Simon Oechsner

Gabriel Martins Dias, Toni Adame, Boris Bellalta et al.

Wireless sensor networks (WSNs) have been adopted as merely data producers for years. However, the data collected by WSNs can also be used to manage their operation and avoid unnecessary measurements that do not provide any new knowledge about the environment. The benefits are twofold because wireless sensor nodes may save their limited energy resources and also reduce the wireless medium occupancy. We present a self-managed platform that collects and stores data from sensor nodes, analyzes its contents and uses the built knowledge to adjust the operation of the entire network. The system architecture facilitates the incorporation of traditional WSNs into the Internet of Things by abstracting the lower communication layers and allowing decisions based on the data relevance. Finally, we demonstrate the platform optimizing a WSN's operation at runtime, based on different real-time data analysis.

Gabriel Martins Dias, Simon Oechsner, Boris Bellalta

In this work, we present a method that exploits a scenario with inter-Wireless Sensor Networks (WSNs) information exchange by making predictions and adapting the workload of a WSN according to their outcomes. We show the feasibility of an approach that intelligently utilizes information produced by other WSNs that may or not belong to the same administrative domain. To illustrate how the predictions using data from external WSNs can be utilized, a specific use-case is considered, where the operation of a WSN measuring relative humidity is optimized using the data obtained from a WSN measuring temperature. Based on a dedicated performance score, the simulation results show that this new approach can find the optimal operating point associated to the trade-off between energy consumption and quality of measurements. Moreover, we outline the additional challenges that need to be overcome, and draw conclusions to guide the future work in this field.

Gabriel Martins Dias, Boris Bellalta, Simon Oechsner

In 2008, the CEO of the company that manages and maintains the public bicycle service in Barcelona recognized that one may not expect to always find a place to leave the rented bike nearby their destination, similarly to the case when, driving a car, people may not find a parking lot. In this work, we make predictions about the statuses of the stations of the public bicycle service in Barcelona. We show that it is feasible to correctly predict nearly half of the times when the stations are either completely full of bikes or completely empty, up to 2 days before they actually happen. That is, users might avoid stations at times when they could not return a bicycle that they have rented before, or when they would not find a bike to rent. To achieve that, we apply the Random Forest algorithm to classify the status of the stations and improve the lifetime of the models using publicly available data, such as information about the weather forecast. Finally, we expect that the results of the predictions can be used to improve the quality of the service and make it more reliable for the users.