Prediction-based Monitoring in Sensor Networks: Taking Lessons from MPEG Samir Goel and Tomasz Imielinski Department of Computer Science Rutgers, The State University Of New Jersey ACM Computer Communication Review Vol. 31, No. 5, October 2001

Outline Background Model PREMON Experiment Conclusion

Background The compression techniques in MPEG-2 –Spatial compression –Temporal compression

Model Large, non-deterministic topology Cluster-based Limited energy Access points Location-aware

PREdiction Based MONitoring Update-mode Centralized approach A base station maintains the database of current reading of all the sensors in the sensor field.

Classes of Prediction Models Spatial –Reading at sensor X in time slot t is the same as reading at sensor Y during the same time slot. Temporal –Reading at sensor X in time slot t is 2 greater than its reading in the previous time slot Spatio-temporal –Reading at sensor X in time slot t is the same as the reading of sensor Y in the previous time slot. Absolute –Readings at sensor X in time slots t, t+1, and t+2 will be 32, 34, and 35 respectively.

Key Characteristics of PREMON Trades computation for communication –Cost(computation) << Cost(communication) Works well if one can tolerate: –“small” amount of errors in predictions –“some” latency in generating prediction models Applicable whenever correlation (temporal, spatial, or spatio-temporal) exists

The Framework Spatial-Temporal Assumption –All sensors within the fall within one cluster. –All sensors operate in the update-mode. The sensors in the

Visualization Monitoring may be seen as watching the snapshot images on a continuous scale

Visualization Monitoring may be seen as watching a video of sensed values

Prediction Operation Monitoring operation: –Initially, all sensors transmit their current reading to the base station –Subsequently, sensors transmit only when their readings change In the visualization: –Initially, the full image is transmitted –Subsequently, only the diffs from the previous image are transmitted  This is analogous to how MPEG encodes a video!

PREMON Apply block-matching algorithm to compute motion-vectors Translate motion-vectors into motion-predictions Frame#2 Frame#1 Frame#3 Is valid ?

Translating Motion-vectors into Prediction Models No-motion case (motion-vector: ): –Generate a Constant Value Prediction General Case (Motion-vector: ): –Generate a Movement Prediction

MPEG Analogy Sensor Field Base station update frames All sensors send data (I-frame) Sensors send updates when their value differs from predicted ones (P-frames) sensor predictions time

Differences between MPEG and PREMON Hard real-time requirements for MPEG Soft real-time requirements for sensor nets Limited energy for sensor networks The number of sensors is small compared to pixels The frame rate is an order of magnitude higher compared to PREMON Non-uniform placement

Architecture Processing at the Base Station –Collect updates from the sensors –Generate prediction model –Send the update –Send a set of prediction models (If the previous model resulted in fewer updates) When low on power, the base station may divide its cluster in spatial blocks and may only send average reading of each block to the access point.

Architecture Processing at the Sensor –Update-mode by default: send an update whenever the reading changes –Receive a prediction-model –After the prediction-model expires, revert back to update-mode.

Prediction Model Gridding –Interpolate or extrapolate the readings at grid points –Assign the closest sensor to a grid point –Or assign the average of the closest sensors –Transparent grid point

Prediction Model Divide the image into macro-blocks Block-matching and find motion vectors Transparent pixel matches any other pixel Only when the percentage of transparent pixels in a macro-block is above threshold

Prediction Model The base station –With 4 most recent frames, apply block- matching to frames 1 and 2 to get MVs. –For each MV, check frames 2 and 3, and 3 and 4.

Prediction Model If a motion vector “holds”, generate an absolute model based on it; otherwise, discard it. Their data is encoded in a more efficient way – depending on the type of sensors. The magnetometers output binary values: LOW/HIGH Only the coordinate of the largest rectangle of 1s is sent and only the prediction model within this range is valid. While no motion, send only one flag to indicate it.

Prediction Model Type –Absolute –Spatial –Temporal –Statio-temporal Model –Tuples( ) or a funciton Destination –A broadcast address, sensor id, or a spatial polygon TTL –Valid time

Experiments 4 MHz processor Radio: 10kbps 8KB program memory 512 bytes data memory Light sensor

Experimental Setup

One-dimensional version of the problem Base-station code fully resides in a mote Cases considered: –Case#1: Default mode: Sensors send their sensed values once every second –Case#2: Constant-value predictions only BS makes a constant value prediction if the value of a sensor doesn’t change for 2 consecutive frames. BS doesn’t transmit movement predictions –Case#3: Constant and movement predictions BS issues both constant-value and movement predictions BS makes a movement prediction based on two correlated motio-sensor readings

Constants Cost of transmission/bit = 1 µJ Cost of reception/bit = 0.5 µJ Cost of computing = 0.8 µJ per 100 instructions Update size = 11 bytes (Tu = 88 µJ, Ru = 44 µJ) Prediction size –Movement Prediction = 8 bytes (Tp = 64 µJ, Rp = 32 µJ) –Constant Value Prediction = 5 bytes (Tp = 40 µJ, Rp = 20 µJ)

Results 950mJ Case#1: default mode Case#2: constant-value predictions only Case#3: constant and movement predictions Summary of Results: - Case#3 performs 5 times better than case#1 - Case#3 performs 28% better than case#2

Conclusion Prediction-based monitoring paradigm can significantly increase energy efficiency Monitoring of sensor data may be visualized as watching a “video” and MPEG-2 algorithms may be adapted for generating predictions