Abstract
Upcoming 5G technology and the demand for near real-time response services raise the need for optimizing current IoT solutions. The aim of this paper is to model and execute the performance analysis of network structures suitable for Edge Computing in IoT. The prior research into different topology and parameter sets have not provided sufficient clarity, on which parameters had a considerable impact on overall system performance; therefore, repetitive simulations were performed to express dispersion of alternating values, as well as determining its confidence intervals. The paper presents Edge Computing service simulation setup on known and newly derived network topologies with different complexity varying network bandwidth and network delay parameters. The experimental investigation has revealed that the IoT configuration network is more sensitive to network topology, while the Internet configuration is more sensitive to network parameters. The discussion on the results received debates possible causes of performance differences in network parameters and device configurations, the comparison to similar state-of-the-art research results has also been presented. Finally, conclusions with recommendations based on the results acquired have been provided.
Highlights
Upcoming 5G technology and the demand for near realtime response services raise the need for optimizing current IoT solutions
In this paper, we describe modelling and performance analysis of different topologies with an idea to propose a dynamic self-organizing network topology in order to increase the performance of Edge Computing
That is why the success of packet delivery events is evaluated by estimating the ratio values: EiS(
Summary
Upcoming 5G technology and the demand for near realtime response services raise the need for optimizing current IoT solutions. The demand for cloud technologies for big data processing is tremendous; the insights into [1], [2] show that with the realization of Industry 4.0 it would not be possible to process such enormous data capacities. The Edge Computing technology is highly suitable for low-power devices, such as wearables, mobile gadgets or even sensors themselves [3] to adopt computing tasks from common cloud technology. The most important undertaking in superior Edge Computing realization is an appropriate job scheduling strategy selection [4] aiming to fulfil timeliness criteria, i.e., to perform a task before its data became obsolete [5]. To support this, the proper law of reorganization of a dynamic network architecture must be determined
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