Gossiping for threshold detection

We investigate the use of gossip protocols to detect threshold crossings of network-wide aggregates. Aggregates are computed from local device variables using functions such as SUM, AVERAGE, COUNT, MAX and MIN. The process of aggregation and detection is performed using a standard gossiping scheme. A key design element is to let nodes dynamically adjust their neighbor interaction rates according to the distance between the nodes' local estimate of the global aggregate and the threshold itself. We show that this allows considerable savings in communication overhead. In particular, the overhead becomes negligible when the aggregate is sufficiently far above or far below the threshold. We present evaluation results from simulation studies regarding protocol efficiency, quality of threshold detection, scalability, and controllability.