In-network Outlier Cleaning for Data Collection in Sensor Networks

Outliers are very common in the environmental data monitored by a sensor network consisting of many inexpensive, low fidelity, and frequently failed sensors. The limited battery power and costly data transmission have introduced a new challenge for outlier cleaning in sensor networks: it must be done innetwork to avoid spending energy on transmitting outliers. In this paper, we propose an in-network outlier cleaning approach, including wavelet based outlier correction and neighboring DTW(Dynamic Time Warping) distance-based outlier removal. The cleaning process is accomplished during multi-hop data forwarding process, and makes use of the neighboring relation in the hop-count based routing algorithm. Our approach guarantees that most of the outliers can be either corrected, or removed from further transmission within 2 hops. We have simulated a spatialtemporal correlated environmental area, and evaluated the outlier cleaning approach in it. The results show that our approach can effectively clean the sensing data and reduce outlier traffic.