Advances in microsensor technology, low power wireless communication and processing opened the possibility of combining one or more sensors (such as temperature, light, acoustic, seismic, and acceleration), digital communication systems, storage,
and processing resources into low-cost, low-power
wireless sensor nodes.
Although sensor nodes are simple devices with limited capabilities,
sensor networks are expected to perform complex tasks by leveraging extensive
collaboration among sensor nodes. Potential applications of such systems
cover many diverse problems, such as early fire detection,
contaminant transport monitoring,
outdoor environmental monitoring, target tracking on a battlefield,
freeway traffic control, etc.
A good introduction in sensor networks is available at
NIST Advanced Network Technology Division.
Location Discovery in Sensor Networks
The location discovery problem is a fundamental task in wireless
ad-hoc sensor networks (WASN), because majority of the proposed
applications for
sensor networks require information about locations of nodes. The goal of
location discovery is to establish as accurately as possible the position
of each node given partial information about location of a subset of nodes
and distances between pairs of nodes.
We have developed a new approach for location discovery in wireless
sensor networks, based on distributed optimization algorithms.
Intuitively, optimization algorithms are most suitable for problems where a change in the state of one object in a system impacts the state(s) of other objects. The problem of location discovery falls into this category naturally,
because accepting one location estimate as correct, the locations of all other nodes that are connected to the initial node through the distance measurements are impacted. Objective function in optimization algorithms capture such complex relationships,
and aid in transforming them into numerical values, which then facilitates simple comparisons between different states of the whole system. One object can make a simple decision, such as either to change its state or not.
By making such simple decisions, the state of the system is changed in a complex way. Such a model corresponds to WASNs, where the nodes are simple objects with limited capabilities, but a WASN as a whole must make complex decisions and run complex tasks.
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F. Koushanfar, S. Slijepcevic, J. Wong, M. Potkonjak, "Global Error-Tolerant Algorithms for Location Discovery in Ad-Hoc Wireless Networks", IEEE ICASSP 2002, Orlando, FL, May 13 - 17, 2002, vol. 4, pp. IV4186.
Statistical Properties of Location Error and Impact of Location Error on Applications
No matter how good a location discovery algorithm is, there is always a
certain amount of error in the location estimates determined by the
algorithm. The main sources of location error are inaccurate initial
location estimates and distance measurements. If applications and
system software in WASNs that use the location estimates know the magnitude of the location error,
they can adjust their operation parameters in such a way that they can guarantee required properties of their results.
For example, a WASN can be set up to reduce the energy consumption in the network by organizing the nodes in mutually exclusive sets, where each set covers the monitored area, and only one set is active at any time.
Without any information about the expected errors in locations, such a network can leave large parts of the area uncovered,
or extensively cover other parts of the network, when the nodes are grouped into the sets assuming that the given location estimates are correct. However, if the parameters of the location error distribution are known, the task can be reformulated,
so that the area is still covered under the expected magnitude of error.
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S. Slijepcevic, S. Megerian, M. Potkonjak,
"Analysis of Location Error in Wireless Sensor Networks",
2nd International Workshop on Information Processing in Sensor
Networks (IPSN 03),
Palo Alto, CA, Apr. 22-23, 2003, pp. 593-608.
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S. Slijepcevic, S. Megerian, M. Potkonjak,
"Location Errors in Wireless Embedded Sensor Networks: Sources, Models, and Effects on Applications", ACM Sigmobile Mobile Computing and Comm. Review, vol. 6,
no. 3, July 2002, pp. 67-78.
Coverage Management and Evaluation
The deployment of sensor nodes is the first step in
establishing a sensor
network. Since sensor networks contain a large number of sensor nodes, the
nodes must be deployed in clusters, where the location of each particular
node cannot be fully guaranteed a priori. Therefore, the number of nodes
that must be deployed in order to completely cover the whole monitored
area is often higher than if a deterministic procedure were used. In
networks with stochastically placed nodes, activating only the necessary
number of sensor nodes at any particular moment can save energy.
We introduce a heuristic that selects mutually exclusive sets of sensor
nodes, where the members of each of those sets together completely cover
the monitored area. The intervals of activity are the same for all sets,
and only one of the sets is active at any time. The experimental results
demonstrate that by using only a subset of sensor nodes at each moment, we
achieve a significant energy savings while fully preserving coverage.
Security in Sensor Networks
Resource limitations and specific architecture of sensor networks call for
customized security mechanisms. Our approach is to classify the types of
data existing in sensor networks, and identify possible communication
security threats according to this classification. We propose a
communication security framework where for each type of data we define a
corresponding security mechanism. By employing this multitiered security
architecture where each mechanism has different resource requirements we
allow for efficient resource management that is essential for wireless
sensor networks.
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