Data Fusion Methods

7/31/2019 Data Fusion Methods

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Data Fusion MethodsNeural Network

Probabilistic model

Time series

Kalman filtering

Other methods:

A-distributed database query methods

B-mobile agent based data fusion

C-processing according to message

categories 11


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Neural Network

neural-networks use unsupervised learningmethods forcategorization of the sensoryinputs

No need for uncertainties or model of sensors

Combine NN and discrete waveform transform(initial data-processing)to extract features

data robustness against malfunctioningsensors

Combine GROUP and Neural network

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Probabilistic modelpredicts the monitored data by maintaining a

pair of probabilistic models over the networkattributes, one distributed in network and

another at base station.

When the gap between predicted data andsensed data is within the required error

bound, the node does not need to report thesensed data, the base station uses thepredicted data as sensed data.

the better the user can tolerate the error, the

more energy can be saved. 55


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Probabilistic modelwhere users need not only the result of the

object, but also all the information aboutevery node.

Where need to get the information as muchaspossible without too much network cost

data is correlative in temporal or space

data within the tolerance value will not betransmitted, So the accuracy is lowest

energy saving depend on the model and

tolerance value. 66


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Time series modelGood scalability

Algorithm divided into three phases: history

data gathering, data predicating and datachecking

Time series models like autoregressivepredicative algorithm can realize more

accurate predication. simpler and moresuitable for WSNs.

little data needs to be transmitted no matterhow high frequency the sample rate is,(save

energy. ) 88


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Kalman FilteringPredict state of dynamic system( position ,velocity )in noisy environment.

reduce the affect of white Gauss noise, so the datais more accurate, while the others are of loweraccuracy

reduce the impact ofabnormal data (environmentinterference or the problem of the sensor node), soas to get more accurate information.

Transmits fused data instead of the raw data (saveenergy).

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Distributed Kalman

Filtering Decomposes KF into n collaborative micro-KFs with

local communication

Distributed algorithm for Kalman filtering

Applicable in large-scale sensor networks withlimited capabilities (e.g. local communication,routing)

Excellent robustness properties regardingvarious network imperfections, including delay,link loss, network fragmentation, andasynchronous operation

Assumes identical sensing models across WSN1111


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Comparision

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Other methods:

A. Distributed databasequerynetwork which the user queries informationaccording to requirement

Consider the WSNs as a distributed database,each node stores very little data of database,

query result returns to the user in the form ofmulti-hop: such as TAG & TINA

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TAG: a Tiny AggregationService for Ad-HocSensor NetworksTAG adopts a query grammar similar to SQL;

COUNT, MIN, MAX, SUM, and AVERAGE.Flooding: sink broadcaststhe request

message, If the message is received not forthe first time, it will be dropped, otherwise the

node sending this message will be taken as itsparent until the requested message reachesevery node in the network, meanwhile anaggregation tree is formed. In the result

returning phase, data is fused during the 1414


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TiNA: A Scheme for TemporalCoherency-Aware in-NetworkAggregationif the query rate is very high or very dense

communication, the network will not be ableto provide exact answers at the specified rate.

reduce the amount of information transmittedand to improve quality of data when not allsensor readings can be transmitted (reducepower consumption by up to 50% without any

loss in the quality of data.)

Data will not be reported if the gap betweenthe data and the one just report is within thetolerance value.(exploiting end-user toleranceto temporal coherency) 1616


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transmitting a single bit of data is equivalent

to 800 instructions.a AA battery pack will allow a sensor to send

5.52 million messages which is equivalent toone message per second every day for about

two months 1717


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B. Mobile Agent Based

Data Fusionsensed data is stored in local node.

mobile agent migrates among the nodes

according to the requirement, and gets thedata of the nodes one by one. Then the agentfuses the data with previous results.

Less demand for bandwidth, lower energy

consumption and latency.

If combined with mobile agent routingstrategy, it could reduce congestioneffectively.

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C. Processing According

to Message Categoriesclassify the message into emergency one and

non-emergency one

For emergency one, the nodes forward itimmediately to meet the requirement ofreal-time system.

in the abnormal detection network to be

reported in time.

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D. Data Compression

for the applications requiring all the raw data

node compresses the data before

transmitting, and the sink node decompressesthe data after receiving it. (some energyconsumption, but better than sending rawdata)

data compression techniques , such as sortingencode based data compression algorithm,relevance based compression algorithm, etc.

sorting encode based data compression:

some nodes value is expressed by other 2020


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Data Compression

Using joint entropy and bit-hop metric toquantify the size of compressed data and totalcost of joint routing with compression

respectively.

Three schemes: DSC, RDC and CDR.

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Distributed Source Coding (DSC): If the sensornodes have perfect knowledge about theircorrelations, they can encode/compress dataso as to avoid transmitting redundantinformation. In this case, each source cansend its data to the sink along the shortestpath possiblewithout the need for

intermediate aggregation. 2222


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Routing Driven Compression (RDC): In this

scheme, the sensor nodes do not have anyknowledge about their correlations and senddata along the shortest paths to the sink whileallowing for opportunistic aggregation

wherever the paths overlap. 2323


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Compression Driven Routing (CDR): As inRDC, nodes have no knowledge of thecorrelations but the data is aggregated closeto the sources and initially routed so as to

allow for maximum possible aggregation at2424


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DSC(Distributed Source Coding ) performs theideal bit-hop metric based on having knownthe correlations, and its bit-hop metric is the

lower bound for any possible routing schemewith lossless compression.

RDC outperforms CDR for low correlation(little data can be compressed) while CDRoutperforms RDC for high correlation.

Neither RDC nor CDR performs well forintermediate correlation. In this case, a hybrid

scheme can be adopted. 2525


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Conclusion and future

studyIn-network data processing is a unique

characteristic that differentiates wirelesssensor networks from conventional data

forwarding networks.

Classify routing schemes supporting datafusion in wireless sensor networks into threecategories: routing-driven, coding-driven, andfusion-driven

Chal1:obtaining data correlation, whichserves as the foundation for coding-driven

and fusion-driven approaches, is still a 2626


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future study

while the fusion process curtails dataredundancy, it also decreases the reliability ofrouting schemes. In lossy and uncertain

wireless environments, a certain degree ofredundancy must be preserved in order toachieve the desired fault tolerance andreliability. Embedding this consideration in the

routing design is desirable.

in-network fusion will inevitably introducedelay in routing schemes, due to either theprocessing itself or the waiting time for side

information (need to provide mechanisms for2727

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Data Fusion Methods

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