statement)

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“…Furthermore, in existing work, fog nodes are placed at fixed locations to support the delay-sensitive applications [9,10] or a federated approach is used for balancing workload [11][12][13]. Thus, fixed location deployment of a fog node can cause a single point of failure, and such solutions are not scalable.…”

Section: Introductionmentioning

confidence: 99%

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

et al. 2022

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The Industrial Internet of Things (IIoTs) is an emerging area that forms the collaborative environment for devices to share resources. In IIoT, many sensors, actuators, and other devices are used to improve industrial efficiency. As most of the devices are mobile; therefore, the impact of mobility can be seen in terms of low-device utilization. Thus, most of the time, the available resources are underutilized. Therefore, the inception of the fog computing model in IIoT has reduced the communication delay in executing complex tasks. However, it is not feasible to cover the entire region through fog nodes; therefore, fog node selection and placement is still the challenging task. This paper proposes a multi-level hierarchical fog node deployment model for the industrial environment. Moreover, the scheme utilized the IoT devices as a fog node; however, the selection depends on energy, path/location, network properties, storage, and available computing resources. Therefore, the scheme used the location-aware module before engaging the device for task computation. The framework is evaluated in terms of memory, CPU, scalability, and system efficiency; also compared with the existing approach in terms of task acceptance rate. The scheme is compared with xFogSim framework that is capable to handle workload upto 1000 devices. However, the task acceptance ratio is higher in the proposed framework due to its multi-tier model. The workload acceptance ratio is 85% reported with 3000 devices; whereas, in xFogsim the ratio is reduced to approx. 68%. The primary reason for high workload acceptation is that the proposed solution utilizes the unused resources of the user devices for computations.

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