CONTROL COMPLEXITY FROM THE SOURCE�A CASE STUDY ON SMART ENERGY WASTE SENSING

Yongcai Wang, Amy Yuexuan Wang

Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University, Beijing, China

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June 29, 2012, in HPC2012, Cetraro, Italy

THE SOURCES OF “BIG DATA”

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Ubiquitous Sensing Systems

Social Networks Scientific Simulation Systems

Complexity of Storage

Complexity of Computing

Big Data Redundant

Correlated

Control Complexity From the Source

Unnecessary

SENSE SMARTLY AND EFFICIENTLY WHILE SATISFYING

THE REQUIREMENTS

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To sense the environment of a city:If 1000 sensors can provide enough information, it is not necessary to deploy 2000 sensors.

But the problem is not trivial, because: 1.

We don’t know how many is “enough”.

2.

Where to place the sensor? The amount of sensors is general related to deployment strategy.

A WASTE DISCOVERING CASE TO ANSWER ABOVE PROBLEMS

Waste Discovering ProblemCompressive Sensing

Fundamentals of Compressive Sensing

Compress Sensing Framework for Waste Discovering.

Recent Results in CS-based Waste Discovering:

Compressive State Tracking for Massive Appliances .

Compressive Occupation Sensing

Conclusion4

BACKGROUNDIn US:

Buildings use 20% of all energy

($370B in 2005 in

US)

30% of energy consumed in buildings is wasted (US2008).

In China

Buildings will use 1,300 billions kwh in 2020[1].

More than 30% is wasted [1].

Electrical energy waste by buildings equals to 200 billions RMB per year.

Supply Demand

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WASTE DISCOVERING IS DIFFICULT IN TRADITIONAL

ENERGY AUDITING

Energy Consumption Pattern of a Working Day

Energy Consumption Pattern of a week

Where and when is the

waste?

We don’t know

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Know in real-time the human persistence and position.

THE ERA OF INTERNET OF THINGS (IOT) ENABLES ENERGY WASTE

DISCOVERY

Berkeley ACme rev.A Tweet-a-Watt Berkeley

ACmeX2

EnergyHubEnistic

Smart AC Sensor Networks

Know in real-time how each WATT is consumed.

Smart Meters

IOT for Energy Sensing

IOT for Human Sensing

Mobile phones Camera Locating SystemUltrasonicInfrared RFID

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PROCEDURE FOR ENERGY WASTE DISCOVERY

Appliances 1-N

Time 1-T

Mining & Learning Problem

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1) The real-time working states of the electrical appliances :

2) The real-time user demands to the electrical appliances.

3) Waste Judgment

State Monitoring

Occupation Sensing

THE CURSE OF DIMENSION EXPLOSION

State space increases exponentially with the number of the electrical appliances:

The On/OFF states of N appliances have 2N space size.

The user using states of N appliances have 2N space size.

Electrical appliances are massive, i.e., N is large.

An office building have more than thousands of electrical appliances.

An community may have millions of electrical appliances.

The dimension curse in Energy Waste Discovery.

A bigger N means extremely “Big Data”

and “Hard

Problem”.

Unbearable sensing cost.

Unbearable data processing and computation cost.

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SPARSITY: THE TRICK TO TACKLE BIG DATA

A n-dimensional signal called k-sparse, if it has only k nonzero elements.

A 3-sparse Signal

10:01

10:02

10:02

State switching

events are sparse

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The on/off states and the using

states of the appliances change sparsely within a short observation interval.

COMPRESSIVE SENSING

Compressive Sensing: sense high-dimension sparse signal by low dimension measurement. [Donoho2006,

Candès2006

].

If X

RN

is k-sparse, k<<N, Y

Rm

=AX

is a m-dimensional measurement vector, then

X

can be precisely recovered from Y,

when

m>O(k log(n/k)) and A

Rm*n

satisfies RIP property.

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A COMPRESSED INTRODUCTION TO COMPRESSIVE SENSING

Sampling Compress (optional)

Transmit or Store

Nyquist rateN

Receive Decompress (optional)

High Sensing, Transmission and Storage Cost

N- dimension

signal

N- dimension

Dynamics

Sensor Network

Data Center

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Traditional Sensing is not Efficient

A COMPRESSED INTRODUCTION TO

COMPRESSIVE SENSINGHigh

dimension signal

Many info is temporally or spatially correlated

It is k-sparse, where k<<N

Compressive Sensing

m=O(klog(N))<<N

Transmit or Store

Receive ReconstructHigh

dimension signal

K sparse representation

Efficient sensing, transmission and storage

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THREE STEPS OF COMPRESSIVE SENSING

x

Nk sparsek<<N

1. Find the basis, i.e. basis to convert X

to

sparse.

2. Design the Compressive sensing matrix

N

S1. Sparse Representation

2. Compressive Sensing

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3. Design Recovery Algorithm: to recover S

from y, ,

f , ,( )3. Signal Reconstruction

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THREE STEPS OF COMPRESSIVE SENSING

A COMPRESSIVE SENSING FRAMEWORK FOR ENERGY WASTE DISCOVERING

States of Appliances

User Demands

1.

Detecting and Tracking The Energy Waste

EnvironmentFactors

CS-based State Monitoring

CS-based Environment

Sensing

CS-based Occupation

Sensing

Mining Techniques

Goals:

Sensing Technique

s

Factors

Electrical Appliances

Users EnvironmentsPlayers

Waste Detection

Our Research Focus

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1. METER DEPLOYMENT OPTIMIZATION FOR CS-BASED STATE MONITORING

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Circuit/breaker panel 

level power monitoring

Load Tree

Mains power 

monitoring

ACme: plug load energy 

monitor and controller

Our Result: How to Deploy The Minimal Number of Smart Meters to Monitor The On/OFF States of Appliances:

1.

Prove minimizing the power meter deployment on the load tree while guaranteeing the decoding accuracy is NP-hard.

2.

Propose a 2-approximation algorithm for minimizing the number of meters while satisfying the decoding requirement.

2. HMM-BASED SEQUENTIAL STATE RECOVERY ALGORITHM

HMM(Hidden Markov Model) is generally used in decoding the state changes of the appliances.

Traditional Viterbi algorithm has complexity. It is terrible. For example, when N=1000, the

computation cost will be t22000

. But we have massive

appliances. Complexity reduction is necessary.

s1 s2 s4s3 sK

y1 y2 y3 yT

State Transition Probability

Observation Probability

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We prove the complexity of FSD is , where n<N, U

is the upper bound of the simultaneous

switching events.

3. COMPLEXITY REDUCTION BY A FAST SEQUENTIAL DECODING

ALGORITHM1.

Parallel Processing.

The load tree is spitted into a forest of mono-meter trees.

State decoding is run in parallel in each mono-meter tree.

2.

Offline state sorting. The most computation intensive step is run offline for only once.

3.

Utilizing event sparseness. Online binary search + backtrack with bounded state differences.

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4. PERFORMANCE OF CS-BASED STATE MONITORING, ACCURACY VS. COST

SAVING

We can save deployment cost remarkably while We can save deployment cost remarkably while preserving good state tracking accuracy. preserving good state tracking accuracy. [Wang12][Wang12]

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CS-BASED OCCUPATION SENSING

Problem

Sense the occupation states of users by very limited number of binary sensors.

Model

Stochastic sensing model, which is proved solvable by CS (compressive sensing).

Algorithm

Convert the problem to a knapsack problem, and propose an route-based greedy algorithm to approximate it.

0 0 0 0 1 0 1 0 0 …Occupation vector

Binary occupation sensorUser

[Song2012, in progress]

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CONCLUSION In appliance state monitoring case, we learn:

1.

By compressive sensing, we can save the sensing cost remarkably.

Avoiding redundant big data.

Avoiding the transmission, storage cost.

2.

By exploiting the event sparseness, we can design polynomial time decoding algorithm.

Better scalability.

Low computation cost

Low Storage Cost.

Many information in the real world is temporally or spatially correlated, which is sparse in some domains (such as FFT).

Many work can be carried out for smart sensing to control the complexity of data source.

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THANKS. Q&A.

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