Data Cleaning publication categorizer

Outliers are very common in the environmen-
tal data monitored by a sensor network con-
sisting of many inexpensive, low ¯delity, and
frequently failed sensors. The limited battery
power and costly data transmission have in-
troduced a new challenge for outlier clean-
ing in sensor networks: it must be done in-
network to avoid spending energy on trans-
mitting outliers. In this paper, we propose
an in-network outlier cleaning approach, in-
cluding wavelet based outlier correction and
neighboring DTW(Dynamic Time Warping)
distance-based outlier removal. The clean-
ing 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 cor-
rected, or removed from further transmission
within 2 hops. We have simulated a spatial-
temporal correlated environmental area, and
evaluated the outlier cleaning approach in it.
The results show that our approach can e®ec-
tively clean the sensing data and reduce out-
lier tra±c.