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Query-based wireless sensor storage management for real time applications. Ravinder Tamishetty, Lek Heng Ngoh, and Pung Hung Keng Proceedings of the 2006 IEEE International Conference on Industrial Informatics (INDIN ’ 06). Outline. Introduction Location Aided data centric storage - PowerPoint PPT Presentation
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Query-based wireless sensor storage
management for real time applications
Ravinder Tamishetty, Lek Heng Ngoh, and Pung Hung Keng
Proceedings of the 2006 IEEE International Conference on Industrial Informatics (INDIN’06)
Outline Introduction Location Aided data centric storage Simulation results Conclusion
Existing schemes for storage External Storage (ES) Local Storage (LS) A significant benefit of data-centric storage
A group of pre-defined Low level sensor data are abstracted to high level concept of event
Use a geographic hash table to map an event type into a geographic
Avoid flooding
Geographic Hash Table for Data-Centric Storage (GHT)
level1 mirror pointsroot point (3,3)
level2 mirror points
♦ d, hierarchy depth
♦ mirrors, 4d -1
e.g. d = 2
(0,100)
(100,0)
(100,100)
(0,0)
The storage nodes are pre-computed and kept at the same location
Keeping the storage nodes doesn’t consider the query space
A potential application
The origin of these queries is tooted to particular region and changes periodically in the network
Propose the shifting of storage node from its initial hashed location
Basic idea
City Center
Sensor node
Storage node
Query node
Old storage node
Location aided data centric storage Storage node’s update
In order to reduce the query traffic The current storage node’s location are not
capable of keeping the data
Sensor node
Storage node
Query node
ai>r+k/2
ai<r+k/2
In the same region
In the different region
Storage node keeps track of the query location in a small table for a
certain amount of time
Query region boundary
Identify the query region boundaries In order to reduce the query traffic
Sensor node
Storage node
Query node
f: query frequency
t: the waiting time for the storage node
f: 4
t: 2 seconds
Shirting Shirting algorithmalgorithm
Shifting algorithm
furthestfurthest
shortestshortest
Sensor node
Storage node
Query node
New storage node
New hashing locationNew query region boundary identifyNew query region boundary identify
The radius covered by regionThe radius covered by region
‘‘rr = ( = (dd + + kk)/2)/2
d: the distance between furthest and shortest query nodes from the storage node
k: an additional constant is added to d as safe step
Sent [c, r] to Sent [c, r] to query nodesquery nodes
Shifting Algorithm New storage node is identified by the hashing f
unction v = H (key)
Where key is data_type + movement Every movement of storage node the movement le
vel is increased by one The new updated hashed location returned to
the querying node and flood in the query region
Shifting Algorithm The current storage node’s location are
not capable of keeping the data
The power level at current storage node < threshold A local shifting
Finds a nearest neighbor and forwards all data and they cache
Simulation results Network size: 200m*100m The number of sensor nodes: 50, 100, 200 The number of event types: 2 to 20 The number of queries: 100 to 200 The number of queries with no shift of storage
node:33% The number of queries with 1st shift of storage
node:33% The number of queries with 2nd shift of storage
node:34%
Simulation results
Simulation results
Simulation results
Conclusion Presented location aided storage
management Shirting algorithm
Shifts the storage nodes location based on the query traffic
The contributions for storage management Query region boundary estimations New storage node formations