Energy Efficient Data Collection In Distributed Sensor Environments

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

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

Quasar (Quality Aware Sensing Architecture) client cache server cache and archive producer & its cache QUERY FLOW DATA FLOW client • 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 3

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 4

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 5

Data Collection Framework query Q 1 (A 1, D) source-initiated update sensor si query Qm (Am, D) … consumer-initiated request consumer-initiated update i=[li, ui] Imprecise data representation • 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 6

Abstract Sensor States radio mode 1 -radio node 2 -radio node Tx on, Rx off Tx on, Rx on sensor state active (a) Tx off, Rx on listening (l) Tx off, Rx off sleeping (s) 7

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 8

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 9

The AL(Active-Listening) model Upon first source-initiated update or probe listening active Ta after processing last source-initiated update or probe 10

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

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+ ); 12

The AS Model (Active-Sleeping) Upon first source-initiated update or after Ts without traffic sleeping active Ta after processing last source- or consumer-initiated update 13

The ALS Model (Active-Listening-Sleeping) sleeping After Tl without traffic listening Upon first source-initiated update or after Ts Upon first source-initiated update or probe active Ta after processing last source-initiated update or probe 14

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 Kth update • Server side – Keep track of the number of sensor-initiated updates and probes of the last k updates – Upon receiving the Kth update from the sensor • Compute the optimal precision range r • Inform the sensor about the new r 15

Adaptive Sensor State Management • Consider the AS model for derivation of optimal Ta 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 Ta=0 if requests are uniformly distributed in interval [0, Ta+Ts]. • In practice, learn (t) at runtime and select Ta 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 16

Adaptive State Management (Cont. ) • ci : 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 ti with probability ci/w • Ta is chosen to be the value tm that minimizes the energy consumption as follows: c 1 c 0 bin 0 t 0 cn-1 c 2 bin 1 bin n-1 bin 2 t 1 t 2 t 3 …… tn-1 tn=Ta+Ts 17

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( Aavg(1 - Avar ), Aavg(1+ Avar )) • Aavg =20 (average accuracy constraint) • Avar=1 (accuracy constraint variation) 18

System Performance Comparison of Proposed Sensor Models 19

Impact of Ta adaptation on System Performance 20

Impact of Range Size Adaptation on System Performance 21

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 22