Biblio

A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.

Vehicular Named Data Network (VNDN) uses Named Data Network (NDN) as a communication enabler. The communication is achieved using the content name instead of the host address. NDN integrates content caching at the network level rather than the application level. Hence, the network becomes aware of content caching and delivering. The content caching is a fundamental element in VNDN communication. However, due to the limitations of the cache store, only the most used content should be cached while the less used should be evicted. Traditional caching replacement policies may not work efficiently in VNDN due to the large and diverse exchanged content. To solve this issue, we propose an efficient cache replacement policy that takes the quality of service into consideration. The idea consists of classifying the traffic into different classes, and split the cache store into a set of sub-cache stores according to the defined traffic classes with different storage capacities according to the network requirements. Each content is assigned a popularity-density value that balances the content popularity with its size. Content with the highest popularity-density value is cached while the lowest is evicted. Simulation results prove the efficiency of the proposed solution to enhance the overall network quality of service.