report fa ga hybrid
DESCRIPTION
reprt fa ga hybridTRANSCRIPT
HYBRID APPROACH FOR ENERGY OPTIMISATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS USING
FIREFLY ALGORITHM AND GENETIC ALGORITHM
A PROJECT REPORT
Submitted in partial fulfillment of the requirement for the award of the Degree of
BACHELOR OF TECHNOLOGY
in
ELECTRONICS AND COMMUNICATION ENGINEERING
By
G. Praveen Kumar Reddy (10BEC0077)Bollapragada.S.S.S.C Surya teja (10BEC0186)
Under the Guidance of
Prof. T. Shankar
(Assistant Professor (SG.), SENSE)
SCHOOL OF ELECTRONICS ENGINEERINGVIT University
VELLORE. (TN) 632014 (APRIL 2014)
1
CERTIFICATE
This is to certify that the Project work titled “Hybrid approach for Energy Optimization
in cluster based Wireless Sensor Networks Using Firefly Algorithm and Genetic Algorithm”
That is being submitted by G. Praveen Kumar Reddy (10BEC0077) and Bollapragada
S.S.S.C Surya Teja (10BEC0186) in partial fulfillment of the requirements for the award of
Bachelor of Technology in Electronics and Communication Engineering, is a record of bona
fide work done under my guidance. The contents of this project work, in full or in parts, have
neither been taken from any other source nor have been submitted to any other Institute or
University for award of any degree or diploma and the same is certified.
Prof. T. ShankarAssistant Professor (SG)
Guide
The thesis is satisfactory / unsatisfactory
Internal Examiner External Examiner
Approved byDean/ programme chair
2
ACKNOWLEDGEMENTS
I owe my Gratitude to our beloved Chancellor, G. Viswanathan, for providing
necessary facilities to carry out and finish the project successfully. I am grateful to all our
Vice presidents for their support and encouragement. I owe my sincere thanks to our
Vice chancellor Prof. Raju, Dean, school of Electronics Engineering, Dr. Rama
Chandra Reddy and Programme chair, school of Electronics Engineering, Prof P.
Arulmozhivarman for their continuous support.
I would like to express my gratitude to my project Guide Prof T. Shankar,
Assistant Professor (SG), SENSE, VIT University, for the useful comments, remarks
and engagement through the learning process of this bachelor’s thesis. Furthermore I
would like to thank him for introducing me to the topic as well for the support on the
way. I would like to thank my loved ones, who have supported me throughout entire
process, both by keeping me harmonious and helping me putting pieces together.
G. PRAVEEN KUMAR REDDY (10BEC0077)
BOLLAPRAGADA S.S.S.C SURYA TEJA (10BEC0186)
3
ABSTRACT
In this project two clustering based meta-heuristic and evolutionary algorithms for
WSNs are discussed. In both types of protocols some special nodes called cluster heads acts as
sink base station for a group of nodes. Each collects data from its member nodes and forwards it
to the sink (base station). Here it considers a homogeneous network where all the nodes in the
network have uniform and limited resource energy. So it is essential to reduce the energy
depletion through long distance transmission. Hence every node in the network takes their turn to
act as a cluster head but only for a limited amount of time. Energy optimization in these
approaches can be obtained by cluster formation, head election. In this thesis hybrid algorithm
proposed is a combination of firefly algorithm and Genetic algorithm. Proposed Hybrid
algorithm implements GA based algorithm until the first node dies as it has higher value than
other implemented algorithms and after it switches to Firefly algorithms to have long network
life time since it has high last node death value. The simulation results show that the proposed
protocol extends the life time of the network by reducing the number of dead nodes when
compared to basic Firefly and Genetic algorithm. It also has better throughput and high residual
energy when compared to Firefly and Genetic algorithm.
4
TABLE OF CONTENTS
ABSTRACT……………………………………………………………………..4
LIST OF FIGURES…………………………………………………………………….8
LIST OF TABLES……..………………………………………………………………9
LIST OF ABBREVIATIONS………………………………………………………….9
INTRODUCTIONPage No
Chapter 1
1.1 Introduction.............................................................................................................. 10
1.2 Wireless sensor node architecture ........................................................................... 10
1.3 Applications of Wireless Sensor Networks ............................................................. 12
1.4 Background Literature Survey ................................................................................. 15
1.5 Thesis Contributions ................................................................................................ 16
1.6 Thesis Outline .......................................................................................................... 17
SURVEY ON CLUSTERING PROTOCOLS
Chapter 2
2.1 Introduction ............................................................................................................. 18
2.2 Clustering Challenges and Design Issues in WSNs ................................................. 19
2.3 Classification of Clustering Protocols in WSNs....................................................... 22
2.4 First order radio model ……..................................................................................... 23
2.5 Direct Transmission................................................................................................... 25
2.6 LEACH (Low Energy Adaptive clustering Hierarchy)…………..………………. 25
5
2.7 Summary ……………………….…………………………………….……………27 ENERGY EFFICIENT PROTOCOLS
Chapter 3
3.1 Introduction .......................................................................................................... 28
3.2 Cluster based protocols......................................................................................... 29
3.3 Firefly Algorithm.................................................................................................. 30
3.3.1 Firefly Pseudo code .............................................................................. 31
3.3.2 Flow Chart of FA……........................................................................... 32
3.3.3 FA idealized Rules ................................................................................33
3.3.4 FA light intensity and brightness concept.............................................. 33
3.3.5 Clustering using FA .............................................................................. 35
3.4 Genetic algorithm............................................................................................ 35
3.4.1 The mathematical implementation of GA ............................................. 36
3.4.2 WSN routing protocols using GA ......................................................... 38
3.4.3 Cluster based routing .............................................................................38
3.4.4 Flow Chart of GA .................................................................................. 40
3.4.5 Pseudo code of GA..................................................................................41
IMPLEMENTATION OF THE PROTOCOLS
6
Chapter 4
4.1 Network design..................................................................................................... 42
4.2 FA implementation................................................................................................ 43 4.3 GA implementation............................................................................................... 46
4.4 Proposed Hybrid Algortihm.................................................................................. 47
SIMULATION RESULTS
Chapter 5
5.1 Simulation metrics.................................................................................................. 51
5.2 Results and analysis................................................................................................ 52
5.2.1 Plotting of nodes ..................................................................................... 52
5.2.2 Hybrid along with other algorithms …………….................................... 53
5.2.3 Number of dead nodes per round for implemented protocols.................. 54
5.2.4 Residual energy per round for implemented protocols............................ 55
5.2.5 Throughput per round for implemented protocols................................... 56
5.3 Comparison table................................................................................................... 57
CONCLUSION
Chapter 6
6.1 Conclusion……..................................................................................................... 60
6.2 Future work............................................................................................................ 60
7
LIST OF FIGURES
Figure 1.1 Architecture of a wireless sensor node
Figure 2.1 First order radio model
Figure 3.1 Direct Transmission
Figure 3.2 Clustering Approach
Figure 3.2 Clustering Approach in FA
Figure 3.4 Flow chart firefly algorithm
Figure 3.6 Main steps of proposed protocol
Figure 3.7 Flow chart genetic algorithm
Figure 4.1 Flow chart Hybrid algorithm
Figure 5.1 Sensor network
Figure 5.2 Alive nodes of after every round in all algorithms
Figure 5.3 Comparison of dead nodes in all algorithms
Figure 5.4 Residual Energy comparison of all algorithms
Figure 5.5 Throughput comparison of all algorithms
Figure 5.6 Comparison of all algorithms for FND, LND, Residual energy & Throughput
LIST OF TABLES
8
Table 2.1 Radio characteristics
Table 4.1 parameters and their initial values
Table 5.1 FND, LND, Residual energy & Throughput comparison
Table 5.2 Residual Energy improvement using Hybrid algorithm
Table 5.3 CPU time taken per round for all algorithms
LIST OF ABBREVIATIONS
WSN Wireless sensor network
RF Radio frequency LEACH Low energy adaptive clustering hierarchy
GA Genetic Algorithm
FA Firefly Algorithm
BS Base station
CH Cluster head
MEMS Micro electro mechanical systems
ADC Analog to digital converter
FPGA Field programmable gate array
MAC Medium access control
CSMA-CA Carrier sense multiple access collision avoidance
QOS Quality of service
FND First node death
LND Last node death CHAPTER 1
9
INTRODUCTION
1.1 INTRODUCTION
Wireless sensor network (WSN) have gained world-wide attention in recent years due to the
advances made in wireless communication, information technologies and electronics field. The
concept of wireless sensor network is based on a simple equation: Sensing + CPU+ Radio =
thousands of potential applications [6]. It is a natural sensing technology where tiny, autonomous
and compact devices called sensor nodes or motes deployed in remote area to detect phenomena,
collect and process data and transmit sensed information to users. The development of low-cost,
low-power, a multifunctional sensor has received increasing attention from various industries
[23]. Sensor nodes or motes in WSNs are small sized and are capable of sensing and processing
data while communicating with other connected nodes in the network, via radio frequency (RF)
channel.
At present, most available wireless sensor devices are considerably constrained in terms of
computational power, memory, efficiency and communication capabilities due to economic and
technology reasons. That’s why most of the research on WSNs has concentrated on the design of
energy and computationally efficient algorithms and protocols. WSNs nodes are battery powered
which are deployed to perform a specific task for a long period of time, even years.
1.2 WSN ARCHITECTURE
The basic block diagram of a wireless sensor node is presented in figure 1.1. It is mainly
made up four basic components:
Sensing unit,
Processing unit,
Transceiver unit and
Power unit
10
Sensing unit
Sensing units are usually composed of two subunits: sensors and analog to digital
converters (ADCs). Sensor is a device which is used to translate physical phenomena to
electrical signals. Sensors can be classified as either analog or digital devises. There exists a
variety of sensors that measure environmental parameters such as temperature, light
intensity, sound, magnetic fields, image etc. The analog signals produced by the sensors
based on the observed phenomenon are converted to digital signals by the ADC and then fed
into the processing unit.
Figure 1.1 Architecture of a wireless sensor network
Processing unit
11
Sensing unitCommunication
unit
Memory
Micro controller
Battery
The processing unit mainly provides intelligence to the sensor node. The processing unit
consists of a microprocessor, which is responsible for control of the sensors, execution of
communication protocols and signal processing algorithms on the gathered sensor data.
Transceiver unit
The radio enables wireless communication with neighboring nodes and the outside world.
It consists of a short range radio which usually has single symmetric channel. There are several
factors that affect the power consumption characteristics of a radio, which includes the type of
modulation scheme used, data rate, transmit power and the operational duty cycle. Similar to
microcontrollers, transceivers can operate in Transmit, Receive, idle and sleep modes. An
important observation in the case of most radios is that, operating in Idle mode results in
significantly high power consumption, almost equal to the power consumed in the receive mode.
Thus, it is important to completely shut down the radio rather than set it in the idle mode when it
is not transmitting or receiving due to the high power consumed. Another influencing factor is
that, as the radios operating mode changes, the transient activity in the radio electronics causes a
significant amount of power dissipation. The sleep mode is a very important energy saving
features in WSNs.
Battery
The battery supplies power to the complete sensor node. It plays a vital role in
determining sensor node lifetime. The amount of power drawn from battery should be carefully
monitored. Sensor nodes are generally small, light and cheap, the size of the battery is limited.
1.3 APPLICATION OF WIRELESS SENSOR NETWORK
Wireless sensor networks may consists of many different types of sensors such as
seismic, low sampling rate magnetic , thermal, visual, infrared, acoustic, and radar. They are able
to monitor a wide variety of ambient conditions that include temperature, humidity, vehicular
movement, lighting condition, pressure, soil makeup, noise levels, the presence or absence of
certain kinds of objects, mechanical stress levels on attached objects, and the current
characteristics such as speed, direction and size of an object. WSN applications can be classified
into two categories:
12
1. Monitoring
2. Tracking
Monitoring
Monitoring applications include indoor/outdoor environmental monitoring, health and
wellness monitoring, power monitoring, inventory location monitoring, factory and process
automation, and seismic and structural monitoring.
Tracking
Tracking applications include tracking objects, animals, humans, and vehicles and
categorize the applications into military, environment, health, home and other commercial areas.
Military applications
The rapid development, self-organization and fault tolerance characteristics of sensor
networks make them a very promising sensing technique for military command, control,
communications, computing, intelligence, surveillance, reconnaissance and targeting (C4ISRT)
systems. Military sensor networks could be used to detect and gain as much information as
possible about enemy movements, explosions and other phenomena of interest, such as battle
field surveillance, nuclear, biological and chemical attack detection and reconnaissance. As an
example, Pinptr is an experimental counter-sniper system developed to detect and locate
shooters. The system utilizes a dense deployment of sensors to detect and measure the time of
arrival of muzzle blasts and shock waves from a shot. Sensors route their measurements to a base
station (e.g., a laptop or PDA) to compute the shooters location. Sensors in the pinptr system are
second-generation Mica2 motes connected to a multi-purpose acoustic sensor board. Each multi-
purpose acoustic sensor board is designed with three acoustic channels and a Xilinx Spartan II
FPGA. Mica 2 motes run on a tiny OS [7] operating system platform that handles task
scheduling, radio communication, time, I/O is processing, etc. Middleware services developed on
tiny OS that are exploited in this application include time synchronization, message routing with
data aggregation, and localization.
13
Environmental applications
Wireless sensor network have been deployed for environmental monitoring, which
involves tracking the movements of small animals and monitoring environmental conditions that
affect crops and livestock. In these applications, WSNs collect readings over time across a space
large enough to exhibit significant internal variation. Other applications of WSNs are chemical
and biological, forest fire detection, volcanic monitoring, meteorological or geophysical
research, food detection and pollution study.
Microscope of redwood [8] is a case study of a WSN that monitors and records the
redwood trees in Sonoma, California. Each sensor node measures air temperature, relative
humidity, and photo-synthetically-active solar radiation. Sensor nodes are placed at different
heights of the tree. Plant biologists track changes of spatial gradients in the microclimate around
a redwood tree and validate their biological theories.
Underwater monitoring study in [9] developed a platform for underwater sensor networks
to be used for long term monitoring of coral reefs and fisheries. The sensor network consists of
static and mobile underwater sensor nodes. The nodes communicate via point-to-point links
using high speed optical communications. Nodes broadcast using acoustic protocol integrated in
the Tiny OS protocol stack. They have a variety of sensing devices, including temperature and
pressure sensing devices and cameras. Mobile nodes can locate and move above the static nodes
to collect data and perform network maintenance functions for deployment, re-location, and
recovery
Healthcare applications
WSN based technologies such as Ambient Assisted Living and Body sensor networks
provide dozens of solutions to healthcare’s biggest challenges such as an aging population and
rising healthcare costs. Body sensor networks can be used to monitor physiological data of
patients. The body sensor networks can provide interfaces for disabled, integrated patient
monitoring. It can monitor and detect elderly people’s behavior, e.g., when a patient has fallen.
These small sensor nodes allow patients a greater freedom of movement and allow doctors to
identify pre-defined symptoms earlier on. The small installed sensor can also enable tracking and
monitoring of doctors and patients inside a hospital. Each patient has small and lightweight
14
sensor nodes attached to them, which may be detecting the heart rate and blood pressure. Doctors
may also carry a sensor node, which allows other doctors to locate them within the hospital.
AT & T recently introduced a tele health monitoring service that uses ZigBee and Wi-Fi.
Mote Track is the patient tracking system developed by Harvard University, which tracks the
location of individual patient’s devices indoors and outdoors, using radio signal information
from the sensor attached to the patients. Heart Home is a wireless blood pressure monitor and
tracking system. Heart Home uses a SHIMMER mote located inside a wrist cuff which is
connected to a pressure sensor. A user’s blood pressure and heart rate is computed using the
oscillometric method. The SHIMMER mote records the reading and sends it to the T-mote
connected to the user’s computer. A software application processes the data and provides a graph
of the user’s blood pressure and heart rate over time.
Home application
With the advance of technology, the tiny sensor nodes can be embedded into furniture
and appliances, such as vacuum cleaners, microwave ovens and refrigerators. They are able to
communicate with each other and the room server to learn about the services they offer, e.g.,
printing, scanning and faxing. These room servers and sensors nodes can be integrated with
existing embedded devices to become self-organizing, self-regulated and adaptive systems to
form a smart environment. Automated homes with personal area network such as ZigBee [10]
can provide the ability to monitor and control mechanisms like light switches and lights, HVAC
(heating, ventilating, air conditioning) controls the thermostats; computers, TVs and other
electronics devices, smoke detectors and other safety equipment; alarm panels, motion sensors,
and other security devices; and electricity, water and gas meters.
Traffic control
Traffic conditions can be easily monitored and controlled at peak times by WSNs.
Temporary situations such as road works and accidents can be monitored in situ. Further, the
integration of monitoring and management operations, such as signpost control, is facilitated by
a common WSN infrastructure.
15
1.4 BACKGROUND LITERATURE SURVEY
In 1981, baker and Ephremides proposed clustering algorithms called – linked cluster
algorithm (LCA) [11] for wireless networks. To enhance network manageability, channel
efficiency and energy economy of MANETS, Clustering algorithms have been investigated in
the past. Lin and Gerla investigated effective techniques to support multimedia applications in
the general multi-hop mobile ad-hoc networks using CDMA based medium arbitration. Random
competition based clustering (RCC) [12] is applicable both to mobile ad-hoc networks and WSN.
RCC mainly focuses at cluster ability in order to support mobile nodes. The RCC algorithm
applies the first declaration wins rule, in which any node can govern the rest of the nodes in its
radio coverage if it is first to claim being a cluster-head.
In recent years, insect sensory systems have been inspirational to new communications
and computing models like bio inspired routing. It is due to their ability to support features like
autonomous, and self-organized adaptive communication systems for pervasive environments
like WSN and mobile ad hoc networks. Biological synchronization phenomena have great
potential to enable distributed and scalable synchronization algorithms for WSN. The first
MANET routing algorithm in the literature to take inspiration from ants is Ant-Colony based
routing algorithm (ARA), Ant Net, Ant HocNet etc. In energy efficient and delay aware routing
algorithms is proposed based on Ant Colony based algorithms. In a bio inspired scalable
networks synchronization protocol for large scale sensor networks is proposed, which is inspired
by the simple synchronization strategies in biological phenomena such as flashing fireflies and
spiking of neurons. A biologically inspired distributed synchronization algorithm introduced in is
based on a mathematical model. It explains how neurons and fireflies spontaneously
synchronize. In the principles of genetics and evolution are adopted to enable service oriented,
autonomous, self-adaptive communication systems for pervasive environments such as WSN and
mobile ad hoc networks. In efficient bio-inspired communication paradigm for WSN is proposed
based on the feedback loop mechanism developed by inspiration from the principles of cell
biology. In a clustering algorithm based on biological quorum sensing mechanism is mentioned.
It helps the sensor nodes to form cluster according to spatial characteristics of the observed event
signal.
16
1.5 THESIS CONTRIBUTION
The work reported herein investigates energy efficient routing algorithm for WSN. This
part investigates clustering techniques for cluster head selection to provide energy efficiency for
WSN. In this thesis proposed hybrid algorithm is a combination of firefly algorithm and Genetic
algorithm, which is seen to provide better performance than traditional algorithm like direct
transmission and LEACH (Low Energy Adaptive Clustering Hierarchy) algorithms. The
performance metrics like network lifetime and total energy consumption have been analyzed for
the above named optimization techniques.
1.6 THESIS OUTLINE
The thesis has been organized in the following manner:
Following this chapter, chapter 2 presents the background survey of wireless sensor
network, energy optimization techniques with different traditional and modern algorithm. It also
focuses on the design issues in wireless sensor networks and routing challenges. We also
discusses about first order radio model. Chapter 3 presents energy efficient protocols in wireless
sensor networks. This chapter presents the mathematical modeling of firefly and genetic
algorithm. Chapter 4 presents the implementation for firefly, genetic and hybrid algorithm.
Chapter 5 presents the simulation result of the entire above discussed algorithm and hybrid
algorithm. We also discuss the results of the simulation in this chapter with changing the value of
the different parameter. Chapter 6 represents the conclusion section following with references.
17
CHAPTER 2
SURVEY ON CLUSTERING PROTOCOLS
2.1 INTRODUCTION
Wireless Sensor Network has their own unique characteristics which create new
challenges for the design of routing protocols for these networks. First, sensors are very limited
in transmission power, computational capacities, storage capacity and most of all, in energy.
Thus the operating and networking protocol must be kept much simpler as compared to the other
ad hoc networks [6]. Second, due to the large number of application scenarios for WSN, it is
unlikely that there will be one-things-fits all solution for these potentially very different
possibilities. Micro sensor networks can contain hundreds or thousands of sensing nodes. It is
desirable to make these nodes as cheap and energy efficient as possible [5].A critical aspect of
applications with wireless sensor networks is network lifetime. Power-constrained wireless
sensor networks are usable as long as they can communicate sensed data to a processing node.
Sensing and communications consume energy, therefore judicious power management and
sensor scheduling can effectively extend network lifetime [22]. The design of a sensor network
routing protocol changes with application requirements. For example, the challenging problem of
low latency precision tactical surveillance is different from that requirement for a periodic
weather task. Thirdly, data traffic in WSN has significant redundancy since data is probably
collected by many sensors based on a common phenomenon. Such redundancy needs to be
exploited by the routing protocols to improve energy and bandwidth utilization. Fourth, in many
of the initial application scenarios, most nodes in WSN were generally stationary after
deployment. However in recent development, sensor nodes are increasingly allowed to move and
change their location to monitor mobile events, which results in unpredictable and frequent
topological changes.
Due to such different characteristics, many new protocols have been proposed to solve
the routing problem in WSN. These routing mechanisms have taken into consideration the
inherent features of WSN, along with the application and architecture requirements [6]. To
minimize energy consumption, routing techniques proposed in the literature for WSN employ
some well-known ad hoc routing tactics, as well as, tactics special to WSN, such as data
18
aggregation and in-network processing, clustering, different node role assignment and data
centric methods. In the following sections, introduce to current research on routing protocols
have been presented.
2.2 CLUSTERING CHALLENGES AND DESIGN ISSUES IN WSNS
Despite plethora of applications of WSN, these networks have several restrictions, e.g.,
limited energy supply, limited computing power, and limited bandwidth of the wireless links
connecting sensor nodes [23]. One of the main design goals of WSN is to carry out data
communication while trying to prolong the lifetime of the network and prevent connectivity
degradation by employing aggressive energy management techniques. In order to design an
efficient routing protocol, several challenging factors should be addressed meticulously. The
following factors are discussed below:
Node deployment
Node deployment in WSN in application dependent and affects the performance of the
routing protocol. The deployment can be either deterministic or randomized. In deterministic
deployment, the sensors are manually placed and data is routed through pre-determined paths;
but in random node deployment, the sensor nodes are scattered randomly creating an
infrastructure in an ad hoc manner. Hence, random deployment raises several issues as coverage,
optimal clustering etc. which need to be addressed.
Energy consumption without losing accuracy
Sensor nodes can use up their limited supply of energy performing computations and
transmitting information in a wireless environment. As such, energy conserving forms of
communication and computation are essential. Sensor node lifetime shows a strong dependence
on the battery lifetime. In a multi-hop WSN, each node plays a dual role as data sender and data
router. The malfunctioning of some sensor nodes due to power failure can cause significant
topological changes and might require rerouting of packets and reorganization of the network.
19
Node/Link Heterogeneity
Some applications of sensor networks might require a diverse mixture of sensor nodes
with different types and capabilities to be deployed. Data from different sensors, can be
generated at different rates, network can follow different data reporting models and can be
subjected to different quality of service constraints. Such a heterogeneous environment makes
routing more complex.
Fault Tolerance
Some sensor nodes may fail or be blocked due to lack of power, physical damage, or
environmental interference. The failure of sensor nodes should not affect the overall task of the
sensor network. If many nodes fail, MAC and routing protocol must accommodate formation of
new links and routes to the data collection base stations. This may require actively adjusting
transmit powers and signaling rates on the existing links to reduce energy consumption, or
rerouting packets through regions of the network where more energy is available. Therefore,
multiple levels of redundancy may be needed in a fault-tolerant sensor network.
Scalability
The number of sensor nodes deployed in the sensing area may be in the order of hundreds
or thousands, or more. Any routing scheme must be able to work with this huge number of
sensor nodes. In addition, sensor network routing protocols should be scalable enough to respond
to events in the environment. Until an event occurs, most of the sensors can remain in the sleep
state, with data from the few remaining sensors providing a coarse quality.
Networks Dynamics
Most of the network architectures assume that sensor nodes are stationary. How-ever,
mobility of both BS’s and sensor nodes is sometimes necessary in many applications. Routing
messages from or to moving nodes is more challenging since routing stability becomes an
important issue, besides energy, bandwidth etc. Moreover, the sensed phenomenon can be either
20
dynamic or static depending on the application, e.g., it is dynamic in a target detection/tracking
application, while it is static for forest monitoring for early fire prevention. Monitoring static
events allows the network to work in a reactive node, simply generating traffic when reporting.
Dynamic events in most applications require periodic reporting and consequently generate
significant traffic to be routed to the BS.
Transmission media
In a multi-hop sensor network, communicating nodes are linked by a wireless medium.
The traditional problem associated with a wireless channel (e.g., fading high error rate) may also
affect the operation of the sensor network. As the transmission energy varies directly with the
square of distance therefore a multi-hop network is suitable for conserving energy. But a multi
hop network raises several issues regarding topology management and media access control. One
approach of MAC design for sensor networks is to use CSMA-CA based protocols of IEEE
802.15.4 that conserve more energy compared to contention based protocol like CSMA (e.g.
IEEE 802.11). So, Zigbee which is based upon IEEE 802.15.4 LWPAN technology is introduced
to meet the challenges.
Connectivity
The connectivity of WSN depends on the radio coverage. If there continuously exists a
multi hop connection between any two nodes, the network is connected. The connectivity is
intermittent if WSN is partitioned occasionally, and sporadic if the nodes are only occasionally
in the communication range of other nodes.
Coverage
The coverage of a WSN node means either sensing coverage or communication coverage.
Typically with radio communication, the communication coverage is significantly larger than
sensing coverage. For applications, the sensing coverage defines how to reliably guarantee that
an event can be detected. The coverage of a network is either sparse, if only parts of the area of
interest are covered or dense when the area is almost completely covered. In case of a redundant
coverage, multiple sensor nodes are in the same area.
21
Data Aggregation
Sensor nodes usually generate significant redundant data. So, to reduce the number of
transmission, similar packets from multiple nodes can be aggregated. Data aggregation is the
combination of data from different sources according to a certain aggregation function, e.g.,
duplicate suppression, minima, maxima and average. It is incorporated in routing protocols to
reduce the amount of data coming from various sources and thus to achieve energy efficient. But
it adds to the complexity and makes the incorporation of security techniques in the protocol
nearly impossible.
Data Reporting Model
Data sensing and reporting in WSNs is dependent on the application and the time
criticality of the data reporting. In wireless sensor networks data reporting can be continuous,
query-driven or event-driven. The data-delivery model affects the design of network layer, e.g.,
continuous data reporting generates a huge amount of data therefore, the routing protocol should
be aware of data aggregation.
Quality of Service
In some applications, data should be delivered within a certain period of time from the
moment it is sensed; otherwise the data will be useless. Therefore bounded latency for data
delivery is another condition for time-constrained applications. However, in many applications,
conservation of energy, which is directly related to network lifetime, is considered relatively
more important than the quality of data sent. As the energy gets depleted, the network may be
required to reduce the quality of the results in order to reduce the energy dissipation in the nodes
and hence lengthen the total network lifetime. Hence, energy aware routing protocols are
required to capture this requirement.
2.3 CLUSTERING
Clustering is a popular data analysis technique to identify homogeneous groups of objects
based on the values of their attributes. Clustering is an important unsupervised classification
22
techniques, where a set of patterns, usually vectors in a multi-dimensional space, are grouped
into clusters (or groups) based on some similarity metric. Clustering is often used for variety of
applications in statistical data analysis, image analysis, data mining and other fields of science
and engineering. Clustering algorithms can be classified into two categories: hierarchical
clustering and partitional clustering [8]. Hierarchical clustering constructs a hierarchy of clusters
by splitting a large cluster into smaller ones and merging smaller cluster into their nearest
centroid [9]. In this, there are two main approaches: (i) the divisive approach, which splits a
larger cluster into two or more smaller ones; (ii) the agglomerative approach, which builds a
larger cluster by merging two or more smaller clusters. On the other hand partitional clustering
[9] attempts to divide the data set into set of disjoint clusters without the hierarchical structure.
The most widely used partitional clustering algorithms are the prototype-based clustering
algorithms where each cluster is represented by its center.
2.4 FIRST ORDER RADIO MODEL
Currently there is a great deal of research in the area of low energy radios. Different
assumptions about the radio characteristics [5], including energy dissipation in transmit and
receive modes, will change the advantages of different protocols. In this thesis work, it assume a
simple model where the radio dissipates Eelec= 70 nJ/bit to run the transmitter or receiver circuitry
and ∈amp = 120 pJ/bit/m2 for the transmit amplifier to achieve an acceptable Eb/N0 (see figure 2.1
and table 2.1). These parameters are slightly better than the current state-of-the-art in radio
design. It also assumes and d2 energy loss due to channel transmission. Thus to transmit a K-bit
message a distance d using our radio model, the radio expends:
ETx(k,d) = ETx-elec(k) + ETx-amp (k,d)
ETx(k,d) = Eelec* k + ∈amp * k * d2 ………..…………………………….. (2.1)
And to receive this message, the radio expends:
ERx(k) = ERx-elec(k)
ERx(k) = Eelec*k ……………………………………….. (2.2)
Table 2.1 Radio characteristics
Operation Energy dissipated
23
Transmitter or Receiver electronics (Eelec) 70nJ/bit
Transmit amplifier (∈amp¿ 120pJ/bit/m2
Fig 2.1 First order radio model
For these parameter values, receiving a message is not a low cost operation; the protocol
thus should try to minimize not only the transmit distances but also the number of transmit and
receive operations for each message.
This thesis makes the assumption that the radio channel is symmetric such that the energy
required to transmit a message from node A to node B is the same as the energy required to
24
Transmit electronics
Tx Amplifier
k bit packet
K bit packet
Eelec*k ∈amp*k*d2
Eelec*K
Receiver electronics
transmit a message from node B to node A for a given SNR. It also assumes that all sensors are
sensing the environment at a fixed rate and thus always have data to send to the end user. For
future versions of these protocols, it will implement an “event-driven” simulation, where sensors
only transmit data for some event occurs in the environment.
Micro sensor networks can contain hundreds or thousands of sensing nodes. It is desirable
to make these nodes as cheap and energy-efficient as possible and rely on their large numbers to
obtain high quality results. Network protocols must be designed to achieve fault tolerance in the
presence of individual nodes failure while minimizing energy consumption. In addition, since the
limited wireless channel bandwidth must be shared among all the sensors in the network, routing
protocols for these networks should be able to perform local collaboration to reduce bandwidth
requirements.
Eventually, the data being sensed by the nodes in the network must be transmitted to a
control center or base station, where the end-user can access the data. There are many possible
models for these micro sensors networks. In this work we consider micro sensor networks where:
The base station is fixed and located far from the sensors.
All nodes in the network are homogenous and energy constrained.
Thus, communication between the sensor nodes and the base station is expansive, and there
are no “high-energy” nodes through which communication can proceed.
2.5 DIRECT TRANSMISSION
Using a direct communication protocol, each sensor sends its data directly to the base
station. If the base station is far away from the nodes, direct communication will require a large
amount of transmit power from each node (since d in equation 2.1 is large). This will quickly
drain the battery of nodes and reduce the system lifetime. However the only reception in this
protocol occur at the base station, so if either the base station is close to the nodes, or the energy
required receiving data is large, this may be an acceptable (and possibly optimal) method of
communication.
2.6 LEACH (low energy adaptive clustering hierarchy)
25
LEACH is a self-organizing, adaptive clustering protocol that uses randomization to
distribute the energy load evenly among the sensors in the network. In LEACH, the nodes
organize themselves into local clusters, with one node acting as the local base station or cluster-
head. If the cluster-heads were chosen a priori and fixed throughout the system lifetime, as in
conventional clustering algorithms, it is easy to see that the unlucky sensors chosen to be cluster-
heads would die quickly, ending the useful lifetime of all nodes belonging to those clusters. Thus
LEACH includes randomized rotation of the high-energy cluster-head position such that it
rotates among the various sensors in order to not drain the battery of a single sensor.
Sensors elect themselves to be local cluster-heads at any given time with a certain
probability. These cluster-head nodes broadcast their status to the other sensors in the network.
Each sensor node determines to which cluster it wants to belong by choosing the cluster-head
that requires the minimum communication energy. Once all the nodes are organized into clusters,
each cluster-head creates a schedule for the nodes in its cluster. This allows the radio
components of each non-cluster-head node to be turned off at all times except during its transmit
time, thus minimizing the energy dissipated in the individual sensors. Once the cluster-head has
all the data from the nodes in its cluster, the cluster-head node aggregates the data and then
transmits the compressed data to the base station. Since the base station is far away in the
scenario we are examining, this is a high energy transmission. However, since there are only a
few cluster-heads, this only affects a small number of nodes. Being a cluster-head drain the
battery of that node. In order to spread this energy usage over multiple nodes, the cluster-head
nodes are not fixed; rather, this position is self-elected at different rounds.
Initially, when clusters are being created, each node decideswhetheror not to become a
cluster-head for the current round. This decision is based on the suggested percentage of cluster
heads for the network (determined a priori) and the number of times the node has been a cluster-
head so far. This decision is made by the node n choosing a random number between 0 and 1. If
the number is less than a threshold T(n), the node becomes a cluster-head for the current round.
The threshold is set as.
T (n )={ p
1−p∗(r mod1p), if n∈G
0∧ , otherwise
…………………………………………………………. (2.3)
26
Where p is the desired percentage of cluster heads (e.g., P = 0.05), r = the current round,
and G is the set of nodes that have not been cluster-heads in the last 1P
rounds. Using this
threshold, each node will be a cluster-head at some point within 1P
rounds. During round 0 (r =
0), each nodehas a probability P of becoming a cluster-head. The nodes that are cluster-heads in
round 0 cannot be cluster-heads for the next 1P
rounds. Thus the probability that the remaining
nodes are cluster-heads must be increased, since there are fewer nodes that are eligible to become
cluster-heads. After 1P
– 1 rounds, T = 1 for any nodes that have not yet been cluster-heads, and
after 1P
rounds, all nodes are once again eligible to become cluster-heads. Future versions of this
work will include an energy-based threshold to account for non-uniform energy nodes. In this
case, we are assuming that all nodes begin with the same amount of energy and being a cluster-
head removes approximately the same amount of energy for each node.
2.7 SUMMARY
Clustering is a revolutionary idea in wireless sensor networks before clustering direct
transmission is used in wireless sensor networks. In direct transmission all the nodes in the
network participate in transmission of data to the Base station due to this energy consumption of
the network is high and another disadvantage of direct transmission is data redundancy will be
there because all the nodes sent their information directly to the base station. All these problems
are solved using the clustering technique in clustering cluster head will have communication
with the BS and nodes will send their data to their cluster head instead of to base station and
cluster head will aggregate the data received and send the non-redundant data to the base station.
All the transmission and reception is implemented using the First order radio model .First order
radio covers the transmission, amplifier and reception losses according to the transmission
distance.
27
CHAPTER 3
ENERGY EFFICIENT PROTOCOLS IN WSN
3.1 INTRODUCTION
Sensor nodes will sense the data from the environment surrounding the node. But this data
has to be sent to the destination (called Base-station or BS) for the further processing. Therefore
these nodes will require some amount of energy to sense the data and to transmit it. So each node
in the system is given a fixed amount of initial energy. Recharging of nodes are not possible
during the processing. But it is observed that, most of the node energy is wasted during the
transmission phase than in sensing state. So to reduce these transmission energy different types
of transmission techniques were introduced.
28
Initially when the sensor systems were introduced direct transmission from node to the
destination was used (As shown in the Fig 3.1). But in this transmission mode the energy
consumption of the nodes which are far from the base station will be high. This is because the
first order radio model [5] shows that transmitting energy required ETx is directly proportional to
the square of the distance (d) and this type of nodes will die quickly.
d1 Base-station (BS)
d2
d3 d4
Fig 3.1 Direct Transmission
ETx∝ d2…………………………………………………………………………………. (3.1)
Where ETx is the transmission energy, d is the distance between nodes and Base station.
So to reduce the transmission energy, transmission distance is to be reduced, so for this purpose
we will go for the clustering approach.
3.2 CLUSTER BASED PROTOCOLS
In this type of protocol entire network is divided into small areas (as shown in fig 3.2) called
clusters. The Base-station informs each node to which cluster they belong, after assigning all
nodes into clusters BS will elect a node from each cluster as head (known as cluster-head CH)
and informs other nodes in the cluster or we go for different algorithm techniques, which select
CH first and then all other node find their distances to all the cluster-head and which cluster-head
is nearer to the node that node will join that cluster in this way various cluster-head and cluster
forms.
29
Base station
Cluster-head (CH) clusters
Fig3.2: Clustering approach
So during the transmission phase, nodes will transmit the data to the respective cluster head
only. Due to this type of transmission the distance to which the data is to be transmitted is to be
reduced. The function of the cluster-head is to gather the data from its cluster node and
aggregates the data and sends it to the base-station. Thus base station only receives data from the
cluster-heads. So the number of reception at the base station also reduced. All these
modifications in the network show that the energy consumption by the nodes is reduced.
3.3 FIREFLY ALGORITHM
A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm that simulates
the flash pattern and characteristics of fireflies [4].Fireflies produce short and rhythmic flashes.
The flashing light is produced by a process of bioluminescence. Fundamental functions of such
flashes are to attract mating partners (communication). The rhythmic flash, the rate of flashing
and the amount of time form part of the signal system that brings both sexes together.
It is well known that the light intensity at a particular distance r from the light source
obeys the inverse square law. That is to say, the light intensity (I) decreases as the distance r
increases in terms of I ∝ r2. Furthermore, the air absorbs light which becomes weaker and weaker
as the distance increases. These two combined factors make most fireflies visual to a limit
30
distance, usually several hundred meters at night, which is good enough for fireflies to
communicate. The flashing light can be formulated in such a way that it is associatedwith the
objective function to be optimized, which makes it possible to formulate new optimization
algorithms.
Fig3.3: Clustering approach in Firefly Algorithm
3.3.1 FA pseudo code
Objective function f(x)
Generate initial population of fireflies xi (i = 1, 2,..., n)
Light intensity Ii at xi is determined by f(xi)
Define light absorption coefficient γ
while (t <Max Generation)
for i = 1 : n all n fireflies
for j = 1 : n all n fireflies (inner loop)
if (Ii<Ij), Move firefly i towards j; end if
Vary attractiveness with distance r via exp[−γ r]
Evaluate new solutions and update light intensity
31
end for j
end for i
Rank the fireflies and find the current global best g¿
end
While Post process results and visualization
3.3.2 Flow Chart of Firefly Algorithm
32
Start
Generate initial population of n=100
Round start
No Yes
Fig 3.4 Flow chart of firefly algorithm
3.3.3 FA-idealized rules
Some of the flashing characteristics of fireflies can be idealized so as to develop firefly-
inspired algorithms. For simplicity in describing this new Firefly Algorithm (FA) which was
developed by Xin-She Yang at Cam- bridge University in 2007, we now use the following three
idealized rules
All fireflies are unisex so that one firefly will be attracted to other fireflies regardless of
their sex;
Attractiveness is proportional to their brightness, thus for any two flashing fireflies, the
less bright one will move towards the brighter one. The attractiveness is proportional to
33
Cluster formation with random cluster head with the near nodes
Energy based switching
Select the nodes having best fitness value that nodes will be
cluster heads of that round
Max Cycle=1800 Stop
Data transmission takes place
R=R+1
Calculate fitness value CH(i).fit=CH(i).E / M(k)
the brightness and they both decrease as their distance increases. If there is no brighter
onethan a particular firefly, it will move randomly;
The brightness of a firefly is affected or determined by the landscapeof the objective
function.
For a maximization problem, the brightness can simply be proportional to the value of the
objective function. Other forms of brightness can be defined in a similar way to the fitness
function in genetic algorithms.
3.3.4FA- light intensity and brightness concept
In the firefly algorithm, there are two important issues: the variation of light intensity and
formulation of the attractiveness [1,4]. For simplicity, one can always assume that the
attractiveness of a firefly is determined by its brightness which in turn is associated with the
encoded objective function.
In the simplest case for maximum optimization problems, the brightness(I) of a firefly at a
particular location x can be chosen as I(x) ∝f(x). However, the attractiveness β is relative; it
should be seen in the eyes of the beholder or judged by the other fireflies. Thus, it will vary with
Distance ‘rij’ between firefly i and firefly j. In addition, light intensity decreases with the distance
from its source, and light is also absorbed in the media, so we should allow the attractiveness to
vary with the degree of absorption.
In the simplest form, the light intensity I(r) varies according to the inverse square law:
I(r) = I s
r2 ………………………………………………………. (3.2)
Where Is is the intensity at the source and r is the distance.
As a firefly’s attractiveness is proportional to the light intensity seen by adjacent fireflies,
we can now define the attractiveness β of a firefly by:
β= β0e−γ r2
……….………………………………………………. (3.3)
Whereβ0 is the attractiveness at r = 0 and γ is the light absorption coefficient. As it is often
faster to calculate1
1+r2 than an exponential function, the above function, if necessary, can
conveniently be approximated as
34
β= β0
1+γ r2 ………………………………………………………. (3.4)
The distance (Rij) between any two fireflies i and j at xi and xj, respectively, is the
Cartesian distance:
Rij= || xi – xj|| = √∑k=1
d
(x i , k−x j , k)2 …………………………………………………. (3.5)
Where xi,k is the k th component of the spatial coordinate xi of theith firefly. In 2-D case, we
have
Rij = √(x¿¿ i−x j)2+( y i− y j)
2¿ …………………………………………………….. (3.6)
The movement of a firefly i is attracted to another more attractive(brighter) firefly j is
determined by
Xi = Xi +β0e−γ rij2
(Xj – Xi ) + α∈i …………………………………………………. (3.7)
Where the second term is due to attraction and third term is randomization with α being
the randomization parameter, and ∈iis a vector of random numbers drawn from a Gaussian
distribution or uniform distribution. For example, the simplest form is ∈i can be replaced by rand
− ½ where rand is a random number generator uniformly distributed in [0, 1]. For most our
implementation, we can take β0 = 1 and α ∈[0, 1].It is worth pointing out that (3.7) is a random
walk biased towards the brighter fireflies. If β0 = 0, it becomes a simple random walk.
Furthermore, the randomization term can easily be extended to other distributions such as Levy
flights.
The parameter γnow characterizes the variation of the attractiveness, and its value is
crucially important in determining the speed of the convergence and how the FA algorithm
behaves. In theory, γ ∈ [0, ∞], but in practice for most applications, it typically varies from 0.1
to 10.
3.3.5 Clustering using firefly algorithm
35
Clustering is a popular data analysis technique to identify homogeneous groups of objects
based on the values of their attributes. For clustering this project assumes energy of the nodes
similar to the light intensity of fireflies in FA algorithm and movement of firefly in FA algorithm
is similar to the change of location of cluster head. As less attractive fireflies moves towards
more attractive fireflies similarly cluster head node with more energy are eligible for cluster head
than those cluster head having less energy.
Initially randomly select k cluster head where k is found by the given clustering
probability. Then clustering will be done and communication takes place after that from the first
round go for energy based switching of cluster head means nodes which have more energy are
more eligible for cluster head than node with less energy. If a node is a cluster head having less
energy than other node in that cluster then that node become cluster head and again clustering
will be done. After clustering get the fitness value of each optimization round and finally at the
end of optimization round get the best set of cluster head which have better fitness value and
final clustering done for best fitness value. Fitness value is given as
CH (k).fit=CH (k ) . E
M (k) …………………………………….…………………. (3.8)
Where CH (k).fit gives the fitness value of kth cluster head.CH(k).E gives the energy of kth
cluster head and M(k) gives the sum of square distances of CHs-BS and sum of square distance
of nodes to their respective cluster heads.
3.4 GENETIC ALGORITHM
Our work introduces a genetic algorithm-based variant of LEACH to determine the
optimal value of p for various base station placements. The GA-based optimization procedure is
performed only once, before the set-up phase of the first round.
3.4.1 The mathematical implementation of the Genetic algorithm
At the beginning of preparation phase, each node initially determines whether or not it
should be a candidate cluster head (CCH), using the following cluster head selection procedure.
First, every sensor node selects a random number r from the interval [0, 1]. If r is smaller than
T(s), based on a prescribed probability pset, then the node is a CCH. The value of pset can be a
large value in our protocol, pset = 0.5, say. Thereafter, each node sends its ID, location
36
information, and whether or not it is a CCH to the BS. As the BS receives messages sent by all
nodes, it performs GA operations to determine the optimal probability, popt= kopt/n, by
minimizing the total amount of energy consumption in each round. Therefore, the objective
function used in the GA can be formulated as
………………………… (3.9)
Where values of xc are one for our binary-GA when it is a CCH, otherwise, it is zero.
ε=εfs and α=2 when d≤d0
ε=εmp and α=4 when d≥d0.
q represents number of member nodes in cluster.
The optimal probability popt is determined by the GA by searching the solution space
through an evolutionary optimization process incorporating probabilistic transitions. Energy
consumption of cluster Head
ECH (l , d )={ l∗[ Eelec (nk−1)+ EDA
nk+Eelec+E fs∗d toBS
2 ]; ifd<d0
l∗[Eelec (nk−1)+EDA
nk+Eelec+Emp∗d toBS
4 ]; ifd ≥ d0}¿
……………(3.10)
Where n=no.of nodes
k=no.of clusters
EDA =represents the energy dissipation for aggregating data
Eelec =Transmitter/ Receiver electronics
Efs =Transmit amplifier (if d to BS<do)
Emp= Transmit amplifier (if d to BS≥do)
Energy consumption of non-cluster Head
…………………………………….……… (3.11)
37
d to CH =distance from node to cluster head
Assuming the shape of clusters is a circle
……………………………………….....……… (3.12)
= (1/π)*M2 /k
M*M= Network area
Energy dissipation in a cluster
…………………………..…………… (3.13)
Total energy dissipation for a round is
…………………(3.14)
l =l-bit message over a distance dd to BS =distance from cluster head to base stationFrom solving the above equation optimal solutions for k opt and Popt
…………………………... (3.15)
Optimal probability:
…………………..… (3.16)
38
0TotalE
k
02
04
;d[ ]
;d[ ]
toBS
toBS
opt
fstoBS
mp toBS
n Md
E dk
En Md
E E d
Assuming the co-ordinates of Base station as (0.5M,0.5M+B) values calculated are
………………………………………………………………….….. (3.17)
……………………………………………………….…. (3.18)
Therefore the values of kopt and popt are related to the total number of sensor nodes,
domain size of sensor field, and the location of BS.
3.4.2 WSN routing protocols using genetic algorithm
The scenarios of WSNs routing protocols using genetic algorithm are developed for the
networks having no global positioning system. The main purpose of the operations of these
protocols is to increase the network life-time by maximizing the number of transferred data
packages with clustering [19]. The clustering mechanism of the proposed protocols is based on
the clustering technique of LEACH protocol where cluster heads perform data aggregation
processes of their clusters. Cluster heads use TDMA MAC in intra-cluster communication and
CDMA MAC communication with the base station. The main operational difference between the
proposed protocols and LEACH is the selection process of cluster heads (CH); clustering head
selection is performed by genetic algorithm in proposed protocols while LEACH uses a random
selection method. The proposed network clustering protocol is based on a centralized control
algorithm that is implemented at the base station. The base station is a node with unlimited
energy supply.
3.4.3 Cluster based routing strategy
In a typical WSN application, sensor nodes collect data nearby and send it to the
destination which is a neighbor node or the base station. In a clustered approach, cluster heads
gather data about the common phenomena from sensor nodes and then aggregate this raw data to
form the final abstract data. The main idea in the aggregation of data is to combine the data
obtained from different sensor nodes in a neighborhood and minimize the total amount of data
transmission before transferring data to the base.
We consider a wireless sensor network model that consists of a base station and a number
of stationary sensor nodes that are grouped into clusters dynamically in each tour as used in
LEACH.
39
Initialization
Setup
Data gathering
Fig. 3.5 Main steps of the proposed protocol
In the first step (setup), cluster organization is made by selecting of the cluster-heads for
the current tour. Then, sensor nodes are joined to the nearest cluster-heads. After this selection
process, periodical data from the network is gathered via the cluster-heads as the second step.
In the proposed approach based on genetic algorithm, selection process of cluster heads is
achieved using calculated optimal probability in the genetic based protocol which the
communication energy is considered as the significant factor. The distance between the
communicating elements is the main concern of energy consumption.
3.4.4 Flow Chart of genetic Algorithm
40
Start
No Yes
Fig 3.6 Flow chart of genetic algorithm
3.4.5 pseudo code of genetic algorithm based energy efficient adaptive clustering protocol
INITIALIZATION
Specify the probability (p), number of nodes (n);
41
Generate initial population n=100
Select the nodes randomly for each iteration of each round
Select the nodes that satisfy the threshold function
Round Begins
R<=Rmax
(1800)Stop
Data transmission takes place
R=R+1
Calculate the optimal probability p_opt.
Einit(s)=E0, s=1,2, …, n;
Calculation of optimal probability
Set-up phase
if (Einit(s)>0 & rmod(1/ popt)≠0) then //popt calculated
r←random(0,1) and compute T(n); //given by threshold eq where popt is used instead of p.
if (r < T(n)) then
CCH{s}=TRUE; //node s be a candidate CH
Else
CCH{s}=FALSE; //node s not be a candidate CH
end if
end if
STEADY-STATE PHASE
If (CH(s)=TRUE) then
Receive(IDi, DataPCK) //receive data from members;
Aggregate(IDi, DataPCK) //aggregate received data;
Tans To BS(IDi, DataPCK); //transmit received data;
// one round is completed.
CHAPTER 4
IMPLEMETATION OF THE PROTOCOLS USING MATLAB
42
Mathematical formulation of firefly and Genetic based algorithm as well as cluster formation is discussed in the previous chapter. This chapter tells about the parameter used for implementing firefly, Genetic and Hybrid algorithm along with the algorithm steps which are used in implementing those protocols and flow charts of the protocols. It is also going to discuss the advantages and disadvantages if any of these protocols and purpose the modifications done in overcome those disadvantages. First we implemented Genetic based algorithm which has higher first node death value and it also uses optimal probability value which is obtained from different network parameters rather than random value. The advantages of firefly algorithm are exploration and exploitation but mostly exploitation in this implemented firefly and high convergence rate but the problem is it uses energy based switching for cluster head selection and to overcome this we implemented Hybrid algorithm which is a combination of Genetic based and firefly which uses fitness value switching i.e (CH will have higher fitness value than other nodes) due to this last node death value increases drastically, this implementation overcome the problems like fast first node death and also have high network life time it has advantages of having both exploration and exploitation characteristics.
4.1 NETWORK DESIGN
In designing the wireless sensor network in all the protocols, the following assumptions are
made
Base station is located far away from the sensing field.
Sensors and the base station are all stationary after deployment.
Every node in the field has the initial energy of 0.5Joules.
All nodes are homogeneous and each node is assigned a unique identifier.
All links are symmetric.
A node can compute approximate distance to other node based on the received signal
strength.
Table 4.1 shows the required parameters whose initial values are assumed as follows
Table 4.1 Parameters and their initial values
43
Parameter Value
Sensor field region (100*100)m
Base station location (50,150) (in meters)
Number of nodes 100
CH probability 0.1
Rmax 1800
Data packet length 4096 bits
Eelec 70nJ/bit
Eamp 120pJ/bit/m2
EDA 5nJ
Initial energy 0.5J
4.2 FIREFLY ALGORITHM IMPLEMENTATION
Step 1: Initialization
Initially provide all the constant value which are used in the MATLAB code. For
example network area, base station location, number of nodes in the network, initial energy
provided to each node, data aggregation energy required in each round, transmitter and receiver
electronics (Eelec) and transmitter amplifier ( ∈amp), number of rounds ( Rmax) , clustering
probability and number of bits transferred (Kb).
Step 2: Generation of sensor network
Now network will be generated with the given number of nodes. Each node gets their
position based on the random location generated by rand function. Ex. the x-coordinate and y-
coordinate of a node is given as
S(i).Xd=rand(1, 1)*Xm;
S (i).Yd=rand (1, 1)*Xm; where Xm, Ym gives the network x-axis and y-axis range.
And Xd, Yd gives the co-ordinate position of the nodes.
Whereas rand(1,1) will generate single random number between 0 and 1.
44
Step 3: Firefly round begins
In this step first initialize the value of dead is equal to zero and then check the energy of
each node, if energy of node is equal to zero then we increment dead value by one. Then
randomly generate the total number of clusters and Cluster head based on the given probability
value and save the result in a structure. After formation of cluster find the distance of each node
with each CH and join the cluster in which cluster head is nearest.
Step 4: Energy based switching
Now in this step first initialize the value of optimization round. Now compare the every
node with the other nodes in the network, if the energy of the node is more than the CH energy
then that node will be eligible for the CH means location of cluster head changed because
previous CH is no more eligible for CH. This is the same thing as firefly change their location if
attracted towards more brighter firefly. After becoming new CH again clustering done, and
comparison process runs till the given optimization round value in that current round.
While comparing if the energy of the CH is more than the node then the CH will not be changed
in that optimization round.
Step 5: Fitness value calculation
After energy based switching step we find the fitness value in that optimization round for
that the clusters.
CH (k).fit=CH (k ) . E
M (k) ……………………………………………….…………… (4.1)
Where CH (k).fit gives the fitness value and CH(k).E gives the energy of the current CH
whereas M(k) is given as the sum of square of distances of all the nodes with their corresponding
CH in addition with distance of CH with the base-station.
Step 6: Getting best CHs
Now in this step first store the fitness value for the clusters of previous optimization
round along with the fitness value for the clusters of the current optimization round. After storing
45
the values get the fitness values in descending order and choose top k cluster-head for the further
process and this step goes on and finally will get the best possible set of CHs.
FIT=0; DER=0;
for k=1:c
FIT=[FIT CH(k).fit];
DER=[DER CH (k).der];
end
FIT=FIT (2: end);
DER=DER(2: end);
Best = [Best FIT];
[Best ID] = sort(Best, ‘descend’);
Best = Best (1: c);
ID=ID (1: c);
Step 7: Energy consumption
After getting best possible CHs, all the nodes starts sending data to their respective CHs.
Cluster-head collects these data and aggregate these data and sends it to the Base-station. All
nodes update their energy and then algorithm goes back for the next round. Where energy
consumption is calculated as:
For transmitting data
ET-x=Eelec*Kb + ∈amp*d2 *Kb ………………………………………………………. (4.2)
Where Kb is the number of bits sent and d is the distance between CH and node
For receiving data
46
ER-x=Eelec*Kb … ……………………………………………………………………… (4.3)
4.3 Genetic algorithm IMPLEMENTATION
For a sensor network with N nodes and k number of clusters, the sensor network can be
clustered as follows
Step 1
Initially network is created using ‘rand’ command using the above parameters and the
base station is located at the given position.
Step 2
Calculate the optimal probability of selecting the Cluster heads using the proposed
formula which is obtained from solving the objective function.
Step 3
For every node a random number is given and check it satisfies the threshold function
and if it satisfies the function it is chosen as cluster head.
Step 4
Clusters are formed considering the distances between the nodes and cluster heads
Step 5
In this step communication takes place between nodes and loss incurred are calculated
and residual energy of the network is calculated.
Step 6
Repeat steps 2 to 5 until the maximum number of round is reached. Each cluster head
receives data from its member node, process the data and send it to the base station for each
round. Check for dead nodes, alive nodes and residual energy for each round. If the energy of
node is less than or equal to zero then it is consider as dead node. Now plot is made for alive
node, residual energy and throughput of network for each round.
4.4 PROPOSED HYBRID ALGORITHM
47
Start
Initialization of value and generation of sensor
network
Yes
NO
No
Yes No
Fig 4.1 Flow chart of hybrid algorithm
In the case of Genetic algorithm First node death round is much more than any other
algorithm so network life will be more than any other algorithm without losing any node. Where
as in the case of proposed algorithm last node dies late compare to any other algorithms which
gives smooth energy consumption throughout the network.
Step 1: Initialization
48
Dead==0
Clustering using Fitness value based switching for CH selection from all the nodes in the
network and get set of best CHs
Communication takes place, energy update of nodes
R <= Rmax (1800) STOP
Implements Genetic algorithm based
protocol
Round starts
R= R+1
Implements Firefly algorithm with Fitness function is S(i).fit=S(i).E/M(i)
Clustering of nodes is done with the selected cluster heads
Provide all the initial value required as like in Genetic and Firefly algorithm
Step 2: Sensor network generation
Nodes are deployed in random manner using ‘rand’ command in the given network area.
Step 3: Round begins
In this step we first initialize the value of dead is equal to zero and then check the energy
of each node, if energy of node is equal to zero then we increment dead value by one. Then
randomly generate the total number of clusters and CH based on the given probability value and
save the result in a structure. After formation of cluster find the distance of each node with each
CH and join the cluster in which cluster head is nearest than other CH.
Step 4: Check below given condition
if (dead= = 0)
go to step 5
else
go to step 6
Step 5: Genetic algorithm begins
All the process of Genetic algorithm is carried out as described earlier until the first node
dies and after the first node death firefly algorithm starts.
Step 6: Random CH Selection and Clustering
Now in this step CH are choosen randomly based on the optimal probability and
clustering is done based on the distances between the nodes and the selected Cluster Heads.
Step 6.1: Fitness value calculation (Fitness value based switching)
49
After energy based switching step we find the fitness value in that optimization round for
every node in the network.
S(i).fit=S (i) . EM ( j)
……………………………………………………….
(4.4)
Where S(i).fit gives the fitness value and S(i).E gives the energy of the every node whereas M(j)
is given as the sum of square of distances of all the nodes with the node in addition with distance of node
with the base-station.
Step 6.2: Getting best CHs
Now in this step first store the fitness value for the clusters of previous optimization
round along with the fitness value for the clusters of the current optimization round. After storing
the values get the fitness values in descending order and choose top k cluster-head for the further
process and this step goes on and finally will get the best possible set of CHs.
FIT=0;
for k=1:c
FIT=[FIT CH(k).fit];
end
FIT=FIT (2: end);
Best = [Best FIT];
[Best ID] = sort(Best, ‘descend’);
Best = Best (1: c);
ID=ID (1: c);
Good=0;
Good=[Good ID];
Good=Good(2:c);
Form the clusters using the nodes from obtained from matrix Good as choosing them as the CH.
Step 6.3:Energy consumption
50
After getting best possible CHs, all the nodes starts sending data to their respective CHs.
Cluster-head collects these data and aggregate these data and sends it to the Base-station. All
nodes update their energy and then algorithm goesback for the next round. Where energy
consumption is calculated as:
For transmitting data
ET-x=Eelec*Kb + ∈amp*d2 *Kb ……………………………………………..…………. (4.5)
Where Kb is the number of bits sent and d is the distance between CH and node
For receiving data
ER-x=Eelec*Kb … …………………………………………………….………………… (4.6)
Clusters are formed as considering the distance between the nodes and the selected
Cluster heads.
As soon as the first node dies, Firefly algorithm starts and follows all the steps of firefly
algorithm and then plots all the results.
All the above algorithms are implemented using MATLAB and simulation results will be
discussed in the next chapter.
CHAPTER 5
51
SIMULATION RESULTS
5.1 SIMULATION METRICS
The main objective of the simulation is to evaluate the performance of each protocol.
Evaluation is made based on the following three metrics
Alive nodes
First node and last node dead
Residual energy of the network
Throughput of the network
Alive nodes
Here the number of nodes alive after finishing every round will be obtained
First node and last node dead
The performance of a network depends on the lifetime of its node. If the lifetime of the
node is high means less number of node dead for longer duration then the network performs well
and also transmits more data to the base station.
Residual energy of the network
Here the residual energy of the network for different algorithm with respect to the
number of nodes is analyzed. Any algorithm is better if their residual energy is greater and
energy graph is more smooth and flatter then only that algorithm is known as energy optimized
algorithm.
Throughput of the network
Throughput of the network shows data sent during the process. If the number of alive
nodes is more, then throughput of the network will be more. Throughput of the network is given
as:
Throughput= Alive nodes * Kb ……………………………………………..…………… (5.1)
52
5.2 RESULTS AND ANALYSIS
Here sensing area is taken as (100*100)m whereas working area is (100*200)m and base
station is located at (50,150). Number of rounds (Rmax) is taken as 1800.
5.2.1 Plotting of nodes
Fig. 5.1Sensor network
Figure 5.1 shows the distribution of the 100 nodes in the given sensing area. Nodes are
randomly distributed in the given 100*100m network whereas base station is placed at (50,150)
location.
53
5.2.2 Hybrid along with all other algorithms
Fig 5.2 Alive nodes after every round in all the algorithms
Fig 5.2 shows dead node comparison of proposed hybrid algorithm along with Firefly, Genetic, Leach and DT. Above fig shows the FND and LND is better for Hybrid algorithm so hybrid algorithm is better than all other algorithms .Number of alive nodes is less in case of DT is less because all the nodes in the network has to send information and leach has higher value than DT because it uses the concept of clustering but it has early first node death because it chooses the cluster head in a random probabilistic manner .GA based algorithm uses calculated optimal probability rather random value therefore it gives better results than LEACH. Firefly has better first node death than leach and lower than GA based because its clustering is done based energy switching concept and it also take much time to find an optimal solution. Hybrid has almost same first node death value as GA based because it implements GA based algorithm until the first node dies and later it uses fitness value based switching. Conclusion of the above result more alive nodes better network life time.
54
5.2.3 Number of dead nodes per round for implemented protocols
Fig 5.3 Comparison of dead nodes among all the algorithms
Fig 5.3 shows dead node comparison of proposed hybrid algorithm along with Firefly, Genetic, Leach and DT. Above fig shows the FND and LND is better for Hybrid algorithm so hybrid algorithm is better than all other algorithms. As mentioned DT involves all the nodes in the network in transmission so its network lifetime is less than other algorithms its last node death occurs at 280 rounds. LEACH has its last node death 780 rounds and firefly and GA based algorithms has their last node death at 750 rounds. The Hybrid algorithm has its LND at 1450 rounds which is a high improvements than the other algorithms.
5.2.4 Residual Energy per round for implemented protocols
55
Fig. 5.4 Residual Energy comparison of all the algorithms
Fig 5.4 shows the Residual energy comparison of Hybrid, Firefly, Genetic, Leach and DT
algorithm. From the above graph one can conclude that the energy is optimized more in the case
of hybrid algorithm than any other algorithms. The residual energy of DT becomes zero at 280
rounds because all nodes are involved in transmission in every round. In LEACH it becomes
zero at 780 rounds. In firefly and Genetic based it becomes zero at 750 rounds because these
algorithms doesn’t consider the location of node while electing the CH. Hybrid has higher value
than other because it selects the nodes which have higher fitness value as CH, fitness function
involves the energy of the node and its distance from other nodes and base station that’s why its
residual energy becomes zero much later than other algorithms i.e at 1450 rounds.
5.2.5 Throughput per round for implemented protocols
56
Fig 5.5 Throughput comparison of all the algorithms
Fig 5.5 shows the Throughput comparison of Hybrid algorithm along with Firefly,
Genetic, Leach and DT. Above fig tells in Overall, Hybrid algorithm will send more bits of data
than any other algorithm throughout the process. So Hybrid algorithm is better than all other
algorithms because it sends more bits of data, their energy is optimized and overall lifetime of
network also increases in the case of hybrid algorithm. Same conclusion is drawn from alive
nodes it is directly proportional to alive nodes.
57
5.3 Comparison Table
Table 5.1 First node dead (FND), Last node dead (LND), Residual Energy & Throughput comparisons:
Algorithms First node dead (in rounds)
Last node dead(in rounds)
Residual EnergyAt 700 rounds(in joules)
ThroughputAt 700 rounds
(in bits*105)Direct Transmission 44 280 0 0
LEACH 114 780 8 3
Genetic 266 750 7 3.5
Firefly 228 750 7 3.4
Proposed Hybrid 260 1450 24 3.9
Table 5.1 compares the FND, LND, Residual Energy and Throughput result of all the above discussed algorithms. From the above bar graph one can see that FND is more in the case of Genetic algorithm, LND is almost same for genetic algorithm and firefly algorithm.so genetic algorithm is used for hybridization with firefly algorithm works for longer duration with full efficiency than any other algorithms.Hybrid algorithm more network lifetime than any other algorithms with FND almost equal for hybrid algorithm and genetic algorithm and LND at 1450th
round. Residual energy for direct transmission is zero at 700 round, for genetic algorithm and firefly algorithm residual energy is almost same but the proposed hybrid algorithm has higher residual energy compared to other algorithms because of that it’s LND is at 1450 rounds. Throughput for direct transmission is zero at 700 round, for genetic algorithm has better throughput compared to firefly algorithm but the proposed hybrid algorithm has higher throughput compared to other algorithms because of that its LND is at 1450 rounds
58
First node death Last node Death Residual Energy Throughput
260
1450 1450 1450
228
750 750 750
266
750 750 750
114
780 780 780
44
280 280 280
Comparison
Proposed Hybrid Firefly Algorithm Genetic based AlgorithmLeach Direct transmission
Output Parameters
No.
of r
ound
s
Fig 5.6 Comparison of all the Algorithms for FND, LND, Residual energy and Throughput
Fig 5.6 compares the FND, LND, Residual Energy and Throughput result of all the above discussed algorithms. From the above bar graph one can see that FND is more in the case of Genetic algorithm, LND is almost same for genetic algorithm and firefly algorithm. so genetic algorithm is used for hybridization with firefly algorithm works for longer duration with full efficiency than any other algorithms. So hybrid algorithm more network lifetime than any other algorithms with FND almost equal for hybrid algorithm and genetic algorithm and LND at 1450th
round
Below table shows that the Residual energy of the network is more in the case of hybrid
algorithm than any other algorithms. The improvement in residual energy is more in the case of
hybrid algorithm with other algorithms are given below
% improvement in residual energy = RH −RO
RO *100……. (5.1)
Where RH = residual energy (In Joule) of the network using hybrid algorithm after 350 rounds.
RO = residual energy (in joule) of the network using other algorithm taken one at a time like DT,
leach, Genetic and Firefly algorithm
59
Table 5.2 Residual energy improvement using hybrid after 700 round
Algorithm Improvement using hybrid (in %)
Hybrid Vs DT 100
Hybrid Vs Leach 200
Hybrid Vs Genetic 242.8
Hybrid Vs Firefly 242.8
Table 5.2 tells about Residual energy improvement of the network using hybrid
algorithm than other algorithms discussed above. In above table column 2 tells about the
percentage improvement of the Residual energy of the network using hybrid algorithm in
comparison to the other algorithms like DT, leach, Genetic and Firefly algorithm. Table 5.1
clearly tells that the hybrid algorithm is better than all the algorithms because network lifetime of
the network is more in the case of hybrid algorithm than any other algorithms.
Table 5.3 CPU time taken per round for all the algorithms
LEACH Genetic Algorithm
Firefly Hybrid
e=Total time 61.18 73.87 498.32 510.34z=Time taken per round
0.062 0.049 0.711 0.283
Initial CPU time t= cputime
Running For r rounds
Total CPU Time taken for that algorithm e= cputime-t…………………………………… (5.3)
Time taken per round z=e/(number of rounds) ……………………………………………. (5.4)
60
CHAPTER 6CONCLUSION
6.1 CONCLUSION
This thesis discussed the various energy efficient algorithms. In LEACH protocol
probability based cluster head (CH) selection process occurred whereas in genetic based
algorithm selection of cluster-head is done based on the calculated optimal probability through
given objective function which will take more number of rounds for the first node to die than
other algorithms. In Firefly Energy based switching of CH takes place which gives the best
possible cluster-head which has more energy than any other nodes, firefly algorithm is
implemented and its drawback is rectified in Hybrid algorithm. The drawback of firefly is
considering the node with high energy as CH but in hybrid fitness value every node is calculated
and nodes with high values are considered as CH which gives more number of rounds for the last
node to die. The network will work with full efficiency in the case of genetic based for longer
duration. The residual energy curve is more smooth and better for hybrid compare to any other
algorithms. Now with taking the advantages of Genetic based and Firefly, Hybrid Algorithm is
proposed. This Hybrid approach increases the life-time of the network. More number of nodes
alive for longer duration in the case of hybrid algorithm. From the residual energy graph one can
conclude that energy is optimized in hybrid than any other algorithms. Hybrid algorithm also
sends more bits of data than any other algorithms.
FUTURE WORK
In all the above algorithms, only randomly distributed network is taken where nodes are
distributed randomly but in future one can think about other possible distribution model and
compare the results and finds out that whether the results are better or not in case of other
distributions.
All the above algorithms assumed that all the sensors sensing the environment at fixed
rate and always have data to send to the end user. But in future version one can think about the
situation when nodes does not have data to send but still taking part in the process which causes
61
loss of battery power. So if nodes do not have data, then nodes should switched-off so that
battery life increases.
References
[1] Afnizanfaizal Abdullah, Safaai Deris, Mohd Saberi Mohamad & Siti Zaiton Mohd
Hashim 2011, ‘A New Hybrid Firefly Algorithm for Complex and Nonlinear Problem,’
S. Omatu et al. (Eds.): Distributed Computing and Artificial Intelligence, AISC 151, pp.
673–680.
[2] Ahmed, G, Khan, NM & Ramer, R 2008, ‘Cluster Head Selection Using Evolutionary
Computing in Wireless Sensor Networks’ Progress In Electromagnetics Research
Symposium, Hangzhou, China, University of New South Wales, Australia, pp. 883-885.
[3] Amir Hossein Gandomi, Xin-She Yang & Amir Hossein Alavi, 2011, ‘Mixed variable
structural optimization using Firefly Algorithm,’ Elsevier, Computers and Structures 89
2325–2336.
[4] Bai Chen , Yaxiao Zhang , Yuxian Li , Xiaochen Hao & Yan Fang 2011, ‘A Clustering
Algorithm of Cluster-head Optimization for Wireless Sensor Networks Based on Energy’
Journal of Information & Computational Science, Institute of Electrical Engineering,
Yanshan University, Qinhuangdao, China vol. 8, no. 11 pp. 2129–2136
[5] Baker, DJ & Ephreminds, A 1981, ‘The architecture organization of a mobile radio
network via a distributed algorithm,’ Transactions on communications, IEEE, vol. 29,
no.11, pp.1694-1701.
[6] Dezhen Song 2005, ‘Probabilistic Modeling of Leach Protocol and Computing Sensor
Energy Consumption Rate in Sensor Networks,’academics_tr_tamu-cs-tr-2005-2-2.
[7] Erfan. Arbab, Vahe, Aghazarian, Alireza, Hedayati , Nima & Ghazanfari Motlagh 2012,
‘A LEACH-Based Clustering Algorithm for Optimizing Energy Consumption in
Wireless Sensor Networks’, 2nd International Conference on Computer Science and
Information Technology (ICCSIT) Singapore, pp. 147-150.
[8] Farahanish, M, Abshouri, AA, Nasiri, B, & Meybodi, MR 2011, ‘A Gaussian Firefly
Algorithm,’ International Journal of Machine Learning and Computing, Vol. 1, No. 5.
[9] Gandomia, AH, Yang b, XS, Talatahari, S & AH, Alavi d, 2013, ‘Firefly algorithm with
chaos,’ Commun Nonlinear Sci Numer Simulat 18 89–98.
62
[10] Handy, MJ, Haase, M, & Timmermann, D 2002, ‘Low Energy Adaptive Clustering
Hierarchy with Deterministic Cluster-Head Selection,’ IEEE_MWCN.
[11] Hill, JL 2003, ‘System architecture for wireless sensor networks,’ University of
California, Berkerley Ph.D. dissertation.
[12] Jenn-Long Liu , Chinya & Ravishankar, v 2011, ‘LEACH-GA: Genetic Algorithm-
Based Energy-Efficient Adaptive Clustering Protocol for Wireless Sensor Networks’,
International Journal of Machine Learning and Computing, Vol.1, pp. 1
[13] Jennifer Yick, Biswanath Mukherjee & DipakGhosal, 2008, ‘Wireless sensor network
survey,’ University of California, J. Yick et al. / Computer Networks 52 , 2292–233.
[14] Mihaleacardei, T, thai & Weiliwu 2005, ‘Energy efficient target coverage in wireless
sensor networks,’ university of texas, IEEE, O-7803-8968-9-05.
[15] Navdeep Kaur & Deepiika Sharma 2013, ‘Genetic Algorithm for Optimizing the Routing
in the Wireless Sensor Network’ International Journal of Computer Applications Vol.
70, No.28, pp. 0975 – 8887
[16] Senthilnath, J, Omkar∗, SN & Mani, V 2011, ‘Clustering using firefly algorithm:
Performance study,’ Swarm and Evolutionary Computation 1 164–171.
[17] Shankar, T, Shanmugavel, S, Karthikeyan, A & Dhanbal, R 2012, ‘Selection of cluster of
head using neural network in wireless sensor network’, European Journal of Scientific
research, Department of SENSE, VIT university, vol.3, no.3, pp. 320-337.
[18] Shankar, T & Shanmugavel, S 2013, ‘Hybrid approach for WSN using ABC and firefly
Algorithm’, International review on computer science and software, vol.8, no.10,
pp.2335-2341.
[19] Shankar, T, Shanmugavel, S & Karthikeyan, A 2013, ‘Hybrid approach energy
optimization in WSN using PSO’, International review on computer science and
software, vol.8, no.6, pp. 1454-1459.
[20] Shankar, T & Shanmugavel, S 2014, ‘Energy optimization in cluster based WSN’,
Journal of Engineering science and technology, vol.9, no.2, pp.246-260.
[21] Shankar, T, Shanmugavel, S & Karthikeyan, A 2013, ‘Modified harmony search
algorithm for energy optimization in WSN’, International review on computer science
and software, vol.8, no.6, pp. 1469-1475.
63
[22] Surafel Luleseged Tilahun & Hong Choon Ong 2012, ‘Research Article Modified Firefly
Algorithm,’ Hindawi Publishing Corporation Journal of Applied Mathematics Volume ,
Article ID 467631, 12 pages doi:10.1155/2012/467631.
[23] Tolle, G, Culler, D & Hong, W 2005, ‘A microscope in the redwoods,’ in proceedings of
the 3rd international conference on embedded sensor systems, San Diego, CA.
[24] Vasilescu, I, Kotay, K, Rus, D, Dunbabin, M & Corke, P 2005, ‘Data Collection,
Storage, Retrieval with an underwater sensor network,’ in proceedings of the third
international conference on Embedded Networked Sensor Systems (Sensys), San Diego,
CA.
[25] Wendi Rabiner Heinzelman, Anantha Chandrakasan & Hari Balakrishnan 2000, ‘Energy-
Efficient Communication Protocol for Wireless Microsensor Networks,’ Proceedings of
the 33rd Hawaii International Conference on System Sciences.
[26] Xin-She Yang, 2010, ‘Nature-Inspired Metaheuristic Algorithms Second Edition,
Published by Luniver Press,’ ISBN-10: 1-905986-28-9.
[27] Xu, K, & Gerla, M 2002, ‘A heterogeneous routing protocol based on a new stable
clustering scheme,’ in proceedings of IEEE Military communication conference, vol.2 ,
Anachim, CA.
64
BIODATA
Name : G. Praveen Kumar Reddy
Reg. No : 10BEC0077
Father’s name : G. Bhaskar Reddy
Address : 10-155, Nehru bazaar,
Piler, Andhra pradesh
Pin code- 517214
Mobile No : 9042303069
Email-Id : [email protected]
Name : B.S.S.S.C Surya Teja
Reg.No : 10BEC0186
Father’s Name : B.V.V.S Narayana
Address : Plot no.402,Gayathri plaza
:Tadepalligudem
: Andhra Pradesh
: Pin No 534101
Mobile No : 08124273347
Email-id : [email protected]
65
66