E FFICIENT E NERGY U TILIZATION P ATH A LGORITHM I N W IRELESS S ENSOR N ETWORKS

International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 2, April 2015
DOI: 10.5121/ijci.2015.4205 53
EFFICIENT ENERGY UTILIZATION PATH
ALGORITHM IN WIRELESS SENSOR NETWORKS
K.Ramanan1
and E.Baburaj2
1
Sathyabama University /Department of Computer Science& Engineering, Chennai,
India
2
Narayanaguru College of Engineering&Technology /Department of Computer
Science& Engineering, Nagercoil, India
ABSTRACT
With limited amount of energy, nodes are powered by batteries in wireless networks. Increasing the life
span of the network and reducing the usage of energy are two severe problems in Wireless Sensor
Networks. A small number of energy utilization path algorithms like minimum spanning tree reduces total
energy consumption of a Wireless Sensor Network, however very heavy load of sending data packets on
many key nodes is used with the intention that the nodes quickly consumes battery energy, by raising the
life span of the network reduced. Our proposal work aimed on presenting an Energy Conserved Fast and
Secure Data Aggregation Scheme for WSN in time and security logic occurrence data collection
application. To begin with, initially the goal is made on energy preservation of sensed data gathering from
event identified sensor nodes to destination. Invention is finished on Energy Efficient Utilization Path
Algorithm (EEUPA), to extend the lifespan by processing the collecting series with path mediators
depending on gene characteristics sequencing of node energy drain rate, energy consumption rate, and
message overhead together with extended network life span. Additionally, a mathematical programming
technique is designed to improve the lifespan of the network. Simulation experiments carried out among
different relating conditions of wireless sensor network by different path algorithms to analyze the
efficiency and effectiveness of planned Efficient Energy Utilization Path Algorithm in wireless sensor
network (EEUPA).
KEYWORDS
Wireless Sensor Network (WSN), Environmental Monitoring, Efficient Energy Utilization Path Algorithm
(EEUPA), Data Collector, Sensor Nodes.
1. INTRODUCTION
Nodes consists of wireless crossing point to transfer the information to each other are broadly
used in Wireless Sensor Networks (WSNs) in various areas such as examination, disaster
liberation, intellectual carrying, surveillance, environmental managing, healthcare, goal tracking,
and more. To collect the information and data in unkind or defensive atmosphere, Wireless
Sensor Networks are more useful. In a Wireless Sensor Networks, data collected by sensor nodes
are preferred to be circulated to destinations (base stations). May be, data kept in few nodes might
not be explicitly transmitted to the destination because the destination is far away from the radio
transceivers of the wireless crossing point of the nodes. So, path protocols are preferred, where
the data packets are transmitted through multi-hop manner, i.e., they are transmitted node next to
node, consequently reaches the destination.

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Generally, nodes in a Wireless Sensor Networks are powered by means of batteries with limited
amount of energy. As a result, network separation may occur as one or more nodes employ
available energy of batteries, to supply conservative path procedure undesirable. Consequently, it
is severe for a Wireless Sensor Networks to start effectively to prepare an efficient energy
utilization path algorithm where the battery energy is drained out fully since the wireless sensor
network has an extended lifetime. Path protocol designed at power-saving and increasing network
lifespan are seriously taken in research community.
A wireless sensor network (WSN) physically consists of large amount of miniature,
multifunctional and resource controlled sensors which are self-organized as an informal network
to examine the physical world. Sensor networks are often used in applications where it is
complicated or impossible to gather wired networks. Examples include wildlife habitat checking,
security and military observation, and target path.
For applications same as military surveillance, opponents have strong motivation to hear on
network transfer to achieve significant intellect. Utilization of such information can source
monetary fatalities or risk human lives. To secure such information, researchers in sensor network
security comprises of intended significant challenge on identifying techniques to offer
characteristic security services such as privacy, substantiation, reliability, and accessibility. But,
these are dangerous security provisions because they are inadequate in various reasons. The
communication samples of sensors can themselves represents a huge transaction of essential
information, which discloses the position information of strict components in a sensor network.
Path in wireless sensor network is very important, mainly in intolerant surroundings. Failure to
protect such information can completely challenge the planned principles of sensor network
applications. Path actions need to be developed to prevent the opponent from determining the
physical location of source sensors and destination. Because of the limited energy life span of
battery-powered sensor nodes and techniques comprises to be energy efficient. Because
communication in sensor networks is more special than calculation, so we use communication
cost to verify the energy consumption of the routing protocols.
To decrease the utilization of energy in the wireless sensor networks, an efficient energy
utilization path algorithm in an improved version is initiated by processing the grouping series
with path mediators. Path series on the particular time is taken with showing alternative form of
gene character. Other hidden alternative form of gene traits is preserved for future sequencing
instance on definite threshold.
2. LITERATURE REVIEW
The field of wireless sensor networks (WSN) is now developed in the research community area
because of its applications in different fields for instance defense security, civilian applications
and medical research. These limitations eliminate the utilization of traditional path protocols
planned for other ad hoc wireless networks. Secondly, the base station cannot verify data
reliability and accuracy via fixing message digests or signatures to every sensing model. To come
across the above two disadvantages, the base station [1]. can regain all sensing data yet these data
has been combined. The multi level data aggregation method are presented [6]. to develop the
data collecting path for a mobile destination as Infrastructure based Data Gathering Protocol
(IDGP) and a Distributed Data Gathering Protocol (DDGP). A k-hop relay mechanism is
established to restrict the number of hops for path data to a mobile sink.
The key point of EEGA scheme [2]. is that accurate data aggregation is attained without
discharging secret sensor readings and without initiating important overhead on the battery-

55
limited sensors. To come across the delay planned an Efficient Data Collection Aware of Spatio-
Temporal Correlation (EAST) that utilizes shortest routes used for forwarding the
collected data toward the sink node [3]. and fully expand both spatial and temporal correlations to
execute near real-time data collection in WSN. Developed [4]. easy, least-time, energy-efficient
path protocol by means of one-level data aggregation which guarantees improved life span for the
network. The energy-efficient data development issue [5]. through person packet delay limitations
to an energy-efficient service curve construction issue is developed and solves the difficulty by
developing a local optimality theorem.
To design a method to mitigate the uneven energy dissipation problem by controlling the mobility
of agents, which is achieved by an energy prediction strategy to find their positions using [7].
energy balancing cluster routing based on a mobile agent (EBMA) for WSNs. To obtain this
while maintaining a good trade-off between the communication overhead of the scheme, the
storage space requirements on the nodes, and the ratio between the number of visited nodes x and
the representativeness of the gathered data using [8].density-based proactive data dissEmination
Protocol (DEEP), which combines a probabilistic flooding with a probabilistic storing
scheme.The QoS of an energy-efficient cluster-based routing protocol called Energy-Aware
routing Protocol (EAP)[9]. in terms of lifetime, delay, loss percentage, and throughput, and
proposes some modifications on it to enhance its performance.
QoS of an energy-efficient cluster-based path protocol called Energy-Aware routing Protocol
(EAP) in terms of lifespan, delay, loss percentage, and throughput, and presents some alterations
on it to improve its results [10]. DHAC [11]. can include both quantitative and qualitative
information types in clustering. It prevents the destination to collect a representative investigation
of the network’s sensed data. Presented efficient and balanced cluster-based data aggregation
algorithm (EEBCDA) splits the network into rectangular grids [12]. with uneven size and creates
cluster heads rotate between the nodes in every grid correspondingly, the grid whose cluster head
utilizes more energy by means of offering unbalanced energy dissipation.
To produce system-level behaviors that show life-long adaptivity to changes and perturbations in
an external environment using [13].bee-inspired BeeSensor protocol that is energy-aware,
scalable and efficient. En Energy Efficient and QoS aware multipath routing protocol (
EQSR)[14]. that maximizes the network lifetime through balancing energy consumption across
multiple nodes, uses the concept of service differentiation to allow delay sensitive traffic to reach
the sink node within an acceptable delay, reduces the end to end delay through spreading out the
traffic across multiple paths, and increases the throughput through introducing data redundancy.
In this approach[15]. shares intermediate results among queries to reduce the number of
messages. When the sink receives multiple queries, it should be propagated these queries to a
wireless sensor network via existing routing protocols. The sink could obtain the corresponding
topology of queries and views each query as a query tree. With a set of query trees collected at
the sink.
The above planned research gap, encouraged us to plan a Fast and Secure Energy Efficient Data
Aggregation Scheme for Wireless Sensor Network matched to applications.
3. METHODOLOGY
WSN is a wireless sensor network relating to the distributed device by means of sensors to
observe the environmental conditions at different positions. A WSN is developed by planned
sensor nodes in an application area. The Sensor Node is a very important factor of WSN, is
prepared with Computation, sensing and wireless Communication unit. Wireless Sensor

56
Networks are created properly for security, environmental monitoring, computerization, habitat
groping and creative industries etc. A block diagram of wireless sensor network system
architecture is shown in Fig.1.
A characteristic node in a WSN is generally consists of four important units:
• A sensor,
• A processing unit,
• Transceiver, and
• A power supply unit
Figure 1. Block Diagram of Sensor Node
Energy utilization of a node is mainly developed as radio communications. The energy costs of
broadcasting and getting a k-bit data packet between two nodes being d meters separately can be,
equally, expressed as
)(),( dEkdkE ampelecTx ε+= (1)
and
elecRx kEkE =)( (2)
Eelec denotes the energy utilization due to digital coding, inflection, filtering, and diffusion of the
signal, etc and eamp is the energy utilized by the source power amplifier. The energy efficiency of
WSN is shown in Fig.2

57
Figure 2. Architecture diagram of the proposed EEUPA
The above figure (figure 2) explains the complete process of the EEUPA system. The EEUPA is
employed here for sending the sensed set of data from event identified sensor nodes to
destination. For sending out the data, efficient energy utilization path algorithm is designed. The
efficient energy utilization path algorithm is included by gathering the sequences of data
depending on gene characteristics sequencing to improve the life span of the network. The gene
characteristic series are estimated depending on the node energy drain rate and the utilization of
energy required for sending out the data.
3.1. Efficient Energy Utilization Path Algorithm (EEUPA)
In the designed EEUPA, energy utilization of sensed data collection in the network environment
is carried out by means of minimum energy utilization path algorithm. Through minimum node
energy drain rate, and minimized message overhead, gene contains two properties,
Hidden property and
Exposed property
On processor node, drain rate series is sustained in gene property called allele. Path series at
particular occurrence is taken with exposed allele of gene traits. Secondly, hidden allele of gene
traits are protected for future sequencing occurrence on exact threshold. Among the frequency
sets, we recognize the active path depending on the arrangement of allele in each occurrence of
the nodes in the network. Consider a set of sensors planned in a field. The EEUPA system offers
the following properties of the Wireless Sensor Networks:
(1) There are only one destination and sensor nodes in the WSN.
(2) The destination is inactive and the topology of the WSN collects data generated by the nodes.
(3) The destination has sufficient power supply while nodes are powered by batteries with
restricted energy.
(4) Nodes do not move after they are planned, to the destination.
(5) All nodes have similar traits
The path node mediators in the network are selected depending on the gene characteristics series
on energy utilization and node energy drain rate. The energy drain rate of the gene character
series are recognized depending on the staying energy and the draining speed of the nodes in the

58
network. The node energy drain rate is calculated depending on the genes (nodes) staying energy
and the time. The energy drain rate is planned as,
PTCT
CEPE
TT
EE
genesDR
−
−
=)( (3)
Where,
DR (genes) – Drain rate of the gene sequences
EPE – Prior assigned energy of the particular gene
ECE – Present energy of the particular gene
TCT – Present time of the gene presence in the network
TPT – Prior time of the gene
In the WSNs, recognize the path node mediators from source to sink. After that for every
occurrence of node, the utilization of energy and the node energy drain rate is calculated. To offer
the effective path, choose the series of gene characteristics which has less energy drain rate and
energy utilization. By tracking the system of choosing the path node mediators, the data packet is
effectively transferred from source to sink in less amount of time. The algorithm below explains
the process of choosing the effective path node mediators.
From Fig.3 primarily packets are sent through the path to reach the destination from source after
selecting the S and D from the sensor networks. The staying energy of all the path series of node
mediators in the network is subsequently calculated. The staying energy of all nodes for
Figure 3. Processing the Path Node Mediators
particular period of time is accumulated in the file. This is used to calculate the energy draining
speed. The staying energy of nodes and node energy drain rate is calculated by means of the
threshold values. Compare the energy utilization of node and the energy drain with their
equivalent threshold values to find out the best path. Depending on the calculated value, choose
the node which has minimum value of energy utilization and drain rate of energy.
The key task of the efficient energy utilization path algorithm is to choose the maximum network
lifespan for a given wireless sensor networks. Consequently, by investigating the properties of
gene, the frequency set is recognized. By means of this set, the path for the particular occurrence
is recognized and leads it. In the proposed EEUPA work, the path series characteristics are

59
accumulated at every exposed allele and the hidden alleles are preserved for future set of path
occurrences.
Figure 4. Process of Choosing Effective Path Node Mediators
4. EXPERIMENTAL DISCUSSIONS
We execute experiments to verify the proposed EEUPA system and evaluate its results. In the
simulations, we used 1/5 of every data as preparation period, and permit the nodes to create their
primary contact history. After the preparation time, we created 1000 messages, all starting from
an arbitrary source node to an arbitrary destination node for every t seconds. The period of the
experiment is set as t = 300s. All messages are consigned a Time-To-Live (TTL) value
representing the majority delay restriction.
For the discussions, we have used three set of parameters to calculate the performance of the
proposed EEUPA system and compared with the existing works like LEACH (Low Energy
Adaptive Cluster Hierarchy), PEGASIS (Power-Efficient Gathering in Sensor Information
Systems). The Performance of EEUPA is measured in terms are listed as,
i) Node Energy Drain Rate,
ii) Message Overhead and
iii) Network Lifespan
5. RESULTS AND DISCUSSION
In this section, we evaluate the attained results with the existing algorithm to calculate the result
of the proposed EEUPA system. The below table and graph explains the performance estimation
of both the existing and proposed methods.

60
5.1. Node Energy Drain Rate
Table 1. No. Of Nodes Vs. Node Energy Drain Rate
No. of Nodes
Node Energy Drain Rate (%)
EEUPA Method LEACH Scheme PEGASIS
25 5 10 7
50 9 15 12
75 15 21 18
100 19 28 25
125 24 35 30
150 28 40 35
175 30 47 42
200 32 58 49
The node energy drain rate is calculated based on the number of active nodes in the wireless
sensor networks and the values of the proposed Efficient Energy Utilization Path Algorithm is
compared with the existing LEACH and PEGASIS schemes and illustrated in Table 1.
Fig.5 explains the drain rate as a method to report for the rate at which energy gets degenerated at
a specified node. Every node observes its energy and preserves its battery power drain rate value
by taking mean of the amount of energy utilization and calculating the energy dissipation per
second. Compared to existing works like LEACH and heuristic model, the proposed EEUPA has
small energy drain rate since it used large amount of energy for sending the data between the set
of nodes in WSN. However in the proposed work, decrease the surplus dissipation of particular
nodes by considering the present traffic situation and by using the drain rate of the staying battery
capability.
Figure 5. No. of Nodes vs. Node Energy Drain Rate
5.2. Message Overhead
The occurrence of message overhead is calculated depending on the number of messages to be
sent into the wireless sensor networks and the values of the proposed EEUPA is compared with
the existing LEACH and PEGASIS schemes is illustrated in Table 2.

61
Fig.6 illustrates the existence of message overhead depending on the number of messages to be
sent into the network. The message overhead is termed as the number of unsuccessful messages
acquired while sending the sensed data from source to destination. Because the path verifying is
completed in the proposed EEUPA, results in minimizing the message overhead on the path series
for the data collection of sensed events by the destination. By comparing to the other existing
works like LEACH and PEGASIS, the proposed EEUPA has fewer messages overhead.
Table 2. No. Of Messages Vs. Message Overhead
No. of Messages
Message Overhead (%)
15 5 22 26
30 11 29 32
45 15 35 39
60 19 42 45
75 23 49 50
90 28 53 59
105 32 59 65
5.3. Network Lifespan
The lifespan of the network is calculated depending on the number of nodes in the wireless sensor
networks and the values of the proposed EEUPA is compared with the existing LEACH and
PEGASIS schemes is described in Table 3.
Fig.7 explains the lifespan of the network depending on the number of nodes in the network.
Because the energy drain rate of the nodes in the network environment is less, the life span of the
network increases correspondingly.
Figure 6. No. of Messages vs. Message Overhead

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Table 3. No. Of Nodes Vs. Network Lifespan
No. of Nodes
Network Lifespan (%)
25 85 65 45
50 87 68 49
75 89 70 52
100 90 74 56
125 93 77 60
150 95 80 64
175 96 83 69
200 98 85 73
Figure 7. No. of Nodes vs. Network Lifespan
Compared to the existing works like LEACH and PEGASIS, the proposed EEUPA work attains
high network lifetime as the utilization of minimal energy drain rate nodes on data collection gene
series increases the lifespan of the node and the network.
6. CONCLUSION
In this paper we proposed a new method which is employed to calculate the lifespan of nodes
constant with present traffic conditions. We described a mechanism, called the EEUPA which
cannot be utilized in any of the present wireless sensor path protocol as a way organization
principle. This metric is good at reflecting the current dissipation of energy without reflecting on
other traffic measurements, like queue length and the number of links passing via the nodes. The
main objective of EEUPA is not only to increase the lifespan of every node, but also to extend the
lifespan of every link by means of selecting the consistent path from source to

63
destination. With NS-2 simulator, the EEUPA method is compared with the existing works like
LEACH and PEGASIS, and the results explained that the EEUPA evades over dissipation of
energy, since the sequence path for the particular node is processed with allele of gene traits.
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