Global Navigation Satellite Systems (GNSS) like GPS have become part of everyday life. From navigation as was its original intent to highly accurate and relatively cheap timekeeping and making games out of walking outside.
However, tools to test GNSS-enabled systems can be expensive, especially for non-GPS systems or they bypass the actual GNSS receiver.
Therefore I made a free and open-source GNSS simulator framework and implemented five different GNSS signals on top of it (GPS L1 C/A, GLONASS L1, Galileo E1, Beidou B1I, and IRNSS L5). This thesis document will explain how the framework works and evaluate the five simulators.
Because easy access to GNSS simulators means more people can abuse it I will also cover spoofing and spoofing detection.
While spoofing can be used for nefarious purposes I show how it can potentially be used to protect from GNSS guided drones.

For my master’s thesis, I developed a novel indoor localization approach that can achieve accurate localization results. Indoor localization is a well-known topic of research, and many attempts have been made to find the so-called holy grail. For a robotic car swarm to be able to execute a specific task in a specific room, each robot in the swarm needs to know its own location with respect to a map of the building. Allowing the robot to deduce this location himself allows for total automation of the swarm and helps mitigate errors along the way due to faulty sensor readings. Numerous works have tried to solve this problem by using beacons or heavily trained machine learning networks. These methods, however, prevent the robot from working everywhere, as either the location needs to be adapted or the robot needs to be trained for the location. To mitigate these issues, the work of this thesis is focused on achieving indoor localization that works everywhere by researching if a combination of a particle filter and sound source localization techniques can achieve high-accuracy indoor localization.

To achieve this, particle filter and source localization techniques are combined based on probability theory. Here, so-called localization tables are used to estimate the robot’s position based on the geo- metrical properties of the map, the DOA, and the distance from a received message. This table is then used to update the position and weight of the particles, which can then be used to make an educated guess of the robot’s position. This approach proved to be effective, as it was able to achieve average RMSE results as low as 3.83 cm, where the robot only needed to drive 363 cm and localization was achieved within 9.52 seconds. The research showed that deploying more cars in the swarm leads to better results, as fewer transmissions are needed, and less distance needs to be traveled. In addition, the research also showed that localization can be achieved while driving even less distance by sharing the localization tables between the robots. This, however, does come at the cost of localization accu- racy, resulting in an RMSE of 19.89 cm while driving only 158 cm. To conclude this research, based on the results of the novel fusion of a particle filter and sound source localization techniques, it can be concluded that the work in this thesis offers a great contribution to the field of indoor localization using audio-based signals.

We consider a system of IoT nodes powered completely by energy harvesting.This work focuses on achieving the time correlation of data measurements ina network of energy harvesting sensor nodes. Time correlation is achieved byhopping a message through the whole network. This message wakes up all thenodes and lets them perform a measurement. Measurement data is added tothe transmitted message and is collected by a gateway at the end. The nodesharvest energy from a Radio Frequency (RF) source and store it in a capacitor.When the capacitor has sufficient energy, the nodes can turn on their system.A low power Wake-Up Receiver (WURx) is turned on and the nodes fall asleepwhile waiting for the incoming request message. Communication is done usingactive transmissions and the ultra-low-power WURx for data reception. Nodesconsume a continuous power of less than 2 µW in sleep mode while the WURx isturned on. The receiving sensitivity is -40 dBm, which limits the communicationrange. The request message hops through the network to overcome distancelimitation. Collisions are avoided with Clear Channel Assessment (CCA) usingthe WURx. The hidden node problem is overcome by toggling an operationalamplifier during CCA. Distance limitation is overcome by a novel network layeralgorithm. The network layer algorithm finds a directed acyclic graph (DAG)based on all nodes, starting in a single special source node and ending in agateway. Data from all the nodes are gathered in a round, where each node cantransmit one message around. The timing interval between the data collection ischosen to be bigger than the required energy divided by the minimal harvestedpower. In this way, all nodes will have sufficient energy in every time interval.The found DAG represents all important links where the nodes should wait forbefore measuring and transmitting. Other data from previous nodes are addedto the transmission of the nodes. In this way, the gateway will receive datafrom all nodes with the minimal time difference between their measurements.Simulations show that a correct gateway oriented DAG solution is always foundfor random networks. In > 92 % of the cases, all nodes are taken into account inthis solution and in 6% of the cases, just one node is missing. Nodes have beendesigned and evaluated. We can power the nodes with a minimal RF input of-15 dBm. The receiving range is found to be 8 m from a 10 dBm On-Off Keying(OOK) transmission. With 6 µW harvested energy, data from all the nodes canbe gathered every 15 minutes.

Locating people inside buildings is still an unsolved problem. There is a lot
of research going on in this field and many different solutions using different
techniques have been proposed. However, there is no widely accepted indoor
localization solution like how GPS is for outdoor localization due to less accuracy, higher hardware requirement, cost etc,. We introduce a system that locates
people indoors more accurately.