ASPIRE · Collaborative signal processing for efficient wireless sensor networks

Final Activity Report Summary - ASPIRE (Collaborative Signal Processing for Efficient Wireless Sensor Networks)

The project - www.ics.forth.gr/aspire/ - pushed the boundaries of wireless sensor networks. By approaching traditional, entrenched problems from innovative new angles, the project tried to break through the "wireless sensor node" glass ceiling. Research Topic and Key Goals: Realising the potential of large, distributed sensor networks requires major advances in the theory, fundamental understanding, and practice of: distributed data processing, self-organised communications, and information fusion in highly uncertain scenarios using sensing/communications nodes that are severely constrained in power, computation, and communication capabilities. Hence, ASPIRE's basic research focused on furthering the basic theory and understanding of WSN by addressing the following problems: adaptive collaborative processing in highly non-stationary scenarios, consistent fusion algorithms for networked sensors and representation/transmission of information in large and uncertain networks. Indeed, this is a highly diverse field which combines disciplines such as signal processing, wireless communications, networking, information theory and data acquisition. In addition to ASPIRE's basic theoretical research, our mission was to investigate challenging, high-impact research application projects. Art, entertainment, and education have always served as unique and demanding laboratories for information science and ubiquitous computing research. ASPIRE demonstrated that WSN can be a provocative catalyst for creative expression. The capturing, processing, coding and transmission of the audio content through multiple sensors - as well as the reconstruction of the captured audio signals so that immersive presence can be facilitated in real time to any listener - were the application goals of this project. Achievements: At ASPIRE we introduced mathematical models specifically directed towards understanding the distributed signal acquisition and representation problem, and further, we developed distributed classification and data compression techniques. In the multi-sensor, immersive audio application, we tested and validated novel algorithms that allow compression of the audio content and at the same time allow for this processing to be performed on resource-constrained platforms such as sensor networks. This methodology is ground-breaking since it manages to combine, in a practical manner, the theory of sensor networks with audio coding. It is the use of the sensor networks theory that allows for audio encoding to be performed on sensors with limited resources regarding: computational power, communications bandwidth and battery life etc. The approach proposed by ASPIRE differs from current state-of-art methods in that it moves the complexity from the transmitter to the receiver, and it takes advantage of the plurality of sensors in a sensor network, so as to encode high-quality audio with a low bit-rate. At the same time, the proposed approach focused on encoding multiple signals in bit-rates which are significantly lower than in other state-of-the-art approaches. This is because ASPIRE's system is based on sparse signal representations and compressive sensing (CS) theory which allows sampling of signals significantly below the Nyquist rate. This is the first time that a feasible high-quality audio coding system has been tested and evaluated in the context of wireless sensor networks. Impact on the Future: ASPIRE developed sensing and information processing technologies that will enable new forms of interactive experience and expression. Our aspirations are geared towards finally implementing exciting new ideas, such as the immersive presence of a user in a concert hall performance in real-time, virtual music performances (where the musicians are located all around the world), and collaborative environments for the production of music.

Data: CORDIS, © European Union