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The fast and unconstrained mobility of Flying Adhoc NETworks (FANETs) brings about the need to develop solutions for packet routing in a highly dynamic topology scenario. Previous works in this direction aim at extending protocols designed for Mobile Ad-hoc NETworks (MANETs) to the more challenging domain of FANETs. Unlike previous approaches, we aim at exploiting the device controllable mobility to facilitate network routing. We propose MAD (Movement Assisted Delivery): a packet routing protocol specifically tailored for networks of aerial vehicles. MAD enables adaptive selection of the most suitable relay nodes for packet delivery, resorting to movement-assisted delivery upon need, which is supported by a reinforcement learning approach. By means of extensive simulations we show that MAD outperforms previous solutions in all the considered performance metrics including average packet delay, delivery ratio, and communication overhead, at the expense of a moderate loss in average device availability.

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Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required for testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism.