20674814 location based spatial query processing in wireless broadcast environments
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LOCATION BASED SPATIAL QUERYPROCESSING IN WIRELESSBROADCAST ENVIRONMENTS
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Geospatial data orgeographicinformation.
Identifies the geographic location of features and
boundaries on Earth(oceans, rivers, hospitals,
restaurants etc).
Usually stored as coordinates and topology.
Spatial data is often accessed, manipulated or analyzedthrough Geographic Information Systems (GIS).
What is spatial data?
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A spatial query is a special type of database querysupported by geodatabases.
Its a query which selects features based on their
location or geographic relationship to others.
Spatial queryprocessing is the process of selecting
features based on location or spatial relationship.
For example, Send me the status of a particular route.
What are spatial queries?
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Location-based spatial queries (LBSQs) refer to spatialqueries whose answers rely on the location of the inquirer.
They represent a set of spatial queries that retrieve
information based on mobile users current locations.
Efficient processing of LBSQs is of critical importance with the
ever-increasing deployment and use of mobile technologies.
For example, "Find the top-three nearest hospitals of some
location x.
What are location based spatial queries?
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ON DEMAND DATA ACCESS MODEL
WIRELESS BROADCAST MODEL
P2P DATA SHARING MODEL
Approaches of mobile data access for spatial
query processing
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Its a simple client-servermodel.
In the simplest approach, a user establishes a point-to-point
communication with the server so that his queries can be answered
on demand.
Here server queues up the query requests issued by the clients and
processes them accordingly. The results of query processing are
returned to the mobile user through the same point-to-point link.
ON DEMAND DATA ACCESS MODEL
(Traditional centralized server
model)
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Queue
Point to point link
Client
c1c2
c3
c4
ON DEMAND DATA ACCESS MODEL
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First, it may not scale to very large user populations.
Second, to communicate with the server, a client must
most likely use a fee-based cellular-type network toachieve a reasonable operating range.
Third, users must reveal their current location and send
it to the server, which may be undesirable for privacyreasons.
Fourth, it is subjected to single point failure of server
which disrupts the entire system.
Disadvantages ofON DEMAND DATA ACCESS MODEL
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In the Wireless broadcast model, the server repeatedlybroadcasts all the information in wireless channels, and
the clients are responsable for filtering the information.
An example of such a system is the Microsoft DirectBand
Network.
To facilitate information retrieval on wireless broadcast
channels, the server usually transmits an index structure,
along with data objects. A well-known broadcast index
structure is the (1, m) indexing allocation method.
WIRELESS BROADCAST MODEL
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The general access protocol for retrieving data on a
wireless broadcast channel involves three main steps
The initial probe
Index search
Data retrieval
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Its a more advanced solution.
It can support an almost-unlimited number of mobile hosts
(MHs) over a large geographical area with a single
transmitter.
With the broadcast model, MHs do not submit queries,
Instead, they tune in to the broadcast channel for information
that they desire. Hence, the users location is not revealed
and his privacy is retained.
The main advantage of the broadcast model over the on-
demand model is that it is a scalable approach.
Advantages ofWIRELESS BROADCAST MODEL
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Limitations of WIRELESS BROADCAST MODEL
1. The broadcast model has large latency, as clients have to
wait for the information that they need in a broadcastingcycle. Furthermore if a client misses the packets that it
needs, it has to wait for the next broadcast cycle.
2. Nearly all the existing spatial access methods are designed
for databases with random access disks. These existingtechniques cannot be used effectively in a wireless
broadcast environment, where only sequential data access
is supported.
3. Since there is significant delay in answering the spatial
queries, the answers provided become invalid especially in
case of mobile nodes.
4. Queries can only be fulfilled after all the required on-air
data arrives.
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The main limitation of preceding on air KNN query lies in its
sequential data access: the access latency becomes longeras the number of data items increases. If we can provide
(approximate) answers to spatial queries before the arrival of
related data packets, we will overcome the limitation of the
broadcast model.
The fundamental idea behind our methodology is to
leverage the cached results from prior spatial queries at
reachable MHs for answering future queries at the local host.
This is known as P2P cooperative caching with resultsharing.
A novel component in our methodology is a verification
algorithm that verifies whether a data item from neighboring
peers is part of the solution set to a spatial query.
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Communication
range of q
P1
P1| P
2
P2|
O2q
O4
O3
1NN candidate
1NNcandidate
o1
P2P cooperative caching with result sharing.
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Mobile hosttransmission range
WirelessBroadcastChannel
Peer-to-PeerChannel
MobileHost
Data
station
System environment
SpatialDatabase
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POINT OF INTEREST
A POI, is a specific point location that someone may
find useful or interesting. Ex:Hospital,resturant etc.
MINIMUM BOUNDED RECTANGLE
A rectangle, oriented to the x and y axes, which
bounds a geographic feature or a geographic data set. It is
specified by two coordinates: xmin,ymin and xmax,ymax.
Any MH p exclusively belongs to an immediatly enclosing
MBRat any instant of time.
MBR
(xmax ,ymax )
(xmin, ymin )p
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VERIFIED REGION
Since memory space is scarce in mobile devices,
we assume that each MH p caches a set of POIs in an
MBR related to its current location.
Since the POIs located inside the MBR were obtained
from the wireless information
server, we define the area bounded by the MBR as
verified region p.V R with regard to ps location.
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Sharing based nearest neighbor verification
When an MH q executes SBNN, it first broadcasts a
request to all its single-hop peers for their cached spatialdata.
Each peer that receives the request returns the verified
region MBR and the cached POIs to q.
Then, q combines the verified regions of all the replying
peers, each bounded by its MBR, into a merged verified
region MVR . The merging process is carried out by the
MapOverlay algorithm.
The core of SBNN is the NN verification (NNV) method,
whose objective is to verify whether a POI oi obtained from
peers is a valid (that is, the top-k) NN of the MH q.
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Sharing based nearest neighbor verification
Let P denote the data collected by q from j peers p1,p2..pj.
Consequently, the merged verified region MVR can berepresented as
MVR=p1.VR U p2.VR U . U pj.VR.(MAPOVERLAY
algorithm)
Suppose that the boundary of MVR consists of k edges,
E={e1,e2,..ek}, and there are l POIs, O={o1,o2..ol}, inside
the MVR. Let es E be the edge that has the shortest
distance to q. An example is given in fig where k=10, and e1
has the shortest distance to q.
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SBNN
At neighboring nodes
1. Let N = { m1,m2 ,.. .. mn } be the set of
mobile nodes
2. If a node mi receives a broadcast request from aquery mobile host q
3. If q.POI = mi.POI
4. mi sends a response message to q consisting of1. Its spatial data as a set of POIs present in itscache and
2.MBR information.
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At q : Algorithm: NNV (q, H, k)
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Euclidian distance between two points p1(x1,y1) and
p2(x2,y2) is given by ||p1,p2||= (x2-x1)2+(y2-y1)2
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Hence we put forth a new strategy for addressing the
categorization of users by making the ordinary SBNNsystem to be equipped with what is called as Profile basedfiltering.
It makes the query mobile host to receive and process
only the relevant data tailored to its profile thus gainingadvantage over ordinary SBNN.
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At neighboring nodes,
1. Let N = { m1,m2,.. .. mn } be the set of mobilenodes each with a profile from the set PR = {pr1,pr2,pr3}
2. If a node mi with profile prmi receives a broadcast request
from a query mobile host q with profile prq3. If q.POI = mi.POI and prmi = prq
4. mi sends a response message to q consisting of
1. Its spatial data as a set of POIs present in its cacheand
2. MBR information.
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At q : Algorithm: NNV (q, H, k)
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Sharing based nearest neighbor verification
The NNV method uses a heap H to maintain the entries
of verified and unverified POIs discovered so far . Initially,H is empty. The NNV method inserts POIs to H as it verifiesobjects from MHs in the vicinity of q.
The heap H maintains the POIs in ascending order in
terms of their Euclidean distances to q. Unverified objectsare kept in H only if the number of verified objects is lowerthan what was requested by the query.
If k elements in H are all verified by NNV, the kNN queryis fulfilled. There will be cases when the NNV methodcannot fulfill a kNN query. Hence, a set that containsunverified elements is returned. If the response time iscritical, a user may agree to accept a kNN data set with
unverified elements, where the objects are not guaranteedto be the top kNNs, otherwise he has to switch into
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Node ID TransmissionRange
Cache Capacity No. of Nodes No. of POIs % Increase inNo. of nodes &
POIs
6
150 3 11 33 -
175 3 20 60 81.81
195 3 28 84 40
215 3 34 102 21.42
PERFORMANCE OF SBNN
Results
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PERFORMANCE OF SBNN
Node ID TransmissionRange
Cache Capacity No. of Nodes No. of POIs % Increase in No. of nodes& POIs
6
150 2 11 22 -
150 3 11 33 32
150 4 11 44 43
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SBNN vs. PFSBNN
Node ID TransmissionRange
Cache Capacity
SBNN PFBNN % Decrease innodes & POIs
No. of Nodes No. of POIs No. of Nodes No. of POIs
6 150 3 16 48 10 30 37.5
27 150 3 13 39 12 36 7.69
15 150 3 15 45 8 24 46.6
31 150 3 13 39 11 33 15.38
34 150 3 16 48 12 36 25
258 150 3 10 30 7 21 30
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Node id->6
Graphs
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Node id->6
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Node id->6
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conclusion
With this novel SBNN algorithm, Thedelay in answering the KNN query issignificantly reduced as it doesnt
need to filter all the informationrequired to satisfy the query.
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Future enhancement
An efficient caching strategy andcache replacement strategy fordistributed storage of spatial data
can further more increase theefficiency of SBNN algorithm.