Abstract
With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of \(586.7 - 1089{\text{ms}}\) over the benchmark algorithm, saving \(18.3 - 39\%\) in energy consumption. The total latency of IMOA is reduced by \(59.8 - 68.5\%\), and the maximum latency is reduced by \(48.8 - 69.2\%\). The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This work is supported by National Key R&D Program of China under Grant NO. 2017YFE0125300.
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Xu, F., Yin, Z., Han, G. et al. Multi-objective fog node placement strategy based on heuristic algorithms for smart factories. Wireless Netw 30, 5407–5424 (2024). https://doi.org/10.1007/s11276-023-03262-3
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DOI: https://doi.org/10.1007/s11276-023-03262-3