Distributed Quad-Tree
for Spatial Querying in
Wireless Sensor Networks (WSNs)
Murat Demirbas, Xuming Lu
Dept of Computer Science and Engineering,
University at Buffalo, SUNY, NY 14260
{demirbas, xuminglu}@cse.buffalo.edu
URL: http://www.cse.buffalo.edu/~demirbas/
iComp
In-network querying in WSNs
In contrast to traditional WSN
applications that perform only data
collection, new generation of WSN
applications require in-network
information querying
– Disaster relief applications
– Battlefield applications
Where is the nearest enemy tank?
A soldier queries the WSN via a
palm device. Energy-efficiency and
latency suffers drastically if queries
are always routed to a centralized
basestation over many hops
In-network querying should satisfy
•
Distance sensitivity
Cost of querying for nearby events
should be lower
•
Low-cost construction
Costly bottom-up constructions
(via flooding) should be avoided
•
Graceful resilience
Node failures should not impact
performance disproportionately
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Our contributions
We present an in-network querying infrastructure that
satisfies all these requirements for event querying
– Distance sensitivity: the cost is at most 2 2(stretch factor) times
the distance d to the nearest event
– Low-cost maintenance: stateless, minimalist infrastructure,
bottom-up construction is avoided
– Graceful resilience: single mote failures are masked and
performance degrades proportionately wrt the severity of holes
(failures of motes in a region)
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Distributed Quad-Tree (DQT)
•
We embed a DQT over the WSN :
DQT is a multi-resolution structure, but
for a lightweight representation of
DQT we use an encoding trick
–
•
Based on the DQT node id, a node
determines which level it is at, which
children, neighbor, parent it has
To achieve low-cost construction, we
exploit location info at the nodes
–
–
Nodes know the boundary coordinates
of deployment, and calculate which
DQT node id they fall into using their
coordinates
We chose the clusterheads closer to
the basestation to avoid backward links
during querying and data collection
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Event Indexing & Querying
Indexing of event information
Event querying algorithm
•
•
•
If query point is not current location, query is
routed to the query point via GPSR
If matching answer is not found, query is sent
to next parent progressively
(until root is reached)
The result is returned to the initiator
Query Propagation Path
A node at level i maintains the event
information of its cluster, as well as
the event information of its neighbors
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Graceful resilience
• DQT achieves resilience via:
– its stateless nature, and
– using GPSR for routing
Stretch factor under different failure rate
• More specifically :
– Mote failures do not often lead to
failure of level 1 node & are masked
– GPSR routes around coverage holes,
& delivers the message to a boundary
node if destination is inside hole
– Since DQT is stateless, any node can
act as a proxy node for another
•
Simulations show that s’ increases
slowly wrt failure rate
s = ratio of DQT querying cost to dist (q, p)
s’ =ratio of DQT querying cost to GPSR cost (q, p)
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Simulation (ns2)
Querying success rate
Settings: 16x16 nodes, unit distance 200m,
transmission range 250m
Case 1: Failures happen before the event
advertisement.
Case 2: The event has already been
published in the structure before the failure
happens.
Stretch factor with node failure: Case 1
Stretch factor with node failure: Case 2
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Related work
•
Geographic Hash Tables (Ratnasamy
•
Distance Sensitive Information
Brokerage protocol (Funke et al[2006].)
– achieves distance-sensitivity
via hierarchically partitions
– relies on communication
protocol and needs costly
construction
•
DIMENSIONS (Estrin et. al. [2002])
– provides a unified view of data
processing and in-network
querying via wavelet
compression and waveRoute
routing protocol
– sacrifices flexibility space to
achieve multi-resolution
capability
et al. [2002] )
– stores and retrieves information
using a geographic hash function
– querying is not distance sensitive
•
Distributed Index for Features in
Sensor Networks (Greenstein et al.
[2003])
– addresses complex querying, not
restricted to event querying
– costly to construct and update
– special purposed routing
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Our current work:
Model-based querying in DQT
The original temperature data
– Only supporting event type of
querying is very limited
– Complex querying is very costly
without prior knowledge of data
– We use modeling to capture the
correlations of sensor values and
reduce the cost of querying
Our modeling of the data at high levels
• We use multi-resolution modeling,
and a query is answered with
approximate values accompanied
by confidence levels
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