Query-based wireless sensor storage management for real time applications

1. 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)

2. Outline • Introduction • Location Aided data centric storage • Simulation results • Conclusion

3. 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

4. root point (3,3) level1 mirror points level2 mirror points Geographic Hash Table for Data-Centric Storage (GHT) (0,100) (100,100) • The storage nodes are pre-computed and kept at the same location • Keeping the storage nodes doesn’t consider the query space ♦ d, hierarchy depth ♦ mirrors, 4d -1 e.g. d = 2 (0,0) (100,0)

5. 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

6. Sensor node Storage node City Center Query node Basic idea Old storage node

7. Sensor node Storage node Query 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 In the different region Query region boundary Storage node keeps track of the query location in a small table for a certain amount of time ai<r+k/2 ai>r+k/2 In the same region

8. Sensor node Storage node Query node Identify the query region boundaries • In order to reduce the query traffic f: 4 t: 2 seconds Shirting algorithm f: query frequency t: the waiting time for the storage node

9. New hashing location New storage node Sensor node Storage node Query node Shifting algorithm New query region boundary identify furthest Sent [c, r] to query nodes 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 shortest

10. 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

11. 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

12. 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%

13. Simulation results

14. Simulation results

15. Simulation results

16. 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