Conference PaperPDF Available

Novel Location Tracking Energy Efficient Model for Robust Routing over Wireless Sensor Networks

Authors:

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.
1
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
2
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
3
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-ecient 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.
4
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
REFERENCES
[1] A. Razaque, K. M. Elleithy, “Automated energy saving (AES)
paradigm to support pedagogical activities over wireless sensor
networks,” in proceedings of 6th Springer/ACM international
conference on Ubiquitous Computing and Ambient Intelligence
(UCAmI). Vitoria-Gasteiz, Spain, December 3-5, 2012, pp.512-519.
[2] A. Razaque and K. Elleithy, “Least Distance Smart neighboring
Search (LDSNS) over Wireless Sensor Network,” In proceedings of
IEEE conference on European Modeling Symposium (EMS),
Manchester, UK, 2013.
5
[3] Wei Ye, F. Ordonez, “Robust Optimization Models for Energy-
limited Wireless Sensor Networks under Distance Uncertainty,” IEEE
Transactions on Wireless Communications, September, 2006.
[4] Khushboo Pawar, Y.Kelkar,”A Survey of Hierarchical Routing
Protocols in Wireless Sensor Network,” International Journal of
Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5,
May 2012.
[5] M. Godwin Premi, K. Shaji, “Impact of Mobility Models on MMS
Routing in Wireless Sensor Networks,” International Journal of
Computer Applications (0975 – 8887) Volume 22– No.9, May 2011.
[6] Jamal N. Al-Karaki, Ahmed E. Kamal, “Routing Techniques in
Wireless Sensor Networks: A Survey” IEEE Wireless
Communications, Volume 11 Issue 6, December 2004, pp. 6-28.
[7] Stefanos A. Nikolidakis, Dionisis Kandris, Dimit rios D, Vergad os,
Christos Douligeris, “Energy Efficient Routing in Wireless Sensor
Networks Through Balanced Clustering,” Algorithms 2013, 6(1), pp.
29-42.
[8] M. J. Handy, M. Haase, D. Timmermann,Low Energy Adaptive
Clustering Hierarchy with Deterministic Cluster-Head Selection,”
Fourth IEEE Conference on Mobile and Wireless Communications
Networks, Stockholm, September 2002
[9] Stephanie Lindsey, Cauligi S. Raghavendra, “PEGASIS: Power-
Efficient Gathering in Sensor Information Systems,” Proceedings of
the IEEE Aerospace Conference, March 2002.
[10] Parminder Kaur, Mrs. Mamta Katiyar, “The Energy-Efficient
Hierarchical Routing Protocols for WSN: A Review” International
Journal of Advanced Research in Computer Science and Software
Engineering - Volume 2, Issue 11, November.
[11] I. Rhee, A. Warrier, M.Aia and J.Min, “Z-MAC: A Hybrid MAC for
Wireless Sensor Networks,” In proceedings of International
Conference on Embedded Networked Sensor Systems (SENSYS), San
Diego, California, USA, 2005.
Article
Full-text available
In this paper, we describe about the performance of different mobility models on MMS Routing. MMS routing is the technique used for large wireless sensor networks where MMS indicates multiple mobile sinks. The mobility of sink is considered in random manner in the basic MMS routing. Here we show the effect of different mobility models in the sink"s mobility. From the observations what we got from simulations we decide that wind mobility will be more energy efficient and it is good to be selected for wireless sensor network.
Article
Full-text available
The wide utilization of Wireless Sensor Networks (WSNs) is obstructed by the severely limited energy constraints of the individual sensor nodes. This is the reason why a large part of the research in WSNs focuses on the development of energy efficient routing protocols. In this paper, a new protocol called Equalized Cluster Head Election Routing Protocol (ECHERP), which pursues energy conservation through balanced clustering, is proposed. ECHERP models the network as a linear system and, using the Gaussian elimination algorithm, calculates the combinations of nodes that can be chosen as cluster heads in order to extend the network lifetime. The performance evaluation of ECHERP is carried out through simulation tests, which evince the effectiveness of this protocol in terms of network energy efficiency when compared against other well-known protocols.
Conference Paper
Full-text available
In this paper, we introduce a novel least distance smart neighboring search (LDSNS) to determine the mostefficient path at one-hop distance over WSNs. LDSNS helps to reduce the energy consumption and speeds up scheduling for delivery of data. It provides cross layering support and linking MAC layer with network layer to reduce the amount of control messages. LDSNS is a robust and efficient approach that isbased on single-hop communication mechanism. To validate the strength of LDSNS, we incorporate LDSN in Boarder Node Medium AccessControl (BN-MAC) protocol [ 15] to determine the list of neighboring sensor nodes and choosing best 1-hop efficient search to avoid collision and reducing energy consumption. Evaluation of LDSNS is conducted using network simulator-2 (ns2).The performance of LDSNS is compared with minimum energy accumulative routing problem (MEAR) [12], asynchronous quorum-based wakeup scheduling scheme (AQWSS) [14] and Minimum Energy Relay Routing (MERR) [13]. Simulation results show that LDSNS is highly energy efficient and faster as compared with MEAR, AQWSS and MERR. It saves 24% to 62% energy resources and improves12% to 21% search at 1-hop neighboring nodes.
Chapter
Full-text available
Fast expansion in ambient intelligence (AmI) has attracted different walks of people. AmI systems provide robust communication in open, dynamic and heterogeneous environments. This paper presents a AES paradigm that introduces wireless sensor networks to control remote servers or other devices at remote place through mobile phones. The main focus of paper is to consume minimum energy for obtaining the objectives. To realize the paradigm, mathematical model is formulated. The proposed paradigm consists of automatic energy saving model senses the environment to activate either the passive or active mode of sensor nodes for saving energy. Simulations are conducted to validate the proposed paradigm; we use two types of simulations: Test bed simulation is done to check practical validity of proposed approach and Ns2 simulation is performed to simulate the behavior of wireless sensors network with supporting mathematical model. The prototype can further be implemented to handle several objects simultaneously in university and other organizations.
Article
Full-text available
Wireless sensor networks consist of small nodes with sensing, computation, and wireless communications capabilities. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. Routing protocols in WSNs might differ depending on the application and network architecture. In this article we present a survey of state-of-the-art routing techniques in WSNs. We first outline the design challenges for routing protocols in WSNs followed by a comprehensive survey of routing techniques. Overall, the routing techniques are classified into three categories based on the underlying network structure: flit, hierarchical, and location-based routing. Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, QoS-based, and coherent-based depending on the protocol operation. We study the design trade-offs between energy and communication overhead savings in every routing paradigm. We also highlight the advantages and performance issues of each routing technique. The article concludes with possible future research areas.
Article
Full-text available
The actual performance of a wireless sensor network (WSN) can be severely influenced by uncertainty present in the environment where it is deployed. For example, the distance between nodes, the quality of the communication channel, and the energy consumed in transmission are all problem parameters that may be subject to uncertainty in real domains and can affect performance. In this paper we consider optimization models of WSN subject to distance uncertainty for three classic problems in energy limited WSNs: minimizing the energy consumed, maximizing the data extracted, and maximizing the network lifetime. We use robust optimization to take into account the uncertainty present. In a robust optimization model the uncertainty is represented by considering that the uncertain parameters belong to a bounded, convex uncertainty set U. A robust solution is the one with best worst case objective over this set U. We show that solving for the robust solution in these problems is just as difficult as solving for the problem without uncertainty. Our computational experiments show that, as the uncertainty increases, a robust solution provides a significant improvement in worst case performance at the expense of a small loss in optimality when compared to the optimal solution of a fixed scenario.
Conference Paper
This paper presents the design, implementation and performance evaluation of a hybrid MAC protocol, called Z-MAC, for wireless sensor networks that combines the strengths of TDMA and CSMA while offsetting their weaknesses. Like CSMA, Z-MAC achieves high channel utilization and low-latency under low contention and like TDMA, achieves high channel utilization under high contention and reduces collision among two-hop neighbors at a low cost. A distinctive feature of Z-MAC is that its performance is robust to synchronization errors, slot assignment failures and time-varying channel conditions; in the worst case, its performance always falls back to that of CSMA. Z-MAC is implemented in TinyOS.
Conference Paper
This paper focuses on reducing the power consumption of wireless microsensor networks. Therefore, a communication protocol named LEACH (low-energy adaptive clustering hierarchy) is modified. We extend LEACH's stochastic cluster-head selection algorithm by a deterministic component. Depending on the network configuration an increase of network lifetime by about 30% can be accomplished. Furthermore, we present a new approach to define lifetime of microsensor networks using three new metrics FND (First Node Dies), HNA (Half of the Nodes Alive), and LND (Last Node Dies).
Conference Paper
Sensor webs consisting of nodes with limited battery power and wireless communications are deployed to collect useful information from the field. Gathering sensed information in an energy efficient manner is critical to operate the sensor network for a long period of time. In W. Heinzelman et al. (Proc. Hawaii Conf. on System Sci., 2000), a data collection problem is defined where, in a round of communication, each sensor node has a packet to be sent to the distant base station. If each node transmits its sensed data directly to the base station then it will deplete its power quickly. The LEACH protocol presented by W. Heinzelman et al. is an elegant solution where clusters are formed to fuse data before transmitting to the base station. By randomizing the cluster heads chosen to transmit to the base station, LEACH achieves a factor of 8 improvement compared to direct transmissions, as measured in terms of when nodes die. In this paper, we propose PEGASIS (power-efficient gathering in sensor information systems), a near optimal chain-based protocol that is an improvement over LEACH. In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. Simulation results show that PEGASIS performs better than LEACH by about 100 to 300% when 1%, 20%, 50%, and 100% of nodes die for different network sizes and topologies.