LEAP: Localized Energy-Aware Prediction for Data Collection in Wireless Sensor Network

1. LEAP: Localized Energy-Aware Prediction for Data Collection in Wireless Sensor Network Hongbo Jiang Shudong Jin Mobile Ad Hoc and Sensor Systems(MASS), 2008

2. Outline • Introduction • Model selection • Prediction model • Energy-efficient algorithm • Algorithm design • Experiment results • Conclusion

3. Introduction Nowadays, sensors’ processing power and compacity have been increased. Sophisticated algorithm on sensors like predictor become possible. A predictor use past input values from sensors to perform prediction, implys sensors do not need to transmit the data if they differ from a predicted value by less than a threshold Simple prediction way is that all sensor nodes send data to base station and only base station do the predictor training and prediction, despite their increasing computing capacity. But it cost high transmission energy, wireless bandwidth and potential high latency.

4. Introduction - localized prediction Base station Clustering-based localized prediction divide the network into clusters and use a cluster-head(also a sensor node) to maintain each cluster member’s history data. Localized prediction may highly energy-efficient due to the reduction of routing path. There is a trade off between communication and computation since prediction computation cost is not negligible in localized prediction.

5. Localized Energy-Aware Prediction (LEAP) • Localized Energy-Aware Prediction (LEAP) • Cluster head – receive data that selective reported by all cluster members and perform local prediction • Cluster member – also perform prediction and transmit to cluster head only if they are not within a specified error bound

6. Prediction model selection Autoregressive(AR) model can be calculated by Yule Walker equation or least square method

7. Prediction model selection Lemma: Given a linear predictor P, a m-step prediction error is less than m times a one-step prediction error .m-step prediction error is . Given an error bound , the predictor can provide confidence level of is cummulative distribution of Gaussion white noise SDVis the standard deviation

8. Energy-Efficient Algorithm Selection • Tradeoff • Communication cost: Without local prediction, all sensor nodes will send original data values to the cluster head. • Computation cost: With local prediction.

9. Energy-Efficient Algorithm Selection Theorem: If the error bound satisfies the scheme with local prediction is more energy-efficient The variance is unknown, use correlation coefficient to represent m-step prediction error Eliminate , we have condition (1), if error tolerence and correlation coefficient satisfythe scheme with local prediction is more energy-efficient

10. Algorithm Design - cluster head Pseudo-code description Cluster head will continuously receive data from cluster members to update the set of history data, or when no data values are received, will use the predicted value instead of update.

11. Algorithm Design - cluster head Periodic process to determine algorithm selection, with or without local prediction.

12. Algorithm Design - cluster member • Each cluster member maintains a set of history data of its own. • Local prediction disable: transmit the data values • Local prediction enable: perform prediction on each data value, if not within the error bound, still have to send the value to the cluster head

13. Algorithm Design – sleep/wake scheduling For some applications may tolerate a few missing value not within error bound. If confidence level(or having data values within the error bound) is very high. There is no need for the nodes to stay awake to obtain data values.

14. Algorithm Design – sleep/wake scheduling Disable local prediction as default. When cluster members is awake, the cluster head checks if the member's data values are within the error bound with high probability. If yes, send a message to power off the member.

15. Algorithm Design – sleep/wake scheduling The condition (2) should be the condition is higher than ,i.e.

16. Algorithm Design – sleep/wake scheduling To remain accurate prediction, periodic but infrequent collection from cluster member is still necessary. Here use a heuristic solution: let be the time interval between two consecutive report. We set a sleep duration of , when a member wakes up, it will continuously perform data reading (and possibly reporting) for the next time. is initially set to , increased if condition (2) holds, or decreased if it does not.

17. Experiment result • Data-set: Intel Berkeley Lab Data • 54 nodes spread around the lab • Temperature data within one week • Nodes close to sink set as cluster heads • Nodes report data every 30 seconds • 10mJ/Byte, 80mJ per message(assume the value is 8 bytes double precision number) • 13 cluster heads and 40 cluster members, only measure the consumption of cluster members and LEAP with control of sleep, set confidence level

18. Experiment result k represents the ratio between transmission/prediction energy consumption.(i.e. k=10 and 100, prediction energy consumption is 8 and 0.8 mJ per value)

19. Conclusion • LEAP is a energy-aware data collection approach • clustering-based: sensor nodes form clusters and cluster head collect and maintain data values • prediction-based: energy-aware prediction is used to find the subtle tradeoff between communication and prediction cost.