Martins Dias G, Nurchis M, Bellalta B. Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning. IEEE World Forum on Internet of Things 2016
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
List of publications acknowledging the funding in Scopus.
The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository 
as well as at the UPF e-repository 
Martins Dias G, Nurchis M, Bellalta B. Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning. IEEE World Forum on Internet of Things 2016
Martins Dias G, Nurchis M, Bellalta B. Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning. IEEE World Forum on Internet of Things 2016
Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity, and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73% the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.
Additional material:
- Data: Intel Lab Data with after-processing to fill missing values as described in the article (if interested, contact the authors)