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Managing energy consumption for computation and communication is a key requirement for flying ad hoc networks (FANET) to prolong the network lifetime. In many applications, the main role of drones is to collect imagery information and relay them to a ground station for further processing and decision making. In this paper, we present a predictive compression policy to maximize the end-to-end image quality penalized by communication and computation costs. The idea is to predict the number of remaining links to the destination for a given routing algorithm and use it to re-compress image frames at intermediate nodes such that the overall energy consumption is minimized. Numerical results confirm that the performance of this method is within 4% of the global optima and higher than the current fixed-rate policies with a significant margin. 

Unmanned aerial vehicles (UAVs), commonly known as drones, are becoming increasingly popular for various applications. Freely flying drones create highly dynamic environments, where conventional routing algorithms which rely on stationary network contact graphs fail to perform efficiently. Also, link establishment through exploring optimal paths using hello messages (as is used in AODV algorithm) deems extremely inefficient and costly for rapidly changing network topologies. In this paper, we present a distance-based greedy routing algorithm for UAV networks solely based on UAVs' local observations of their surrounding subnetwork. Thereby, neither a central decision maker nor a time consuming route setup and maintenance mechanism is required. To evaluate the proposed method, we derive an analytical bound for the expected number of hops that a packet traverses. Also, we find the expected end-to-end distance traveled by each packet as well as the probability of successful delivery. The simulation results verify the accuracy of the developed analytical expressions and show considerable improvement compared to centralized shortest path routing algorithms. 

Emerging Internet of Things (IoT) provides connectivity to a wide range of mobile nodes including indoor wireless users, pedestrian, ground robotics, vehicles, and flying objects. Such decentralized network require rethinking user-centric communication protocols which accommodate extremely dynamic environments of autonomous nodes. The authors recently proposed a predictive routing algorithm, which enables a delay-optimal communication through incorporating network topology prediction into the Dijkstra's shortest path algorithm. In this work, we extend the proposed solution to jointly optimize the end-to-end latency and total transmission power. Further, we develop a ground robotics platform in order to study the utility of the proposed algorithm in real-world applications. The simulation results which verified by the test platform, confirm the superiority of the proposed algorithm compared to the conventional shortest path algorithms by improving the delay and power consumption by a factor of 10% to 15%.