Novel framework of retaining maximum data quality and energy efficiency in reconfigurable wireless sensor network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 4, August 2019, pp. 2893~2901
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i4.pp2983-2901  2893
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Novel framework of retaining maximum data quality and
energy efficiency in reconfigurable wireless sensor network
B. Prathiba1
, K. Jaya Sankar2
, V. Sumalatha3
1,3
Department of Electronics and Communication Engineering, JNTUA, Anantapur, India
2
Department of Electronics and Communication Engineering, MGIT, Hyderabad, India
Article Info ABSTRACT
Article history:
Received Sep 20, 2018
Revised Mar 24, 2019
Accepted Apr 1, 2019
There are various unseen and unpredictable networking states in Wireless
Sensor Network (WSN) that adversely affect the aggregated data quality.
After reviewing the existing approaches of data quality in WSN, it was found
that the solutions are quite symptomatic and they are applicable only in a
static environment; however their successful applicability on dynamic and
upcoming reconfigurable network is still a big question. Moreover, data
quality directly affects energy conservation among the nodes. Therefore, the
proposed system introduces a simple and novel framework that jointly
addresses the data quality and energy efficiency using probability-based
design approach. Using a simplified analytical methodology, the proposed
system offers solution in the form of selection transmission of an aggergated
data on the basis of message priority in order to offer higher data utilization
factor. The study outcome shows proposed system offers a good balance
between data quality and energy efficiency in contrast to existing system.
Keywords:
Data quality
Data qggregation
Energy efficiency
Pervasive network
Wireless sensor network
Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Prathiba B.,
Department of Electronics and Communication Engineering,
JNTUA,
Anantapur, India.
Email: balireddyprathibha@gmail.com
1. INTRODUCTION
The rapid development of digitalization has led organizations to focus on low-cost, low-power,
multi-functional devices and distributed network environments. However, WSN emerged as a required
network application platform that leverages cost-effective implementation services to facilitate a flexible
network infrastructure and also adapts to, many changes based on application requirements.
WSN incorporates with hundreds of resource constraints tiny sensor nodes which have ability to sense
the environment after it collects data, processes data and perform communication via radio link channel
among other sensor devices [1, 2]. The main purpose of designing WSN is to detect the events and transmit
sensed data to relative user. WSN’s are highly adapted to monitor services for various environments such as
military-defense, weather forecasting, biological healthcare, surveillance & security and disaster-relief
work [3]. The functioning of these applications is mainly based on the real-time information. Due to resource
constraints nature of sensor devices WSN tends to be less reliable. The raw data collected by the sensor is
affected by various factors such as structural changes, signal strength changes, multipath propagation, and
interferences and also sometimes it provides redundant data, which does not guarantee end-to-end
reliability [4]. Therefore, in the field of WSN research, energy efficiency with data reliability becomes a very
challenging issue. The term ‘data reliability’ refers to a state that satisfies the quality requirements such as
data is relevant, sufficient, valid and unaltered [5]. There are several approaches based on retransmission and
redundancies are discovered by many researchers for providing reliable data transmission in WSN [6, 7].
However, the overhead and transmission load problems of these approaches results in a higher amount of

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Int J Elec & Comp Eng, Vol. 9, No. 4, August 2019 : 2893 - 2901
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energy exhaustion. Hence there is a need of multi-objective approach which ensures both energy-efficiency
and data reliability for real-time WSN applications. Moreover, it has been observed that many existing
researches have carried towards in the domain of energy efficiency with ensuring reliable data
processing [8-10]. Also, various and different existing works of data reliability for energy efficient WSN
application are discussed in the review of literature section. The prime contribution of the proposed system is
to address the problems associated with the data reliability as well as energy efficiency jointly so that this
problem can be investigated for its effect over the network lifetime performance.
The proposed system also discusses about a message prioritization scheme that perform selection
transmission of data packet on the basis of time factor and sensor node. The organization of the proposed
paper is as follows: Section 1.1 discusses about the existing literature where different techniques are
discussed for detection schemes used in power transmission lines followed by discussion of research
problems in Section 1.2, the problem statement describes in section 1.2.1 and proposed solution in 1.3.
Section 2 discusses about algorithm implementation followed by discussion of result analysis in Section 3.
Finally, the conclusive remarks are provided in Section 4.
This section discusses about the existing research works that carried in the division of energy
efficient data reliability in WSN. The work carried out by Alipio et al. [11] have concentrated on the issue
related with reliable data transmission and proposed cache based traffic control protocol which utilizes
information resource from the cache management program to recover the packet losses in WSN.
The experimental outcomes of presented approach achieves good improvement compared to base line
protocols and existing cache based transport protocols in terms of end-to-end delay and cache utilization.
Alshaheen and Rizk [12] have considered energy problem and packet loss in body sensor network.
The author have used linear network coding approach to improve data reliability by computing
the probability of successful packet receiving at the sink node. The work of Duan et al. [13] have design an
improved software defined network for addressing the issue of energy consumption and reliability in
industrial WSN.
Elsafrawey et al. [14] have presented self-recovery shuffled distributed approach using proactive
and reactive protocol to enhance the quality of data collected by unattended WSN. The study of He and Jiang
[15] have applied approach of Quantum mechanics to evaluating and modelling the consistency of sensor
reports. Jin et al. [16] have constructed novel optimization framework for Industrial WSN. The presented
framework composed of channel management layer for improving reliability and ﬂow-scheduling layer for
resolving temporality factor in network. Kamezaki et al. [17] have presented detection scheme for measuring
the reliability of data collected in Hydraulic Manipulators. The work of lei et al. [18] have examine
the energy management policy and designed scheduling and allocation algorithm for reducing heavy packet
loss for energy harvesting industrial WSN.
A robust ant-jamming routing protocol is adopted in the study of Liu et al. [19] in order to address
the data transmission problem in high medium noise and electromagnetic interference environment.
In the study of Long et al. [20] have developed a data collection scheme that ensures the required quality of
service for optimizing the total energy consumption and improving data reliability. Another work carried out
by Luo et al. [21] has presented a new approach for getting reliable data. The author has used fusion
mechanism on redundant data with utilizing approximation algorithm for chain and tree topologies.
In the study of Pielli et al. [22] have studied to combine energy-efficient factor with over all required QoS of
network and used machine learning algorithm in order to make sensor node able to take self-decision for
utilizing their energy for transmission. Sun et al. [23] have presented novel model for estimating reliability of
end-to-end packet delivery and optimizing the transmission link quality for the industrial WSN. The outcome
of presented model achieves good performances for reliable data transmission at network level.
Teng et al. [24] have formulated a novel optimization scheme based on broadcast technology for
enhancing the reliability and minimizing the communication delay in the network. Wang et al. [25] have
introduced novel analytical model using smart feeder for improving reliability factor of integrated cyber
distributed systems. The work of Yan et al. [26] have, presented a closed loop based measurement model to
enhance the accuracy of vibrant sensors. The outcomes of presented approach reveals that it achieve
beneficial property for removing human factor error and is more suitable compared to open loop technique.
A loss function based Inference technique is used in the work of Yand et al. [27] for enhancing data
reliability for large distributed data from the WSN.
The study of Zhu et al. [28] have carried analysis for investigating the reliability problem in WSN of
which topology switches between the possible available connection operated by Markovian chain.
The findings of presented work suggested that the reliability factor dependents on three points that are
network parameters and topology, and Markovian chain. In the same way another work carried out
by Zhu et al. [29] have presented a novel technique based on the evidence theory for estimating the reliability
constant of sensors established on training data. Therefore, there are various scheme implemented for

Int J Elec & Comp Eng ISSN: 2088-8708 
Novel framework of retaining maximum data quality and energy efficiency in reconfigurable ... (B. Prathiba)
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the purpose of data reliability but all these above scheme is associated with beneficial points as well as non-
beneficial points. Samara et al. [30] have illustrated efficient power, price decreases, and QoS based routing
method for WSNs. Chisab et al. [31] demonstarted a method for power saving in WSN unfder preservative
white Gaussian noise way. The study focused to enhance IF-Algorithm for data aggregation methods in
WSNs by Ingle et al. [32]. The next section discusses about identified research problem from existing
literature.
The significant research problems are as follows:
a. Existing system does not offer much simple and computationally effective solution towards data
reliability problems.
b. There is lack of any consideration of WSN to be integrated with pervasive networks or no discussion of
reconfigurable network and the problems associated with it.
c. Exising system does not discuss about any possible linkage of message prioritization with the data
reliability as well as energy efficiency.
d. There are very less discussion of solutions that offers a good balance between scheduling, precise data
transmission, and energy efficiency considering heterogeneous network.
The problem statement of the proposed study can be stated as “Developing a mechanism to solve
the data reliability problems with energy efficiency on reconfigurable networking state of WSN is a
computationally challenging task”. The next section discusses about proposed solution.
The proposed study is continuation of our prior research work [33-35] emphasizing towards data
reliability in WSN. The prime aim of the proposed work is to develop a simple and novel model that is
capable of performing integration of WSN with any pervasive network via gateway system with higher
degree of energy efficiency as well as data reliability. The implemented architecture of proposed system is
shown in Figure 1.
Reconfigurable Network
Wireless Sensor
Network
Pervasive
Computing
Construct
priority-
information Sensor-based prioritization
Time-based prioritization
Compute Utilization Factor+Energy Efficiency
Gateway System
Algorithm
Figure 1. Proposed methodology
Figure 1 highlights the proposed methodology where a reconfigurable network is modeled in order
to construct a communication bridge between WSN and pervasive computing (i.e., cloud, Internet-of-Things,
Optical Network, etc.). This environment is intentionally constructed as it represents the real-world situation
of heterogenous communication system that still suffers from data reliability problems. The proposed system
introduces a simple construction of algorithm that uses time parameters as well as sensor node. An interesting
fact introduced in the proposed system is that it offers a selection mechanism via the gateway system to
choose the forwarding of the data packets on the basis of the selected WSN. Another interesting part of
the proposed methodology is that it offers prioritization of the messages that are generated dynamically from
the traffic of WSN. This will mean that proposed system constructs a many-to-one networking between WSN
and pervasive network and implements its algorithm that has capability to perform selective transmission of
the data packet on the basis of priorit with a sole motive of ensuring higher degree of data utilization and
lower rate of depletion of the transmission energy. The applicability of the proposed algorithm is more on
those areas that demands consistent monitoring activity and forwarding of the aggregated data packet in large
scale. The next section discusses about the algorithm implementation followed by discussion of the outcome
obtained from its implementation.

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2. ALGORITHM IMPLEMENTATION
The proposed algorithm is constructed retaining the utilization of WSN with pervasive computing in
mind and hence a pervasive reconfigurable network is considered for modeling. The agenda of the design
principle is to offer a good balance between an effective utilization of aggregated data as well as enhancing
the data reliability with a terger of better energy conservation. The challenges of data quality is handled by
introducing a mechanism where a particular set of data is selected for forwarding on the basis of time and
urgency level. The discussions of the algorithm are as follows:
Algorithm for Constructing Priority Information
Input: N (number of networks), n (number of sensors), u (number of dynamic users), Eo (initialized energy)
Output: MAT (matrix to reposit priority information)
Start
1. init N, n, u, Eo
2. (x, y)rand_dist(n)
3. For i=1:t
4. For j=1: u
5. μ(i,j)f(rand(N))
6. End
7. End
8. For i=t
9. For j=1:u
10. c.μ(i,j) [c.μ(i,j)+1]
11. End
12. cc/∑c
13. MATc
14. End
End
The above algorithm is responsible for establishing a concrete foundation of data reliability
considering a case study where WSN will be deployed in reconfigurable network for its upcoming
technologies. The algorithm performs random distribution of the sensors (Line-2) as well as the complete
network (Line-4) along with a placement of a gateway in order to assist in communication between WSN and
pervasive network. The algorithm also introduces a mechanism to perform communication with respect to
the dynamic user u (Line-5) as well as upon certain time period t (Line-3). An explicit function f(x) is created
that takes the input of random value of number of WSN in order to generate request and all the generated
request are stored in an explicit matrix μ(x)(Line-5). The proposed system also constructs an empty matrix
MAT that considers the dimension of number of WSN and maximum value of time. The next part of
the study is to compute the probability of each sensor for a given instance of time and the entire dynamic user
(Line-8 and Line-9). The system than uses the same function μ and obtains its count c value that is a direct
representation of probability of the entire service request (Line-10).
Finaly probability is computed by dividing the count with sum of all the count (Line-12).
All the respect information of count c is now stored in matrix MAT. This algorithm on successful
implementation assists in analyzing probability of data request with respect to the various time instances of a
day, which is nearly equivalent to any real time sensory operation. It should be known that basedon
the information of MAT, the proposed system allows the sensor to forward only particular set of information
to the different network (pervasive) on the basis of priority and time for the dynamic user. This information is
used for taking a decision of routing on the basis of the time factor and priority factor. From a different
viewpoint, this algorithm could be also seen as a substitute of scheduling and clustering approach as well as
this algorithm also offers a good extension to perform routing from WSN to different reconfigurable network
using a gateway services. The information retained within MAT is consistently updated as depending on this
updated information, the algorithm assist the other side of the network (i.e., pervasive network) to select
the set of nodes (i.e., N) on the basis of their relative priority level.
Although, this algorithm does not directly involve any kind of energy modeling that is carried out in
the next part of the algorithm, but its complete operation saves a significant amount of energy that is required
for data aggregation. It is also successfully capable of processing highly scheduled data aggregation on
the basis of data priority and time that directly increases the utilization and reliability level of the data.
The next algorithm is responsible for invoking an energy efficient modeling that initiates by loading
the information retained with the MAT (Line-1) that yields computed energy as an output. The significant
implementation steps of this algorithm are as follows:

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Algorithm for Energy Efficiency
Input: MAT (matrix to reposit priority information)
Output: E (Energy)
Start
1. load MAT
2. For i=1:size(x)
3. For j=1:length(tx)
4. AdjNodes(nix= =j)
5. End
6. End
7. For k=1:u
8. c(treq(k))=[c(treq(k))+1]
9. End
10. MAT100*c/∑c
11. For i=1:length(P1)
12. k=∑(Ei)≥0
13. [v, ri]sort(E(i))
14. For j=1:lenth(tx)
15. Ni(ri= = Nix)
16. For k=1:n1
17. E(j, rix(k)f(E(j, rix(k))
18. End
19. End
20. End
End
The algorithm constructs a loop where it reads all the value of positional information x of the nodes
(Line-2) as well as all instantaneous time-based information tx (Line-3). It then extracts all the adjacent nodes
using Euclidean distance d approach followed by comparing the distance with range and extract information
about the adjacent sensors (Line-4). This step is also followed by elimination of node identity that is finally
stored in a matrix called as AdjNodes (Line-4). The algorithm than access information of priorityfrom
the MAT and reperform the process of generation of the new demands for current time period followed by
computation of probability of service request and updating the MAT matrix (Line-7 and Line-8).
The proposed system also introduces logic in order to compute the degree of data utilization. For this
purpose, the algorithm computes the demand by considering all the count c corresponding to total length of
count c matrix.
It also accesses the corresponding readings of the sensors and computes the utilization degree
bysubtracting demand from reading value. If the value of this utilization degree is positive then the empirical
value of the utilization factor is computed from demand factor itself (which is nothing by corresponding
counts). This process directly assists in computing the reliability of the data. The next step is to construct a
matrix k which considers nodes with sufficient residual energy (Line-12). It is further followed up by
searching total number of alive nodes followed by ranking the sensors on the basis of their residual energy
(Line-13 to Line-16). This process also assists in obtaining percentile informationof the MAT matrix
followed by extracting total number of communicating sensors.
The algorithm constructs a matrix rix in order to apply random permutation of the matrix of value
equivalent to length of tx. For all the communicating nodes (n1 is in percentile), the proposed system applies a
function f(x) that performs reduction of energy. The complete process of implementation assists in obtaining
information related to residual energy, number of nodes that still have good residual energy left,
and utilization degree. Therefore, the proposed system offes a comprehensive solution using simple approach
for maintaining good balance between data reliability and energy efficiency. The proposed system also
ensures that there is always a lesser extend of overhead factor as complete process of selection is carried out
by the pervasive network or any other form of networks. However, the algorithm is required to be run not
only among the sensors but also within the gateway node too. The next section outlines the outcomes that are
obtained after implementing the above mentioned algorithm of the proposed system.

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3. RESULT ANALYSIS
This section discusses about the outcome obtained from the implementation of the proposed system.
The study considers that there is only single pervasive unit and 10 users with 10 different number of sensor
networks where the user resides. The analysis of the proposed study was carried out using 500-1000 sensor
nodes with a presence of one singlegateway node that performs the heterogenous communication system.
The simulation area of 1000x1200 m2
is considered with 50 meter as the transmission range and 10 joules of
initialized energy. Considering 1000 data packets and 10 bytes of packet length, the analysis is carried out
considering four performance parameters that are discussed as below. The study is also compared with
existing LEACH [36] and PEGASIS [37] for assessing their performance.
Figure 2 highlights the comparative analysis of the utilization degree that directly relates to the data
reliability of transmitted data of the sensor node. The outcome shows that proposed system offers better data
utilization factor as compared to exitsing LEACH and PEGASIS algorithm. The prime reason behind this is
LEACH offers centralized data aggregation scheme which increases maximum events of the data redundancy
as well as stale data whose utilization factor is very less. At the same time, PEGASIS uses chain-based data
aggregation approach that does not caterup the dynamic demands of multiple data points to single
heterogeneous network.
Figure 2. Comparative analysis of utilization factor
3.1. Performance evaluation
The proposed system offers streamline capability to forward only reliable data that is selected by
the pervasive network on the basis of time and sensor network. Figure 3 and Figure 4 offers the comparative
performance analysis with respect to energy efficiency. A closer look into the graphical outcomes will show
that proposed system offers significant level of energy saving by retaining maximum remaining energy and
number of nodes in contrast to existing system. The prime reason behind this is majority of the task of
forwarding the data packet is outsourced to the pervasive network via gateway node on the basis of the sensor
network and specific prioritization level. This will mean that on the basis of data priority level as well as
location of origination point of data, the proposed system performs the complete process of data aggregation.
This operation offers significantly lesser amount of load on any node (member / clusterhead) causing saving
of residual power (Figure 3) that directly ensures more number of alive nodes till the end of simulation
(Figure 4). However, LEACH and PEGASIS offers highly iteratve steps along with no much ensurity of data
reliability. This causes adverse effect on data utilization factor that significantly increase the process of data
retransmission causing more depletion of energy in WSN.

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Figure 3. Comparative analysis of residual energy
Figure 4. Comparative analysis of alive nodes
The proposed system is also computationally cost effective as it offers significantly lesser
computational resources as well as time to perform processing. The complete algorithm is less iterative in its
modeling and it encourages more progressive operation that leads to potential reduction in processing time.
Apart from this the algorithm does not need any extra gain on memory system in order to offer compiled-
based outcome that are quite realistic in nature. Hence, the algorithm offers both time and space complexity
reduction capability to show that it is cost effective solution to ensure a good balance between data reliability
and energy efficiency. The proposed system has been developed in matlab under normal 32-bit windows
platform.
Figure 5. Comparative analysis of processing time

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4. CONCLUSION
This paper introduced a method where a selective transmission is carried out using many to one
direction. The complete network is divided into many forms of WSN which performs many communications
with one pervasive network via gateway system. The significant contribution of the proposed systems are as
follow i) the proposed system offers selective transmission scheme that is significantly beneficial for all
the heterogenous network with emergency-sensory application, ii) the proposed system offers good saving of
dissipation power among the sensors irrespective of its number of deployment, iii) the processing speed of
the proposed system is significantly faster than any existing standard energy efficienct algorithms, and iv)
the utilization degree offers direct usability of the aggregated data in the WSN.
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