Supervised Learning in Sensor Networks: New Approaches with Routing, Reliability Optimizations

Abstract

Routing in sensor networks maintains information on neighbor states and potentially many other factors in order to make informed decisions. Challenges arise both in (a) performing accurate and adaptive information discovery and (b) processing/analyzing the gathered data to extract useful features and correlations. To address such challenges, this paper explores using supervised learning techniques to make informed decisions in the context of wireless sensor networks. In consideration of the unique characteristics of sensor networks, our approach consists of two phases: an offline learning phase and an online classification phase. We use two case studies to demonstrate the effectiveness of our approach. In the first, we present MetricMap, a metric-based routing protocol that derives link quality using our classifiers when the traditional ETX-based approach fails. In the second, we present SHARP, an extension to the PSFQ protocol, which uses knowledge gathered in the training phase to control its caching policy for saving constrained storage space. Evaluation is performed on a 30-node real-world testbed and a multihop sensor network in our lab. Our results show that MetricMap can achieve up to 300% improvement in data delivery rate for a high data-rate application, without compromising other performance metrics; SHARP can reduce the memory footprint of PSFQ by 46.4% with a modest increase of 4.7% in fetch miss rate