Energy sustainability and efficiency of energy use are one of the leading Sustainable Development Goals established by the United Nations. Similarly, the 3rd Generation Partnership Project (3GPP) targets the decrease of power consumption of the Internet of Things and wearable devices as the primary requirement for enabling next-generation connections and networks.
In 2022, almost 500 million wearables were sold and shipped worldwide, and the market with smart watches, fitness bands, eTextiles and smart glasses is expected to continue its exponential growth.
These connected devices measure, store, process, transmit and receive vast amounts of data of various types, and developing the means to minimize their volume, as well as to optimize the algorithmic complexity directly leads to valuable savings in computational and storage resources.
Lossy compression mechanisms as energy savers
Sensor-based, time-series data, such as heart rate, electrocardiogram (ECG), or respiration measurements, represent a large part of the wearable-based data gathered directly from a wearable device. In her thesis, M.Sc. Lucie Klus introduces lossy compression mechanisms that can save energy in delay-sensitive data gathering, transfer, and storage.
Compared to the lossless compression methods, lossy compressions are capable of greatly reducing the volume of data while removing redundancies and noise, leaving the useful information coherent. To further boost the energy efficiency of wearable devices, Klus’ thesis provides methods to optimize positioning capabilities in indoor scenarios.
“Performing the positioning task quickly and efficiently on all connected devices, including wearables, becomes crucial in industrial applications, eHealth, or security”, Klus says.
Location awareness becomes a critical function across smart devices and in her thesis, Klus proposes a multitude of radio map reduction techniques supporting accurate positioning while saving resources in data storage and transfer without damaging the accuracy. Klus also proposes several solutions to minimize the computational complexity of the positioning algorithms applied to voluminous datasets.
Klus conducted her research during 2019–2022 under a joint doctoral degree programme in Dynamic Wearable Applications with Privacy Constraints at Tampere University, Tampere, Finland and Universidad Jaume I., Castellón de la Plana, Spain, funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska Curie grant agreement No. 813278, A-WEAR.
Public defence on Thursday 27 April
The doctoral dissertation of M.Sc. Lucie Klus in the field of communications titled From Compression of Wearable-based Data to Effortless Indoor Positioning will be publicly examined at the Faculty of Information Technology and Communication Sciences at Tampere University at 12 o’clock noon, Thursday 27.04.2023 at Hervanta campus, Sähkötalo building, in the SA207 S4 Auditorium. The Opponents will be Dr. Tobias Feigl from Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, and Dr. Christos Laoudias, University of Cyprus, Cyprus. The Custos will be Professor Jari Nurmi from Faculty of Information Technology and Communication Sciences, Tampere University.
Photo: Petr Olišar