Query-based wireless sensor storage management for real time applications Ravinder Tamishetty, Lek Heng Ngoh, and Pung Hung Keng Proceedings of the 2006.

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Presentation transcript:

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 points root point (3,3) level2 mirror points ♦ d, hierarchy depth ♦ mirrors, 4 d -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 a i >r+k/2 a i <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 algorithm

Shifting algorithm furthest shortest Sensor node Storage node Query node New storage node New hashing location New query region boundary identify The radius covered by region ‘ r = (d + k)/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 query nodes

Shifting Algorithm  New storage node is identified by the hashing function v = H (key)  Where key is data_type + movement Every movement of storage node the movement level 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 1 st shift of storage node:33%  The number of queries with 2 nd shift of storage node:34%

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