Sensors can have big advantages in the process of mapping planets and inspecting the prevailing conditions on their surfaces. We can obtain broader and more thorough pictures about the solar system bodies around us. We can not only gain new information about them, but we can observe how fast and in what way the already known data changes, and how these planets forms. For this purpose, I examined a sensor network that can withstand the environment on a planet in our solar system.
During the abstract development phase, I analyzed the network’s performance as well as its cost-effectiveness. The network contains some higher performance sensors, whose dedicated purpose is to collect data from the other-, (smaller) sensors, and forward this information to the satellites, which can send it back to Earth. I created the sensor network’s structure in a specific way, which allows to pinpoint the location of these devices. Localization is an essential part of the mapping of any planet in the solar system.
I used the triangulation technique for the positioning which allows locating a fourth sensor using three other one. The previously mentioned higher performance sensors know their own positions, therefore a smaller sensor’s position can be calculated within a small margin of error. However, the measurement errors build up with a multi-level localization method, leading to the malfunction where the sensors’ position cannot be calculated precisely, because the prediction has shifted so much. I examined this effect from multiple viewpoints.
For modeling the sensor network, I developed a C++ simulation program. I designed the simulation program in such a way that the different components can be included with their parameters. It can be easily calculated how much the measurement error build-up depends on the number of sensors, their movement and by increasing the allowed error percentage how many sensors do we lose from the trackable area. I considered the different environmental factors which can develop on a planet, e.g., ground relief’s effect on the mobility, dust storms which can lead to the sensors losing each other for a period of time.