Energy-scalable protocols for battery-operated MicroSensor networks

In wireless sensor networks, the goal is to gather information from a large number of sensor nodes and communicate the information to the end-user, all under the constraint of limited energy resources. Network protocols minimize energy by using localized communication and control and by exploiting computation/communication tradeoffs. In addition, data fusion algorithms such as beamforming aggregate data from multiple sources to reduce data redundancy and enhance signal-to-noise ratios, thus further reducing the required communications. We have developed a sensor network system that uses a localized clustering protocol and beamforming data fusion to enable energy-efficient collaboration. We compare the performance of two beamforming algorithms, the Maximum Power and the Least Mean Squares (LMS) beamforming algorithms, using the StrongARM SA-1100 processor. Results show that the LMS algorithm requires less than one-fifth the energy required by the Maximum Power beamforming algorithm with only a 3 dB loss in performance, thus showing that the LMS algorithm is better suited for energy-constrained systems. We explore the energy-scalability of the LMS algorithm, and we propose an energy-quality scalable architecture that incorporates techniques such as variable filter length, variable voltage supply and variable adaptation time.