1. Energy Efficient Data Collection In Distributed Sensor Environments Qi Han, Sharad Mehrotra, Nalini Venkatasubramanian {qhan, sharad, nalini} @ics.uci.edu QUASAR Project University of California, Irvine School. of Information & Computer Science

2. 2 Ubiquitous Sensor Environments Sensor Networks Battlefield Monitoring Habitat Monitoring Earthquake Monitoring Oceanographic current monitoring Medical Condition Monitoring Traffic Congestion Detection Target TrackingIntrusion Detection Video Surveillance Generational advances to computing infrastructure –sensors will be everywhere Continuous monitoring and recording of physical world and its phenomena –limitless possibilities New challenges –limited bandwidth & energy –highly dynamic systems System architectures are due for an overhaul –at all levels of the system networks, OS, middleware, databases, applications

3. 3 Quasar (Quality Aware Sensing Architecture) Hierarchical architecture –data flows from producers to server to clients periodically –queries flow the other way: if client cache does not suffice: –query routed to appropriate server if server cache does not suffice: –access current data at producer –this is a logical architecture producers could also be clients a server may be a base station or a (more) powerful sensor node servers might themselves be hierarchically organized the hierarchy might evolve over time server client client cache server cache and archive producer & its cache QUERY FLOW DATA FLOW

4. 4 Quasar: Observations & Approach Applications can tolerate errors in sensor data –applications may not require exact answers: small errors in location during tracking or error in answer to query result may be OK –data cannot be precise due to measurement errors, transmission delays, etc. Communication is the dominant cost –limited wireless bandwidth, source of major energy drain Quasar Approach –exploit application error tolerance to reduce communication between producer and server and/or to conserve energy –two approaches Minimize resource usage given quality constraints Maximize quality given resource constraints

5. 5 This Paper… Explore data collection protocols for sensor environments that exploits the natural tradeoff between application quality and energy consumption at the sensors –Consider a series of sensor models that progressively expose increasing number of power saving states –For each of the sensor models considered, develop quality-aware data collection mechanisms that ensure quality requirements of the queries while minimizing the resource consumption

6. 6 Data Collection Framework If query quality tolerance satisfied at server –Answer query at the server Else –Probe the sensor –Sensor guaranteed to respond within a bounded time D consumer-initiated update … source-initiated update consumer-initiated request query Q 1 (A 1,D) query Q m (A m,D) i =[l i,u i ] sensor s i Imprecise data representation

7. 7 Abstract Sensor States radio modesensor state 1-radio node2-radio node Tx on, Rx offTx on, Rx onactive (a) Tx off, Rx onlistening (l) Tx off, Rx offsleeping (s)

8. 8 Problem Statement Objective: minimize sensor energy consumption in the process of answering all queries –Given user queries with varying accuracy constraints and latency bound Formally stated: Issues –How to maintain the precision range r for each sensor Larger r increases possibility of expensive probes Small r wastes communication due to source-initiated updates –When to transition between sensor states Powering down might not be optimal if we have to power up immediately Powering down may increases query response time

9. 9 Our Approaches We solve the energy optimization problem by solving two sub-problems –Optimize energy consumption by adjusting range size under the assumption that the state transition is fixed –Optimize energy consumption by adapting sensor states while assuming that the precision range for sensor is fixed Progressively expose increasing number of sensor power saving states –AA: Always Active –AL: Active-Listening –AS: Active-Listening –ALS: Active-Listening-Sleeping

10. 10 The AL(Active-Listening) model listeningactive T a after processing last source-initiated update or probe Upon first source-initiated update or probe

11. 11 Analysis of the AL Model re-write sensor energy consumption equation: sensor state transition probabilities steady state probabilities: sensor energy consumption is minimized when normalized sensor energy consumption: probabilities of source- or consumer- initiated updates:

12. 12 Range Size Adjustment for the AA/AL Model Optimal range can be realized by maintaining the probability ratio Can be done at the sensor Assuming that is the ratio of consumer-initiated update probability to source-initiated update probability: for source-initiated update: with probability min{,1}, set r = r(1+ ); for consumer-initiated update: with probability min{1/,1}, set r = r /(1+ );

13. 13 The AS Model (Active-Sleeping) sleeping active Upon first source-initiated update or after T s without traffic T a after processing last source- or consumer-initiated update

14. 14 The ALS Model (Active-Listening-Sleeping) sleeping listening active Upon first source-initiated update or after T s After T l without traffic Upon first source-initiated update or probe T a after processing last source-initiated update or probe

15. 15 Range Size Adjustment for the AS/ALS Model Not possible to express the ratio in terms of other parameters –Need to monitor parameters such as K 1, K 2 etc. Sensor side –Keep track of the number of state transitions of the last k updates –Piggyback the probability of state transitions with the K th update Server side –Keep track of the number of sensor-initiated updates and probes of the last k updates –Upon receiving the K th update from the sensor Compute the optimal precision range r Inform the sensor about the new r

16. 16 Adaptive Sensor State Management Consider the AS model for derivation of optimal T a to minimize energy consumption –Assuming (t) is the probability of receiving a request at time instant t, the expected energy consumption for a single silent period is –E is minimized when T a =0 if requests are uniformly distributed in interval [0, T a +T s ]. In practice, learn (t) at runtime and select T a adaptively –Choose a window size w in advance –Keep track of the last w silent period lengths and summarizes this information in a histogram –Periodically use the histogram to generate a new T a

17. 17 Adaptive State Management (Cont.) c i : the number of silent periods for bin i among the last w silent periods estimate by the distribution which generates a silent period of length t i with probability c i /w T a is chosen to be the value tm that minimizes the energy consumption as follows: bin 0 bin 1 bin 2 bin n-1 t 0 t 1 t 2 t 3 …… t n-1 t n =T a +T s c0c0 c1c1 c2c2 c n-1

18. 18 Performance Study Modeling sensor –Sensor values: uniformly from the range [-150, 150]; perform a random walk in one dimension: every second, the values either increases or decreases by an amount sampled uniformly from [0.5,1.5]. Modeling queries –query arrival times at the server are Poisson distributed mean inter-arrival time = 2 seconds. –each query is accompanied by an accuracy constraint A A=uniform( A avg (1- A var ), A avg (1+ A var )) A avg =20 (average accuracy constraint) A var =1 (accuracy constraint variation)

19. 19 System Performance Comparison of Proposed Sensor Models

20. 20 Impact of Ta adaptation on System Performance

21. 21 Impact of Range Size Adaptation on System Performance

22. 22 Conclusions Explored the tradeoff between sensor data accuracy and energy consumption for sensor data collection in distributed sensor environments Both theoretical analysis and experimental results validated the effectiveness of our approaches –The AS model consumes the least amount of sensor energy –Our proposed strategies of adaptive sensor state transition reduce energy consumption to a great extent –Optimized range size adjustment works effectively with corresponding sensor models and saves more energy than using static range or instantaneous values