A Review on State of Art Variants of LEACH

Transkript

Sensors & Transducers, Vol. 186, Issue 3, March 2015, pp. 25-32
Sensors & Transducers
© 2015 by IFSA Publishing, S. L.
http://www.sensorsportal.com
A Review on State of Art Variants of LEACH Protocol
for Wireless Sensor Networks
*
Yuvaraj P., Vikram K., Venkata Lakshmi Narayana K.
School of Electrical Engineering, VIT University, Vellore, Tamilnadu, 632014, India
*
Tel.: +91 9865324440
*
E-mail: [email protected]
Received: 4 February 2015 /Accepted: 5 March 2015 /Published: 31 March 2015
Abstract: Recent advances in wireless communication lead to many improvements in application specific
wireless sensor network (WSN) deployment. Sensing different data from different environments is essential to
monitor and control the situations. For instance, it is very important to sense the forest fire as early as possible to
control the upshot. So efficient and timely gathering of the data from a network of small sensor nodes is
necessary. In WSN, the small sized sensor nodes are working with very small batteries with limited energy.
Since those are randomly deployed over a wide area, replacement of battery or recharging is not feasible. So, for
getting prolonged life time of WSN, energy efficient operation is the key factor. Among many protocols
proposed for enhancing the life time of WSN, the clustering based hierarchical protocols are popular and
gaining the attention of researchers because of their high energy efficiency. Low Energy Adaptive Clustering
Hierarchy (LEACH) is energy efficient hierarchical, clustering based protocol. It is considered as the base of
many hierarchical clustering protocols. In this paper, some of the recent tailored protocols proposed to
strengthen LEACH are examined. Copyright © 2015 IFSA Publishing, S. L.
Keywords: LEACH protocol, Energy efficient protocol, Lifetime, Cluster, Wireless sensor networks.
1. Introduction
Wireless sensor network (WSN) is a network with
many number (from hundreds to several thousands)
of small, battery operated sensor modules (generally
called as nodes), formulated for a particular
application [1-5]. A node comprises of a sensor,
central processing unit, memory device, and the
antenna. The architecture of the sensor node is shown
in Fig. 1. The different sensors are now available for
measuring temperature, moisture, vibrations,
brightness, and sounds etc. These sensors can sense
the data from the environment and communicate to
location where the data is required (generally called
as Base Station (BS)). That data can be utilized for
analysis, monitoring and controlling. The WSNs can
http://www.sensorsportal.com/HTML/DIGEST/P_2619.htm
be used in a variety of regular life activities. A
standard application of WSNs is monitoring.
Fig. 1. Wireless sensor node architecture.
25
In interested area of monitoring, the WSN is
deployed over a wide region to monitor some event.
The sensor nodes are randomly deployed in ad-hoc
manner over the interested area using helicopters.
The applications include habitat observation [6-7],
military application for monitoring enemy territory
[8], environment monitoring like earth quake, wild
fire monitoring and Tsunami forecasting etc. [9],
traffic management and road safety [10], under water
surveillance system [11], biomedical signal
monitoring [12], and agricultural applications for
measuring the condition of soil, etc. [13].
send the data directly to the BS. But the nodes which
are far away from the base station need to spend very
high energy for transmitting the data since the energy
spent for communication is directly proportional to
the distance between the sender and the receiver.
Therefore, those nodes will drain their complete
energy soon, and similar kind of data is obtained in
the base station from the nodes which are closely
deployed with in a region.
1.1. Energy Efficiency in WSN
Many energy efficient protocols are prescribed in
the past [14-16]. The battery of a sensor node in
WSN is drained in various operations like sending
and receiving the signal (data), memory operations,
data aggregation, retransmissions due to loss of data.
Among all the above aspects, the majority of the
power is drained in:
1.1.1.
Communicating the Data to Other
Nodes and BS
1.2.2. Multi hop Communication
From the first order wireless radio model, it is
valuable to note the energy needed for sensor node to
send one data to other node is given in Equation (1).
ETX(k, d)=Eelec×k+Eamp×k×d2,
(1)
where ETX is the transmitter energy, Eelec is the
electronics energy, k is the number of bits of data,
Eamp is the amplifier energy and d is the distance
between the sender and the receiver node. From
Equation (1) it is easily identified that when the
distance increases, the total energy dissipated is
increased to the square of the distance.
1.1.2.
The Multi hop communication is illustrated in
Fig. 3. Here, the nodes deployed far away from the
base station send the data to the nearest node which is
in the path of Base Station. Thus long distance
communication is eliminated, but the nodes deployed
closer to the base station are in operating condition
every time because the data should be routed through
them. The duplicate data from the nodes from the
same region are obtained from this type of
communication.
Data Aggregation
When more sensor nodes are densely placed in a
particular location, the data sensed by several nodes
are almost same. In that case, only the consolidated
data can be sent to the base station. This is known as
data aggregation and considerable amount of energy
is drained for this operation. But it is necessary to
avoid the duplicate data because we need the state of
an area rather than individual numbers. By doing this,
we are eliminating unnecessary data transmission to
the base station and increasing the life time of the
node as well as the network.
1.2. Types of Communication
1.2.1. Direct Communication
The Direct communication is illustrated in Fig. 2.
In the earlier stage, the sensor nodes are instructed to
26
Fig. 2. Direct communication.
Fig. 3. Multi hop Communication.
1.2.3. Clustered Communication
The Clustered communication is illustrated in
Fig. 4. In Clustered communication, the closer nodes
are grouped in to a cluster. One leader (generally
called as Cluster Head (CH)) is elected for gathering
all the data from that cluster. After gathering all the
similar data, the CH aggregates the data and sends
the aggregated data to the BS. By doing this, the
transmission between very far nodes is avoided and
the duplicate data received by the BS is eliminated.
Fig. 4. Clustered communication.
This paper is further organized as follows.
Section 2 describes the LEACH in detail. Section 3
illustrates the energy efficiency of LEACH over
conventional methods. Section 4 states various
modified versions of LEACH. In Section 5, Table 2
compares all the discussed modified LEACH
versions. Section 6 concludes the paper.
k

: Ci (t ) = 1

N
Pi (t ) =  N − k × ( r mod )
,
k

0 : Ci (t ) = 0
(2)
where is Pi(t) the probability of ith node to become
CH, k is the desired percentage of nodes to be CHs, N
is the total number of nodes in the network, r is the
current round.
After the CHs are decided, the CH sends an
advertisement message to the other nodes that they
are elected as CH. Based on the signal strength, the
nodes which receive that advertisement message will
join the cluster. In the second stage, the Time
Division Multiple Access (TDMA) schedule is
created for each sensor node to send their data to the
corresponding CH. After receiving all the data from
the normal sensor nodes, the CH performs the data
aggregation in form of Beam forming algorithm or
Data Fusion. Finally The CH sends the data to the
Base Station.
2. LEACH Protocol
When clustered communication was introduced, a
fixed cluster and the cluster head is arranged for
communication. CH is drained quickly since it is
gathering all the data from the sensor nodes and it has
to aggregate that data. After aggregating the data it
has to send the data to the base station which is far
away generally. Hence it is identified that rotation of
CH among the nodes is mandatory. So based on the
concept of rotating the CH, researchers proposed
many clustered communication protocols [17-21].
LEACH [22] is the very popular protocol for
WSN, based on rotation of CHs using probabilistic
method. It is developed in the year 2002. Still it is
used by many researchers as a reference [23]. In
LEACH, the sensor nodes can organize themselves
based on the received signal strength, in to a cluster
which is known as distributed clustering method.
Every node has the possibility of becoming the CH at
least once to ensure the proper load balancing. The
non-cluster nodes use the least energy for joining the
cluster. That is the other advantage of LEACH
protocol. All non-cluster nodes send the data to the
CH. So the distance for which the communication to
be done, is reduced. The CH performs the data
aggregation locally in the cluster and sends the data
to the BS. LEACH consists of two important phases.
One is setup phase and the other is communication
phase. In setup phase, an optimum percentage of
nodes which would be acting as CH are decided. The
cluster formation flow chart is shown in Fig. 5.
The CH is selected based on the threshold value
shown in Equation (2).
Fig. 5. Cluster formation process of LEACH.
3. Simulation Results
Direct
communication,
Static
clustering
communication, and LEACH protocol are simulated
using MATLAB and the life times of the networks
are compared.
In direct communication, all nodes send the data
directly to the base station. If distance between the
base station and the nodes are much, it needs a large
amount power for transmission. In static clustering,
all the nodes send the data to the CHs. The CHs send
the data to the base station. But here the CH is fixed.
27
So the burden of the CHs is more compared to
ordinary nodes. In LEACH the CHs are rotated for
every round. Each node becomes CH at least once.
For simulation, simple first order radio model is
considered. For sending the data, Equation (1) is used
and for receiving the data Equation (3) is used.
E RX (k , d ) = Eelec × k
(3)
The simulation parameters are listed in the
Table 1 and simulation results are displayed in Fig. 6.
Table 1. Simulation parameters.
Nodes
100
Base station location
(50, 175)
Data size
2000 bits
Radio Electronics Energy, Eelec
50 nJ/ bit
4.2. Optimized LEACH (O-LEACH)
2
Amplifier energy, Eamp
100 Pj / bit / m
Data aggregation Energy, EDA
5 nJ / bit / signal
Fig. 6. Network life time of Direct, Static clustering
and LEACH when initial energy of each node = 0.5 J.
The simulation results show that the
clustering communication with rotating CHs
is better than the direct communication and static
clustering communication. In fact, the energy
efficiency of static clustering is worse than the
direct communication.
4. LEACH Based State of Art Protocols
4.1. Security Authentication LEACH
(SA-LEACH)
Yanhong Sun and Ming Tang [24] proposed
SA-LEACH; a Security Added LEACH. The authors
are claiming that with some modifications, the energy
consumption of total network can be reduced and
hence the lifetime can be improved. Further, security
28
is added for safe operation within WSN.
SA-LEACH is divided in to three stages:
1) Preparations before the network deployment;
2) Cluster formation stage;
3) Stabilization stage.
In the First stage, unique random ID is allotted for
every sensor node. That Id is valid for that particular
round only. By verifying the ID in both the sender
and the receiver side, safe transmission is ensured. In
the Second stage, when the clusters are formed, the
ordinary nodes which are having the security key of
nearest CH can only join with that cluster. Some
nodes won’t be in the range of the selected CHs. The
left out nodes act as Individual cluster and can send
the data to base station or they can be set out to sleep.
By this way, the energy consumption is reduced to
some extent compared to LEACH. The stabilization
stage is designed as it is in the LEACH.
Salim EL Khediri, et al. [25] introduced
O-LEACH with some optimization in CH selection.
Instead of selecting the CHs based on the
probabilistic rotation, the authors are taking the
dynamic residual energy alone for selecting the CHs
and concluding that O-LEACH increases the stability
of the network and it can be used for dynamic
networks. Moreover, it uses the centralized concept
of electing the CHs. The BS is responsible for
making efficient clusters based on the residual
energy. The BS begins the making of clustering
process. The CH is elected by considering the energy
of the sensor nodes. Nodes which have energy greater
than 10 percentage of minimum residual energy are
considered for the selection of CHs. If the energy of
nodes are lesser than 10 percentage of minimum
residual energy, the original LEACH method is
followed for selecting the CHs. Thus, by adding this
new factor when selecting the CH, O-LEACH is
competing with LEACH.
4.3. Energy Efficient LEACH Protocol
(EELP)
Abdullah Erdal Tumer and Mesut Gunduz [26]
introduced a more practical protocol called EELP
which is not designed for the ad hoc wireless sensor
networks. It is specifically designed for the network
of sensor nodes, whose locations are predetermined.
The general application of EELP is to identify the
hazardous gases in residential or commercial
buildings. In EELP, two thresholds are set namely
Lower Threshold (LT) value and Higher Threshold
(HT) value. If the sensed data is below the LT, it will
not be sent to the BS. If the sensed data (dangerous
level) is greater than the HT, there is no need to wait
for the CH. It can send the sensed data directly to the
BS. Another important aspect is EELP uses XOR
operation for eliminating the duplicate data sensed by
the nearby nodes.
4.4. Cognitive LEACH (CogLEACH)
4.7. Improved LEACH
Rashad M. Eletreby, et al. [27] developed
CogLEACH for cognitive radio sensor networks. The
authors are utilizing the potentials of cognitive radios
and WSNs to form a new high-speed, distributed,
spectrum-aware protocol. CogLEACH uses the
sensor nodes as SU (Secondary Unit) and some PU
(Primary Units). The sensor nodes are free from
congestion bands and can utilize less crowded bands
with the capacities of cognitive radios. The threshold
is fixed based on the vacant channels mainly. It has
very less control overhead, and can support for
different PU models. The CH is selected based on
availability of more number of idle channels of
nodes. It considerably improves the throughput and
network life time of WSN.
Sandeep Sharma and Sapna Choudhary [30]
developed improved LEACH. In this, three types of
nodes are used namely normal, intermediate and
advanced with different energy levels. Normal nodes
are used for getting the data from the area of interest.
Intermediate nodes which are having some higher
energy level compared to normal nodes are elected as
CHs. The advanced nodes are used as the router
between CHs and BS. Initially the intermediate nodes
are selected as CHs based on the threshold value as in
the LEACH protocol. After collecting all the
information from the normal nodes which are
associated with the particular clusters, these CHs are
doing the data aggregation once and send the data to
either the adjacent CH or to the advanced nodes. The
advanced nodes once again aggregate data collected
from various CHs and forward the result to the BS.
4.5. LEACH with Spare Management
(LEACH-SM)
Bilal Abu Bakr and Leszek T. Lilien [28]
introduced the concept of managing spare nodes for
the primary CHs. LEACH has two demerits. One is
the hotspot problem which is occurred due to
additional workload of CHs. The second is redundant
data transmissions to CHs. Both the problems are
rectified when spare nodes are used. When the
primary CH loses its entire energy, the spare node
takes the responsibility and thus avoiding the
discontinuation in obtaining data. LEACH-SM also
elects the appropriate spare nodes and decides how
long the spare nodes should be in idle condition. The
authors are claiming that using this protocol, life time
of LEACH can be extended up to 50 %.
4.6. Air Quality Monitoring LEACH
(AQM-LEACH)
Henady M. Abdulsalam, et al. [29] developed
AQM-LEACH specifically for Monitoring the Air
Quality. Unlike LEACH, the nodes do not
continuously send the data to the BS at every round.
This protocol is an event driven protocol in which the
nodes are mostly in sleep mode. When any
abnormality occurs in the Air Quality, the nodes tend
to send more details to the BS regarding the
abnormality. Based on some preset Air Quality Index
(AQI), it is decided to send the data to the BS or not.
The AQI is a measure of air quality to indicate the
level of pollution in the surrounding. It is used for
forecasting the level of danger for human health. The
value is different for different countries. When any
one of the senor nodes senses the event which is
abnormal, more data is collected from the nodes.
Thus by keeping the nodes idle for most of the time,
the life time of the network is increased.
4.8. Energy Based LEACH
Mohammad Shurman, et al. [31] introduced two
different approaches for enhancing the life time of
the network. The first approach uses the transmission
radius of nodes, i.e. node coverage for finding the
threshold along with probabilistic approach as in
LEACH. In Second approach, the residual energy and
the distance between the nodes are considered for
selecting the CH. In both the approaches, the number
of CHs is not defined randomly. It is based on the
transmission range of the nodes.
4.9. Optimized LEACH (Op-LEACH)
Sapna Gambhir and Nida Fatima [32] introduced
Op-LEACH. It is the optimized version of LEACH in
which the TDMA slots are utilized wisely. LEACH
uses a TDMA based MAC protocol to maintain
balanced energy consumption. But it distributes the
slots uniformly. Since Op-LEACH is an event driven,
after the cluster formation, when the sensor node
does not have any data to send, then the TDMA slot
is allotted for some other sensor node to send its data.
So, latency of the overall network is reduced
considerably. By keeping the idle nodes in sleep
mode, the overall life time is increased. Op-LEACH
reduces the network traffic.
4.10. LEACH with Mobile Sink and
Rendezvous Nodes
Saeid Mottaghi and Mohammad Reza Zahabi [33]
introduced a Mobile Sink (MS) and Rendezvous
points (RP) for collecting the information from the
WSN effectively with reduced energy consumption.
MS can traverse between the nodes either randomly
or in the preset MS trajectory. When MS is travelling
in a fixed trajectory, the Rendezvous Nodes (RN) is
29
helpful in getting the data from the sensor nodes.
Cluster formation is done as in LEACH protocol, and
the CHs send the aggregated data to the RN. The RN
nodes send it to MS when MS is travelling through
the Trajectory and passing via RNs.
4.11. Multi-Level LEACH
Ravi Kishore Kodali and Naveen Kumar
Aravapalli [34] introduce the different layer level
concept for collecting the information from the
sensor network. In Level 1, every node is considered
as leaf nodes with equal energy. The first round is
setup same as that of LEACH Protocol. In
consecutive rounds, the CHs maintain a list of its
members. Level 2 CHs are elected based on the
residual energy and distance between nodes. Now the
Level 2 CHs also maintain the Level 1 CH members.
Level 2 CHs collect the aggregated data from the
Level 1 CHs and store it. Similarly Level 3, Level 4,
Level 5 and Level 6 are created and thus provide the
multi-hop mode of communication between the
nodes and the base station. Thus Multi-level LEACH
cleverly utilizes the concept of multi hop inter cluster
connectivity and improves the life time of the
network.
4.12. Percentage LEACH (PR-LEACH)
Mahmoud M. Salim, et al. [35] introduced
PR-LEACH; an event driven, multi-hop interclustering protocol. Initially CHs are selected
randomly as in LEACH and data transmission is
taken place. After first round, each node sends the
residual energy to the corresponding CH. After
receiving data from the nodes, CH sends the data to
adjacent CHs with their residual energies. The same
operation is done in every cluster. BS calculates the
Network Energy Range (NER) based on a predefined
threshold value. If residual energy of the node is less
than the threshold value, then that node is considered
as ordinary node, and if it is maximum of all the
nodes in the cluster, that node is elected as CH for
further round. Since it is an event driven protocol, the
data transmission occurs between the nodes randomly
in every round.
5. Comparison of LEACH Based
Protocols
Table 2 compares all the discussed modified
LEACH versions.
Table 2. Comparison of LEACH based protocols.
LEACH
Inter Cluster
Connectivity
Single hop
LEACH-C
Single hop
LEACH-F
Single hop
SA-LEACH
O-LEACH
EELP
Single Hop
Single Hop
Single Hop
Parameters
Clustering
Cluster
Cluster Head Selection
Method
Count
Distributed Variable
Random
Energy and
Centralized Fixed
simulated annealing
algorithm based
Energy and
centralized Fixed
simulated annealing
algorithm based
Random
Energy based
Centralized Fixed
Purely energy based
CogLEACH
Single Hop
Distributed
Variable
Random
LEACH-SM
Single Hop
Distributed
Variable
Random
9. AQM-LEACH
10. Improved
LEACH
11. Enhanced
LEACH
12. Multi-level
LEACH
13. Op-LEACH
14. LEACH with
mobile sink and
rendezvous nodes
15. PR – LEACH
Single hop
Distributed
Variable
Random
Multi Hop
Distributed
Variable
Energy based
Single hop
Distributed
Variable
Energy & distance based
Multi Hop
Distributed
Variable
Energy Based
Scalability
Single hop
Distributed
Variable
Random
Multi Hop
Distributed
Variable
Energy Based
Single Hop
Distributed
Variable
Energy Based
Event Driven
Mobile Sink
Rendezvous
nodes
Event Driven
S.
No.
1.
2.
Protocol Name
3.
4.
5.
6.
7.
8.
30
Value added
with LEACH
Centralization
Fixed Cluster
Security
Stability
Spectrum
awareness
Cognitive Radios
Energy
Efficiency
Event driven
Multi hop
communication
Reduced number
of clusters
6. Conclusions
Energy efficiency of WSNs is a vital point to be
discussed because of their wide application. The
LEACH protocol is considered to be a basis for all
hierarchical protocols proposed in the literature. It
has many merits like load balancing, energy
efficiency and self-organization. But it has some
demerits like poor scalability for very large network
areas and it is not considering the residual energy of
individual sensor nodes. Hence, to overcome the
demerits many researchers are being still proposing
the modified versions of LEACH protocol. In this
article, we have reviewed some of the state of art
LEACH based protocols. It has been found that there
is a scope of improvement in energy efficient
hierarchical clustering algorithms for real time
applications.
References
[1]. Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal,
Wireless sensor network survey, Computer Networks,
Elsevier Publications, Vol. 52, Issue 12, 2008,
pp. 2292-2330.
[2]. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam,
E. Cayirci, Wireless sensor networks: a survey,
Computer Networks, Vol. 38, Issue 4, 2002,
pp. 393-422.
[3]. Feng Zhao, Leonidas Guibas, Wireless Sensor
Networks – An Information Processing Approach,
Morgan Kaufmann Publishers, 2004.
[4]. C. Siva Ram Murthy, B. S. Manoj, Ad Hoc Wireless
Sensor Networks – Architectures and Protocols,
Pearson Education, 2006.
[5]. Holger Karl, Andreas Willig, Protocols and
Architectures for Wireless Sensor Networks, Wiley –
India Edition, 2005.
[6]. E. S. Nadimi, R. N. Jorgensen, V. Blanes-Vidal,
S. Christensen, Monitoring and classifying animal
behavior using ZigBee-based mobile ad hoc wireless
sensor networks and artificial neural networks,
Computer and Electronics in Agriculture, Vol. 82,
2012, pp. 44-54.
[7]. Joseph
Polastre,
Robert
Szewczyk,
Alan
Mainwaring, David Culler, John Anderson, Analysis
of Wireless Sensor Networks for Habitat Monitoring,
Wireless sensor Networks, 2004, pp. 399-423.
[8]. Sang Hyuk Lee, Soobin Lee, Heecheol Song, Hwang
Soo Lee, Wireless sensor network design for tactical
military
applications:
Remote
large-scale
environments, in Proceedings of the Conference on
IEEE
Military
Communications
Conference
(MILCOM’09), Boston, MA, 18-21 October 2009,
pp. 1-7.
[9]. Yuan Guo, McNair J., Fault Tolerant Three
Dimensional Environment Monitoring
using
Wireless Sensor Networks, in Proceedings of the
Conference on IEEE Military Communications
(MILCOM’06), Washington, DC, 23-25 October
2006, pp. 1-7.
[10]. A. Pascale, M. Nicoli, F. Deflorio, B. Dalla Chiara,
U. Spagnolini, Wireless sensor networks for traffic
management and road safety, IET Intelligent
[11].
[12].
[13].
[14].
[15].
[16].
[17].
[18].
[19].
[20].
[21].
[22].
[23].
[24].
Transportation Systems, Vol. 6, Issue 1, 2012,
pp. 67-77.
Ian F. Akyildiz, Dario Pompili, Tommaso Melodia,
Underwater acoustic sensor networks: research
challenges, Ad Hoc Networks, Vol. 3, 2005,
pp. 257-279.
V. Naveen Kumar, K. Venkata Lakshmi Narayana,
Implementation issues of body area sensor networks
for ubiquitous long-term biomedical signal
monitoring and conditioning, in Proceedings of the
IEEE International Conference on Green Computing,
Communication and Conversation of Energy
(ICGCE’13), Chennai, 12-14 December 2013,
pp. 934-937.
Aqeel-Ur-Rehman, Abu Zafar Abbasi, Noman Islam,
Zubair Ahmed Shaikh, A review of wireless sensors
and networks' applications in agriculture, Computer
Standards & Interfaces, Vol. 36, Issue 2, 2014,
pp. 263-270.
V. Naveen Kumar, K. Siva Sankar, L. Srinivasa Rao,
I. Santiprabha, Comparative analysis of QoS-aware
routing protocols for wireless sensor networks,
Journal of Innovative Systems Design and
Engineering, IISTE, Vol. 3, Issue 4, 2012, pp. 1-5.
Nikolaos A. Pantazis, Stefanos A. Nikolidakis,
Dimitrios D. Vergados, Energy-Efficient Routing
Protocols in Wireless Sensor Networks: A Survey,
IEEE Communications Surveys & Tutorials, Vol. 15,
Issue 2, 2013, pp. 531-591.
V. Naveen Kumar, I. Santiprabha, Comparative
analysis of energy-efficient cluster-based QoS-aware
routing protocols for wireless sensor networks,
Sensors & Transducers, Vol. 142, Issue 7, July 2012,
pp. 23-32.
Ameer Ahmed Abbasi, Mohamed Younis, A survey
on clustering algorithms for wireless sensor
networks, Computer Communications, Vol. 30, 2007,
pp. 2826-2841.
M. Mehdi Afsar, Mohammad-H. Tayarani-N,
Clustering in sensor networks: A literature survey,
Journal of Network and Computer Applications,
Vol. 46, 2014, pp. 198-226.
W. Heinzelman, A. Chandrakasan, H. Balakrishnan,
Energy-efficient routing protocols for wireless
microsensor networks, in Proceedings of 33rd Annual
Hawaii International Conference on System Sciences
(HICSS’00), 4-7 January 2000, pp. 1-10.
Pratyay Kuila, Prasanta K. Jana, Energy efficient
clustering and routing algorithms for wireless sensor
networks: Particle swarm optimization approach,
Engineering Applications of Artificial Intelligence,
Vol. 33, 2014, pp. 127-140.
Md Azharuddin, Pratyay Kuila, Prasanta K. Jana,
Energy efficient fault tolerant clustering and routing
algorithms for wireless sensor networks, Computers
& Electrical Engineering, 2014, pp. 1-14.
Wendi B. Heinzelman, Anantha P. Chandrakasan,
Hari Balakrishnan, An Application-Specific Protocol
Architecture for Wireless Microsensor Networks,
IEEE Transactions on Wireless Communications,
Vol. 1, Issue 4, 2002, pp. 660-670.
Triana Mugia Rahayu, Sang-Gon Lee, Sang-Gon
Lee, Survey on LEACH-based Security Protocols, in
Proceedings of IEEE International Conference on
Advanced Communication Technology (ICACT’14),
Pyeongchang, 16-19 February 2014, pp. 304-309.
Yanhong Sun, Ming Tang, An Enhanced Protocol for
LEACH Based Wireless Sensor Networks, in
Proceedings of the IEEE International Symposium on
31
[25].
[26].
[27].
[28].
[29].
[30].
Computer, Consumer and Control (IS3C’14),
Taichung, 10-12 June 2014, pp. 344-347.
Salim El Khediri, Nejah Nasri, Anne Wei,
Abdennaceur Kachouri, A New Approach for
Clustering in Wireless Sensors Networks Based on
LEACH, Procedia Computer Science, Vol. 32,
pp. 1180-1185.
Abdullah Erdal Tumer, Mesut Gunduz, An improved
leach protocol for indoor wireless sensor networks, in
Proceedings of IEEE International Conference on
Signal Processing and Integrated Networks
(SPIN’14),
Noida,
20-21
February
2014,
pp. 432-437.
Rashad M. Eletreby, Hany M. Elsayed, Mohamed M.
Khairy, CogLEACH: A Spectrum Aware Clustering
Protocol for Cognitive Radio Sensor Networks, in
Proceedings of the 9th International Conference on
Cognitive Radio Oriented Wireless Networks and
Communications (CROWNCOM’14), Oulu, 2-4 June
2014, pp. 179-184.
Bilal Abu Bakr, Leszek T. Lilien, Comparison by
Simulation of Energy Consumption and WSN
Lifetime for LEACH and LEACH-SM, Procedia
Computer Science, Vol. 34, 2014, pp. 180-187.
Hanady M. Abdulsalam, Bader A. Ali, Anwar Al
Yatama, Eman S. Al Roumi, Deploying a LEACH
Data Aggregation Technique for Air Quality
Monitoring in Wireless Sensor Network, Procedia
Computer Science, Vol. 34, 2014, pp. 499-504.
Sandeep Sharma, Sapna Choudhary, Heterogeneous
Multi-hop LEACH routing protocol, in Proceedings
of the IEEE International Conference on Green
[31].
[32].
[33].
[34].
[35].
Computing
Communication
and
Electrical
Engineering (ICGCCEE’14), Coimbatore, 6-8 March
2014, pp. 1-6.
Mohammad Shurman, Noor Awad, Mamoun F. AlMistarihi,
Khalid
A.
Darabkh,
LEACH
Enhancements for Wireless Sensor Networks Based
on Energy Model, in Proceedings of the IEEE 11th
International Multi-Conference on Systems, Signals
& Devices (SSD’14), Barcelona, 11-14 February
2014, pp. 1-4.
Sapna Gambhir, Nida Fatima, Op-LEACH: An
Optimized LEACH Method for busty traffic in
WSNs, in Proceedings of the IEEE Fourth
International Conference on Advanced Computing &
Communication Technologies (ACCT’14), Rohtak,
8-9 February 2014, pp. 222-229.
Saeid Mottaghi, Mohammad Reza Zahabi,
Optimizing LEACH clustering algorithm with mobile
sink and rendezvous nodes, International Journal of
Electronics and Communications (AEU), Vol. 69,
Issue 2, 2015, pp. 1-8.
Ravi Kishore Kodali, Naveen Kumar Aravapalli,
Multi-level LEACH Protocol model using NS-3,
in Proceedings of the IEEE International Advance
Computing Conference (IACC’14), Gurgaon,
21-22 February 2014, pp. 375-380.
Mahmoud M. Salim, Hussein A. Elsayed, Salwa H.
El Ramly, PR-LEACH: Approach for Balancing
Energy Dissipation of LEACH Protocol for
Wireless Sensor Networks, in Proceedings of the
IEEE 31st National Radio Science Conference
(NRSC’14), Cairo, 28-30 April 2014, pp. 252-259.
___________________
2015 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved.
(http://www.sensorsportal.com)
32

A Review on State of Art Variants of LEACH

Transkript

Benzer belgeler

Forest Fire Detection with Wireless Sensor Networks

epmark inc

1 Ultrasonic sensor UB4000-F42-I-V15

Ariadne: Secure On-Demand Routing in Ad-Hoc Networks