Latency Minimizing Tasking for Information Processing Systems

Abstract

Real-time cyber-physical systems and information processing clusters require system designers to consider the total latency involved in collecting and aggregating data. For example, applications such as wild-fire monitoring require data to be presented to users in a timely manner. However, most models and algorithms for sensor networks have focused on alternative metrics such as energy efficiency. In this paper, we present a new model of sensor network aggregation that focuses on total latency. Our model is flexible and enables users to configure varying transmission and computation time on a node-by-node basis, and thus enables the simulation of complex computational phenomena. In addition, we present results from three tasking algorithms that trade-off local communication for overall latency performance. These algorithms are evaluated in simulated networks of up to 200 nodes. We've presented an aggregation-focused model of sensor networks that can be used to study the trade-offs between computational coverage and total latency. Our model explicitly takes into account transmission and computation times, and enables users to define different values for the basestation. In addition, we've presented three different tasking algorithms that operate over model to produce aggregation schedules of varying quality. In the future, we expect to continue exploring distributed tasking algorithms for information processing systems. We've shown that the gap between highly optimized schedules that use global information is quite large relative to our distributed algorithms. This gives us encouragement that future distributed tasking algorithms can still make large gains.