The application of wireless sensor networks has grown rapidly in the past decades. They can be found in many areas of our lives, such as healthcare, agriculture, communication networks and even more. Therefore, based on a new concept they could be used in space research as well, since their effectiveness has been proven many times on Earth. On the surface of a distant celestial body our resources are limited, and distant interference is difficult to carry out due to the large distance. In the case of wireless sensor networks, the combination of thousands of small, low-cost devices enable the realisation of new measurement methods.
The first step of asteroid mining is exploring raw materials, in which wireless sensor networks can be of great assistance. Before launching such a mission, we must ensure that each element of the network is working based on the most appropriate algorithm. The so-called swarm intelligence solutions can provide an excellent basis for this, which are characterised by collective behaviour.
In my thesis, I examine one of the most common swarm intelligences techniques, the Particle Swarm Optimization algorithm. I suppose the map of an unknown planet where I scatter some elements of the sensor network and then I implement an optimum search process with the help of the algorithm. Along with the mentioned model, I also created a simulation program in Matlab.