DF AMS Proposed Solutions for Multi Sensor Data Fusion in Wireless Sensor Networks

DF-AMS: Proposed solutions for multi-sensor data fusion in wireless sensor networks Duong Viet Huy Science, Technology and Environ ment Depart ment Ministry of Culture, Sports and Touri

DF-AMS: Proposed solutions for multi-sensor data fusion

in wireless sensor networks

Duong Viet Huy Science, Technology and Environ ment Depart ment

Ministry of Culture, Sports and Tourism

Hanoi, Vietnam huy.duongviet@gma il.co m

Nguyen Dinh Viet University of Engineering and Technology Vietna m National Un iversity, Hanoi

Hanoi, Vietnam vietnd@vnu.edu.vn

Abstract— When using multiple sensor nodes in wireless sensor

networks (WSNs) for monitoring (measuring) parameters of

the target and sending the result to the base station (BS ), data

redundancy is an inevitable problem The measured data often

contains the same information, and sending redundant data to

BS causes the waste of energy of sensor nodes and the risk of

congestion Multi-sensor data fusion in WSNs is a technology

of gathering and processing data applied from node to BS It

improves the performance of surveillance systems by allowing

the obtained sensed information from multiple sensor nodes

aggregated to one unified format data packet to send to BS to

make decision In this paper, we propose a solution namely

DF-AMS (Data Fusion - Average Median Sampling) for

sampling and fusing data from the sensor nodes in the cluster

to optimize the energy conservation of sensor nodes in clusters

and cluster head node

Keywords- wireless sensor networks; data fusion; data

aggregation; median sampling; DF-AMS

I INT RODUCTION (H EADING 1)

Data fusion (or data aggregation) has been a key proble m

in the monitoring system using sensor nodes increasing in

size (nu mber of sensor nodes, the scope of monitoring) and

comple xity (nu mber of para meters being monitored, fineness

of measurement) Typically, sensor nodes are powered by

batteries with limited capacity thus energy saving of node

and network are a lways priorit ised in WSNs research

There are many advantages in using multip le sensor

nodes to track a target such as [1]: tolerance of error

(because the results of rema ining non-error sensors could be

used); better stability and reduction in perceived noise data

(as many sensor nodes will increase the dimension of

measurement space); multiple independent measurements

fro m multip le sensors improve spatial resolution

measurements and provide a better source of input data;

mu ltiple sensors ensure the continuity of available data, and

improve the ability of detection of missed-track events

However, DF from mu ltiple sensors also poses many

challenges [1] Current technology cannot create sensors

which are able to record accurately absolute change of

events The data processing in the transmission step from

target to BS cannot fix the previous processing step Results

of data fusion from mult iple sensor for sensing the same

event/object may be worse than data of one single good

sensor There is no algorith m that can satisfy all criteria It is

very difficult to quantify the overall quality of data compared

to the actual situation because it is difficult to quantify all events from sensing signal and this data is transmitted through a long chain of intermediate nodes to the destination (BS)

Therefore, DF in WSNs is studied to increase the accuracy of the conclusions deduced from uncertainty sensing data while saving the energy consumption of sensor

as well as the whole network

The model uses multiple sensor nodes (each node can monitor many parameters) to track 1 target then send the result to the destination station (BS) Th is causes the wasted energy of sensor nodes and increases the risk of network congestion due to transmission of redundant data (Fig 1)

DF on the intermediate nodes between the first sensor node (directly tracks the target) to the BS helps save energy by limit ing the movement of packets on the network (but still preserve the integrity of monitoring data) There a re many studies in DF such as Kalman filtering [2], De mpster-Shafer [3], fu zzy logic [4], neural network [5], information entropy [6] ect Most of DF studies have applied to WSNs with clustering, each cluster has a sensor node selected as the cluster head - CH, it processes data from all me mber nodes

in this cluster and sends data fusion result to BS

In this paper, we propose a solution namely DF-AMS which includes two phases: sampling sensor nodes (in clusters) by the properties of the sensor nodes; CH performs data fusion of parameters sensed by selected sensor nodes with the application of median method and ma ximu m method The paper consists of two main parts: the first part presents scientific basis and proposed solution of DF-AMS;

Fig 1 Data fusion model with n sensor node, each sensor node

measure l parameters (l≥2) of the target

1 2 l

1 2 l

1 2 l

S1

CH

1 2 l 1 2 l 1 2 l

BS

Sensor node

Target

Base Station

Sensing data

Data

2015 Seventh International Conference on Knowledge and Systems Engineering

2015 Seventh International Conference on Knowledge and Systems Engineering

2015 Seventh International Conference on Knowledge and Systems Engineering

the second part presents analysis of effic iency and simulation

evaluation of the proposed solution using NS-2 network

simulator

II DF-AMSSOLUT ION

A Concepts

1) Properties conditions

The Rough set theory [7] has been studied and proved to

be applicable to multi-sensor data fusion in wireless sensor

networks [8] The paper will apply the concept of conditional

attributes to model "knowledge" data based on the semantics

of sensor nodes at the time of DF, inc luding: the semantics of

sensor nodes (such as distance, remaining energy, ect.) and

semantics of data sense (such as accuracy, number of packets

to transmit, ect.) Suppose that a sensor network has n nodes,

each sensor node is characterized by the combination of m

condition attributes, then the semantic data table has n rows

and m colu mns CH bases on this condition attributes to

select sensor node, then transmit or fuse data of this

particular sensor node to the BS

2) Data attributes, sensor node attributes

Data attributes and sensor node attributes are conditional

attributes which are used on the semantic aspects, meaning

of sensor nodes at the time of DF inc luding physical

characteristics and properties of the sens ed data of the target

The number of data attributes, sensor node attributes can be

changed and quantified by different values depending on the

method and the level of required accuracy in modelling

sensor network by data attributes Data and sensor node

attributes can be used to model sensor networks at a certain

time as an information system reflecting (assessment,

describing) the current state of the network to serve different

purposes

In the multi-sensor data fusion problem presented in this

paper, data attributes and sensor node attributes are

quantified by the values for the most accurate description of

sensor nodes and sensing data at the time just before

imple menting of data fusion Data attributes and sensor

nodes attributes are used as inputs in selecting the sensor

nodes (output) based on attribute information

3) The average value, the median value

For wire less sensor networks, sensor nodes are often

scattered randomly which results in different distance values

and the relative distances between sensor nodes are often not

equal During network operation, the sensor nodes process

and transmit electromagnetic waves leading to the loss of

energy of nodes When a node transmits data to the

destination node by radio waves, it has to amplify radio

transmitting power by a function of the square of the distance

[9], and the energy consumption of node exponentially

proportional to the distance between them At a point,

re maining energy of sensor nodes in the network usually

have different values If the energy is arranged increase (or

decrease), the separation between two random node is not

uniform When the network starts operation, the energy of

the nodes is full and the variation is small, the median value

is often greater than the average value Conversely, when

sensor nodes operate longer, the energy difference is larger and the median value is smaller than average In this study the node with the highest rema ining energy is chosen to transmit data Therefore, we can choose flexibly the median value if it is greater than the average value, or conversely Several studies have applied solutions Sum, Ma x, Average, Median to multi-sensor data fusion, for exa mple in [10, 11, 12] Data fusion in the cluster is the role of CH The sensor nodes in the cluster send data to the CH alternately in round, frames and time slots in accordance with CDMA and TDMA access In one round, total number of data packets that CH received fro m the nodes in the cluster are usually not much different Therefore, DF solution with median value and mean value can have the similar results With the current measurement by sensor technology, it is difficult to re ly on one sensor node which has the largest measured value (Ma x) Therefore, we use the combined values of median, average, ma x to support each other to increase reliability and improve energy efficiency

B DF-AMS 1) Sampling

A WSN can be divided into several clusters, in this paper, the term c luster is understood as the sub-WSN which cannot be further subdivided and data fusion will be applied

to this cluster We use one level data fusion model, means that the CH has only one intermediary ro le node between the sensor in clusters and BS (Fig.1) The sample was selected

on both data transmission stages: First, in n sensors of cluster, select q sensor node (q n) based on the remaining

energy of sensor node CH, then require q sensor node to

send data to CH; Second, at CH, each q sensor is a sensing data set (each set with k elements, each element is one

parameter measurement target) For each para meter, CH

chooses q/2 (if q is even) or (q+1)/2 (if q is odd) sensor fuse

the data into data set about the target and send this result to the BS

Suppose CH has selected q node in n node of cluster satisfying the conditions resulting data fusion with q sensors which have similarly good (or better) results with n sensors

We use the remaining energy properties of sensor nodes to select suitable sensor node immed iately after the cluster is established CH can determine the energy of nodes and is arranged in ascending

ES = {ES 1 , ES 2 , ES k ,ES n-1 , ES n }, ES 1 =Min, ES n =Max.

Let E avg is average remain ing energy, E Med is median value of n sensors in the cluster,

n ES E

n i

1













(2)

Let SDF I = {S i | 1 i n, ES i ≥ E Avg or ES i ≥ E Med } a

collected sensor which satisfied (1) or (2) and is selected first

ES (n+1)/2 i f n is odd

(ES n/2 + ES (n+1)/2 )/2 if n is even

E Med=

by CH, k is the number of nodes which is not selected by CH

(kn), q is the number of nodes (cardinal number) of SDF I,

2) Data Fusion

With SDF.ii SDFI, 1  ii  q, VSDF.ii is the set of l

measurements parameter values of sensor S ii where VSDF.ii =

{VSii.M1, VSii.M2,…, VSii.Ml } and l≥2 Then, table data received

(at CH) fro m q sensor quantitative measurements of l

parameters of the target is a matrix size (q x l) in Table 1

TABLE 1 – MEASURED DAT AAT DATAFUSION

Sensor

node

Measurement parameters

In this paper, we assume that the higher the amount of

data (packets / data) which sensor nodes produce the more

accurate and more knowledge of the target properties

Suppose, for each parameter, the sensor can measure the

number of d iffe rent levels This is consistent with the fact

that although sensor manufacturers strive to measure the

value change to reflect the best target to ensure the smooth

and asymptote to the variation of the target to be monitored

However, it is very difficult to manufacture this sensor

Suppose, for each parameter (M 1 , M 2 ,…, M l ), sensor can

measure different level, in Table 2 A value VSii.Ml in

Table 1 is getting a VX in Table 2

Fro m Table 1, sorted ascending S DF.q measured values of

M l respectively

Let Med.S q.M1 = V SDF.µ.Ml be M l median value at median

position µ of q sensor, where µ=(q/2)+1 (if q is even) or

µ=(q+1)/2 (if q is odd) SDF IIis set of sensor node satisfying

conditions V SDF.ii.Ml ≥ Med.S q.M1 (µii q ) SDF IIis a matrix

size (µ x l) (4)

Let Avg(M l , µ) be a M l average value of µ sensor node

Let Vmax.q Ml = Max (V SDF.q.Ml ) be M l ma ximu m value of M l

parameter measurement of q sensor node (5)

Then, CH sends sensing data to BS (including l values)

about the target at time of DF as follows:

V DF.Mj sent = (Avg(M l , µ) + Vmax.q Ml )/2 (6)

3) DF-SM algorithm a) Convention

Sign Signification

S i Sensor i th of cluster (1in)

ES i Remaining energy of S i

E Select Energy landmark for reference/select SDF I Set of sensor with minimum energy at E Select

q Cardinal number of SDF I , q = |SDF I |

S DF.ii Sensor ii th of SDF I (1iiq)

M j Parameter j th (1jl) Number of measurement level of M l

SDF II Set of sensor from µ, |SDF II |=µ Med.S q.M1 M l value at µ

Avg(M l , µ) M l average value of µ sensor node

V DF.Mj sent M j last results that CH sent to BS b) Data processing model

The algorith m consists of two ma in processing phases

as shown on Fig 2 Phase 1, immed iately a fter the c luster is

established, based on the re main ing energy of sensor node,

CH selects sensor node with more energy than average (or

med ian) to be in SDF I, only the selected nodes are allo wed

to continue sending data to the CH in ne xt steps

Phase 2, just before the end of the cycle, CH selects M l

measure ment results of the q sensor nodes and chooses the corresponding med ians M l The selection of node based on

med ian values of M l has SDF II (with µ sensor node) With each parameter M l, CH will fuse the data by average values and ma ximu m value, then send this result to the BS

TABLE 2 – LEVEL, MEASURE VALUES OF THE PARAMETERS

Le vel

Value of the paramete r measurement

(each parameter m ay have a different level)

X

CH sorted by energy

( n nodes)

CH

SDF I

(q nodes)

Phase 2

M 1

Median M 1

SDF II

( µ) M 1 M 2 … M l

AVG MAX

M 1 sent

Base Station (BS)

M 2 sent M l sent

…

…

…

Phase 1

…

E Avg / E Med

M 2

Median M 2

M l

Median M l

…

Fig 2 Data processing model DF-AMS

c) Algorithm

1 Set n = num_cluster_nodes; SDF I = Ø

2 For {set i 1} {$i <= $n } {incr i}

3 Sorted (Si, n, ESi, AtoZ)

4 E Avg = Avg(ES i , n)

5 E Med = Median (ES i , n)

6 If E Avg E Med then E Select = E Avg

7 Else E Select = E Med

8 If ES iE Select

9 Then Addsensor (Si, SDF I )

10 Return SDF I

11 Set q = num_SDF I _nodes;

set l = num_measure; SDF II M j = Ø

12 For {set ii 1} {$ii <= $q } {incr ii}

13 For {set j 1} {$j <= $l } {incr j}

14 Sorted (SDF I , q, M j , AtoZ)

15 Select (SDF I , µ, Med.S q.Mj , SDF II M j )

16 Return SDF II M j

17 V DF.Mj = Avg(M j , µ)

18 V DF.Mj sent = (V DF.Mj + Vmax.q Mj )/2

19 Send to BS (V DF.Mj sent)

20 End.

Immediate ly after the cluster is established (has nodes

in the cluster and CH) CH applies DF-AMS a lgorithm with

2 phases: Phase 1, line 1-3: CH arranged ascending (AtoZ)

ES i energy of n nodes ; line 4, 5: Ca lculate average value and

med ian value of n nodes; line 6, 7: choice an energy

landma rk; line 8: select sensor nodes according to the

landma rk energy E Select and line 9, 10: add sensor nodes

satisfying conditions to SDF I set Phase 2, line 11-13: CH

repeat scan until q sensor node of SDF I and l measured

parameters; line 14, for each parameter M j, CH a rrange

ascending value (AtoZ) of q sensor in SDF I ; dòng 15, 16:

references according to median value of each parameter to

select the µ sensor in q sensor of SDF I, add this sensor to

SDF II M j (SDF II = {SDF II M j where 1  j  l }); line 17:

calculate M j average va lue of µ sensor is selected; line 18:

calculate average of this value with M j ma ximu m va lue in μ

(also in q) of the sensor; send results to the BS, ending cycle

at line 19, 20.

III SIMULATION

A Configure simulation

We use NS2 simulat ion software, version 2.34 installed

on Ubuntu 12.04 operating system and source code from

MIT (Massachusetts Institute of Technology) The

parameters of DF-AMS simu lation are in Table 3:

TABLE 3 THE MAIN PARAMETERS

Parameter Value

Initial stored energy of sensor nodes 2 J

Amplification factor radio transmissions 10pJ/bit/m 2

Sensing data size (sig_size) 500 Byte

Level measurement for parameters ( ) Option

B Analysis and evaluate efficient

Simu lations with 01 BS, 100 sensor nodes (stored

energy 2J/node) are randomly distributed, network

automated clustering with LEACH algorith m For c luster,

time per sampling is 10s, t ime per round and data fusion T =

20s (can change T) At the beginning of each cycle , sensor

network (inc luding 100 sensor nodes) is divided into clusters, the number of sensor nodes in each cluster may be diffe rent In each round, we will e xa mine the c luster has the most sensor nodes, apply DF -AMS a lgorith m to ensure repeatability and representation of the sampling size The rate of the number clusters are surveyed and compared with the total number of sensor node of network at each T = 20s

in Fig 3

Immediate ly after c luster is established, the DF-AMS algorith m is applied During the operation of network and cluster, the energy of sensor nodes will be consumed For each survey cluster, the ratio between the median values

E Med and average values E Avg is reducing in accordance with the operation time as shown in Fig 4 CH selects sensor

nodes based on E Select

Fig 3 The number of cluster (including CH) has examined, evaluated

During the 20s to 180s, E Med > E Av g, there is change from

180s to 200s After 200s, E Med < E Av g, the sensor node has

consumed more energy which increases the difference level

CH only selected nodes in the cluster which have higher

power than E Med or E Avg to send The effic iency in selecting

notes of each round is presented in Fig 5

Therefore, in the Phase 1, DF-AMS algorithm has

effic iently limited number of packets that (n-q) nodes in the

cluster to transmit to CH Th is restriction is proportional to

the saved energy of the nodes which is calculated by

determin ing the energy required to transmit one bit of data

fro m the sensor node to CH The Fig 6 shows the difference

of efficiency in transmitting data between DF-AMS and

LEA CH

After selecting the sensor nodes with higher energy to

transmit sensing data to CH, at CH, Phase 2 of the DF-AMS

algorithm is processed by selecting the parameters measured value at least equal to the median value For e xa mp le, the

simulation results in the 200 th second, cluster which has the

highest sensor nodes has 27 nodes (including CH) The

energy of nodes in the cluster immed iately after c luster was established is presented in Table 4

TABLE 4 ENERGY OF NODES IN THE CLUSTER

ID Sensor node Ene rgy remains

Sensor node Ene rgy remains (J)

1 S64 0.19448999079361 14 S5 0.65913075467570

2 S66 0.20677067744723 15 S89 1.12126991147126

3 S93 0.25040697454776 16 S61 1.13665628124961

4 S47 0.26613698088662 17 S52 1.17191220310141

5 S42 0.31557190501557 18 S50 1.18431619852745

6 S97 0.36129589356154 19 S55 1.19451328998839

7 S27 0.39927476632118 20 S1 1.19564554291688

8 S9 0.44017552807918 21 S40 1.29382501427092

9 S10 0.44093077969050 22 S56 1.29528679594018

10 S69 0.45218943657088 23 S54 1.30253668746033

11 S39 0.45963518880449 24 S24 1.39874617508751

12 S98 0.46680686461839 25 S79 1.41500537472045

13 S74 0.49558099115671 26 S36 1.44151193539112

27 S96-CH 1.37788451247948

Sensor node S96 is CH, E Med = 0.57735587291621 and

EAvg = 0.79075469778057, E Select = EAvg CH selected 12

sensor nodes with ID fro m 15 to 26 SDF I= {S89, S61, S52,

S50, S55, S1, S40, S56, S54, S24, S79, S36} with |SDF I | = q= n – k =12 (in the formula (3)) Right before the end of the

round (at 200 thsecond), CH will start Phase 2 using the total number of sensing data that CH received (fro m sensors of

SDF Iset) Assuming that the total number of sensing data

measurement with 3 para meters components {M1, M2, M3}

= {Temperature, Humidity, Wind speed}, data measured at

200 th second in Table 5 In which (a ) the total number of sensing data, (b) the sensing data with parameter measurement components and (c) the fusion sensing data TABLE 5: SENSING DATA OF CLUST ER AT 200THSECOND

Node Total sensing data

Te mpe rature (M 1 )

Humidity (M 2 )

Wind speed (M 3 )

S89 8 S52 1 S52 0 S55 1 S61 10 S50 1 S36 0 S54 1 S52 8 S1 1 S40 1 S56 2 S50 10 S36 1 S89 2 S24 2 S55 8 S61 2 S55 2 S89 3 S1 10 S89 3 S54 2 S79 3 S40 8 S40 3 S79 2 S40 4 S56 10 S56 3 S61 3 S61 5 S54 8 S24 3 S24 3 S50 5 S24 8 S55 5 S50 4 S1 5 S79 10 S54 5 S1 4 S52 7 S36 10 S79 5 S56 5 S36 9

Fig 4 E Avg and E Med to select E Select

Fig 5 Efficient selection sensor nodes based on E Select

Fig 6 The efficiency of data transmission between DF-AMS and LEACH

Value

Parameter measurement

Temperature (M 1 )

Humidity (M 2 )

Wind speed (M 3 )

V DF.Mj sent 4.43 4.25 7.21

Total sensing data

(c) Therefore, total sensing data that CH will be sent to BS at

200 th second with 3 para meters are {M1, M2, M3} =

{Temperature, Humidity, Wind speed} = {4.43, 4.25, 7.21} =

15.89 (data) That means at 200 th second, CH only sent to

BS 4.43 data unit sensing data of temperature M 1 , 4.25

sensing data of humidity M 2 and 7.21 sensing data of wind

speed M 3

The results of the experiment of stimu lation times are presented in the Fig 7 In applying the DF-AMS algorith m,

CH sends the asymptotic measurement result which is the

ma ximu m measured value of all selected nodes to measure that parameter

CONCLUSION When you do not rely upon a sensor node, you must use

mu lti-sensor data fusion DF has to balance at acceptable level a mong three factors: precise measurement signal, completeness measurement data and energy consumption of sensor nodes when transmitting data to the BS The solution

of fusion value of DF-AMS a lgorithm asymptote ma ximu m value and CH filter raw data, re moval of weak measurement signals to send single result to the BS is energy efficiency of sensor nodes in that round Therefore, the DF-AMS is a suitable choice for sensor networks with many nodes, the node energies are diffe rent (not equal), target to be measured

by many parameters and measured values of the sensor nodes are largerly difference

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(a)

(b)

(c) Fig 7 Data fusion results of 3 parameters measurement

results a) of M 1 , (b) of M 2 and (c) of M 3

“Approaching Multi- sensor Data Fusion in Wireless Sensor Networks

by Rough Set Theory", Proceedings of the 7th National Conference...

determin ing the energy required to transmit one bit of data

fro m the sensor node to CH The Fig shows the difference

of efficiency in transmitting data between DF- AMS and

LEA... weak measurement signals to send single result to the BS is energy efficiency of sensor nodes in that round Therefore, the DF- AMS is a suitable choice for sensor networks with many nodes, the node