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Vision-based Localization
Beacon-assisted Localization
This work addresses the sensor network localization problem in a novel way, in
that visual observations made by image sensors from a beacon agent
are used to estimate network node coordinates.
Based on visual observations of a
moving beacon by the network nodes, the proposed technique employs
simple image processing functions to produce equations that contain
the node positions and orientation angles as the unknown parameters.
Observations made at the nodes relate the position of the observed beacon
to the physical coordinates of the node via the mapped position of the
object in the node's image plane. Multiple observations by a network node
from a moving beacon with
known coordinates result in a system of equations with a rank-deficient
matrix. Hence, the solution for the desired node coordinates lies in
the null space of the data matrix. The proposed algorithms are based on in-node processing and
hence are scalable to large networks. This approach is decentralized and no collabotion between the network nodes is ncessary. Hence, the network nodes can stay passive and only receive the beacon broadcasts during localization.
Application areas in
which the image sensor node location and orientation information
can be used include target detection and tracking, robot tracking and
control, estimation of traffic speed in roadways, and implementing
geographic routing schemes for wireless sensor networks.

Target-based Localization
A collaborative vision-based technique is develop for localizing
the nodes of a surveillance network based on observations
of a non-cooperative moving target. The proposed
method employs lightweight in-node image processing and
limited data exchange between the nodes to determine the
positions and orientations of the nodes participating in synchronized
observations of the target. A node with an opportunistic
observation of a passing target broadcasts a synchronizing
packet and triggers image capture by its neighbors.
In the cluster of participating nodes, the triggering node and
a helper node define a relative coordinate system. Once a
small number of joint observations of the target are made by
the nodes, the model allows for a decentralized or a clusterbased
solution for the localization problem. No images are
transferred between the network nodes for the localization
task, making the proposed method efficient and scalable.

Robot-assisted Topology Discovery
A localization solution focusing on network topology
discovery for vision-enabled wireless sensor networks
is developed in this project. A controllable robot is introduced
to assist the localization process of image sensors
deployed on the ceiling with image planes parallel to
the ground. The scenarios in which the robot does not know
its global coordinates is considered. Two cases where
the sensors have overlapping and non-overlapping fields
of view (FOVs) are investigated. In order to implement
the discovery algorithms for these two different cases, a
forest structure is introduced to represent the topology
of the network. We consider the collection of sensors
with overlapping FOVs as a tree in the forest. The
robot searches for nodes in each tree through boundary
patrolling, while it searches for other trees by a radial
pattern motion.

Publications
Collaborative Node Localization in Surveillance Networks using Opportunistic Target Observations
H. Lee and H. Aghajan,
ACM Multimedia Workshop On Video Surveillance and Sensor Networks (VSSN), Oct. 2006.
Robot-Assisted Localization Techniques for Wireless Image Sensor Networks
H. Lee, H. Dong, and H. Aghajan,
IEEE Conf. on Sensor, Mesh, and Ad Hoc Communications and Networks (SECON), Sept. 2006.
Subspace Techniques for Vision-Based Node Localization in Wireless Sensor Networks
H. Lee, L. Savidge, and H. Aghajan,
Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), May 2006.
Vision-Enabled Node Localization in Wireless Sensor Networks
H. Lee and H. Aghajan,
COGnitive systems with Interactive Sensors (COGIS), March 2006.
Collaborative Self-Localization Techniques for Wireless Image Sensor Networks
H. Lee and H. Aghajan,
Asilomar Conference on Signals, Systems and Computers, Oct. 2005.