Chapter-3 Theoretical Background -...
Transcript of Chapter-3 Theoretical Background -...
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
1
Chapter-3
Theoretical Background
3.1 Domain Introduction
Wireless Sensor Networks (WSN) are networks of typically small, battery-powered,
wireless devices, equipped with on-board processing, interaction, and sensing
capabilities. Especially wireless sensor network suffers from excessive packet loss, over
hearing, retransmission of the packets due to node mobility and constant energy
dissipation. Current advancements in wireless interaction technologies and therefore the
developing of less expensive wireless equipment‟s have past part to the introducing least-
power wireless sensing element systems. Because of their ease allocation and therefore
the multi-functionality of the sensing element devices, wireless sensing element systems
are used for several of real-time uses like human health-care, target-following, and
environment and weather observation. The important responsibility of the sensing
element devices in every application is to observe as well as sense the topographic point
and forward their consolidated data to the sink sensing device for more operations.
Resource limitations of the sensing element nodes and unreliableness of least-power
wireless interaction links, together with numerous performance requirements various
real-time uses impose several challenges in coming-up with economical interaction
protocols for wireless sensing element networks. Meanwhile, designing suitable routing
algorithms to fulfill different performance requirements and demands of various real-time
uses is considered as an important issue in wireless sensing networking systems. A
current technique for routing and transmitting the data does not take into account of
optimizing the transmission through Energy-Balancing (EB). There is several power and
energy aware algorithms that claim to compensate for the energy losses. The main
fundamental of most of the techniques is to route the packets through the highest energy
nodes which lead to quick battery drainage of those node, so the network lifespan
decreases gradually.
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
2
WSNs are a rapidly rising technology which is able to have a robust affect on
analysis and it‟ll become very closer to our part of lives with strong bindings between us
and sensing technology within a future decade. The massive application field of WSNs-
systems covers national security, police-investigation, military, health-care, and
surroundings observation and lots many more. We thankful to their wide-range of
potential real-time uses, WSNs having meaningful extensive analysis interest in recent
and forth coming years.
Wireless sensing system is self-collected of an enormous quantity of less-power,
least-priced sensing element devices that square measures allocated near to interested
area and which are connected via a wireless-interface. Sensing element nodes square
measure small devices consist of sensible-hardware, transceivers, computing and storage
resources and batteries. Basically, particular the sensing devices square measure allocated
haphazardly and not needed to be designed or planned. It permits quick random
allocation in in-accessible terrains or disaster relief operations. So, this allocation of the
sensing node haphazardly needs that sensing element algorithms must have self-dominant
organizing capacities.
3.2 Factors affecting Network Lifespan in WSNs
We are listing below significant network characteristics that influence the network
lifespan. Network structural design identifies how sensing elements supposed to be report
the data to the Access Points (APs). There are 3 kind of network structural design contain
be regard as in the literature survey: i) Flat ad-hoc, ii) Hierarchical Ad-hoc and
iii) Sensor Network with Mobile Access (SENMA). Below the flat ad-hoc structural
design, sensing elements communicate every additional data in multiple hops to the APs.
Within hierarchical wireless sensor networks, sensing elements appearance groups and
reports their data toward the group-leaders that are accountable for transfer the
comprehensive data to the application. In Sensor Network with Mobile Access, sensing
elements communicate straightforwardly with mobile APs affecting approximately the
sensor sports ground.
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
3
3.2.1 Initiation for Data Collection
Concurrence to the real-time uses, data compilation in a wireless sensor network is able
to be beginning through the interior clock of sensing elements, the occasion of
concentration, or the command of the end user. Inside clock-driven wireless sensor
networks, sensing elements assemble and transmit data at prearranged time intermission.
In occurrence driven or command driven wireless sensor networks, data compilation are
activated through an incident of attention or a demand from the APs.
3.2.2 Channel and Energy Utilization Model
The power utilization representation characterizes the foundation of power utilization in
the network. Concurrence to the speed of power expenses, we categorize power
utilization into 2 general categories: the incessant power utilization as well as the
coverage power utilization. The permanent power utilization is the minimum power
required to maintain the network through its lifespan with no data compilation. It consists
of, for instance, battery leakage as well as sensor sleeping power. The coverage power
utilization is the extra power consumed in data compilation. It depends on the velocity of
data compilation and the channel reproduction as well as the network structural design
and procedures. It consists of the power inspired in transmission, response, and probably
channels achievement. We notice out that power utilization might come from additional
sources such as network preservation whose power spending rate is neither incessant nor
connected to data compilation.
3.2.3 Lifespan Definition
Network lifespan is the moment span beginning the operation to the immediate while the
network is measured nonfunctional. Once a network be supposed to be regard as
nonfunctional is, conversely, application-specific. It is able to be for instance, the
instantaneous when the foremost sensor dies, a proportion of sensing elements expire, the
network separation, or the loss of exposure occurs.
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
4
Usually, two different mechanisms are exists to the issues of saving-power in
wireless sensing element networks. Primarily, make schedules for sensing element nodes
to active mode which permits the opposite sensing element nodes to travel into least
energy non-sleeping mode, which in-turn enables the other sensing elements to get-into
least-power sleeping mode. Secondly, the sensing coverage of network elements should
be adjusted to frequent range.
A number of approaches have been proposed and developed for maximizing the
lifespan of wireless sensor networks. Few dominant techniques have already been
discussed in previous chapters. Here in this thesis the author has implemented
protocol and based cross-layered approach for comparing the results obtained by
implementing proposed system design of elephant swarm optimization based cross-layer
design. In order to provide a better understanding and research work, here in this thesis
the author has intended to present a brief description of technologies or protocols being
implemented.
In the preceding section brief of parallel techniques implemented in research work
have been presented.
3.3 LEACH: Low Energy Adaptive Clustering Hierarchical
3.3.1 Introduction
Heinzelman et. al. (2000) states that commenced a grouping with hierarchical technique
designed for sensing element networks known as Low-Energy-Adaptive-Clustering-
Hierarchy (LEACH). LEACH is the foremost hierarchical group base routing procedure
designed for wireless sensor network which separation the nodes inside group, in every
group a committed node through additional privileges identify Group Leader or Cluster
head (CH) is accountable for generating and manipulating a Time division multiple
access (TDMA) timetable and sending comprehensive data through nodes to the BS
wherever these data is needed using Code division multiple access ). Residual
node is group associate.
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
5
LEACH sorting the sensing element nodes within the network into little groups
and selects one amongst them as a group-leader. Usually sensing node initial senses its
reachable point so transfers the relevant data to its group-leader. In the later stage the
group-leader collects the data and kept it in comprising format, which actually received
from every sensing element nodes and sends it to the bottom sensing node. The sensing
element nodes opting s because the group-leader drain out additional power as compared
to the opposite sensing element nodes because it is needed to transfer knowledge to the
bottom system which perhaps way situated. Therefore LEACH applies random
circulation of the sensing element nodes needed to be the group-leaders to equally
distribute power utilization within the network. Generally, Time division multiple
accesses and Code division multiple access MAC is utilized to erase inter-group as well
as intra-group collisions. This protocol is used were a constant monitoring by the sensing
element s are required as data collection is centralized (at the base system) and is
performed periodically.
The current interest in wireless sensor networks has led to the emergence of much
application oriented protocols of which LEACH is the most aspiring and widely used
protocol proposed by Jichuan Jha et. al. (2005). LEACH can be described as a
combination of a group-based design and multi-hop routing. The term group-based can
be explained by the fact that sensing elements using the LEACH protocol functions are
based on group-leaders and group members. Multi-hop routing is used for inter-group
interaction with group-leaders and base systems. Simulation results shown by
Heinzelman et. al. (2002) that multi-hop routing consumes less energy when compared to
direct transmission.
It has been stated that wireless sensing elements sense data, aggregate them and
then send data to the base system from a remote area using the radio transmission scheme
as interaction medium. Data which is consolidated by the sensing elements is sent to the
base system. During this process a lot of problematic issues occur, such as data collision
and the data aggregation. LEACH algorithm gives best result which helps to minimize
the aggregation of data problems employing a native information fusion that does a
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
6
comprising of the quantity which is consolidated by the group-leader before it transfers
this to the bottom system. Every sensing element generates a self-organized network by
give and take policy of a group-leaders a minimum of once. Group-leader which is
actively takes the charges for exchanging the information that's consolidated by the
sensing elements devices to the bottom system. Which is manage to adjusting the
dissipating of power among the network and increasing the networks life-period by rising
the life-period of-the sensing elements which is presented by Rappaport (1996).
3.3.2 Operation in LEACH Protocol
A LEACH computations has been alienated into following sections
1. Setup section
2. Steady section
During the setup section, the groups has been created as well as a group-leader/ or
cluster-head (CH) is selected for every group. Whereas within the steady section, data has
been sensed as well as drive to the middle support position.
The steady section may maximum as compare to the setup section. This is completed in
arrange to reduce the overhead price.
3.3.2.1 Setup Section
Again set-up section includes following sub sections.
(1) Advertisement Section, as well as
(2) Group Setup Section
Consider setup originated part, whole sensing element nodes among a network cluster
itself into few regions of groups by interacting with one another through piece-of-
messages. At some extent of transmission time one detector within the network works
like a group-leader and sends piece-of-messages among the network to all or any of the
opposite remaining sensing elements. The sensing elements favor to be a part of those
teams or set of teams which all are fashioned by the group-leaders, relying upon the
signal strength of the messages sent by the group-leaders. Sensing elements fascinated by
connecting a selected group-leader or response is send back-to group leaders from
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
7
region by causing a response signal indicating their acceptance to re-join. Therefore the
setup part finishes, stated by Heinzelman et. al. (2000). The group-leader will take the
decision the optimum variety of group-mates it will be handle or needs. Earlier to that
move into the steady state section, agreed parameters are conceived, like the constellation
in conjunction with the relative values of evaluation against the interaction. Consider
TDMA Schedule which is used to any or every members of the groups to forward
information‟s to the group-leader, then to the group-leader towards the bottom system.
Fig: 3.1 below shows 2 sections of a sensing element during a LEACH Technique: every
sensing elements construct as group members to the group-leaders and within the second
section group-leaders performs the transmission of information to the sink during a multi-
hop structure. A right away transmitting mechanism additionally shown in the following
diagram:
Fig: 3.1 LEACH process shows setup, steady state sections used multi-hop and direct
Transmission
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
8
3.3.2.2 Steady State Section
Steady state section consists of 2 sub-sections identify,
(1) Schedule Creation section and
(2) Data transmission section.
When a group-leader is chosen for a district, every group members of this region forward
the detected information in their assigned TDMA slots to the group-leader. And very next
group-leader transmits this consolidated information in the format of zipped to the bottom
system that finishes the second section, known as the Steady State section. When the
information forwarded to the sink is completed by steady-state section, the full method
involves a finish and a brand new rummage around for the constructing of group-leaders
for a set of group and new group-member construction starts. Briefly telling that, it may
be same that a brand new setup section and steady-state begins with the tip of
transmitting of information is done to the end of the sink. Conforming that, selecting
different choice of group-leaders at intervals the region that is carried within the sensing
elements during an equipped-itself manner helps in reducing/or least the energy that's
already used. There‟s an open chance that every sensing elements may not be very closer
to the group-leader that the quantity of energy that's consumed by the farther sensing
element isn't adequate to the quantity of energy consumed by the closest node. So as to
attenuate this, group-leader‟s construction or-else the role of group-leader is executed by
a circulation among every node within the group. LEACH reduces standard power
observe via allocating the load of the system to every node or group members at totally
different periods, stated by Heinzelman et. al. (2000).
Every group-leaders transfer the information that is consolidated towards the
bottom system in a zipped form. Every group-leaders might not be near the bottom
system so that they send the compressed information to the subordinate group-leaders,
and during this means, a multi-hop routing network is constructed. LEACH acts as a
haphazard circulation of the group-leader so as to save lots of the high energy which does
disperse at the time of transmitting information to the bottom system. This circulation is
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
9
ascertained inside every detectors thus as to not drain the energy or battery of one
detector.
Researchers made some comparisons and come to realistic issues concern to
LEACH over some transmission techniques. If we have a tendency to think about a
haphazard network wherever there will be a zero or hundred percent group-leaders, the
quantity of energy dispersed from the group-leaders and their group member is adequate
to the energy that's dispersed through direct interaction. This shows us that suppose
we‟ve best range of group leaders in the network which works will be involving in
forwarding the consolidated information from their individual group members. High
throughput and good outcome can be achieved by saving dissipating the energy across
the networks.
Consider that there square measure n1groupleaders within the network that
maintains excellent energy reconciliation within the sensing elements with respect to
WSNs. Suppose the quantity of group nodes is a smaller amount than n1, every nodes
within the network need to transmit the consolidated information at a better transmitting
coverage in order to achieve a specific group leader. Suppose existing are extensible
measure than n1 group-leaders, the distributed sensing node within the network need to
broad-cast the composed information to its neighbor group-leader, that don't reduce the
property, stated by Rappaport (1996), and Heinzelman et. al. (2000).
3.3.3 Multiple Grouping
Consider that group A is forwarding/or sharing information with group B. Suppose this
transmitting the data impacts the nearest group C, the information may be jumbled or lost
by the interfering of the subordinate group C. So as to scale back this issue, LEACH has
given a new approach like CDMA technique, i.e. once a node during a group has
determined to become a group-leader, it select a code from the list of disperse codes on
haphazard and announces it inside the network and therefore the group. This helps in
filtering or segregating for the information that‟s get-backed from alternative teams
containing completely various dispersed codes, described by Heinzelman et.al. (2000).
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
10
3.3.4 LEACH Protocol in Surveillance Applications
LEACH refers group-construction exploiting the nodes allocated on the network to
sensing the knowledge and then sends it to the bottom system. Now, we have a tendency
to concentrate on monitoring real-time uses (may be dynamic monitoring or static
monitoring), it's impossible to simply agree that LEACH refers a less quantity of sensing
elements to create groups, i.e. of group members needed by a group-leader is prescribed
as a result of an oversized number of group members will produce overhead or maximum
traffic-loads at the sink. During a monitoring application continuous information-delivery
model is chosen by LEACH to send a large quantity of information to the sink. Suppose
we have a tendency to use LEACH during a habitat-monitoring application like scanning
of membrane, there is a chance that the performance is far higher because the network
density is little and needs just one time-node allocation. These lead it to cause least
latency and high measurability with bigger network lifespan. The one problem that is
unnoticed in LEACH is that the quality-of-service (QoS). At the time of concentrating on
power decay constructing groups to send information, the QoS problem is put it at least
which exhibits that if a group-leader is failing in forwarding information there's no
alternative path-to re-forwarding lost information packet. The topology i.e. the structure
of group construction modifies at every occasion a transmitting of knowledge is finished
with the sink or bottom system.
The System implementation of LEACH can be easily understood by the following way:
The algorithm for the Least Energy Adaptive Grouping Hierarchy (LEACH)
implemented is:
Setup section:
i.
ii. )
iii. ) ;
iv. ) ) ( )) ;
v. ) ) ( )) ) ( ))
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
11
Steady section:
i. ) ) ( )) ( )) ;
ii. )
Table1 Table for Symbolic Presentation which are used above
CN Candidate sensing element selected as a group-leader
R Random Variable (0 < r < 1)
T(n) Threshold Variable
CH Cluster Head ( or Group-Leader)
G Network which contain severy sensing elements
Id Number for Identification
Join_adv Advertising for connecting to the group/or cluster
A Node in normal mode
T Transmitting the sensed Information for Time-slot
==> Broadcast
Unicast
LEACH protocol has described better results in numerous scenarios, but the
results obtained were not sufficient and was a huge gap for further development. The
emergence of evolutionary computing has ignited a number research enhancement and
protocol development. Initially Genetic algorithm based approach were used dominantly
but considering the swarm behavior and its characteristics made researchers to think
about its implementation for protocol development and optimization. Here in this
research work the author has implemented two parallel systems for comparison, first was
LEACH and it was further compared with a robust technique called as PSO, and it has
also demonstrates an enhanced result as evaluate to further conservative techniques.
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
12
LEACH out present numerous static grouping algorithms through needed nodes to
volunteer to subsists high-energy group-leaders as well as acclimatize the corresponding
groups stand on the nodes that prefer to be group-leaders on a certain instance. At diverse
period, every node has the load of acquiring data commencing the nodes in the group,
combining the data to acquire a cumulative indication, and transmitting this
comprehensive indication to the bottom system. LEACH is completely dissipated,
administer information is not required by the bottom system/or location, furthermore the
sensing elements are not required the information of the worldwide network within
assemble for the LEACH algorithm to proposed.
LEACH is identified based on the three factors:
1. Extension of network lifespan
2. Power utilization of each senor node„s is reduced.
3. Data aggregation helps to reduce the traffic between communicating messages from
every sensing elements.
The utilizing of groups for transmit data to the bottom system leverages the
compensation of little transmit distance for the majority nodes, needing merely a several
nodes to transmit far detachment to the support system.
3.4 PSO: Particle Swarm Optimization
3.4.1 Introduction
PSO is formerly credited to Kennedy, Eberhart (1995) has been foremost proposed for
simulating societal behavior, like a stylized demonstration of the association of organisms
in a bird group or fish school. The algorithm be abridge and it has been experiential to be
presenting optimization. The book presented by Kennedy and Eberhart has been
describing several philosophical aspects of in addition to swarm intelligence. In
computer science, PSO stated by Tillet et. al. (2004) has a computational technique that
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
13
optimizes a difficulty through iteratively annoying to develop a candidate resolution with
observe to a specified measure of excellence. PSO optimizes a difficulty through having a
population of candidate explanation, here dubbed subdivision, and poignant these
particles approximately in the search-space according to straightforward arithmetical
formula above the particle's location and rapidity. Every particle's progress is influenced
through its local most excellent recognized position and is also guided in the direction of
the greatest identified positions in the search-space, which be simplified as improved
positions are establish through additional particles. This is predictable to progress the
swarm toward the greatest solutions.
The PSO, stated by stated by Tillet et. al. (2004) algorithmic rule is a biological
process computing approach, sculptural once the social behavior of a flock of birds.
Within the context of PSO, a swarm refers to variety of potential results to the optimum
issue, wherever every potential result is named as a particle. The goal of the PSO is to
search out the particle position that presents with simplest analysis of a given fitness
operate. Within the data formatting method of PSO, every particle is given primary
parameters arbitrarily and is “flown” via the multi-dimensional search area. Throughout
every reproduction, every particle refers the knowledge regarding its earlier better
individual position and global better position to maximizing the chance of moving
towards a stronger results area that may lead to a stronger fitness. Once fitness is higher
than the individual stronger fitness is found, it'll be applied to exchange the individual
better fitness and updating their candidate results based on the subsequent equations
presented by Kennedy, Eberhart (1995):
)1....())........1((
))1(()1()(
22
11
txpc
txpctvwtv
gdgd
idididid
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
14
Table 2 List of variables used in PSO Equations
3.4.2 PSO Algorithm for Group Setup
The procedure of our procedure is based on an innermost organize algorithm that is
execute at the bottom system, which have been a node through a huge amount of power
supply. The planned protocol operates in surrounding, where every around start on with a
setup section on which groups be formed. This is subsequent by a stable state stage in
which we utilize a parallel approach as in. At the preliminary of every setup section,
every nodes send information about their present power position and locations to the
bottom system. Stand on this information, the base system calculate the standard power
level of every nodes. To make certain that merely nodes by an enough power are chosen
as group-leaders, the nodes among with a power level higher than the standard are
eligible to be a group-leader applicant for this surrounding. Subsequently, the base
system runs the PSO algorithm to conclude the best K group-leaders that is able to
minimize the price function, as distinct through:
)2..(....................)1(cos 21 fft
)3.......(........../,max ,, ,.,2.11 kpknieCp kpikk CCHndf
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
15
)4...(....................)(/)(1 ,12
K
k kp
N
i i CHnEf
Where f1 is the greatest average Euclidean preserve of nodes to their connected group-
leaders and Cpk is the amount of nodes that belong to group Ck of element . Function
f2 is the ratio of whole preliminary power of every nodes ni, in the network
among the totality present power of the group-leaders candidates in the present
surrounding. The invariable β is a user distinct invariable used to weigh the payment of
every of the sub-objectives. The strength function distinct over has the object of
concurrently minimizing the intra-group.
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
16
Start
Initialize position and
velocity of each particle
Calculate fitness of each particle
pid=partical best, pid=p&d
Iteration, t=1 particle p=1 and start loop
Interval confinement:
If particle is <xmin
particle <xmin
Particle >xmin
Map the new position with the closest
(x, y) coordinate
Evaluate the fitness of each
particle
If particle fitness < pid ; then
update pid
If pid<pgd then update < pgd
Iteration=Max
Output result
Stop
Y
n
Update particle velocity and position
Set p=1, increment t
P>S?
Increment p
n
Y
Figure 3.2 Functional Flow chart of the PSO algorithm for setting up a group
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
17
Indifference between sensing elements as well as its group-leaders, as calculate from f1;
beside with also of optimizing the authority capability of the network as enumerate
through f2. Accordingly to the cost operate separate above, a little value of f1and f2
suggest compacted groups by the majority complimentary set of sensing elements that
have sufficient authority to in present the group-leader responsibilities.
Figure 3.2 has been shown the flowchart of algorithm sensitive during the
group setup segment. Designed for a sensor system between N nodes as well as K
approved numeral of groups, the network is intelligent to be grouped as following:
1. Initializing particles to include arbitrarily elect group-leaders within the eligible
group-leader candidates.
2. Appraised the value operate of every particle:-
i. For every node →
- Estimate remoteness ) among node as well as all
Group-leaders
- allocate sensing node to group-leader wherever;
ii. Evaluate the price operate utilizes equations (2) to (3).
3. Discover the individual as well as global better for every particle.
4. Modernize the particle‟s rapidity as well as location using equation (1).
5. Boundary of modify during the particle‟s location worth.
6. Plan the novel reorganized location with the closest ) coordinates.
7. Reproduce steps 2 to 6 pending the greatest amount of permutations are
accomplishment.
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
18
Further algorithms are re-arranged below:
3.4.3 Categories of Grouping in PSO
During this research work researchers considered -grouping that can hold four
alternative of ( by example undependable immobility weight),
(PSO by instant untrustworthy speeding up constants),
(hierarchical ) as well as ( with administrator intellectual
form) intended for power aware grouping in wireless sensing systems. This technique
will be suitable only while all nodes has permanent Omni-directional broadcast diversity,
the sensor ground have to be drawing enthusiastic on a two-dimensional area and node is
arbitrarily dispersed. Following utilization of the nodes, the nodes are stationary and the
situation of the nodes is recognized to the bottom system. The bottom system run the
grouping algorithm with modernize nodes as regards their group-leader and every nodes
ought to have similar transmission ranges as well as hardware configurations.
3.4.3.1 Centralized-PSO (PSO-C)
It will be a centralized - algorithms, in which the nodes it contain power above
standard power resource are opted like the group-leaders, stated by Latiff et. al. (2008). In
this thesis researchers also contrast this algorithm with protocol as well as with
Simulation results demonstrate that out present to and
in period of network life moment and throughput etc. It is also out present
and stand grouping algorithms.
3.4.3.2 Minimum Spanning Tree-PSO (MST-PSO)
It is stated by Co et. al. (2008) that a minimum spanning tree- support grouping
algorithm of the weighted graph for . The optimized route among the nodes and its
group-leaders is investigated from the whole most favorable tree on the foundation of
power utilization. Determination of group-leader is bottom on the power obtainable to
nodes and Euclidean coldness to its national node in the most favorable tree. Additional
contain completed that network life occasion does not depend on the bottom system
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
19
location or remaining power of the node. On one occasion the topology determined to
then network life moment becomes approximately settled.
3.4.3.3 Distributed PSO (PSO-D)
PSO management algorithmic rule, presented by Tillet et. al. (2004), attempts to
minimize radio power at the time of trusted interconnecting to the network. During
designing this technique researcher projected a vital metric for a sensing element
topology that involve thought of hidden nodes and uneven links. It reduce s the amount of
hidden sensing elements and uneven links at the expense of accelerating the transmitting
power of a sub-set of the sensing elements could indeed increase the longevity of the
sensing element network. The Researcher explored a distributed-biological-process
mechanism to optimize this new metric. The Researcher forming the topologies with
fewer hidden sensing elements and uneven links than a comparable algorithmic rule and
presents some results that indicate that newly formed topologies deliver a lot of
information and last longer.
3.5 Cross-Layer Mechanism in Network Optimization
In conventional interaction networks, the ISO-OSI layered design has been widely
adopted and has served many interactions systems well in the past; however, evolving
wireless networks of today are seriously challenging this design philosophy. The layered
design defines a stack of protocol layers in which each layer operate within its well-
defined function and boundary, and thus allowing changes to the underlying technology
at each layer without imposing the need to change the overall system design. This
approach has been successful in its ability to provide modularity, transparency and
standardization in the wire-line networks but might be unsuitable in the wireless networks
domain. Although wireless networks, such as cellular networks, wireless local area
networks (WLANs), mobile ad-hoc networks (MANETs) and wireless sensor networks
(WSNs) are considerably different in terms of their real-time uses and design, a common
theme in every network is the use of the wireless channel for interaction. Unlike the wire-
line networks, the wireless channel has several unique characteristics that need to be
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
20
taken into account when designing wireless networks. First, the broadcast nature of the
wireless channel requires elaborate medium access control (MAC) protocols for channel
access and second, the transmitted signal that propagates through the wireless medium is
affected by attenuation and degrades more rapidly with distance as compared to the wire-
line channels. In addition, the wireless channel is often affected by factors, such as
interference, mobility issues and multipath fading. Every factors need to be taken into
consideration when designing protocols at different layers of the protocol stack. For
example, rapid time variations in channel characteristics due to fading may require a
more advanced modulation and coding techniques at the physical layer to avoid frequent
packet losses, and it will be more difficult to provide quality of service (QoS) to support
future real-time uses, such as multimedia streaming, which demands higher data rates
over heterogeneous wireless networks with different transmission characteristics. Hence,
designing for wireless networks poses more stringent requirements than wire-line
networks, and when the 2 layered approaches to designing wire-line network is used to
wireless networks, it might often lead to a sub-optimal solution and inefficient use of
network resources. One typical assumption is that each layer can be optimized
independently and performance gains within each layer will be sufficient for the wireless
networks as in the equivalent wire line networks.
Considering that there are several direct merging and transactions between the
physical and higher-layer, cross-layer style is one among the rising approach in recent
studies that scholars and researchers are expanding to optimize the efficiency in wireless
networks. Earlier analysis work gave concentration on many alternative areas in wireless
networking; this brand new mechanism of optimizing the efficiency by cross-layer
transactions aims to attain advantages in overall system efficiency in wireless networks,
like increase in network capability, efficiencies in energy utilization and QoS to support a
wider variety of services, and therefore the technique could also be located to support
across a range of wireless and wired networks. The centric plan of cross layer style is that
by put together optimizing the management and exchange of data over more-than two-
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
21
layers, and vital performance enhancements will be achieved through exploiting the
interactions between numerous layers of the protocol stack.
Though, the disadvantage to such a proposed mechanism is that the possibility to
obliterate modularity, as well as therefore creation the in broad system delicate. The
lessons of cross layer propose designed for wireless networks is a motivating
investigating part along with it will be the subject intended for researcher proposed thesis.
3.5.1 Cross –Layer Design
Consider the stratified approach to planning networks; the network is often well planned
as a set of various layers. The aim of every layer is to supply bound services to following
higher-layer, and which provides a level of transparency by encapsulating the upper-
layers from the small print of however the lower-layer services area unit being enforced.
This mechanism helps scale back complexness by render the network into tiny modules
with completely different operations therefore every function are often addressed
additional manageably, and indirectly it conjointly facilitates the incident of latest
protocol standards at numerous layers of the protocol stack. This kind of well-structured
mechanism to network style supports to produce simple standardization, interlayer ability
and peer-to-peer bindings among completely different networks and components, stated
by Raymond et. al. (2004).
In wireless sensing networks, the dynamic interactions of the wireless channel
possessing several troublesome challenges. The traditional protocol stack is inflexible as
numerous protocol layers interacting during a strictly defined approach. In these
situations, the layers area unit designed to control below the worst conditions as critical
adapting to ever-changing conditions and this usually results in inefficient consumption
of accessible frequency spectrum and energy resources. An approach shift is additionally
setting out to occur as a wireless interaction evolves between circuit-switched
infrastructures to a packet-based infrastructure, which is stated by Sanjay et. al. (2003),
and a particular level of QoS is also needed to support future real-time uses in wireless
networks. The question now could be the way to offer and maintain a particular level of
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
22
QoS during a dynamic environment? One potential various is by cross-layer style and
adaptation.
3.5.2 Concept of Cross-Layering
The thought of cross layer style is concerning sharing of knowledge among completely
various protocol layers for adapting reason and to extend the interlayer transactions.
Here, adaptation uses the capability of network protocols and real-time uses to look at
and reply to changes in channel conditions. A standard thought is that the superimposed
approach should be fully eliminated and every one layers should be integrated and
together optimized. In wireless networks, there's a good mutuality between layers. Cross-
layer style will facilitate to use the transactions between layers and promotes adaptation
at numerous layers supported info changed. However, this kind style has to be
fastidiously coordinated to omit the unrelated consequences. It is very difficult to
characterizing the transactions between protocols at completely different layers and
therefore the connected optimistic approach across the layers might result in advanced
techniques, which might later outcome in issues with planning/and adaptation, correction,
enhancing and standardization, presented by Vikas, Kumar (2005). When the
performance of neighbor layers is interrelated, it's equally necessary to completely
perceive this reciprocal relationship and punctiliously analyze their responses as
improvement processes at completely different layers might get into opposite directions.
We contemplate an easy example within the case of WSNs; it contains wireless
sensing device nodes which communicating themselves through multi-hop routes.
Routing algorithms in WSNs may change rely upon the category of application and
specification of the network, and there are various routing algorithms which specifies the
liabilities of creating and maintaining the routes in a dynamic network type. Therefore,
most routing algorithms are developed with low priority on the problems at lower-layers
such as the variable link capability at the physical-layer or the unsteady contention level
at the MAC-layer, described by Jamal, Kamal (2004). The lower-layer data is extracted at
high level through cross layer approach and might be performance advantages are
obtained. Fig: 3.3 presents the cross-layer thought. In the physical-layer, estimation of the
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
23
channel is takes place to get the fast signal-noise-ratio (SINR) of a link, and this data is
employed to pick the information rate, that impacts the transmitting delay. In the
network-layer, the routing algorithm then that takes a decision based upon the delay
related to every link, that it'll then equally unfold the network load distributions across
the obtainable links and therefore optimizing the performance of the lower-layers.
Figure 3.3 Concept of Cross-layer structure
3.5.3 Cross-Layer Structure
Presently, there's no well outlined supporting structure within the study of cross layer
style optimization, as great number of collaborated optimization mechanisms are
available and perhaps performed at different layers of the protocol stack and every
merging of layers are distinctive to a selected optimization goal. Few occurrences of
cross layer presents supporting structures are established according to following authors,
like – Raymond et. al. (2004), Sanjay et. al. (2003), Vikas, Kumar (2005), Jamal, Kamal
(2004), Vijay, Sridhar (2003). The possible benefits and liabilities for a variety of
approaches and reviewed a quantity of obtainable task in this exacting area. A review on
the reimbursement of cross-layer plan optimizations in wireless procedure stacks was
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
24
proposed by Sanjay et. al. (2003), where the we planned the employ of cross-layer
criticism to expand the appearance of mobile devices to hold up future heterogeneous
networks as the obtainable protocol stacks are architected and utilize in a layered method
and do not function professionally in mobile wireless environments. Dynamic multi-
attribute cross- layer design (DMA-CLD) framework has been projected by Vikas,
Kumar (2005) for cross-layer interactions in wireless ad-hoc and sensor networks, in
which multiple, and possibly conflicting (single-layer, cross-layer, nodal, and
networking) objectives can be met. DMA-CLD allows interactions between the network
layer to both upper and lower-layers of the OSI model. It employs Analytic Hierarchy
Process (AHP) for creation multiple, and perhaps contradictory decision. Cross-layer
optimization can also be classified into several categories, based on the order in which
the optimizations are performed; for example, top-down, bottom-up, application centric,
and MAC centric and integrated approaches, which is described by Ahmed (2004).
Consider present research work and thesis we propose an evolutionary computing
based supporting structure for our study of cross-layer optimization for wireless sensor
networks (WSNs) as given in Fig: 3.4. The design contains a projected optimization
coordinator/or agent (OC); that makes simple transactions among varied methods layer
via partition as core storage or anytime required data/or information like node id number,
hop-count, energy-state, status of the link etc. square measure temporarily maintained and
square measure applied as facet data, that square measure feedback to alternative layers
across the protocol stack. It is often being trivially different from the superimposed model
approach as data and will solely be changed directly across 2 adjacent layers in an
exceedingly ordered manner.
The transactions between numerous layers will be classified as either intra-layer
(among contiguous layers) or interlayer connections (crossways 2 or further neighboring
layers) and all these transactions possibly are either from base-upward or prime-
downward. Base-upward connections are ready to be justifying because the feature critic
technique employed in manageable systems, like forwarding of feedback info to the
higher protocol layers to stable the system efficiency. Consider an example, info/data
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
25
attain regarding circumstances of the channel at the physical-layer maybe used to inform
and feedback to the link layer to become to be used the techniques for its error
management or to the request layer to use its distribution rate. Prime-downward
transactions are ready to be describing because the forwarding of imperative messages
like prioritized traffic (e.g., link-down and forwarding of re-routing table entries to
alternative wireless-sensing elements) from the conventional execution or information
flow, in that situation the direction of the info flow may be directly from application-
layer straight-down to the medium access control layer. We will take one more example,
the transiting power at the physical-layer may be fine grain adjusted by the medium
access control layer to extend the transmitting area.
Figure 3.4 Concept of Cross-layer optimization structure
The layout of the OC (i.e., in the above Fig: 3.4 we use OC instead of OA for
convenience purpose) presents a modern and highly expandable design which
accommodates change to the protocol stacks for non-similar network requirements or
real-time uses, different in a quantity of the planned cross-layer approaches, stated by
Miheala, Shankar (2005), and Marco et. al. (2004); which shall optimizing the
performance between 2 nearest layers (e.g., MAC & N/W- layers), researcher projected
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
26
research work expands the cross layering method to each and every protocol layers as
crucial in sequence container be changed across the layers therefore the efficiency at each
independent layer are often totally optimized. To enhancing and supporting future real-
time uses for present wireless networking wherever they demand high QoS and reliable
packet transmission over the extremely dynamic atmosphere, it'd entail the OC to supply
the libertines to modify and incorporate themselves to modifies within the atmosphere
and additionally to the changes within the efficiency at every individual protocol layer
(like, applying to dynamic network conditions or adapting to the application needs). This
kind of adaptability over all protocol-layers is totally segregated from different projected
mechanisms wherever the most prioritize is on the enhancements and improvement
across more than one protocol layers and that they don't take into account the
consequences caused by the dynamic in operation surroundings. The utilization of
dispersed queues and cross layer data removes back-off-periods and collisions in
transmitting of information packet and it does the efficiency of a system freelance of the
amount of transmission systems and it conjointly providing the sustainability in heavy
loaded circumstances.
Consider the paper presented by Wang et. al. (2005), researcher achieves efficient
flooding or duplicating the packets towards wireless sensor networks due to the
maximum denser and critical problem over power usage and utilization in wireless
sensing networks region through MAC and PHY-layer. The purpose to present a probable
and stretchy move in the direction of to resolve the comparisons connecting the supplies
of large-scale, expanded lifespan, and dual-reason wireless sensing network and the
modification of small-bandwidth, least-battery capability, and incomplete node capital.
Table: 3 is depicts according to author Zhang, Liang (2003) gives few useful statistics to
the defined fields wherever optimal enhancements may occur at every ISO/OSI layer and
also the available schemes and methods are categorized based on 3 important optimal
goals of network quantifiability, system lifespan and node skillfulness. As an example,
new conversation mechanism like ultra-wide-band-modulation (UWBM) which is to be
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
27
planned at the level of physical-layer to make use of its qualitative and potent advantages
in-terms of energy usage, utilization and savings in maximum rate.
Consider an additional instance, the problem of energy utilization in WSNs will
able enhanced through implementing energy utilization with saving routing algorithms at
the network layer that will facilitate to enhance energy potency of WSNs by using
awareness of energy and load routings at the network wide and levels of individual
detector node.
Table 3. Representation of Optimization Schemes in each OSI layer
Optimization
Approaches
Application-
Layer
Transport-
Layer Network-Layer MAC-Layer
Physical-
Layer
Network-
Scale
Data-fusion,
Compression
Boundary-
Delay
Node naming,
Efficient
routing,
Efficient-node
discovery
Contention
control,
Channel
reuse
Ultra-wide
band
Lifespan
Of System
Power aware
mode control
QoS-Power
tradeoff
Power-aware
routing,
Reduced
overhead
Synchronized
sleep,
transmission
range control
Least-
power
design,
Powerful
battery
Node
Versatility
Load-
detection,
Automatic
mode
decision
Load-aware
transport
control
Load-aware
routing,
simplified node
discovery,
Distributed
storage
Load-aware
channel
allocation
Attach
specific
accessories
(GPS)
3.5.4 Resource Allocation in Cross-Layer Approach
The propose of networking protocol for multi-hop wireless ad hoc and sensor networks
be able to be understand as the dispersed explanation of resource portion difficulty at
dissimilar layers. Resource portion in the backdrop of multi-hop wireless networks is
lengthily deliberate in the previous few years, classically through the objectives of exploit
the network lifespan, minimizing the power utilization, and maximizing the network
capability. Though, the majority of the obtainable revise decomposes the resource portion
difficulty at dissimilar layers, and regard as distribution of the property at every layer
unconnectedly. Resource allowances troubles are extravagance moreover heuristically, or
Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime
28
with no allowing for cross-layer interdependencies, or through consider pair off wise
interactions among inaccessible pairs of layers. A characteristic instance of the tight
coupling among functionalities handle at dissimilar layers is the interaction between the
congestion control and energy authority control mechanisms. The overcrowding manage
regulates the allowable source rates so that the whole traffic load on several link does not
surpass the obtainable ability. In distinctive congestion control troubles, the capability of
every link is unspecified to be fixed and prearranged. Though, in multi-hop wireless
networks, the possible capability of every wireless link depends on the interfering levels,
which in twist depend on the power organize policy. Therefore, congestion control and
power control are intrinsically coupled and be supposed to not be treated unconnectedly
when competent solution are required. In addition, the considerable, medium access
control (MAC), and routing layers collectively affect the disputation for network
possessions. The physical layer has a straight affect on manifold accesses of nodes in
wireless channel through affecting the meddling at the receiver. The MAC layer
concludes the bandwidth owed to every transmitter, which obviously affect the
presentation of the physical layer in conditions of productively detecting the preferred
indication. On the additional hand over, as a result of broadcast schedules, elevated
packet delays and/or least bandwidth are able to occur, forcing the steering layer to
modify its route decision.