<span>Temperature and humidity are among the parameters that significant to the industrial and agricultural. Traditionally, these elements are monitored inefficiently through wired monitoring system that caused higher implementation and maintenance cost. In addition, the device to detect the temperature such thermometer is not suitable for real-time monitoring since it need a longer response time to measure. With the advent of wireless technology, the temperature and humidity are monitored remotely and effectively. This paper aims to describe the implementation of an embedded real-time temperature and humidity monitoring system, using Arduino for Internet of Things (IoT) application. The system integrates the Arduino node with a dashboard system call Node-FRED, which interfaced to the LoRa radio through the Things Network gateway. This IoT application is deployed on both indoor and outdoor environment, to investigate the relation between the temperature and humidity level in order to manage the environment at more comfort level.</span>
In this paper, an implementation of vehicle ventilation system using microcontroller NodeMCU is described, as an internet of things (IoT) platform. A low-cost wireless fidelity (Wi-Fi) microchip ESP8266 integrated with NodeMCU provides full-stack transmission control protocol/internet protocol (TCP/IP) to communicate between mobile applications. This chip is capable to monitor and control sensor devices connected to the IoT platform. In this reserach, data was collected from a temperature sensor integrated to the platform, which then monitored using Blynk application. The vehicle ventilation system was activated/deactivated through mobile application and controlled using ON/OFF commands sent to the connected devices. From the results, the vehicle ventilation system built using NodeMCU microcontroller is capable to provide near real-time data monitoring for temperature in the car before and after the ventilation system was applied.
Indoor positioning systems (IPS) have witnessed continuous improvements over the years. However, large-scale commercial deployments remain elusive due to various factors such as high deployment cost and lack market drivers. Among the state of the art indoor positioning approaches, the Wi-Fi fingerprinting technique, in particular, is gaining much attention due to its ease of deployment. This is largely due to widespread deployment of WiFi infrastructure and its availability in all existing mobile devices. Although WiFi fingerprinting approach is relatively low cost and fast to deploy, the accuracy of the system tends to deteriorate over time due to WiFi access points (APs) being removed and shifted. In this paper, we carried out a study on such deterioration, which we refer to as fingerprint health analysis on a 2 million square feet shopping mall in South of Kuala Lumpur, Malaysia. We focus our study on APs removal using the actual data collected from the premise. The study reveals the following findings: 1) based on per location pin analysis, ~50% of APs belong to the mall operator which is a preferred group of APs for fingerprinting. For some location, however, the number of operator-managed APs are too few for fingerprinting positioning approach. 2) To maintain mean error distance of ~5 meters, up to 80% of the APs can be removed using the selected positioning algorithms at some locations. At some other locations, however, the accuracy will exceed 5m upon >20% of APs being removed. 3) On average, around 40%-60% of the APs can be removed randomly in order to maintain the accuracy of ~5m.
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