Enhanced Payload Data Reduction Approach for Cluster Head (CH) Nodes

TELKOMNIKA, Vol.15, No.3, September 2017, pp. 1477~1484
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v15i3.7217  1477
Received May 29, 2017; Revised August 18, 2017; Accepted August 30, 2017
Enhanced Payload Data Reduction Approach for
Cluster Head (CH) Nodes
N. A. M. Alduais*, J. Abdullah, A. Jamil
Wireless and Radio Science Center (WARAS), Faculty of Electrical and Electronic Engineering,
Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat, Johor, Malaysia
* Corresponding author, e-mail: naifalduais@gmail.com
Abstract
In this paper, we suggested two approaches to minimizing the CH packet size by considering the
accuracy of prediction of sensed data at the base station. The proposed coding schemes based relative
difference (CS-RD) and based the factor of precision (CS-FP) instead of the absolute change method that
has been used in recent work. The aim is to enhance the accuracy of prediction data at the base station.
Therefore, the performance metric was evaluated in term of the accuracy of prediction data at the base
station. Simulation results showed that the proposed approaches performed better in term of the accuracy
of prediction data at the base station. Specifically, the distortion percentage and average Absolut error in
the CS-RD and CS-FP method decreased by 50% and 88% better than the current new aggregation
method (ADATDC). However, our proposed CS-FP showed a low reduction ratio for some states.
Keywords: WSN; Data Collection, Accuracy, CH Payload packet size, Coding Scheme
Copyright © 2017 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
Recently, WSN has become an enabler technology for the IOT applications, thus
extending the physical reach of the monitoring capability. WSN, as it is, possesses several
constraints such as a limited energy availability, a low memory size, and a low processing
speed that are the principal obstacles to designing effective management protocols for
WSNs [1]. This also concerns WSN-IOT integration. In IOT based WSN, the basic issues are
concerned with the mechanism to reduce the energy consumption of the CH nodes that will
result in prolonging the lifetime of the CH nodes. It is a very significant consideration since the
energy consumed by transmitting one bit via WSN is higher than running numerous
microcontroller instructions [2]. There are various assumptions in modeling ‫‏‬‫‏‬‫‏‬‫‏‬ energy‫‏‬consumption
of sensor‫‏‬ nodes‫‏‬. Most of this modeling focuses on calculating energy consumption while
considering the cost of energy consumption in transmitting and receive modes, ignoring the cost
of the process because the cost of transmitting one bit via WSN is higher than running
numerous instructions in terms of energy consumption.
CH node packed size of the sensed data aggregation through clustering is the most
common issue. Moreover, knowing the number of nodes that transmit sensed data to the CH
still affects the CH payload data size. Furthermore, the cluster packet size is limited, where the
aggregation data from the nodes must be equal or less than the payload data size. Reducing
the packet size will also decrease energy consumption by the CH as well as prolong its lifetime.
Recent work has proposed an approach to reducing the packet size for the CH node based on a
coding scheme. Therefore, the aim of work reported in this paper is to enhance the payload
data reduction approach for cluster head nodes.
2. Related Works
Collecting and reducing data at a CH node in the network is dependent on the
Collecting and reducing data at the CH node in a given network is dependent on the
performance of aggregation methods. That method is classified as a method used for
aggregation without reducing the data and aggregation with reducing data. When the packet
size for the CH node is assumed to be large enough to accommodate a large aggregate of data,
the aggregation without reducing packet size approach is used. However, when the CH packet

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TELKOMNIKA Vol. 15, No. 3, September 2017 : 1477 – 1484
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size is limited, we need to reduce the collected data using some other techniques, such as
taking the mean, median, and min/max values of samples of collected data as these
approaches require a minimum amount of bits. The idea behind this method of using one value
to represent the set of collected data is that these techniques can give the lowest accuracy as
some of the individual sensors need to sense the data.
In previous studies [3-7], the authors assumed that the CH was selected only for highly
spatially correlation among the member nodes for the same CH. The idea behind this was to
send only the CH node sensed data as a representative of all cluster member sensed data.
In [8], the authors developed an adaptive method of data aggregation with the aim of
exploiting the spatial correlation between sensor nodes (ADATDC). ADATDC is a method that
is applied on the cluster head node level with univariate data and each cluster member node
supports a single sensor. They also used two bits for representing the sign (+/-) change value
between the sensor node and the median of sensed data from all cluster members.
In this paper, we suggested two approaches to minimizing the CH packet size by
considering the accuracy of prediction of sensed data at the base station. The proposed coding
schemes are the based relative difference (CS-RD) and the based factor of precision (CS-FP)
instead of the absolute change method that used in ADATDC. The relative difference/change
has been employed as an update data strategy (sensor node level) with a fixed threshold used
in recent work [9]. Therefore, in this study, we employed the CS-RD and CS-FPinstead of the
absolute change to enhance the accuracy of prediction of data at the base station.
3. Proposal of Novel Approaches to Minimize the Packet Size for CH Node
3.1. CS-RD
In the section, we explain the CS-RD approach to reducing the packet size for the
cluster head.
Problem Formulation: It is well known that the number of nodes that transmit
sensed data to the CH has an effect on the CH payload data size ( ). In addition, the cluster
payload size is very dependent on the number of nodes in the CH. Furthermore, the cluster
packet size is limited, where the aggregation data from the nodes must be equal or less than the
payload data size .
Figure 1. The Proposed method flowchart
Algorithm Description: We divide the algorithm into two distinct phases: the CH node
level, and the Gateway level as shown in Figure 1.
CHNode Level: In this phase, the steps taken to reduce the CH node payload packet
size is described.
Step 1: Let us consider that the number of sensor nodes in CH is { };
is i-th sensor, then the sensed data by the sensor nodes are { }; i-th

TELKOMNIKA ISSN: 1693-6930 
Enhanced Payload Data Reduction Approach for Cluster Head (CH) Nodes (N. A. M. Alduais)
1479
sensor node and its sensed data i-th respectively, { }, and the number of
sensor nodes. The median of the sensed data is defined as in Equation (1).
{ (1)
The correlation between the sensed date is by a sensor node and the median
sensed data for all sensor nodes is defined as in Equation (2), where [ ] when =1,
which means that the sensed data has a high correlation. Conversely, = 0 means that the
sensed data is weak or has no correlation with and the allowed amount of the
difference is selected by the sink, where its value is totally dependent on the application error
tolerance.
{ √ , (2)
This study focuses on the accuracy of the predicted data by the sink and the number of
bits coding. For this reason, we assume all aggregated data are correlated.
Step 2: Estimations of the percentage of the difference between the sensor node value
and the median sensed data for the CH node samples are as defined in Equation (3).
( ) (3)
Step 3: isa one bit for the sign(+/-) based on the difference between the data , and
as shown in Table 1.
Table 1. One-bit coding for the sing Data Message
If
0
1
Step 4: Representing the percentage of similarity in binary format based
Decimal to binary conversion (BCD) is required for | | as shown in Table 2. From Equation
(4) we can estimate the number of bits required to send both and value in bits’ form
and respectively.
(4)
Table 2. coding for the value of
…..
0/1 0 0 0 0%
0/1 …. 0 0 1 1%
0/1 …. 0 1 0 2%
0/1 … 1 1 1 7%
Gateway level: The base station can predict the real sensed data for each sensor node
{CH} as defined in Eqnuation (5).
(5)

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Algorithm 1 (CH node Level) Encoding the Sensed Data
1 Inputs:{ } sensed data array for the sensor nodes {S} in the CH; number of sensor nodes in CH
2 Output: { } : array of the difference, The CH payload data
3 Begin: //Arrange the sensed data array {D} from smaller to larger value
4 Calculate // as defined in Eqn. (1).
5 Convert
6 For Do // i=1, 2,n ;
7 Set ⁄ / as defined in Eqn. (3).
8 IF Then
9 Else
10 End if
11 Set ; Set // Convert the u(i) from decimal to binary
12 [ ] //
13 Next
14 Set { } // Sensed data” in bits’ format” for all sensor nodes in CH
15 Send ( ) To BS // Send the data to the base station (BS)
16 End Algorithm
Algorithm 2 (Sink Level) Prediction the sensed Data
1 Inputs { } sensed data packet for the sensor nodes {S} in the CH
2 Output: { }: Received array for sensed data array by the sensor nodes {S} in the CH
3 Begin:
4 //Estimate Sb(i) bu(i) bits from the data for each sensor separately
5 For Do // i=1,2,..,n ;
6 // If Sb(i) ==1 Then
7
8 // as defined in Eqn. (5)
9 Else
10
11 // Next
12 End Algorithm
3.2. CS-FP
CS-FP is a method to enhancing the accuracy of predicted sensed data at the BS. In
this part, we discuss another solution based on the factor of precision (ω) by modifying the main
proposed CS-RD algorithm as illustrated in the following (i) Equation (3) and replacing
Equation(5) by Equation (6) and Equation (7), respectively.
⌈| | ⌉ (6)
( ) (7)
For example, if u =0.5 by applying ADATDC Round (0.5) becomes u=1 and by applying
CS-FP (0.5× ω) becomes u=5 where ω=10, and at the BS, we return it again by dividing 5/ ω.
More details about benefits and cons of CS-FP analysis compared to the main proposed
method (CS-RD), and ADATDC as well as their performance will be assessed through
simulations in the next section.
4. Performance Evaluation
The main differences between our proposed methods and the original work ADATDC
are:
1. ADATDC represents the distance between for CH samples, and all members are
based on the absolute change with nearest the value to near integer decimal number ⌈|
|⌉ defined as:
(8)

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For more accuracy, we proposed the percentage of difference formula or used the
factor of precision (ω) instead of the absolute change to estimate the difference between for
CH samples and all members as defined in Equation (3) and Equation (6), respectively.
2. ADATDC uses a 2-bit format for representing the sensed bit (Sb), where Sb=00
when the Sb=01 when the and Sb=10 when In our proposed
methods, we only need one bit to represent the sign-bit as shown in Table 1.
As we mentioned early, the performance metric for the algorithm evaluation is the
accuracy of data predicted at the sink. Hence, to test our proposed approaches and ADATDC
accuracy, we applied the percentage of distortion equation and average absolute Error equation
as defined in Equation (9) and Equation (10), respectively.
Percentage of Distortion %
| |
(9)
Average absolute Error
∑ | |
(10)
4.1. Mathematical Evaluation And Analysis
To mathematically evaluate our proposed methods with those methods proposed in
recent work, specifically in [8], both methods were applied for the same example in [8]. Suppose
that there are 13 sensor nodes in a cluster sent to their cluster head with ID = #99, then, the
data is measured by the nodes as shown in the 2nd column in Table 3. The distortion
percentage between the real sensed data and the received data is defined in Eqn. (9). From
Figure 2., it obvious that the proposed CS-RD and CS-FP methods show more accuracy than
ADATDC, where the average distortion percentages between the real sensed data and the
received data for all sensor nodes in the CH are 0.28 % and 0.00%. In contrast, the
average distortion percentage by applying the ADATDC method is 0.53 %. But CS-FP has
limitations in data reduction ratio, which is further discussed in the next section.
Table 3. Table on a CH to Collection Correlated Data
No.ID
Sensed Data Received data at Sink Distortion Percentage %
ADATDC CS-RD CS-FP ADATDC CS-RD CS-FP
99 35.36 35.46 35.49 35.36 0.28 0.37 0.000
11 33.44 33.46 33.43 33.46 0.06 0.03 0.001
22 33.44 33.46 33.43 33.46 0.06 0.03 0.001
33 34.46 34.46 34.46 34.46 0.00 0.00 0.000
47 32.9 32.46 32.74 32.86 1.34 0.49 0.001
49 34.3 34.46 34.46 34.26 0.47 0.47 0.001
62 34.98 35.46 34.8 34.96 1.37 0.51 0.001
75 34.98 35.46 34.8 34.96 1.37 0.51 0.001
81 34.46 34.46 34.46 34.46 0.00 0.00 0.000
83 35.36 35.46 35.49 35.36 0.28 0.37 0.000
85 35.36 35.46 35.49 35.36 0.28 0.37 0.000
93 34.46 34.46 34.46 34.46 0.00 0.00 0.000
94 32.9 32.46 32.74 32.86 1.34 0.49 0.001
Average Distortion Percentage % 0.53 0.28 0.000
Figure 2. Performance Evaluation of the Prediction
99 11 22 33 47 49 62 75 81 83 85 93 94
32
33
34
35
Cluster Members (Sensor Nodes)
SensedData
Sensed Data(CH)
ADATDC(BS)
CS-RD(BS)
CS-FP(BS)

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4.2. Simulation And Analysis
The detailed simulation study that tested the performance and accuracy of our
approach is presented in this section. The evaluation was performed based on numerous
scenarios. The dataset used in this study was extracted from the WSN deployments for Intel
Berkeley Research Lab (IBRL) [10]. Subsets of this dataset were selected for assessing the
proposed algorithms. MATLAB software was used to write the code for evaluating the
performance of the proposed methods.
From Figures 3, 4 and 5, we can observe that the proposed methods in this study show
better performance than ADATDC in terms of accuracy, where the prediction sensed data at the
sink by our methods is more accurate than that of the ADATDC. The average Absolute error
(ABE) and the percentage of distortion were also estimated for each CH sample by applying the
equations as defined in Eqn. (9) and Eqn. (10), respectively. The ABE for six nodes cluster
members with 14 samples as an outcome of applying the ADATDC are {0.219 and 0.195} for
temperature and humidity sensors, respectively. Conversely, the ABE after applying our
methods are {0.103 and 0.067} for temperature and humidity sensors, respectiveThus, based
on the results, it is clear that the performance of CS-RD and CS-FP is better by approximately
50% and 88% than that of the ADATDC. It is worth noting that the CS-FP method showed the
best accuracy at all. But it has a low data reduction percentage as displayed in Figure 6. for the
humidity sensor. To explain that, if u=3.5 by applying ADATDC Round (3.5) become u=4 and by
applying propoesd2 (3.5× ω) becomes u=35 where ω=10 implies that the CS-FP showed a low
reduction ratio. We can estimate that CS-FP is benefited merely when the (u) value was in the
range [0- 0.7]. Otherwise, the main proposed (CS-RD) showed the best solution in terms of the
accuracy and reduction ratio.
The result is very dependent on the method used, wherein the ADATDC is used to
represent the distance between for CH samples and all members are based on the absolute
change with nearest the value to the near integer decimal number ⌈| |⌉, which means that
if the distance between ( ) = {0.5, 0.4}, it will become {1,0} respectively. In this case, if the
originally sensed data value is 35.5 and 35.4 the sink will receive it as 36 and 35, respectively.
This problem is solved by the CS-RD and CS-FP methods based on the relative difference/
factor of precision(ω), thus increasing the accuracy as defined in Eqn. (3) and Eqn.(6),
respectively. More details and example are discussed in 3.1, the “Mathematical Evaluation and
Analysis” section in this study. In addition, for the advantage of our proposed methods, we used
only one bit to represent the state of the data message. As shown in Table 1, the ADaTDC
method used two bits for that purpose to show how it assists in decreasing the number of bits.
Figure 3. Performance Evaluation of the Prediction sensed data for temperature
and humidity sensors

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Figure 4. Average Absolute Error
Figure 5. Percentage of Distortion %
Figure 6. Maximum number of bit requiring for cluster member aggregated data at
CH per epoch
0 2 4 6 8 10 12 14
0
0.1
0.2
0.3
0.4
Humidity Sensor
AverageAbsolutError
Samples
CS-RD
ADATDC
CS-FP
0 2 4 6 8 10 12 14
0
0.1
0.2
0.3
0.4
Temperature Sensor
AverageAbsolutError
Samples
CS-RD
ADATDC
CS-FP
1 2 3 4 5 6
0
0.5
1
Humidity Sensor
PercentageofDistortion%
Samples
CS-RD
ADATDC
CS-FP
1 2 3 4 5 6
0
1
2
3
Temperature Sensor
PercentageofDistortion%
Samples
CS-RD
ADATDC
CS-FP
8
16
24
32
The Cluster Members Aggregated Data at CH
Maximumnumberofbits
Temp-ADATDC
Temp-CS-RD
Humidity-CS-RD
Temp-ADATDC
Temp-CS-FP
Humidity-CS-FP

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5. Conclusion
In this study, we proposed two approaches to reducing the CH packet size by
considering the accuracy of prediction of sensed data at the base station. The proposed coding
schemes, namely; CS-RD and CS-FP were used in this study instead of the absolute change
method used in recent work where ADATDC improves the accuracy of approximation data at
the BS. Therefore, the performance metric was evaluated in terms of the accuracy of prediction
data at the BS. Simulated results showed that the proposed approaches performed better in
term of the accuracy of prediction of data at the base station, where the distortion percentage
and average Absolut error in the CS-RD and CS-FP method decreased by 50% and 88% than
those of the ADATDC approach. However, the CS-FP showed the lowest reduction ratio in
some states.
Acknowledgement
The authors would like to thank the staff of Wireless and Radio Science Center
(WARAS) of Universiti Tun Hussein Onn Malaysia (UTHM) for support. This research is
supported by the Fundamental Research Grant Scheme(FRGS) vot number 1532 from the
Ministry of Education Malaysia.
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