Funded by National Science Foundation, NASA and Department of Defense

1. Data Management in Mobile Ad-Hoc and Sensor Network DatabasesLe GruenwaldUniversity of Oklahoma and National Science Foundationhttp://www.cs.ou.edu/~databaseggruenwald@ou.edu Funded by National Science Foundation, NASA and Department of Defense

2. Two Projects • Data REplication in real-time Ad-hoc Mobile network databases (DREAM) • Window Association Rule Mining to estimate missing data in sensor network databases (WARM)

3. DREAM: System Architecture • Servers (Large Mobile Host LMH) • Classical workstations with high memory, power and computing capabilities • Contains the complete DBMS • Clients (Small Mobile Host SMH) • Computers with reduced memory, power and computing capabilities • Clients contain the Query Processing Module of the DBMS Server1 Client4 Client5 Client1 Server3 Client2 Server4 Server2 Client3 Client6

4. DREAM: Research Objective To design and prototype a data replication technique for a Mobile Ad-Hoc Network (MANET) database management system that takes energy restriction, mobility, disconnections and network partitions, and transaction real-time constraints into consideration.

5. DREAM: Data and Transaction Types Contacted the Norman Fire department and OU Military department for data and transaction model requirements. Data Items Temporal Data Items Read-Only Data Items Persistent Data Items Periodic Update Aperiodic Update Periodic Update Aperiodic Update Transactions Read Transactions Write Transactions Use Current Value Overwrite Current Value MRV MRVP OD Insert/Delete MRV – Most Recent Value MRVP – Most Recent Value in a Partition OD – Outdated Data

6. DREAM: What and Where to Replicate • Replicates hot data items before cold data items at servers that have high remaining power • Replicates data items that are accessed frequently by firm transactions before those that are accessed frequently by soft transactions • Replicates data items that are accessed frequently by Non-MRV /Non-UCV transactions before those that are accessed frequently by MRV/UCV transactions. • A data item with a higher age relocation ratio is replicated before the one with a lower age relocation ratio. • Reduces replica redundancy between neighboring servers by replacing redundant data items with other unallocated hot data items if the links connecting them is stable.

7. DREAM: How to Access Replicas • Firm transactions are sent to the nearest server with the least workload • Soft transactions are sent to the highest energy server with the least workload • Read-only data items and Outdated transactions can be executed at any available servers • MRV transactions are executed at the server which has the most recent value of the requested data item • MRVP transactions are executed at the server which has the most recent value of the requested data item within a network partition • Update transactions are forwarded to the primary copy server of the requested data item • Non-UCV transactions are forwarded to any available server if the primary copy server of the requested data item cannot be reached

8. DREAM: How to Synchronize Replicas Maintains two timestamps: • Primary Update Timestamp – Time when the primary copy server of a data item is updated • Local Update Timestamp – Time when the replica is updated • Server which contains the most recent update timestamp of a data item synchronizes its value with other servers during a certain time interval

9. DREAM:Performance Evaluation (1) • Compared DREAM with No Replication and Hara’s model • DREAM has more successfully executed transactions and is the most balanced model in terms of energy consumption distribution among servers

10. DREAMPerformance Evaluation (2)

11. DREAM Performance Evaluation (3)

12. WARM: Objective • Develop an efficient data estimation algorithm • to compensate for missing and/or corrupted data ; • to inform intrusion detection component about possible anomalies.

13. Possible Approaches Possible Approaches Ignore Ask sensor 2 or Estimate the again more sensors missing value Averaging Use existing techniques relations between sensors WARM Approach

14. WARM: Technical Approach • Mine frequent itemsets from sensor data streams; • Generate association rules according to discovered frequent itemsets; • Use generated association rules to identify sensors related to the sensor MS that has missing/corrupted data; • Develop a weighted function to estimate the missing/corrupted data at MS using the data collected at the sensors related to MS.

15. Sensor1 Sensor1 Sensor2 Sensor2 MS MS Sensor3 Sensor3 SensorN SensorN WARM: DSARM Framework Apriori Framework DSARM Framework a a b c Find only the rules between pairs of sensors, in which the MS is a consequent, w.r.t. a particular state, satisfying both minSup and minConf Find all valid rules, satisfying both minSup and minConf

16. WARM • The WARM Approach is an implementation of the DSARM Framework + using an averaging technique to estimate the missing value in the cases association rule mining cannot produce an estimation; • The WARM Approach is a combination of: - the Buffer, the Cube, and the Counter – data structures to store information received by the sensors; - checkBuffer(), update(), and estimateValue() algorithms that use the data in the structures.

17. WARM: The estimate( )Algorithm Purpose: To estimate a missing sensor reading, using the data in the Counter, the Cube, and the Buffer. • Step 1: Determine eligible states e for MS; • Step 2: Distribute sensors into appropriate eligible StateSets based on their states in the buffer; • Step 3: Determine eligible sensors Si|e-> MS|e; • Step 4: Determine weights for all eligible sensors; • Step 5: Calculate estimated value: missingValue = ( StateValue_e * e) / totalWeight

18. A collection of 108 sensor nodes deployed on city streets. The sensors collect and report the number of the vehicles detected for a given time interval. sensor nodes server The data is obtained from [AFIDA 03]. B A WARM: Simulation Results (1)

19. WARM: Simulation Results (2) Evaluation of the Accuracy for different approaches

20. WARM: Simulation Results (3) Evaluation of WARM TMMAT for different error rates p

21. WARM: Simulation Results (4) Evaluation of WARM Memory Space Requirements

22. Thanks!