Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor networks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 1, February 2019, pp. 496~504
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i1.pp496-504  496
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Efficient energy, cost reduction, and QoS based routing protocol
for wireless sensor networks
Ghassan Samara, Mohammad Aljaidi
Computer Science Department Zarqa University, Jordan
Article Info ABSTRACT
Article history:
Received Jan 29, 2018
Revised Jul 31, 2018
Accepted Aug 14, 2018
Recent developments and widespread in wireless sensor network have led to
many routing protocols, many of these protocols consider the efficiency of
energy as the ultimate factor to maximize the WSN lifetime. The quality of
Service (QoS) requirements for different applications of wireless sensor
networks has posed additional challenges. Imaging and data transmission
needs both QoS aware routing and energy to ensure the efficient use of
sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost,
(ECQSR) quality of service routing protocol for sensor networks which can
run efficiently with best-effort traffic processing. The protocol aims to
maximize the lifetime of the network out of balancing energy consumption
across multiple nodes, by using the concept of service differentiation, finding
lower cost by finding the shortest path using nearest neighbor algorithm
(NN), also put certain constraints on the delay of the path for real-time data
from where link cost that captures energy nodes reserve, energy of the
transmission, error rate and other parameters. The results show that the
proposed protocol improves the network lifetime and low power
consumption.
Keywords:
Cost reduction
Quality of service
Energy efficiency
Multipath routing
Nearest-neighbors search
(KNN)
Service differentiation
Simulated annealing (SA)
Wireless sensor networks Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ghassan Samara,
Computer Science Department Zarqa University, Jordan.
Email: gsamara@zu.edu.jo
1. INTRODUCTION
Wireless Sensor Networks (WSNs) are rapidly emanating as an important and influential factor in
mobile computing, wireless systems, and vehicular- ad hoc networks [1], Also, WSNs play a major role in
facilitating the work of applications in a wide range of areas, such as earthquake monitoring, data traffic in
transportation, tracking goals in battlefields, habitat monitoring, fire system detection [2, 3, 4].
Wireless Sensor Networks (WSNs) may have millions of nodes, which are deployed over the wide
sensing area in different parameters. These Nodes broadcast their link quality, which is depending on radio
frequency environment [5]. The main purposes of WSNs are to monitor, analyze, combine and respond to the
data which are gathered by hundreds or thousands of sensors which are distributed in some sensing
field [6].
The power of WSNs lies in the capability of deploying significant numbers of tiny nodes that have
been configured by themselves, each of these sensing nodes contains a microcontroller, external memory,
power source, and transceiver. Figure 1 shows the internal components of a sensor node [1].
In this paper, (ECQSR) protocol proposes; Efficient Energy, Cost Reduction, and QoS based routing
protocol for WSNs, to achieve load balancing by splitting the data traffic among set of nodes which are
distributed in the field, in addition to efficiently balance the consumption of energy through multiple
nodes [7, 8].
Moreover, ECQSR works on increasing the reliability of the data delivery using forward error
correction (FEC) technology, also, increasing the throughput via presenting data redundancy, Data-

Int J Elec & Comp Eng ISSN: 2088-8708 
Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor… (Ghassan Samara)
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redundancy provides type of flexibility on the path failure, in addition to recover lost data, also it has the
possibility of reconstructing the original messages, ECQSR protocol finds the shortest path which has the
least cost by using the nearest neighbor algorithm (NN). This path meets the end to end delay requirements
[7], Figure 2 shows Pseudocode of the nearest neighbor algorithm (NN).
Figure 1. Internal components of a sensor node
Figure 2. KNN kernel algorithm pseudocode

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 1, February 2019 : 496 - 504
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Figure 3 shows the flow chart of the shortest path which is determined by the NN algorithm.
Figure 3. Shortest path flow chart
2. RESEARCH PROBLEM
Providing energy efficient, least cost, and QoS based routing protocol between sensor nodes in a
particular sensing field is a challenging task, because of power consumption [9, 10, 11], data loss, topology
control, coverage, mobility, routing and other parameters [1]. All of these parameters are considered in the
proposed protocol. This protocol will work on finding an optimal solution for all of these challenges so that
the proposed ECQSR protocol based on the idea of extending the routing approach and determining the path
with least cost and energy efficient that satisfy certain end to end delay during transmission [7, 12, 13].
The link cost function is a function that captures transmission energy, reserve energy, error rate, and
other parameters. Our protocol aims to get the shortest path with least cost by using the nearest neighbour
algorithm (NN), which should meet the end to end delay requirements.

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3. LITERATURE REVIEW
QoS routing in wireless sensor networks (WSNs) is a challenging problem because of the poor
resources of the sensor nodes. Therefore, this issue has received increasing interest from the researchers,
where they have made many proposals; here are some of these proposals which relate to our protocol
(ECQSR):
Sequential Assignment Routing (SAR) protocol, which is one of the oldest protocols that concern in
routing protocols that provide some QoS. SAR is a multiple path routing protocol that helps in routing
decisions based on three aspects: QoS on each path, energy resources, and packet priority level. However,
SAR protocol suffers from the overhead of dealing with routing tables and QoS metrics at each sensor
node [1, 7].
K. Akkaya and M. Younis in proposed a cluster-based QoS aware routing protocol that employs a
queuing model to deal with both non-real time and real-time traffic. This protocol suffers from the
transmission delay which is not considered in the estimation of the end-to-end delay [1]. SPEED is
considered as another protocol of QoS based routing protocol that supports a soft real-time end to end
pledges; But the main problem of this protocol, that it does not consider the energy metric in its routing
protocol [1, 7].
Message initiate Constrained Based Routing (MCBR) mechanism. MCBR is composed of route
constraints, QoS requirement of messages, and specifications of constraint-based destinations, but it suffers
from a large number of control packets that yields more overhead [1]. Felemban et al. proposed multiple
paths and multiple speed Routing Protocol (MMSPEED) for probable QoS guarantee in WSNs, QoS is
provided in two domains: reliability domain, and timelines domain, this protocol suffers from dealing with
data redundancy [1].
X. Huang, Y. Faun has proposed a multi-constrained QoS multiple path routing (MCMP) protocol,
the objective of this protocol is to utilize the multi-path to maximize the performance of the network with a
minimum cost of energy, but it does not get over the problem of power consumption completely [1].
Efficient, Least Cost, Energy-Aware (ELCEA) QoS Protocol, which aims to extend the routing and finding
an efficient path using Dijkstra’s algorithm and least cost with consideration of end-to-end delay during the
current connection, but it failed in giving more importance on Quality of Service requirements [1, 7].
4. RESEARCH OBJECTIVES
There is a set of objectives, which can be achieved through the use of our protocol (ECQSR),
all these objectives must be compatible with each other. Figure 4 shows the relations between these
objectives [8, 14].
Figure 4. ECQSR objectives relationship

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where: (N-B)=network-battery life; (QoS)=quality of service; (D)=delay; (EE)=energy efficiency;
(Cost)=total cost of the system; (T)=throughput system; (Cov)=coverage; (PER)=packet error rate.
These different objectives may conflict or not with each other; Different objectives are associated
together with various types of lines depending on the relationship between these objectives. The red line
connects two objectives which are a conflicting relationship, for example, maximization the coverage
conflicts with the delay, packet error rate, total cost of the system, and the network-battery lifetime. Whereas,
the blue dashes and dots lines that connect two objectives mean that they have no direct relationship with
each other [14].
The most important desired objectives of the ECQSR protocol are summarized as follows:
 To Keep the sensor nodes working as much as possible, Therefore, extending the lifetime of the network
 To Reduce the cost by selecting the shortest path which is produced by using nearest neighbor algorithm
(NN), which meets the end to end delay requirements (minimizing the cost of the system)
 To Maximize the coverage and the throughput of the network.
 To Minimize energy consumption and packet error rate.
5. METHODOLOGY
This paper aims to make the routing between the sensor nodes more energy efficient, least cost and
increasing QoS. Consequently, the network lifetime will be extended. To achieve these goals, the
methodology of this paper based on the way of sensor nodes distribution, also using appropriate algorithms
that help to make the technique of exchanging data between these nodes more flexible and shorter, these
algorithms are the nearest neighbor algorithm (NN), and the simulated annealing (SA) algorithm.
These sensor nodes were distributed in the flat sensing filed according to certain coordinates (x, y),
this data set which contains the sensor node coordinates was inserted into both algorithms (NN and SA) to
find the shortest path of exchanging data between these sensors, each one of these algorithms has its special
way to find this route. After implementing the two algorithms, simulation results show clearly that the NN
algorithm attained better results than the SA algorithm. Section (7.3.3) shows the comparison results between
the two algorithms.
6. PROPOSED ALGORITHM
Nearest Neighbour (NN) algorithm is used to find the optimal path between the sensors that were
distributed in the sensing field taking into consideration of power consumption and cost reduction, the next
section of this paper (Experimental Evaluation) will show the implementation of the algorithm with the
simulation results.
7. EXPERIMENTAL EVALUATION
In this section, the experimental evaluation of ECQSR protocol will be shown, whether through the
nearest neighbour, or simulated annealing algorithm, which will be implemented in MatLab version 7.9.0.529
R2009b 32-bit (win32).
7.1. Implementation
The simulation process of ECQSR protocol was evaluated and tested via nearest neighbor algorithm,
which has been implemented using MatLab, to ensure the performance and the effectiveness of ECQSR
protocol. Also, the simulated annealing algorithm was implemented in MatLab to make a comparison
between the results of the two algorithms.
The Simulation process consists of 2000 nodes that are randomly distributed in some sensing field;
all sensors are identical in battery source, memory, and a processing unit. Also, all these nodes were located
in the same external environment.
The simulation results of the two algorithms have shown that the implementation of the ECQSR
protocol with nearest neighbor algorithm has achieved a shorter path to travel between all nodes than
simulated annealing, which means that the nearest neighbor will reduce the power consumption, cost
reduction, and some other parameters. The simulation results of ECQSR protocol, whether using the nearest
neighbor algorithm (NN) or simulated annealing algorithm, will be discussed in the next sections of this
chapter.

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7.2. Data set
To make an implement for the two algorithms we have to find a data set that represents the locations
of the distributed node sensors, also must be compatible with the concept of the algorithms. The data set
which is used in the implementation is organized as coordinate of a point, which are written as ordered pair
as it is shown below, letter P represents the location of the node. The location of these nodes is determined
using an integrated function that exists in MatLab.
P (14991 8390)
The simulation was applied at different locations of 2000 node sensors; all these sensors were distributed
randomly on some sensing field.
7.3. Simulation results
After the implementation of the two algorithms, we obtained the following results; each result of
these algorithms will be discussed separately:
7.3.1. Implementation of nearest neighbor (NN) algorithm
NN algorithm was implemented as a solution for finding the shortest path between all distributed
node sensors; it has been implemented in two different programming languages:
a. MatLab:
Which achieved the desired results, with regard to finding the shortest path between all nodes,
Figure 5 shows the execution result of NN on MatLab.
Figure 5. NN implementation on MatLab for length 730231.4981
The above figure shows the path between the nodes (2000 nodes) that are distributed in the sensing
field using the nearest neighbor algorithm which is implemented in MatLab.
b. Java
NN also implemented by using Java that is also achieved a significant result, as shown in Figure 6.
Figure 6: NN implementation in JAVA

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The above figure illustrates the path between the nodes (2000 nodes) that are distributed in the
sensing field using the nearest neighbor algorithm which is implemented in JAVA. Table 1 shows the
implementation results of the NN algorithm in both PL (MatLab and Java).
Table 1. NN Implementation Result in MatLab and Java
Algorithm
Programming
language
COST TIME
NN
MatLab 730231.4981 3.66 Min
Java 734095 25.60 Sec
7.3.2. Implementation of simulated annealing (SA) algorithm
SA algorithm was implemented in MatLab; Figure 7 shows the execution result of SA on MatLab.
Figure 7. SA implementation on MatLab
The above figure shows the path between the nodes (2000 nodes) distributed in the sensing field
using the simulated annealing algorithm which is implemented by MatLab; this figure should be compared
with Figure 5. Table 2 shows the implementation results of the SA algorithm (MatLab).
Table 2. SA Implementation Result in MatLab
Algo
Programming
language
COST TIME
SA MatLab 1071055.255 40 Sec
When comparing Table 1 with Table 2, SA scored faster results than NN and higher cost.
7.3.3 Comparison between NN and SA algorithm according to path length
Figure 8 shows the average path-length of the ECQSR protocol, using the NN algorithm, and SA
algorithm, it is clear to conclude that the SN is having a longer path than NN when the ECQSR is
implemented.
Figure 8: ECQSR Path-Length average

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8. CONCLUSION
In this paper; Efficient Energy, Least Cost, quality of service based routing protocol for sensor
networks protocol (ECQSR) has been presented specially for wireless sensor networks to extend the life of
the network, finding lower cost by determining the shortest path using nearest neighbor algorithm (NN), put
certain constraints on the delay of the path for real-time data, minimize the power consumption and error rate
and other parameter.
Through the computer simulation, ECQSR multiple routing protocols have been implemented and
evaluated, and it has proven its ability to maximize the performance of the WSN and extend the life of the
network, through its capability in finding the shortest path which helps in reducing the power consumption,
and cost reduction. As future research, it is intended to study the efficiency and the performance of ECQSR
protocol, and analyze the effect of the path length, buffer size, and network size, on the metrics of the
performance of ECQSR protocol.
ACKNOWLEDGEMENTS
This research is funded by the Deanship of Research and Graduate Studies in Zarqa University,
Jordan.
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BIOGRAPHIES OF AUTHORS
Dr. Ghassan Samara holds BSc. and MSc. in Computer Science, and PhD in Computer
Networks. He obtained his Ph.D, from Universiti Sains Malaysia (USM) in 2012. His field of
specialization is Cryptography, Authentication, Computer Networks, Computer Data and
Network Security, Wireless Networks, Vehicular Networks, Inter-vehicle Networks, Car to Car
Communication, Certificates, Certificate Revocation, QoS, Emergency Safety Systems.
Currently, Dr. Samara is an assistant professor at the Computer Science Department, Zarqa
University, Jordan.
Mohammad Aljaidi received his BSc in Computer Science with honors degree from Zarqa
University in 2014, and MSc in Computer Science with honors degree from Zarqa University in
2017. He is currently working as a system administrator in the Royal Jordanian Air Force
(RJAF). His research interests include Wireless Sensor Networks (WSNs), Vehicular Adhoc
Networks (VANETs), and Network Security.

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