chapter-3 theoretical background -...

Elephant Swarm Optimization in Wireless Sensor Network to Enhance Network Lifetime

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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.


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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.


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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.


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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.


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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


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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


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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


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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


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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).


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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. ) ) ( )) ) ( ))


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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.


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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


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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


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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


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)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.


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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


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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.


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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


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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


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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-


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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


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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


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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


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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


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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


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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


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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


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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.

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