Distributed coordination for fast iterative optimization in wireless sensor/actuator networks

Large-scale coordination and control problems in sensor/actuator networks are often expressed within the networked optimization model. While significant advances have taken place in both first- and higher-order optimization techniques, their widespread adoption in practical implementations has been hindered by a lack of adequate programming and evaluation support. This motivates the two major contributions of this paper. First, we extend the distributed programming framework proposed in [1] with a synchronization primitive to implement different versions of the subgradient technique and perform extensive evaluation with varying deployment and algorithmic parameters. Second, the insights — obtained by observing the variability in practical metrics such as response time and incurred message cost — lead us to exploit the spatial locality inherent in these large-scale actuator control applications, and propose a novel consensus algorithm applied to the subgradient method. We show using simulations that there is at least 99% improvement in response time and the message cost is reduced by more than 90% over prior consensus based algorithms.