IMPROVEMENTS IN ROUTING ALGORITHMS TO ENHANCE LIFETIME OF WIRELESS SENSOR NETWORKS

International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.2, March 2018
DOI: 10.5121/ijcnc.2018.10203 23
IMPROVEMENTS IN ROUTING ALGORITHMS TO
ENHANCE LIFETIME OF WIRELESS SENSOR
NETWORKS
D. Naga Ravikiran1
and C.G. Dethe2
1
Research Scholar, ECE Department, Priyadarshini Institute of Engineering and
Technology (PIET), Nagpur, Maharashtra.
2
Director, UGC-Human Resource Development Centre, RTM Nagpur University,
Nagpur, India.
ABSTRACT:
Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited
computation, communication, memory, and energy resources that are being used fora huge range of
applications. Clustering in WSNs is an effective way to minimize the energy consumption of sensor nodes.
In this paper improvements in various parameters are compared for three different routing algorithms.
First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering
mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system
initiated CH selection algorithm for LEACH. Then Artificial Bee Colony (ABC)which is an optimisation
protocol owes its inspiration to the exploration behaviour of honey bees. In this study ABC optimization
algorithm is proposed for fuzzy rule selection. Then, the results of the three routing algorithms are
compared with respect to various parameters.
KEYWORDS
Wireless Sensor Network (WSN), LEACH, Clustering, Artificial Bee Colony (ABC), Fuzzy logic system.
1. INTRODUCTION
Wireless Sensor Networks (WSN) is a group of spatially dispersed dedicated sensors to
monitor/record an environment’s physical conditions and to organize collected data at a central
location (Xiao 2004).
Clustering is used for communication between nodes and BS, as it is energy efficient compared to
single/multi-hop routing. In clustering, a sensor node in a cluster is elected Cluster Head (CH)
and relays data from a sensor to a remote receiver [3]. Few CH nodes are heavily loaded, in
clustering when energy depletion occurs.CH performs aggregation function on data received and
sends it to BS where it is needed. LEACH is a popular routing protocol using cluster based
routing to reduce energy consumption [1].LEACH divides communication into rounds with a
round including a set-up phase and a steady-state phase [4].
Figure1.Cluster Based Wireless Sensor Network

24
Clustering based schemes are the most energy efficient routing protocols. In a cluster, a node is
elected as CH while others are member nodes who in their respective clusters sense ambient
conditions in the environment and transmit measured data to corresponding CHs [7]. CHs collect
data from member nodes, aggregate them, and finally forward it to either a neighbouring CH
(multi-hop) or directly (single hop) to BS. Clustering leverages advantages of small transmit
distances for most nodes, requiring only a few to transmit farther distances to a BS [29].
Every sensor node in the group is associated to a single cluster and interacts only with the
respective CH [17]. Hence, this means that the appropriate CH should be selected to optimise the
consumption of energy by the CH; If not so, it may cause the death of CH because of additional
load for data collection and forwarding. Many of the routing algorithms that are based on cluster
technique first select CH at random or by probability and thereafter form the cluster.
This paper gives a view about comparing the results with respect to fuzzy rule selection with the
use of LEACH and ABC optimisation algorithms. Section 2 lists the literature of previous work,
Section 3 illustrates the used technique, Section 4 explains the comparison of the result and
Section 5 includes the conclusions.
2. LITERATURE REVIEW
An energy efficient CH election protocol (LEACH-HPR) proposed by Han [8] used a minimum
spanning tree algorithm to construct inter-cluster routing. An improved LEACH called partition-
based LEACH (pLEACH) which partitioned a network in to an ideal number of sectors, and
chose a node with the highest energy as sector head was proposed by Gou and Yoo [9] using
centralized calculations. A Multi-hop Routing with LEACH (MR-LEACH) protocol was
presented by [10] to prolong WSN life. MR-LEACH partitioned a network into different cluster
layers. BS selected upper layer CHs to act as super CHs for lower layer CHs.
Singh, et al., (2010) surveyed and summarized recent research works focused mainly on the
energy efficient hierarchical cluster-based routing protocols for WSNs [6]. Due to the scarce
energy resources of sensors energy efficiency was the main challenge in the design of protocols
for WSNs. The ultimate objective of the protocol design is to keep the sensors operating for as
long as possible, thus extending the network lifetime.
MS-LEACH was proposed by [11] to enhance S-LEACH security by giving data confidentiality,
and a node to CH authentication using pair wise keys shared by CHs and cluster members. The
new MS-LEACH’s security analysis showed that it had efficient security properties achieving all
WSN security goals compared to LEACH protocol’s current security solutions. MS-LEACH’s
simulation based performance evaluation proved the effectiveness of new MS-LEACH preserving
the energy efficiency was a critical and a challenging task.
Biradar, et al., (2009) analyzed the design issues of WSNs and presented a classification and
comparison of routing protocols. Recent advances and convergence of micro electro-mechanical
systems technology wireless communications, integrated circuit technologies, microprocessor
hardware and nano technology, distributed signal processing, Ad-hoc networking routing
protocols and embedded systems created the WSN concept where nodes were limited regarding
energy supply, restricted computational capacity and communication bandwidth [18].
Elrahim et al (2016) proposed an energy efficient data forwarding protocol [16] called
Energy Aware Geographic Routing Protocol (EAGRP) for WSNs to extend the life time of
the network. In EAGRP, both position information and energy were available at nodes used to
route packets from sources to destination. This prolongs the lifetime of the sensor nodes hence the
network life time, higher packet delivery ratio and minimal compromise of energy efficiency
were performed. The routing design of EAGRP was based on two parameters: location and
energy levels of nodes.

25
Hancke, et al., (2007) introduced a Simple Energy Efficient Routing Protocol (SEER) to improve
network lifetime by limiting the number of messages that were sent through the network [25].
The nature of WSN necessitates specific design requirements, of which energy efficiency is
paramount. SEER uses a flat network structure for scalability and source initiated
communication, along with event-driven reporting to reduce the number of message
transmissions. Computational efficiency was achieved by using a relatively simple method for
routing path selection. Routing decisions were based on the distance to the base station as well as
on remaining battery energy levels of nodes on the path towards the base station.
Zogović, et al., (2010) research on WSN focused mostly on providing energy-efficient operation
for every node that ensures a long life for WSN. It was important to consider QoS provisioning in
addition to taking into account energy-efficiency. Keeping in mind, that throughput, average
delay and jitter (delay variance) were important QoS parameters at Medium Access Control
(MAC) layer, it led to reviews of fundamental energy-efficiency vs. delay36 trade-off, and
throughput vs. capacity in wireless communications.
Nikravan, et al., (2011) affirmed a routing protocol in wireless sensor networks to achieve real-
time communication besides the energy efficiency [24]. With demand increasing for real time
WSN services, Quality of Service (QoS) based routing is now an emerging research topic.
Providing QoS guarantee in sensor networks is challenging. A fuzzy logic-based Energy Efficient
scheme for real-time packet transmission in WSN was proposed. Here a Fuzzy Logic System
(FLS) was used as a decision mechanism for next hop node selection. Both transmission rate and
energy were chosen parameters for choosing the next-hop node in real-time packet transmission.
Simulation results showed that this scheme provided improvement in real-time transmission and
energy efficiency performance, low energy consumption and high packet delivery ratio within
deadline compared with some other routing protocols when operating in varying real-time
environment.
Kumar, et al., (2015) proposed a novel approach with an energy efficient hierarchical clustering
technique using the Fuzzy Logic method [26]. The Fuzzy search algorithm was applied for cluster
formation and cluster head selection in the distributed hierarchical clustering environment. The
fuzzification functions and rules optSEPimized the simulation [27]. The proposed approach
results in Mat lab simulation outperformed the existing results. The evaluation of the proposed
approach was compared with LEACH protocol. The result showed the algorithm scaled well in
dynamic and energy deficient wireless sensor networks.
Sobral, et al., (2013) proposed a Fuzzy Inference System to help the Directed Diffusion routing
protocol to choose a route for the communication between any nodes in the network. A new
approach helped to choose the best route based on Fuzzy Inference Systems and Ant Colony
Optimization (ACO). The Fuzzy System was used to estimate the degree of the route quality,
based on the number of hops and the lowest energy level among the nodes that form the route.
The ACO algorithm was used to adjust the rule base of the fuzzy system in order to improve the
classification strategy of the route, and hence increased the energy efficiency and the survivability
of the network. The simulations showed that it was effective from the point of view of the energy,
the number of received messages, and the cost of received messages when compared to other
approaches.
Zhou,et al., (2017) proposed a multiple dimensional tree routing protocol for Multi-sink WSNs
based on listening and ACO. Taking into consideration hops, packet losses, retransmission, and
delay account, a distributed ant colony algorithm was proposed. When nodes selected routes in
the data transmission, the algorithm was utilized to realize the real-time optimization by
coordination between nodes. The simulation results showed that the ACOMSR protocol realized
the QoS optimization for Multi-sink wireless sensor networks, and its performance was better
than the routing protocol of minimum hop numbers.

26
3. PROPOSED METHOD
This section discusses fuzzy-based CH selection, ABC and fuzzy ABC-based selection of CH.
Figure 2 shows the flowchart of the proposed methodology.
Figure 2: The flowchart of proposed methodology.
3.1 FUZZY LOGIC SYSTEM
Fuzzy logic implements decision using fuzzy decision sets, each given by a separate term like
``small'', ``medium'', or ``large''. Fuzzy logic system performs de-fuzzification, and fuzzy
inference. Fuzzification converts a crisp input to a fuzzy value. Generally used membership
functions use triangular/trapezoidal membership functions.
Fuzzy input parameters are: Remaining energy, farthest node distance in cluster from Cluster
head and hops to sink. Heinzelman’s energy model for sensor networks, Energy required to
transmit a k-bit message to distance d is given by:
3.2 FUZZY-BASED CH SELECTION
In this technique, it is assumed that a node in WSN node receives its coordinates. LEACH-FL
organisation comprises three sections, four fuzzification functions, an inference engine
(concluding 27 rules) as well as defuzzification module [13]. Defuzzification module is a mere
formula and hence is not depicted in the figure and will be given later (Ran et al., 2010).
• Fuzzification module: Consider that three different node attributes influences the selection of
CH, in order to utilise the input functions to change the system input into the fuzzy set, namely
distance, the density of node and battery power level. Every input function consists of three

27
membership functions to exhibit the various degrees of function. The count after the membership
functions exhibits the membership function degree and illustrates therelationship between the
input function is arithmetical [20].
• Knowledgebase: Any system consists of 27 rules in fuzzy inferences. The forms of regulations
are: IF A and B and C THEN D. Here, A, B, C and D represent battery level, node density,
distance as well as probability correspondingly. The rules are with respect to the equation (1):
Probability = batterylevel *2 + node density + (2 − distance) .......... (1)
The formula illustrates the association among various input function. Battery level is the key
feature in the CH election probability.
• De-fuzzification module: After collating the conclusion by every rule, defuzzification technique
is needed to obtain crisp value. General defuzzification technique is the centroid that gives the
value of centre area in a fuzzy set to obtaina collated conclusion [22].
As illustrated in Figure 2 node in G1 starts to try to become CH at time 0, later the node from G2
starts with a setback, and then the node from G3 starts with a setback after the nodes in G2 is
completed. At the time of execution, node sends station information to BS:
Si → BS :Loc(Si ) ………… (2)
The BS transmits information on delay to the remaining nodes:
Bs→Si :Delay (Si )………. (3)
Set Num(Giveup) to 0. Start with nodes in G1. If a CH is produced from G1, forward a Hello
package and Num(Giveup).
H j →broadcast :Hello, Num(Giveup)….. (4)
Fig 3: selecting CH’s
Well-organised CH selection has an influence on cluster organisation. Using FRD to choose CH
is different from current methods like CBRP, WACA, SCAM, and secured clustering algorithm
(SCA). Selecting CH is difficult and imprecise in CBRP, with respect to Lower ID, MOBIC as
per mobility, and SCA based on the value of trust. Existing methods select CH based on one of
the following variables: ID, mobility, as well as trust value. The suggested technique applies
variables together to choose CH. CH is elected by fuzzy relevance degree (FRD) and decided by
power availability, the strength of signal and distance between the nodes.

28
FRD of a node represented a degree of consistency that a network neighbour node provides.
Fuzzy relevance-based CH selection algorithm (FRCA) system selects CH with respect to fuzzy
relevance, power availability, mobility, and internodes distance [23].Available node power,
internode distance and node mobility are responsible for the maintenance of energy consumption
balance of a node. Inter-node distance and mobility are responsible for the equilibrium of cluster.
FRCA executes clustering as per parameters detailed above and selects a CH for effective
clustering.
After the employment of sensor node, it obtains information on the location of the node (via GPS
technology or known before employment) and informs it to a BS. The BS determines Delay (Si)
distribution of the sensor node. Delay (Si) = 0 for those in regions to begin first (Abad and
Jamali, 2011).
3.3 LOW ENERGY ADAPTIVE CLUSTERING HIERARCHY (LEACH)
LEACH is a cluster-based algorithm uses distributed clustering formation algorithm which is a
cluster routing based data aggregation algorithm [14]. This algorithm is represented in rounds
with two phases: setup phase and steady state phase. In setup phase, % of sensors are
randomly chosen as CHs based on a threshold [28].
Here is a required number of CHs, current round, and a set of nodes that are not CHs in the
last 1/ rounds ensuring that a sensor chosen to be CH is not selected in next rounds till all
network sensors become CHs [12]. This leads to fair energy consumption and increases network
life. LEACH functioning is represented in rounds, which starts with a set-up phase when clusters
are organized, then steady-state phase [21].
Set-up phase: Nodes in LEACH takes independent decisions to define clusters using a distributed
algorithm without centralized control.
Steady-State Phase: Steady-state operation is divided into frames with nodes sending data to CH
at one per frame during given transmission slot. Set-up phase will not assure nodes being evenly
distributed among CH nodes. So, nodes per cluster vary in LEACH, and data a node can send to a
CH depends on the number of nodes in a cluster [15].
3.4 ABC ALGORITHM
The ABC protocol was invented by Karaboga in 2005 for optimisation of numerical problems
that comprises three groupings of bees: employed bee, onlooker bee, finally, scout bee. The bee
that carries out a search in random is called a scout bee. The bee moving to food source visited by
it previously and sharing the information with other type of bees is named as employed bee and
the bee that waits in the dancing area is called onlooker bee. The onlooker bee as well as scout
bee is also known as unemployed bees. The quality of nectar of food sources represents the
fitness cost of the related solutions.
3.5 FUZZY ABC-BASED CH SELECTION
Inthe fuzzy method, FRD to select CH varies from existing method like CBRP, WACA, SCAM,
as well as SCA. Selecting CH is difficult and imprecise in CBRP, with respect to Lower ID,
MOBIC as per mobility, and SCA based on the value of trust. Existing methods select CH based
on any one of the variables: ID, mobility, as well as trust value. The suggested technique applies

29
variables together to select CH. CH is elected by FRD and decided by power availability, the
strength of signal and distance among the nodes. FRD of a node represented a degree of
consistency that a network neighbour node provides. FRCA system selects CH with respect to
fuzzy relevance, power availability, mobility, and internodes distance [19].
Available nodes power, internode distance and node mobility are responsible for the maintenance
of energy consumption balance of a node. Inter-node distance and mobility are responsible for the
equilibrium of cluster. FRCA executes clustering as per parameters detailed above and selects a
CH for effective clustering. ABC is used for optimising the rule selection. ABC refers to a
population-based technique that imitates the behaviour of honey bees [5].
Bees in a hive are classified as employed bees, onlookers, as well as scout bees.
1 Employed bee: Employed bee hunts for nectar and gets linked with the source. The collected
information is shared with the onlooker bee by waggle dance.
2 Onlooker bee: Onlooker bee waits for employed bee in the dancing area of the hive to get
thedetail of food source and decides on selecting the food source.
3 Scout bee: Scout bee carries out the search randomly near the quality food source. In short, they
conduct local searches.
The Fuzzy-ABC algorithm flow chart

4. RESULTS AND DISCUSSION
Parameters considered for improvement in each routing algorithm implementation are
clusters formed, Average end to end delay, Packet loss rate and
software tool used to implement different algorithms was using MAT Lab a
(i) Number of clusters formed
From the above table 1 it is clear that
fuzzy based ABC algorithm in multihop
(ii) Average end to end delay
From the above table 2 it is clear that
based ABC algorithm in multi hop wireless sensor networks.
(iii) Packet loss rate
ISCUSSION
Parameters considered for improvement in each routing algorithm implementation are
Average end to end delay, Packet loss rate and Number of nodes alive.
software tool used to implement different algorithms was using MAT Lab and OPNET simulator.
Table 1: Number of clusters formed
it is clear that Number of clusters formed increases by 9% due to
fuzzy based ABC algorithm in multihop wireless sensor networks.
Table 2: Average end to end delay
From the above table 2 it is clear that end delay decrease by 5% due to implementation of fuzzy
Table 3: Packet loss rate
30
Parameters considered for improvement in each routing algorithm implementation are Number of
Number of nodes alive. The
nd OPNET simulator.
9% due to using
end delay decrease by 5% due to implementation of fuzzy

31
From the table 3 it is clear that packet loss rate decreases by 5% due to implementation of fuzzy
(iv) Number of nodes alive
Table 4: Number of nodes alive
From the table 4 it is clear that Number of nodes alive increases by good number after completion
of so many numbers of rounds of iteration due to the implementation of fuzzy based ABC
algorithm in multi hop wireless sensor networks.
5. CONCLUSION
Wireless network is considered as most common service used in industrial and commercial
application due to its technological enhancement in the process, interaction and utilisation of low
power embedded computational devices. Energy consumption and lifespan are the most vital
concerns in heterogeneous WSN as it increases the energy consumption equilibrium and hence
increase in the lifespan of network. In the current study, a novel hybrid fuzzy ABC is suggested
that is built through the integration of fuzzy logic with ABC to optimise the CH selection [2].
ABC is utilised to optimise the rule selection. The ABC optimisation with fuzzy rule selection
approach is given for improving the network lifetime. The experimental results compared with
other routing algorithms proved that the suggested method enhances the network’s lifespan as
well as reduces the delay from end to end delays and rate of packet loss in comparison with other
selection methods.
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