Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques
نویسندگان
چکیده
Major fields such as military applications, medical fields, weather forecasting, and environmental applications use wireless sensor networks for major computing processes. Sensors play a vital role in emerging technologies of the 20th century. Localization sensors needed locations is very serious problem. The environment home to every living being world. growth industries after industrial revolution increased pollution across environment. Owing recent uncontrolled development, measure levels surroundings are needed. An interesting challenging task choosing place fit sensors. Many meta-heuristic techniques have been introduced node localization. Swarm intelligent algorithms proven their efficiency many studies on localization problems. In this article, we introduce an industrial-centric approach solve problem network. First, our work aims at selecting areas sensed location. We random forest regression methodology select polluted area. Then, elephant herding algorithm used These two combined produce best standard result localizing nodes. To check proposed performance, experiments conducted with data from KDD Cup 2018, which contain name 35 stations concentrations air pollutants PM, SO2, CO, NO2, O3. normalized tested algorithms. results comparatively analyzed other swarm intelligence algorithm, particle optimization, machine learning decision tree multi-layer perceptron. Results can indicate suggest more meaningful topology. Our method achieves lower root mean square value 0.06 0.08 Stations 1 5.
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ژورنال
عنوان ژورنال: Computer systems science and engineering
سال: 2022
ISSN: ['0267-6192']
DOI: https://doi.org/10.32604/csse.2022.021681