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Novel Location Tracking Energy Efficient Model for Robust Routing over Wireless
Sensor Networks
Fatma Almajadub and Khaled Elleithy
Department of Computer Science and Engineering
University of Bridgeport, CT-06604, USA
falmajad@my.bridgeport.edu, elleithy@bridgeport.edu
Abstract - Detecting the location of a mobile sensor node using
signal strength has developed an area of dynamic research. The
major issue in this situation curtails from the difficulty of how
signals spread through space, particularly in the presence of
hurdles such as people, walls and buildings. From another
perspective, sensors are available with limited power capacity
and energy resources. The signal strength guides the node to
choose the right node for forwarding the data to the base
station. In this paper, we propose a novel location tracking
energy efficient (LTEE) model for wireless sensor networks.
The presented model in this paper tracks the neighbor node
based on the signal strength used for forwarding the data to the
next-hop node. LTEE reduces energy consumption and
prolongs the network lifetime. The simulation results
demonstrated that LTEE consumed 8% to 12.5% less power as
compared to other protocols.
Keywords: Location tracking energy efficient, Wireless Sensor
networks (WSNs), Energy consumption, signal strength, network
routing protocols.
I. INTRODUCTION
Wireless Sensor Networks (WSNs) comprises small size
sensor nodes that are dispersed in the area of interest for
monitoring the events used for gathering the data.
Meanwhile, WSNs face several challenges at all network
stacks such as excess energy consumption, scalability,
mobility, coverage and decrease of throughput due to latency
[1, 2]. Several protocols have been introduced at each layer,
but the network lifetime is of utmost importance. To
enhance the efficiency and prolong the network lifetime of
WSN, specifically, at the network layer, many routing
protocols have been implemented.
In addition, several studies were conducted for the
optimization of routing protocols as well as the optimization
of robust models in order to improve the performance of all
WSNs. These optimizations included the attempts to find
solutions for major problems such as maximizing the
extracted data and prolonging the network lifetime as well as
minimizing the consumed energy. [3] Although WSN
network performs many tasks, the most important factor in
optimizing the performance of WSN is the distance between
the nodes. However, the distance values might be changed
because the movement of node or be estimated by the
algorithm using the signal strength indirectly.
On the other hand, advances in WSNs have led to sensor
nodes with low-cost as well as provided the capability of
different sensing within modern technology, wireless
communication, the physical environmental conditions and
data processing. Thus the different sensing capabilities result
in wastefulness of the application areas [4]. Although the
sensor nodes of WSNs have limited ranges of data
transmission, limited ways of data processing, limited
storage capabilities and energy resources, we can say that
the achieving the most advantages of WSNs need superior
methods for supporting data transmission. Therefore,
designing efficient routing protocols and robust transmission
models are major issues of WSNs.
Some studies as [5] improved WSNs performance by
using various mobility models on multiple mobile sinks
(MMS) routing technique which supports large wireless
sensor networks. Generally, MMS routing technique uses
sink mobility in random methods. However, the study of [5]
showed the effect of various mobility models on the sink’s
mobility. In addition, [5] determined that the wind mobility
is a good choice for the optimization of WSNs because the
wind mobility achieves more efficient energy and can be
applied on more data with minimum time. Thus, this
technique saves the energy of the whole WSN network,
increases the lifetime of the network and allows more
collection of data. However, the study of [5] did not use this
technique for optimizing the distances based on mobility
models.
The study of [6] presented a survey of design challenges
for routing protocols and a survey of various routing
techniques such as hierarchical, flat and location of routing
within various routing protocols depending on protocol
operations such as multipath, negotiation, QoS, query and
coherent. The study in [6] investigated the trade-off between
energy consumption and the overhead savings of
communication for each routing as well as determined the
advantages and disadvantages of these techniques and
performance problems of each routing technique in the order
to extend the lifetime of WSN network without
compromising data delivery. Nevertheless, there are many
problems which need further investigation with regards to
optimization of WSNs.
The authors in [7] proposed to use ECHERP protocol for
energy conservation within balanced clustering by using the
algorithm of the Gaussian elimination in order to minimize
the whole network energy and prolong the network lifetime.
Furthermore, this protocol adopted a multi-hop routing
scheme for transferring data. Although [7] demonstrated by
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simulation that the protocol outperformed other protocols as
LEACH, PEGASIS and BCDCP, the protocol didn’t use the
metrics of QoS, time constraints, and nodes distances to
enhance ECHERP protocol.
Many studies reported in literature introduced techniques
to enhance the performance of the routing protocol as
LEACH, PEGASIS, TEEN and (LTEE) model without
considering the nodes distances and the signal strength
algorithm.
In this paper, we present a novel location tracking energy
efficient (LTEE) model for WSNs. This algorithm tracks the
neighbor node based on the signal strength technique and the
node distances for forwarding the data to the next-hop node.
Based on the simulation results, the proposed model of
LTEE reduces energy consumption and prolongs the
network lifetime.
II. PROPOSED LOCATION TRACKING MODEL
In this paper we deploy a novel tracking model for finding
the nearest router to forward the data based on the signal
strength. A major component of the tracking model is to
define the probability of an observation at the various
locations in the network. Before we illustrate the features of
our model for tracking, and demonstrate how to measure the
signal strength, let us assume that ‘A’ is the set of training
samples that can be obtained from a noisy process.
The noisy samples can be sampled as:
Where, is an input sample and is the target value
and is a zero mean.
Since the node does not have direct access to the
destination node but is based on the probability function, it is
important to show the related covariance function for a noisy
process. The covariance function is used to change the
variables. Similarly, sensor nodes can change the positions
which can be explained as Gaussian observation noise.
Here, is the noise in the network and is a
constant variable whose value is 1 if a = b and otherwise 0.
Input values for the whole set is ‘Y’, thus, the covariance for
related observation ‘X’ can be explained as:
Where ‘Z’ is the covariance matrix n x n for input values
that can be explained as:
We observe that matrix ‘Z’ can be generated for any set of
values ‘Y’ then we sample a set of related targets ‘X’. The
sampled value can be expressed as:
.
Here, we use the prediction function for determining the
arbitrary point ‘y+’ for sensor node on training data Y, x.
Thus, Gaussian function with mean and variance value can
be applied to identify the point’ y+’.
where
Equations (5) and (6) describe the benefits of the signal
strength. It further shows the regression model using training
data.
Input values for ‘Y’ relates to locations at ‘x’ for the signal
strength localization can be obtained. Thus, the observation
measurement can be accumulated using equations 5 and 6.
Now, it is possible to determine the exact distance of each
neighbor node based on the training data Y, x. Also, we can
also estimate the directions by capitalizing the log
probability of observations x. Let us assume ;
thus the observation can be made as follows:
It shows that observations are mutually proved through (7)
and that they can also be capitalized by using the conjugate
gradient descent function as explained in equation (8).
We use this model to determine the shortest possible
distance between sources to destination node based on the
signal strength. Figure 1 demonstrates and creates the
shortest route discovery process that helps to reach the
destination using a short distance.
Figure 1: Showing node tracking process based on signal strength
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III. SIMULATION SETUP AND ANALYSIS OF
RESULTS
We have implemented the location tracking energy
efficient (LTEE) model for routing over WSNs. We have
compared the performance efficiency of LTEE with other
known routing protocols; Equalized Cluster Head Election
Routing Protocol (ECHERP) [7], Low Energy Adaptive
Clustering Hierarchy (LEACH) [8], Power-Efficient
Gathering in Sensor Information Systems (PEGASIS) [9],
Threshold Sensitive Energy Efficient (TEEN) [10] and
Location tracking energy efficient (LTEE) model. The
sensor nodes are randomly distributed in the network area
and are placed at different locations of the network. When
the simulation starts, the nodes take time to warm-up and
move back and forth in the network due to their mobility.
Each simulation run is carried out for 5 minutes. At the
transport layer, we have used transmission control protocol
(TCP). The size of each packet is 128 bytes for transferring
the data. The MAC protocol for this simulation is Zone
medium access control (Z-MAC) [11]. We have focused on
sensor application modules with Quality of Service (QoS)
parameters. The radio transmission range is set to 30 meters.
We hereby show the simulation parameters in Table I.
TABLE I: SIMULATION PARAMETERS
Parameters Value
Start time of BN-
MAC
(0,40) Seconds
Sink location in each
region
(45, 25)
Mobility model Random way- point
Simulation time 5 minutes
Initial pause time 10 Seconds
Size of network 500 * 500 square meters
Type of network Hierarchal network
Packet transmission
rate
25 Packets/Sec
Transmission Range 30 meters
Routing algorithms (ECHERP), (LEACH),
(PEGASIS) (TEEN) and
(LTEE)
MAC protocol Z-MAC
Packet size 128 byes
Sensing Range of
node
12 meters
Initial energy of node 20 Joules
Bandwidth of node 45 Kb/Sec
Delivery of data at
varying sensing range
100, 200, 300,
400,500,600 and 700
meters
To evaluate the performance of LTEE protocol, we will
discuss in this section the energy efficiency and the network
lifetime.
A. Energy efficiency
We have analyzed the performance of LTEE and
compared it with other routing protocols such as: Equalized
Cluster Head Election Routing Protocol (ECHERP), Low
Energy Adaptive Clustering Hierarchy (LEACH), Power-
efficient Gathering in Sensor Information Systems
(PEGASIS) Threshold Sensitive Energy Efficient (TEEN).
Based on the simulation results, we have observed that
LTEE outperforms other competing routing protocols. LTEE
has a capability to sense the nearest node based on the signal
strength whereas other routing protocols lack this feature. In
Figure 2 and 3, we observe that LTEE is producing superior
outcomes than other routing protocols. Each routing protocol
performs its job and uses 180 rounds. After completion of
the intended task, we observed that LTEE consumed less
energy that is 1.4 joules after 180 rounds, whereas, other
competing routing protocols consume from 1.52 Joules to
1.71 Joules. This statistical data shows that LTEE is an
energy efficient routing protocol and as a result, the network
lifetime is much better improved.
B. Network lifetime based on base station
Routing in WSNs is reasonably varied due to numerous
partial limitations. The performance of the network usually
depends on the elasticity of the routing protocol. From
another perspective, a robust energy-efficient routing
protocol is a challenge for an energy-constrained network. In
this experiment, our aim is to determine the network lifetime
based on the distance of the sensor node from the base
station. We have observed the first and last node depletion
time in rounds at variable distance. In Figure 4 and 5, we
have analyzed the performance of LTEE by setting a
variable distance of a node from the base station. We have
observed that when the distance of the node increases from
base station, the network performance reduces in context of
rounds. We have assumed that the sensor nodes have a
distance of 100 meters to 300 meters from the base station to
determine the exact distance required to set the sensor nodes
at the network.
We have concluded that, distance affects network lifetime
as well as performance. Whenever distance increases,
performance decreases. Thus, the network distance is
inversely proportional to the lifetime of the network. It is
also validated that before designing the sensor network, we
must keep in mind the position of sensor nodes. Wrong
position of sensor nodes in the dispersed area can also affect
the throughput of the network as well as increase the
network latency.
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IV. CONCLUSION
In this Paper, we have presented a novel location
tracking energy efficient (LTEE) model for WSNs in order
to address the problem of minimizing the energy
consumption in WSNs, including energy consumption for
tracking. This was done by generating a novel model which
tracks the neighbor node depending on the signal strength
algorithm used for forwarding the data to the next-hop node.
In addition, our proposed model (LTEE) can reduce the
energy consumption and prolong the network lifetime, thus;
our proposed model of LTEE can optimize the performance
of the whole WSN network over shortest paths. Moreover,
we presented a model based on the signal strength algorithm
to find the nearest router. In addition to the mathematical
model, the simulation results demonstrated LTEE protocol
outperforms other known routing algorithms such as
ECHERP, LEACH, PEGASIS and TEEN algorithms. The
simulation results show that LTEE has reduced the energy
consumption from 8% to 12.5 %.
Figure 2: Average energy consumption at various network lifetime rounds
Figure 3: Showing nodes alive numbers VS network lifetime
Figure 4: Last node depletion time in rounds VS node ratio as base station is
located at 100 meters, 150 meters, 200 meters, 250 meters and 300 meters
ACKNOWLEDGEMENTS
The authors of the paper would like to acknowledge Mrs.
Doreen for review the language of this paper. Also the
authors of the paper would like to acknowledge Mr. Abdul
Razaque for his help with the simulation.
Figure 5: First node depletion time in rounds VS node ratio
as base station is located at 100 meters, 150 meters, 200
meters, 250 meters and 300 meters
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