Cooperative data muling using a team of unmanned aerial vehicles
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Unmanned Aerial Vehicles (UAVs) have recently o ered signi cant technological achievements. The advancement in related applications predicts an extended need for automated data muling by UAVs, to explore high risk places, ensure e ciency and reduce the cost of various products and services. Due to advances in technology, the actual UAVs are not as expensive as they once were. On the other hand, they are limited in their ight time especially if they have to use fuel. As a result, it has recently been proposed that they could be assisted by the ground static sensors which provide information of their surroundings. Then, the UAVs need only to provide actions depending on information received from the ground sensors. In addition, UAVs need to cooperate among themselves and work together with organised ground sensors to achieve an optimal coverage. The system to handle the cooperation of UAVs, together with the ground sensors, is still an interesting research topic which would bene t both rural and urban areas. In this thesis, an e cient ground sensor network for optimal UAVs coverage is rst proposed. This is done using a clustering scheme wherein, each cluster member transmits its sensor readings to its cluster head. A more e cient routing scheme for delivering readings to cluster head(s) for collection by UAVs is also proposed. Furthermore, airborne sensor deployment models are provided for e cient data collection from a unique sensor/target. The model proposed for this consists of a scheduling technique which manages the visitation of UAVs to target. Lastly, issues relating to the interplay between both types of sensor (airborne and ground/underground) networks are addressed by proposing the optimal UAVs task allocation models; which take caters for both the ground networking and aerial deployment. Existing network and tra c engineering techniques were adopted in order to handle the internetworking of the ground sensors. UAVs deployment is addressed by adopting Operational Research techniques including dynamic assignment and scheduling models. The proposed models were validated by simulations, experiments and in some cases, formal methods used to formalise and prove the correctness of key properties.