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CHAPTER 5

ENERGY EFFICIENT HYBRID CODE

COMBINING ALGORITHM

5.1 INTRODUCTION

An energy efficient hybrid code combining technique is proposed

for a cluster based cooperative wireless network. This method uses a hybrid

of selective repeat ARQ and low density parity check (LDPC) for code

combining techniques. In the existing cluster based code combining

techniques, energy consumption is more in the cluster heads and hence the

energy level of the cluster head is drained before the data reaches the

destination. In order to overcome this, a clustering technique is proposed in

which the selection of cluster heads is based on the connectivity and the

residual energy of each node. The clustering architecture consists of source

cluster, destination cluster and relay clusters. Initially the ARQ technique is

used as the code combining technique when the energy level of the nodes in

cluster is more. The encoding and the decoding are done at each cluster using

the LDPC codes until the data reaches the destination cluster.

5.2 CO-OPERATIVE NETWORKS

Cooperative networks are gaining an interest in information and

communications technologies since such networks can improve

communication capability and provide an environment for the development of

context-aware services. Cooperative communications and networking

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represent a new paradigm which involves both transmission and distributed

processing, promising significant increase of capacity and diversity gain in

wireless networks. From one hand, the integration of long-range and short-

range wireless communication networks (e.g., infrastructure networks such as

3G, wireless ad hoc networks, and wireless sensor networks) improves the

performance in terms of both area coverage and quality of service (Quos). On

the other hand, the cooperation among nodes, as in the case of wireless sensor

networks, allows a distributed space-time signal processing which enables

environmental monitoring, localization techniques, distributed measurements

with a reduced complexity or energy consumption per node.

Anna Scaglione (2003) proposed an efficient flooding of a wireless

network with information from a source, which is referring to as the leader.

At the same time, it permits us to transmit reliably to far destinations that the

individual nodes are not able to reach without consuming rapidly their own

battery resources, even when using multihop links (the reach-back problem).

5.3 CODE COMBINING TECHNIQUES

A technique of combining noisy packets to achieve error-free

results for all channels with bit errors below 50 percent is known as code

combining. Code combining optimizes the code rate and minimizes the delay

required to decode a given packet by allowing a receiver to combine the

minimum number of packets. Code combining applications include spread-

spectrum systems, packet communication systems, two-way links, and

multiple hop networks. Diversity combining provides a large variety of

schemes for improved bit-error-rate (BER) in packet communications. The

majority of the schemes are designed to combat noise. The scheme considers

metric ratio combining (MRC), Chase combining and code combining. The

proposed work also gives simulation and derives the average throughput of a

diversity combining scheme employing turbo coding over MIMO fading

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channels. The Selection of hybrid ARQ schemes based on a signal-to-

interference-plus-noise-ratio (SINR), in the presence of co channel

interference. The SINR reduces to an average signal-to-noise ratio (SNR) when no

co-channel interference exists. Hybrid-ARQ schemes result in a higher effective

signal-to-noise (SNR) and a correspondingly lower bit-error-rate (BER).On a

decoding error, this ARQ scheme discards erroneous packets and sends a

retransmission request to the transmitter. The entire packet is retransmitted on

receipt of the NACK. The packets are combined based on either the weighted

SNRs of individual bits or soft energy values, in which case the technique is

termed Chase combining. The maximum likelihood decoder in the code

combining will select the codeword, which raises the conditional probability

between the received sequence and the repeated codeword. Repeated code

words are transmitted over BSC channels with bit error rate. The

retransmission codes are designed using sub-optimal partition chains of the

MSTTC super-constellation using a relatively simple search. The MSTTCs

designed using the sub-optimal partition chains are not optimal codes. But

when combined with the coded packet used for previous transmissions, they

provide better error control than using the same code for all transmissions.

Hybrid-ARQ error control combines forward error correction and error

detection in an attempt to improve the throughput of ARQ-based techniques.

Code combining, introduced by Chase combines multiple copies of a

codeword to produce a lower rate codeword with improved performance.

When code combining is used in conjunction with error detection,

transmissions provide the multiple copies of the codeword as shown in

Figure 5.1.

Kim and Han Jo (2005) proposed a new technique to evaluate the

performance of turbo-like codes and design better codes. The performance of

serial-concatenated codes that utilize turbo code as inner codes and

rectangular parity check code (RPCC) as outer codes is computed with

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reliability-based hybrid ARQ (RB-HARQ) and also the authors investigated

the performance of turbo codes with RB-HARQ and rate adaptation. Ragnar

Thobaben (2008) presented a multi-user hybrid-ARQ scheme for a wireless

multiple-unicast scenario where a base station supplies a group of users with

individual messages.

Each new copy of the codeword is combined with previous copies

to form code words from successively lower rate codes. The advantages of the

hybrid satellite/terrestrial system can be used effectively by complementary

code combining. A hybrid automatic repeat-request (HARQ) code combining

scheme employs different multidimensional space time trellis codes

(MSTTCs) over a multiple-input, multiple output (MIMO) channel. Using

sub-optimal partition chains of the MSTTC super-constellation, the

retransmission codes are designed. The reliability of the communication link

can be improved by the ARQ error control techniques which are based on

error detecting codes and which rely on multiple transmission of the same

code word. Diversity combining offers a large variety of schemes which

considers the metric ratio combining (MRC), Chase combining and code

combining for improved bit-error-rate (BER) in packet communications

which are designed to combat noise.

Qian Zhang et al (1999) proposed an application of a type-II hybrid

ARQ protocol in a slotted direct-sequence spread-spectrum multiple-access

(DS-SSMA) packet radio system. Moreover, it is shown that for each fixed

input load, there is an optimal retransmission probability under the finite user

population assumption.

Shyan Hwang et al (2007) proposed an energy efficient clustering

technique (EECT) for multicast routing protocol, where each node uses

weight cost function based on the transmission power level, residual power

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and node speed to form cluster in the neighboring area and the node with the

minimum weight value is selected as the cluster head.

Figure 5.1 Channels Encoding and Decoding

In all types of data communication systems, errors may occur.

Therefore error control is necessary for reliable data communication. Error

control involves both error detection and error correction. Previously error

detection can be done by Cyclic Redundancy Check (CRC) codes and error

correction can be performed by retransmitting the corrupted data block

popularly known as Automatic Repeat Request (ARQ). But CRC codes can

only detect errors after the entire block of data has been received and

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processed. In this work a new and continuous technique for error detection

namely, Continuous Error Detection (CED) using arithmetic coding. This

CED technique improves the overall performance of communication system

because it can detect errors while the data block is being processed. This

algorithm focuses only on ARQ based transmission systems and the proposed

CED technique can improve the throughput of ARQ systems by up to 15%.

5.3.1 Introduction to HARQ Schemes

Forward error correction (FEC) and automatic repeat request

(ARQ) are two fundamental error-control techniques used in communication

systems. Both error control techniques have some drawbacks. A drawback of

ARQ is that the throughput of the system decreases rapidly as the channel

error rate increases. In a FEC system, it is difficult to achieve both high

system reliability and high throughput. To avoid errors in decoding, allow

code rate may be required, which reduces throughput. Hybrid ARQ is

combination of the two fundamental error-control techniques. Hybrid ARQ

schemes can be divided into three categories: type-I, type-II and type-III

.These three types of HARQ are classified based on what kind of bits are

requested retransmitted and decoded. When an uncorrectable error pattern is

detected in the received information, a type-I hybrid ARQ scheme discards

the whole received packet and requests retransmission of the same packet

until successful decoding is accomplished.

In type-I HARQ, when an error is detected, the original received

information packet is kept and a parity packet is requested. These two packets

are used to do error-correction decoding. If decoding still fails, the receiver

requests either the original information or parity packet depending on the

retransmission strategy. This retransmitted packet is combined with the

previously received packet and then error-correction decoding is performed

again. These request and combine continues until successful decoding is

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accomplished. All received packets are used for decoding in type-II HARQ but not in type-I HARQ.

Type-II HARQ is applied to systems using Reed-Solomon (RS) and

Reed-Muller (RM) respectively. Type-III hybrid ARQ is a variation of type-II

HARQ. In type-III HARQ, a complementary punctured convolutional (CPC)

code is used to offer redundancy for correct decoding. Every retransmission in

type-III HARQ includes both data and parity bits. Utilizing packet combining

can improve the performance of type-II and type-III hybrid ARQ.The HARQ

scheme developed in this work utilizes incremental parity retransmission and

packet combining.

Johan Roman et al (2009) discussed the performance of different

hybrid ARQ and the results show that in addition to the pure energy gain due

to the transmission of additional symbols a diversity gain can be achieved.

These diversity gains need to be taken into account in the higher layer

simulations in order to obtain realistic throughput results in an overall

network simulation. Simple simulator interfaces between the physical layer

and the higher layers only take into account the additional energy.

5.3.2 Channel Encoder and Decoder

Channel coder used to introduce some redundant bits which is

correlated with information bits. Channel decoder checks if signature and

information match each other. Modulation provides some robustness against

errors and cannot guarantee zero error. In the cluster-based code combining

method, the main drawback is the energy consumption of cluster nodes and

the cluster head. This is because, the nodes in each cluster consumes more

energy by using the forward error correction followed by the automatic repeat

request. This will decrease the energy level of the cluster head before they

reach their destination, resulting in data loss at the receiver. Certain overheads

are produced by the Additional FEC packets which depend upon the size of

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the FEC groups. Smaller the FEC group, larger the overhead and vice-versa.

Larger delays are due to larger FEC groups. High bit rate leads to decrease in

delay which consumes extra bandwidth for retransmission of packets. But

these FEC systems are not highly reliable, since the probability of decoding

error is usually greater than the probability of an undetected error. From

Tables 5.1 and 5.2, it can be inferred LDPC has lower encoding and decoding time period when compared to other approaches.

Table 5.1 Run Time Comparison for Encoding

Sl. No.

Size(KB) Reed-Solomon Codes (Sec)

TORNADO Codes (Sec)

LDPC Codes (Sec)

01 250 KB 4.6 0.11 0.9 02 500 KB 19 0.18 0.16 03 1MB 93 0.29 0.20 04 2 MB 442 0.59 0.20 05 4 MB 1717 1.01 0.50 06 8 MB 6994 1.99 0.9 07 16 MB 30802 3.93 1.5

Table 5.2 Run time Comparison for Decoding

Sl. No.

SIZE Reed-Solomon Codes (Sec)

TORNADO Codes (Sec)

LDPC Codes(Sec)

01 250 KB 2.06 0.18 0.10 02 500 KB 8.4 0.24 0.20 03 1 MB 40.5 0.31 0.25 04 2 MB 199 0.44 0.40 05 4 MB 800 1.74 0.65 06 8 MB 3166 1.28 1.10 07 16 MB 13629 2.27 1.95

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5.3.3 Forward Error Correction

A large number of pattern scan be corrected in order to achieve

high system reliability. Constant throughput even with poor channel

conditions can be achieved by combining the benefits of high reliability ARQ

and advantages of FEC. As the channel rate increases, the throughput of the

system decreases. So in a FEC system, both high system reliability and high

throughput cannot be achieved. Due to the underlying packet structure, the

amount of incremental redundancy may be high when it is applied to packet-

based transmission systems, which is a drawback in hybrid ARQ techniques.

The unwanted complicated ARQ mechanism will cost in terms of processing

power, ease of implementation and possibly also in interoperability. This may

decrease the appreciation and the value of the whole MAC standard. Not

much bandwidth is consumed in ARQ. The aim of the thesis is to develop an

energy efficient clustering technique for cooperative wireless networks. In

this technique, the cluster heads are selected based on their residual energy.

The node with more residual energy is selected as a cluster head. Initially the

ARQ technique is used as the code combining technique when the energy

level of the nodes in cluster is more. When the energy level of the cluster

nodes reduces beyond a threshold, it chooses FEC as the Code combining

technique.

Younis and Fahmy (2004) proposed a new energy-efficient

approach for clustering nodes in ad-hoc sensor networks. Based on this

approach and the authors presented a protocol, HEED (hybrid energy-

efficient distributed clustering), that periodically selects cluster heads

according to a hybrid of their residual energy and a secondary parameter, such

as node proximity to its neighbors or node degree.

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5.4 PROPOSED METHOD

5.4.1 Hybrid Selective Repeat ARQ

In Type II HARQ, an information frame is associated with multiple

HARQ packets. When a NACK is received for a specific HARQ packet, a

physically different packet of the set is sent over the channel, since, according

to the HARQ principle, a “retransmission” refers to sending additional

redundancy for a given information frame, rather than repeating the corrupted

packet. In SRARQ, the sender retransmits only the negatively acknowledged

packets and then resumes the transmission process from the last packet sent so

far. In such a scenario, the delays experienced by different packets are related,

since the packets must be released in-order, the actual delivery of a packet

only occurs after the correct reception of all packets with lower identifier.

5.4.2 Low Density Parity Codes

The decoder architecture always operates on the same low

complexity decoding hardware. Thus, energy consumption and circuit size

can be reduced. If the initial redundancy is not sufficient, only additional

parity packets rather than entire packets must be retransmitted. As a result, the

throughput degrades gracefully with a rising noise level. The novel type-II

hybrid ARQ scheme which is based on the construction of RC-LDPC codes

first transmits the packets using highest rate code. An NACK is fed back if

not correctly decoded. A set of the parity bits which are equivalently

punctured in the preceding case at higher rate are retransmitted which

replaces the corresponding item in the preceding codeword. Figure 5.2 depicts

the CRC encoding and decoding algorithm namely the EEHCC algorithm.

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Figure 5.2 Proposed EEHCC Algorithm

CRCCheck

Start

Receive cell of initial transmission and decode

by the rate m/k

CRCCheck

Received repeated cell

Combining Successive cells and decode it

CRCCheck

Stop

Cluster head Selected

Error Detected

No Error

No Error

Error Detected

Error Detected

No Error

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Ossama Younis proposed a novel distributed clustering approach

for long-lived ad-hoc sensor networks. This method is investigated node

capabilities, other than the availability of multiple power levels in sensor

nodes and also presented a protocol, HEED (Hybrid Energy-Efficient

Distributed clustering), that periodically selects cluster heads according to a

hybrid of the node residual energy and a secondary parameter, such as node

proximity to its neighbors or node degree.

5.5 PROPOSED ALGORITHM

The clustering algorithm is fully distributed, each node transmits

only one message during clustering operation, and the algorithm terminates in

appropriate time without iterations.

1. Initially, the residual energy (RI) and the connectivity (CN) of

the nodes are measured.

2. Then cost = RI+CN, is determined for each node.

3. Each node exchanges HELLO message to its neighboring

nodes along with its cost so that each node stores the cost of

its neighboring nodes.

4. Now cost of each node is checked and the node having the

highest cost is elected as the cluster head.

5. The cluster head announces itself as a cluster head and

broadcast a CH declaration message along with a cluster ID to

all its neighbors.

6. After receiving the clustering message, node checks whether

the node ID and the Cluster ID is same and so message is

transmitted from the cluster head.

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7. If the receiving node doesn’t belong to any cluster and if the

received cost is better than the node cost, the node will mark

down the advertised cluster and wait for its time to send

messages.

8. If the receiving node belongs to some cluster and received

cost is better than its node’s cost, two cases are considered.

a. Current node not a cluster head- node can immediately be

marked down as the best cluster and we can wait until the

scheduled announcement.

b. Current node is the cluster head- receives best cost and

node may switch to better cluster.

9. If a node receives the CH declaration message with same cost

from two or more nodes, then the node which sent the

message first is declared as the cluster head.

Figure 5.3 Clustering Architecture

Let the source cluster be Cs and the destination cluster is Cd. Let

there are n intermediate clusters which act as relay clusters namely

Cr1,Cr2,…..Crn. The numbers of cluster members in the cluster

Cs,Cd,Cr1,Cr2,Crn are Ks,Kd,Kr1,Kr2,Krn respectively as shown in Figure 5.3 .

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5.6 SIMULATION RESULTS

5.6.1 Network Simulator

Wireless Network is a computer network that is wireless, and

it is commonly associated with a telecommunications network whose

interconnections between nodes are implemented without the use of wires.

Wireless telecommunications networks are generally implemented with some

type of remote data transmission system that uses electromagnetic waves,

such as radio waves, for the carrier and this implementation usually take place

at the physical level or "layer" of the network. The reasons for using wireless

network are cost effectiveness of network deployment, and its applicability to

environments where wiring is not possible or it is preferable solution

compared with wired networks. When designing wireless networks and/or

studying their behavior under various conditions, software simulation tools

are often used.

Figure 5.4 Structure of a Unicast Node

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Ns-2 provides a highly modular platform for wired and wireless

simulations supporting Different network element, protocol, traffic, and

routing types. In general, ns-2 provides users with a way of specifying

network protocols and simulating their corresponding behaviors. Result of the

simulation is provided within a trace file that contains all occurred events.

The instance procedure node constructs a node out of more simple

classifier objects Section sec: node: classifiers. The Node itself is a standalone

class in OTcl. However, most of the components of the node are themselves

TclObjects. The typical structure of a (unicast) node is as shown in Figure 5.4.

This simple structure consists of two TclObjects: an address classifier

(classifier_) and a port classifier (dmux_). The function of these classifiers is

to distribute incoming packets to the correct agent or outgoing link.

Figure 5.5 Internal Structure of a Multicast Node

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All nodes contain at least the following components:

an address or id_, monotonically increasing by 1 (from initial

value 0) across the simulation namespace as nodes are

created,

a list of neighbors (neighbor_),

a list of agents (agent_),

a node type identifier (node type_), and

a routing module By default, nodes in ns are constructed for

unicast simulations. In order to enable multicast simulation,

the simulation should be created with an option ``-multicast

on'', e.g.: set ns [new Simulator -multicast on] as shown in

Figure 5.5.

5.6.2 Simulation Model and Parameters

The Energy Efficient Hybrid Code Combining (EEHCC) technique

is applied in the bounded region of 1000 x 1000 sqm, through NS2 simulation

using a uniform distribution. To assign the power levels of the nodes such that

the transmission range and the sensing range of the nodes are all 250 meters.

In this simulation, the channel capacity of mobile hosts is set to the same

value: 2Mbps. Distributed coordination function of IEEE 802.11 as the MAC

layer and constant bit rate is used in this simulation. There are two basic

assumptions. 1. The power level can continuously be adjusted from 0 to some

level Pmax. 2. The power levels can only be chosen from a discrete set {0,

p1…pm}. In this simulation, the channel capacity of mobile hosts is set to the

same value: 2 Mbps. The distributed coordination function (DCF) of IEEE

802.11 is used for wireless LANs as the MAC layer protocol. The simulated

traffic is Constant Bit Rate (CBR).

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5.6.3 Performance Metrics

In terms of power consumption, there is an improvement in the

proposed EEHCC algorithm with Hybrid automatic repeat request (HARQ)

technique. At the outset, the result infers that the EEHCC yields an improved

performance when compared with the simple ARQ technique. Table 5.3

illustrates the various simulation parameters which is used in EEHCC

algorithm.

Table 5.3 Simulation Parameters for EEHCC

No. of Nodes 30,60,90 and 100 Area Size 1000 X 1000 sqm Simulation Time 50 sec Traffic Source CBRPacket Size 512 bytes Rate 500 Kbps Transmit Power 0.360 w Receiving Power 0.355 w Idle Power 0.305 w Routing Protocol AODV

5.7 RESULTS

5.7.1 Comparison of Parameters

The following table shows that the delivery ratio increases in

EEHCC when compared to the HARQ. If energy is less another node with

maximum energy within the cluster will be changed to cluster head. From

Figure 5.6, it can be inferred that the EEHCC provides better delivery ratio

when compared with HARQ.

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Nodes Vs Delivery Ratio

0

0.2

0.4

0.6

0.8

1

20 40 60 80 100

Nodes

HARQ

EEHCC

Figure 5.6 Simulation Results for Delivery Ratio with 100 Nodes

HARQ offers a low throughput at low Signal to Noise Ratios

(SNR) due to a large number of retransmissions. This scheme exploits the

advantages of the conventional ARQ and HARQ systems by combining them

effectively. The ARQ protocol allows the retransmission of erroneous packets

instead of delivering them to the user The main drawback of HARQ is that the

incremental bits are not self decodable. That is, the decoder must relay on

both the initially transmitted packet as well as the incremental bits for

decoding. In situation where the first transmitted packet is severely damaged,

all decoding processes will fail. Therefore, it is desirable to have a scheme

where incremental bits are self decodable To improve the throughput, EHCC,

wherein the data plus error detection bits are LDPC, is commonly employed

when the nodes are increased, EEHCC achieves good delivery ratio when

compared with the HARQ scheme.

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Nodes Vs Energy(J)

0

1

2

3

4

5

20 40 60 80 100

Nodes

HARQ

EEHCC

Figure 5.7 Simulation Results for Energy Consumption with 100 Nodes

The energy consumptions in all the schemes grow with number of

nodes. The main reason is that more power is dissipated for overhearing when

every node has more neighbors. However, compared with the other schemes,

the EEHCC is quick and dramatic because of code combining. From

Figure 5.7, it can be inferred that the number of nodes increases and energy

consumption values are considerably less in EEHCC when compared with

HARQ scheme. The average energy consumed by the nodes in receiving and

sending the data for EEHCC is less when compared with HARQ. Figure

5.8represents the energy consumption for various nodes for EEHCC and

HARQ methods.

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Figure 5.8 Simulation Results for Packet Drop with 100 Nodes

The actual packet drop rates are measured during the simulation at

all the traffic sinks in the network and are summed together to get the total

packet drop rate. In HARQ the Packet loss can be caused by a number of

factors including signal degradation over the network medium due to multi-

path fading, packet drop because of channel congestion, corrupted packets

rejected in-transit, faulty networking hardware, faulty network drivers or

normal routing routines. The proposed scheme provides clustering of nodes in

order to avoid the retransmission again from the source. From Figure 5.8, it

can be inferred that the EEHCC provides less packet drop when compared

with HARQ When the nodes are increased, EEHCC achieves less packet drop

when compared with the HARQ scheme.

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Figure 5.9 Simulation Results under ‘Heavy’ Jamming with Delivery Ratio and Variable Rate-500kbps

Figure 5.9 represents the Delivery ratio for various bit rates for

EEHCC and HARQ methods. When the rates are increased, it is clear that

EEHCC achieves good delivery ratio when compared with the HARQ

scheme.

Figure 5.10 Simulation Results under ‘Heavy’ Jamming with Energy

Consumption and variable rate -500kbps

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Most energy is consumed by sending and collecting data, while

other energy consumption can be omitted. The energy consumption is

calculated based on residual energy, node grade, distance from current node

and other elements of neighbor nodes, establish a routing weight. In terms of

energy consumption, the EEHCC method is on an average 7% less than

HARQ method shown in Figure 5.10.

Figure 5.11 Simulation Results for Packet Drop with variable rate-500

kbps

In terms of packet drop, the EEHCC method is on an average 7.4%

less than HARQ method and is illustrated in Table 5.11. If the rate is

increased, it is seen that EEHCC has less packet drop when compared with

the HARQ scheme.

5.7.2 Based on Varying Nodes

In the first experiment, we vary the number of nodes as 20, 40, 60,

80 and 100. Figure 5.6 presents the packet delivery ratio when the nodes are

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increased. It is clear that EEHCC achieves good delivery ratio when

compared with the HARQ scheme. Figure 5.7 shows the average energy

consumed by the nodes in receiving and sending the data. The values are

considerably less in EEHCC when compared with HARQ scheme. Figure 5.8

presents less packet drop in EEHCC when compared with HARQ.

5.7.3 Based on Varying Rates

In the second experiment, we vary the rates as 100, 200, 300, 400

and 500 Kbps. Figure 5.9 presents the packet delivery ratio when the rates are

increased. It is clear that EEHCC achieves good delivery ratio when

compared with the HARQ scheme. Figure 5.10 shows the average energy

consumed by the nodes in receiving and sending the data. The values are

considerably less in EEHCC when compared with HARQ scheme.

Figure 5.11 shows the less packet drop in EEHCC when the rates are

increased.

5.8 SUMMARY

This chapter has dealt with the proposed algorithm of EEHCC.

EEHCC method is 15.25% less packet drop and 7% delivery ratio than

HARQ method. In the clustering algorithm, the selection of cluster heads

based on the connectivity and the residual energy of each node so that energy

of the cluster head doesn’t get drained before reaching the destination. In

proposed code combining technique, the clustering architecture is designed

which consists of source cluster, destination cluster and relay clusters. Each

source node’s message is distributed to all the member nodes in the source

cluster and then encoded using LDPC. The cluster head of the first relay

cluster receives the encoded data from the source cluster and it decodes the

data. If the data sent by the source cluster is not correctly decoded, the cluster

head sends a NACK using the selective repeat ARQ. Otherwise, the cluster

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head divides the data among its cluster members and again encode the data

and sends to the cluster head again. The cluster head sends the encoded data

to the cluster head of the next relay cluster. This process continues until the

data reaches the destination cluster which again decodes and sends it to the

intended destination node. Since decoding is done at each cluster error

recovery time is minimized and reliability is increased. Immediate error

recovery with less energy consumption is obtained using this technique.