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In the last decade, Indonesia shifted from one of the cleanest countries in the world to one of the countries with high air pollution, especially in big cities like Jakarta, Surabaya, etc. This paper presents a preliminary study of the building of a smart sensor node system to monitor air quality for urban environments. Sampled air parameters were CO, CO2, and CH4. Incoming sensor data were classified using rules from the calculation of the entropy and information gain done before in the Decision Tree Algorithm rule finding steps. The classification is done on a Raspberry Pi device using conditional if-else on the programs. The if-else condition makes the program could achieve air quality results. The decision results in the form of warning alert or data reports packed on a Raspberry Pi device. By then using LoRa transceiver, a medium-range communication system, these results sent to a PC server. Reliability testing has been carried out starting from the sensors, algorithm processing capability, until the transmission process with Time Division Multiplexing technique. Data is sent alternately between one node to another. Based on the testing results, the CO sensor shows good performance at pollutant levels up to 300 ppm, so do the CO2 sensor at values up to 500 ppm, and the CH4 sensor at values up to 250 ppm. Test measurements have been carried out in outdoor environments for feasibility of the transmission system that has designed.

Nowadays, the number of private vehicles increase significantly and cause the congestion. And as the result, almost all of the people have the same problem in failing to get a vacant parking slot People usually need to take time for about 30 minutes just to obtain a vacant parking slot. In this paper, we tackle this urgent issue and propose a Smart Parking Reservation System with Rate Display Occupancy denoted as SORAY. Our system uses simulated annealing based heuristic algorithm to optimize the queue method by considering three parameters such as the driver status (member or non-member), distance, and parking duration. This system also uses the Haversine formula to calculate the distance from the current position of the vehicle to the parking lot at the time of sending the reservation request. As the proof of reservation verification process, SORAY uses Quick Response Code. This SORAY system is also equipped with a secure member registration process by implementing SHA-256 algorithm to generate random OTP. The graph of the occupancy rate is displayed to predict the density of vehicles in the parking lot. Results show that this system successfully decreases the time needed to make a reservation process by almost 20 times faster than the conventional method for searching parking slots. For the simulation results, the heuristic method works and got the smallest cost: 1.97 with the condition of driver who makes a reservation was a member and has an hour parking duration with 5.4 kilometers distance. The results show that the average time to compute the SHA-256 java code for generating random OTP is 4.10 seconds.

GPS is widely extended to various scenarios, owing to its trust and utilities. The GPS on smartphones is more accurate than the GPS receivers. On average, modern smartphones are already using A-GPS to help the global navigation system quickly lock-in smartphone satellites. Although the GPS receiver is used for renewable technologies, such as automated drone applications for farming that require high accuracy, there is a clear need for large-scale precision farm monitoring to increase productivity to meet rising population demands. Drones need accurate GPS to map the agricultural land and precisely spray the land GPS Coordinate data from GPS receiver has often been due to inaccuracies caused by many different factors that GPS signals have made. The main cause of error affecting the accuracy and precision of GPS positioning is the lack of the number of satellites in the sky. We paid attention to the GPS calculation method itself. We adapted the Kalman filter to calculate the positioning of the GPS. Kalman filter is used by direct GPS measurements to make better location estimates than those given. The Kalman filter parameter has been adapted to improve the accuracy and accuracy of SPS positioning in stand-alone mode in worse situations. The error can, therefore, be reduced by integrating the Kalman filter as a post-processing filter. The test result shows that Kalman Filter produces a position that is more accurate than the covariance noise value parameter (R) = 10–6 and the covariance noise (Q) = 10–6.