A real-time data association of internet of things based for expert weather station system

Abstract

The wind carries moisture into an atmosphere and hot or cold air into a climate, affecting weather patterns. Knowing where the wind is coming from gives essential insight into what kind of temperatures are to be expected. However, the wind is affected by spatial and temporal variabilities, thus making it difficult to predict. This study focuses on finding data associations from the weather station installed at Hasanuddin University Campus based on internet of things (IoT) using Raspberry Pi as a gateway that associated all the meteorological data from sensors. The generation of association rules compares the Apriori and FP-growth algorithms to determine relations among itemsets. The results show that high humidity and warm temperature tend to associate with a westerly wind and occur at night. In contrast, conditions with less humid and moderate temperatures tend to have southerly and southeasterly wind.

A genetic algorithm coupled with tree-based pruning for mining closed association rules

“…Outliers may sometimes become a separate cluster instead of being ignored MCDA-ELECTRE [33] -Apply the ELECTRE method to find the best association rule -Measures are taken as attributes and association rules as alternatives and a decision matrix is constructed -Desired rules are obtained with maximum interestingness Redundancy of the rules is not addressed FP-Growth [35] -Uses Association Rule Mining to analyze skill-job relationships -Jobs are analyzed using the FP-Growth algorithm able to retrieve and process large amounts of data posted online -Construction of FP-tree is difficult -The memory may be insufficient if the database is large TAR [36] -Uses Apriori and FP-growth to generate itemsets -Generates association rules to find the relation between weather data variables accompanied by time scale parameters.…”

“…The relationship between skill and job was analyzed using this algorithm [35]. FP-growth and Apriori algorithms were employed to find the relation between weather data variables [36]. Hidden patterns were evaluated and related rules were extracted here [37].…”

Insights on the internet of things: past, present, and future directions

“…To prevent delays in data relay to the server, its analysis, and the final signal to operate the 'thing,' data processing is performed near to the monitor and control system. 2) Gateways and data acquisition systems [43], [59], [66], [71], [113], [140], [158], [209]- [219] A data acquisition system (DAS) collects the data given by the sensors and converts it to a digital-analog format. The DAS combines and prepares this data before sending it to the next level of processing through Internet gateways such as wireless wide area network (WANs), such as cellular or wireless fidelity (Wi-Fi), or wired WANs.…”

“…IoT is critical for businesses, in addition to delivering smart gadgets for home automation [7], [58], [66], [73], [78], [122]. IoT provides organizations with a real-time perspective of how their systems are doing, providing insights into machine performance, supply chain, and logistical operations [71], [97]. The IoT allows businesses to automate operations and save labor costs.…”