E3: Towards energy-efficient distributed least squares estimation in sensor networks

Domain-specific applications, such as structural health monitoring, have been one of the main drivers that motivates the real-world deployment of wireless sensor networks. Due to their data-intensive nature, it is typical for these applications to make heavy uses of least squares estimation as a foundation for their algorithms, which is a standard approach to compute the approximate solution of sets of equations in which there are more equations than unknowns. Due to the very limited amount of energy and computation power available on the sensors, it is imperative to design new algorithms to perform least squares estimation in a distributed fashion. While we wish to conserving energy by minimizing communication with our design, constraints on communication delays will also need to be satisfied. In this paper, we propose E³, a new distributed algorithm specifically designed to guarantee the precision of least squares estimation in sensor networks, with the objective of minimizing the energy consumption incurred during communication, while observing constraints on application-specific communication delays. Compared to previous works, we show that E³ maintains the same level of estimation precision while incurring much lower energy costs.