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