In this paper we propose a wireless sensor network (WSN) designed for monitoring and risk management of landslides, where data collected by sensors are delivered through the network to a remote unit (RU) for on-line analysis and alerting. To ensure fast deployment, robustness in harsh environments, and very long lifetime, the sensor nodes and the communication protocol have been specifically conceived so that the network is self-organizing, fault tolerant, and adaptive. The WSN has been installed on a landslide located in Torgiovannetto (Italy) for an experimental campaign of several months where performance metrics, such as radio link and path statistics as well as battery levels, have been collected. These metrics demonstrated the effectiveness of the network protocols to manage self-organization, node failures, low link quality and unexpected battery depletion. With negligible human intervention during the pilot experiment, the WSN revealed a very high level of robustness, which makes it suitable to monitor landslides in critical scenarios.

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In this paper we propose a wireless sensor network (WSN) designed for landslides monitoring and risk management. The WSN is self-organizing, has fault tolerance capabilities, and its behavior is driven by the events to be monitored, to guarantee fast deployment, robustness in harsh environments, and very long lifetime. Data collected by sensors are delivered through the network to a remote unit (RU) for on-line analysis and alerting. The WSN has been installed on a landslide located in Torgiovannetto (Italy) for an experimental campaign of several months where performance metrics, such as path statistics and battery levels, have been collected. These metrics demonstrate the effectiveness of the network protocols to manage self-organization, node failures, low link quality and unexpected battery depletion. With negligible human intervention during the pilot experiment the WSN revealed a very high level of robustness, which makes it suitable to monitor landslides in critical scenarios.

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Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional work and fine-tuning to make the setup work effectively. In order to increase the use of DRL with real robots and reduce the gap between simulation and real world robotics, we propose an open source toolkit: robogym 1 . We demonstrate a unified setup for simulation and real environments which enables a seamless transfer from training in simulation to application on the robot. We showcase the capabilities and the effectiveness of the framework with two real world applications featuring industrial robots: a mobile robot and a robot arm. The distributed capabilities of the framework enable several advantages like using distributed algorithms, separating the workload of simulation and training on different physical machines as well as enabling the future opportunity to train in simulation and real world at the same time. Finally we offer an overview and comparison of robo-gym with other frequently used state-of-the-art DRL frameworks.