Energy Efficient Data Forwarding in Disconnected Networks Using Cooperative UAVs

Data forwarding from a source to a sink node when they are not within the communication range is a challenging problem in wireless networking. With the increasing demand of wireless networks, several applications have emerged where a group of users are disconnected from their targeted destinations. Therefore, we consider in this paper a multi-Unmanned Aerial Vehicles (UAVs) system to convey collected data from isolated fields to the base station. In each field, a group of sensors or Internet of Things devices are distributed and send their data to one UAV. The UAVs collaborate in forwarding the collected data to the base station in order to maximize the minimum battery level for all UAVs by the end of the service time. Hence, a group of UAVs can meet at a waypoint along their path to the base station such that one UAV collects the data from all other UAVs and moves forward to another meeting point or the base station. All other UAVs that relayed their messages return back to their initial locations. All collected data from all fields reach to the base station within a certain maximum time to guarantee a certain quality of service. We formulate the problem as a Mixed Integer Nonlinear Program (MINLP), then we reformulated the problem as Mixed Integer Linear Program (MILP) after we linearize the mathematical model. Simulations results show the advantages of adopting the proposed model in using the UAVs\u27 energy more efficiently

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods