Energy-Scalable Protocols for Battery-Operated MicroSensor Networks

To maximize battery lifetimes of distributed wireless sensors, network protocols and data fusion algorithms should be designed with low power techniques. 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 have implemented two beamforming algorithms, the Maximum Power and the Least Mean Squares (LMS) beamforming algorithms, on the StrongARM (SA-1100) processor. Results from our experiments 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. The energy requirements of the LMS algorithm was further reduced through the use of variable-length filters, a variable voltage supply, and variable adaptation time.