We certify that we have read this thesis and that, in our opinion, it is satisfactory in

scope and quality as a thesis for the degree of Master of Science in Electrical

Engineering.

THESIS COMMITIEE:

/

Chairperson

ii

ACKNOWLEDGEMENT

I wish to take this opportunity to express my sincere appreciation to Professor Anna

Hac for her very valuable advice in the preparation and the completion of this

manuscript. In addition I wish to thank Professor Dobry and Professor Sasaki for

their important inputs.

iii

Abstract

WIreless sensor networks (WSN) can provide valuable services in scientific

data collection and other military applications. Recent technological advances have

led to the development of small, low-cost, and low-power microsensors. Almost all

of these microsensors are battery-powered. They maybe placed in some rough

terrain or an area where a certain type of data needs to be collected. These sensors

send data back to a base station (BS) through wireless transmission. Data mayor

may not be sent back periodically depending on the types of application. Most of

the time, it is very difficult to replace batteries for these sensors in the field. As a

result, conservation of energy rather than QoS has become the most important factor

to consider in the routing of wireless sensor networks. There are many routing

algorithms designed for a variety ofWSN applications from periodic data collection

to intrusion detection. Each of them targets a certain type of application and has its

own advantages and disadvantages.

LEACH (Low-Energy Adaptive Clustering Hierarchy) [1] is a cluster-based

routing protocol for WSN. It is designed for periodic data sensing and transmission.

Compared with many other routing protocols for this application, it is superior in

the following ways. First it is an energy efficient cluster-based protocol with

random cluster-head (CH) rotations. This allows better uniform energy distribution

and data aggregation. As a result, network lifetime is extended. Secondly, it has

very good scalability in terms of number of sensor nodes. Sensor nodes can be

added to the WSN at any time and the system will self-configure and adapt to all the

changes. Finally the operations in LEACH are completely distributed with no

iv

central control. This makes it very robust with no "hot spots" or bottlenecks.

However, LEACH is a single-hop protocol. The CH node collects the data

from non cluster-heads (non-CH) nodes and transmits the aggregated data back to

the base station. Due to energy constraint, the base station has to be within the

transmission range of every node in the sensor network. Thus, a WSN that

implements LEACH has a small radius (less than 200m). In other words, although

LEACH is very energy efficient and robust, it is not suitable for large-area WSN.

In this thesis, we propose a routing protocol called Multilevel-LEACH (ML­

LEACH). It retains all the key features of LEACH, such as its cluster-head election

algorithm and cluster-formation scheme. ML-LEACH is also designed for periodic

data collection. However unlike LEACH, ML-LEACH is a multi-hop protocol

which allows sensors nodes faraway from the base station to transmit data back to

the base station through data forwarding. This protocol allows the network area of

WSN to be extended by many times and yet maintains fairly long network lifetime

and satisfactory data transmission efficiency. The energy cost to build an n-Ievel

LEACH will also be studied and verified by MATLAB simulations.

v

Outline

1. Introduction

1.1 Introduction to Wireless Sensor Network 1.1.1 Background 1.1.2 Applications 1.1.3 Characteristics

1.2 Routing in Wireless Sensor Network 1.2.1 Challenges 1.2.2 Data-centric routing 1.2.3 Location-based routing 1.2.4 QoS-aware routing 1.2.5 Hierarchical routing

1.2.5.1 TEEN 1.2.5.2 LEACH

1.2.5.2.1 1.2.5.2.2 1.2.5.2.3 1.2.5.2.4 1.2.5.2.5

Background Protocol architecture

1.3 Motivation

1.4 Thesis contribution

1.5 Thesis organization

2. ML-LEACH protoeol

2.1 Protocol architecture

Cluster head selection algorithm Cluster formation scheme Steady-state phase

2.2 Cluster head selection algorithm 2.3 Cluster formation scheme 2.4 Steady-state phase

3. Energy dissipation comparison between LEACH and 2-level LEACH

3.1 2-level LEACH energy dissipation equation 3.2 LEACH energy dissipation equation 3.3 Energy difference equation

4. MATLAB simulation results for energy dissipation of ML-LEACH

vi

4.1 Simulation topology and parameters 4.2 Simulation proof to Section 3 calculation 4.3 Perfonnance gain for ML-LEACH (comparison with LEACH) 4.4 Solutions to improve network lifetime for ML-LEACH

4.4.1 Method I: Increase initial energy oflevel-I sensor nodes 4.4.2 Method 2: Increase the number of sensor nodes in level I 4.4.3 Method 3: Decrease network radius for level 1 4.4.4 Method 4: Combine Method 1 and Method 3 4.4.5 Comparison of results in network lifetime, energy dissipation per

round, and data received per unit Joule of energy

5. Discussion and Conclusion

5.1 Heterogeneous network 5.2 Multi-hop intra-cluster transmissions

6. Glossary

7. Reference

vii

1 Introduction to Wireless Sensor Network

1.1 Background

Recent advances in Ie and micro-electro-mechanical systems (MEMS) allow

Wireless Sensor Networks (WSN) to be constructed with numerous low-power

consuming, inexpensive micro sensors. A graphic illustration is shown in Figure I

where the gray circles represent sensor nodes and the hollow circle represents the

BS. By networking large numbers of tiny sensor nodes, it is possible to obtain data

about physical phenomena that is difficult or impossible to obtain in more

conventional ways. As the cost of manufacturing sensor nodes continues to drop,

an increase in deployments ofWSN is expected in the coming years [5]. There are

two main areas of research regarding WSN . The first area concentrates in the signal

processing of the collected data, such as beam forming. This is in general called

data-aggregation. The other area is concerned with the routing algorithms for WSN.

This thesis focuses on the latter, the routing algorithms for WSN .

• • • • • • ---- 0 • • ~ • • • sensor nodes

Figure I: Sensor nodes transmit from the field to Base Station

1.2 Applications

There are a variety of applications for WSN. Large-scale WSN can be used in

the field of environmental monitoring [6] , medical monitoring, surveillance,

8

intrusion detection, and military operations. For instance, a WSN can be

established in an island to collect data on a certain type of birds. In addition,

suppose there is a need to detect wild fires in a forest, then a WSN composed of

heat or smoke sensors can be used to accomplish the task. Furthermore, in a

military operation, acoustic sensors can be deployed in the battle field via artillery

or aircraft to provide surveillance or reconnaissance. The above are just a few

examples of the usages of WSN. In the future, because of its low cost and ease of

deployment, WSN may become essential to everybody's daily life. Figure 2 shows

di fferent types of applications such as machine monitoring, habitat or animal

monitoring, medical monitoring, etc. for WS [6] .

Anirr~

Monitorin

Ship "-'onitorng Data AIMIIIIsIticIn

... tw.rk

W r.less Sensor

Figure 2: Applications of Wireless Sensor etwork (diagram from [6])

1.3 Characteristics

Since WSN is consists oflow-cost and low-energy sensor nodes, it is different

from traditional ad-hoc networks in a number of ways. First, all of the sensor nodes

are battery-powered. In addition, bandwidth for transmission in WSN is very small

since sensor nodes conserve energy by communicating at low bit rate. Most

importantly, it is very difficult to replace batteries for every node in the sensor

network once they run out of power since a single WSN are possibly composed by

9

thousands of nodes. Because of the constraint in energy, sensor nodes can only

perfonn limited computation and have low bit rate in transmission. A base station

(BS) usua1Iy resides not very faraway from the sensor network to collect data from

the network. Energy is dissipated in the sensor network through sensing, data­

aggregation, transmissions, and receptions. Long-distance transmission is the most

energy-intensive task because amplifier energy required is proportional to distance.

1.4 Routing In Wireless Sensor Network

1.4.1 Challenges

Routing protocols for WSN are different from those used for existing mobile

ad-hoc networks in the following way. Because of energy constraint, energy

dissipation efficiency instead of QoS is the most important factor to consider in

routing of WSN. With this in mind, there are several challenges to overcome

during the design process of a routing protocol for WSN. First the routing protocol

should be application-specific. A routing protocol may be energy efficient for a

certain type of application such as intrusion detection, but not another type of

application such as periodic data collection. For example, in intrusion detection,

even-driven routing protocols are preferred. Besides that, transmissions must be

reliable in order to have a quick response to the event detected. On the other hand,

in periodic data sensing, some packets are allowed to be dropped since there maybe

a lot of duplicate data coming from sensor nodes within the vicinity of each other.

Thus there is no "one-size-fits-all" solution for all the applications [5].

Secondly, WSN often has a many-to-one traffic pattern which creates a "hot

spot" problem. For example, in a traditional mobile ad-hoc network, this may not

10

be a problem in terms of energy dissipation, but in a WSN, some sensor nodes may

run out of power quickly if overused, and this affects the lifetime of the network.

Thirdly, there is a need for al1 the sensor nodes to be self-configuring or

adaptive with no human intervention. In other words, it is preferred that sensor

nodes in the network are autonomous and require no central control. Furthermore,

network protocol complexity must be considered since computing overhead also

consumes a lot of energy.

Finally scalability and sensor network area must be considered. A WSN can be

consisted of a large number of nodes. This requires energy efficient routing

algorithms to reduce the number of transmissions and to extend the system lifetime

by conservation of energy. On the other hand, WSNs may have very large network

area (lkm), so sensor nodes far away from the base station must be able to transmit

data efficiently back to the base station. An energy-efficient algorithm distributes

the energy-intensive tasks among all the sensor nodes and extends the network

lifetime. In this thesis, a new protocol called ML-LEACH which is an extension

based on LEACH, is proposed as an energy efficient routing solution for large-area

WSN.

1.4.2 Data-centric routing

There are mainly four types of routing protocols for Wireless Sensor Networks,

Data-centric, Location-based, QoS-based, and Hierarchical [7]. The fol1owing

paragraphs wil1 briefly describe the characteristics of each of them as well as their

advantages and shortcomings. Several wel1-known routing protocols from each of

the four classes are discussed. Data-centric routing protocols are discussed first.

11

Due to limited energy and large number of sensors within a network, it may not

be feasible to assign a global identifier to each node (e.g. IP) so that queries cannot

be sent to a particular node. This means that redundant packets are sent in network

in order for it to reach the destination. In data-centric routing, the BS sends queries

to certain regions and waits for data from the sensors located in the selected regions

[7]. One of the most popular protocols in this category is Sensor Protocols for

Infonnation via Negotiation (SPIN). The most distinguishing feature of SPIN is its

use of meta -data. Meta-data are descriptors of the data to be transmitted. They

contain only a few bytes and are exchanged via a data advertisement mechanism

before the actual data transmission takes place. Each node upon receiving new data,

advertises it to its neighbors and interested neighbors, i.e. those who do not have the

data, retrieve the data by sending a request message [8]. SPIN's meta-data

negotiation strategy solves the classic flooding problem such as redundant

infonnation passing and overlapping of sensing areas [8]. As a result, network

energy is conserved because less number of bytes is transmitted. Figure 3 illustrates

the operations of SPIN [7].

(a) (b) (e)

12

Figure 3: Operations of SPIN, diagram from [7]

In Figure 3, Node A advertises its data to node B. Node B sends back a REQ

message to show its interest. Node A follows by sending the entire data packet to

node B. Then node B advertises this data to all of its neighbors, and the operation

continues until all interested parties receive the data.

However SPIN's biggest shortcoming is its inability to guarantee the delivery

of data. If the nodes that are interested in the data are far away from the source

node and the nodes between source and destination are not interested in that data,

such data will not be delivered to the destination at all. Comparing with LEACH,

SPIN has a shorter network lifetime determined in [9]. In addition, SPIN is a query­

based protocol and may not be suitable for periodic data collection. Other

prominent protocol in this category includes Directed Fusion [10].

1.4.3 Loeation-based routing

In this type of routing, sensor nodes are addressed by their location information.

Relative positions of neighboring nodes can be obtained by exchanging location

information between neighbors [9]. GPS (Global Positioning System) can also be

used if the sensor nodes are equipped with a small low-power GPS receiver. Some

location-based schemes demand that nodes go to sleep if there is no activity.

Yu et al. discussed the use of geographic information while disseminating queries to

appropriate regions since data queries often include geographic attributes [11]. This

protocol is called Geographic and Energy Aware Routing (GEAR) and it uses

energy aware and geographically-informed neighbor selection heuristics to route a

packet towards the destination region. The main idea behind this protocol is to limit

13

the mnnber of receiving sensor nodes by considering only a certain region rather

than broadcasting the queries to the entire network.

There are two phases in this algorithm. The first phase involves forwarding

packets towards the target region. Upon receiving a packet, a node checks its

neighbors to see if there is one neighbor which is closer to the target region than

itself. The nearest neighbor to the target region is selected as the next hop. If the

node's neighbors are all further than the node itself, then one of the neighbors is

picked to forward the packet based on a learning cost function. The second phase is

forwarding the packets within a region after the packets have reached the target

region. This can be done by either recursive geographic forwarding or restricted

flooding. In high-density networks, recursive geographic flooding is more energy

efficient and vice versa. The operation of recursive geographic flooding is depicted

in the following figure [7]. In Figure 7, after the data reaches the destination region,

the region is divided into four sub-regions, and data is sent to one of the nodes from

each of the four sub-regions. This process repeats itself until all the nodes in the

entire region have received the data

0 0 0 0

fo o~ , , , , , , _0

' , 0 '~ : 0 'iIf.- __

--1' -0

_f.. -0 o

Figure 4: recursive geographic flooding in GEAR, diagram from [7]

14

There are several drawbacks for GEAR. First, there is the problem with

''holes''. That happens when a node finds none of its neighbors are closer to the

target region than itself. This is not energy efficient because now the sender needs

to pick a random neighbor as its next hop stop, and this neighbor is farther away

from the destination than the sender. Secondly GEAR does not provide good

scalability for WSNs because routing is dependent on information exchanged from

the node's neighbors. Any change in topology triggers new path calculation.

Finally GEAR needs global information to estimate a cost function to the

destination region. As a result, its operations are not localized.

1.4.4 QoS-aware routing

In QoS-based routing protocols, the network has to balance between energy

consumption and data quality. The network has to satisfy certain QoS metrics, such

as delay, bandwidth, capacity, etc [9]. Sequential Assignment Routing (SAR) [12]

introduces the notion of QoS in the routing decisions. Routing decisions in SAR are

dependent on three factors: energy resources, QoS on each path, and the priority

level of each packet. To avoid single route failure, a multi-path approach is used

and localized path restomtion schemes are used. To create multiple paths from a

source node, a tree rooted at the source node to the destination nodes is built. The

paths of the tree are built while avoiding nodes with low energy or QoS guarantees.

At the end of this process, each sensor node will be part of multi-path tree. As such,

SAR is table-driven multi-path protocol that aims to achieve energy efficiency and

fault tolerance.

15

However SAR maintains a routing table and any topology change triggers path

re-computation. This increases the complexity of the protocol. In addition, as the

number of sensor nodes becomes large, it is even more difficult to manage the

routing table with the given energy resources so it doesn't provide good scalability

in terms of density of the nodes in the network. As such, QoS-based routing

protocols are not very popular in WSN routing currently. In the future when image

and video sensors are needed for certain applications, QoS-based routing will play

an important role because in these types of data transmission, a certain level of QoS

must be met.

1.4.5 Hierarchical routing

The focus of this thesis is on Hierarchical routing. It is also called cluster-based

routing. The main goals for Hierarchical routing are to improve scalability of the

WSN and communication efficiency. As a matter of fact, the single-gateway

architecture is not scalable for a large set of sensor nodes covering a wide area of

interest since the sensors are not capable oflong-haul communication. To allow the

system to cope with additional load and be able to cover a fairly large area of

interest without degrading the service, network clustering approach has been studied

[7]. This means that cluster formation and the use of cluster-heads to process

energy intensive tasks can improve overall system scalability, lifetime, and energy

efficiency [9]. In addition, by forming clusters, the CHs can perform data­

aggregation to decrease the number of transmitted messages to the base station

since data sent from intra-cluster sensor nodes to the CH are typically correlated.

However this type of routing requires precise scheduling and coordination because

16

• •

sensor nodes in the network share a common transmission medium and scheduling

helps resolve contention problem in transmissions.

1.4.5.1 Threshold-sensitive Energy Efficient Protocols (TEEN)

TEEN is a hierarchical protocol designed for time-critical applications such as

intrusion detection [13]. In TEEN, sensor nodes sense the medium continuously,

but the data transmission is done less frequently. A cluster head sensor sends its

members a hard threshold, which is the threshold value of the sensed attribute and a

soft threshold, which is a sma\l change in the value of the sensed attribute that

triggers the node to switch on its transmitter and transmit. Thus the hard threshold

tries to reduce the number of transmissions by allowing the nodes to transmit only

when the sensed attribute is in the range of interest. The soft threshold further

reduces the number of transmissions that might have otherwise occurred when there

is little or no change in the sensed attribute. "A smaller value of the soft threshold

gives a more accurate picture of the network, at the expense of increased energy

consumption" [9].

A variation of TEEN, called Adaptive Periodic Threshold-sensitive Energy

Efficient Sensor Network) or APTEEN [14] is later developed to accommodate

periodic data sensing. There are three drawbacks in TEEN. First TEEN is not

suitable for periodic data collection applications since the user may not get any data

at all if the thresholds are not reached. APTEEN differs from TEEN in that when a

node does not send data for a certain amount of time, it is forced to sense and

transmit data Secondly for both of them, the base station is required to assign the

clusters. This means that their operations are not distributed and they are not

17

•

suitable for some types of applications where decentralized protocols must be used.

Finally the system complexity becomes an issue with the implementation of hard

and soft threshold functions. especially when multiple levels of clusters are formed.

In addition there is an ambiguity between packet loss and unimportant data (not

reaching the threshold). Although TEEN and APTEEN outperforms LEACH in

network lifetime [7], they are not the optimal solutions for periodic data sensing

with large-area sensor networks since it is difficult for BS to assign clusters to nodes

too far away from itself that communication is impossible.

1.4.5.2 Low Energy Adaptive Clustering Hierarchy (LEACH)

LEACH stands for Low Energy Adaptive Clustering Hierarchy. Its operations

are explained in the next section. Compared with the above protocols, LEACH has

the following advantages. First its sensor node activities are autonomous and self­

configuring, requiring no central control. Secondly its cluster-heads rotate

randomly to ensure uniform energy dissipation. In addition it is a relatively simple

protocol and shows good network lifetime. Finally it demonstrates good scalability

in terms of overall density of the sensor nodes in the network. However LEACH

assumes that all nodes can transmit with enough power to reach the BS if needed.

Therefore it is not applicable to networks deployed in large regions [9]. In the

following paragraphs, the operations of LEACH are discussed in detail.

1.4.5.2.1 LEACH Background

LEACH is a cluster-based protocol. Cluster heads in LEACH performs data

aggregation for all of its cluster members. In general, sensor nodes in the vicinity of

each other form a cluster. However unlike some of the static clustering hierarchical

18

protocols, cluster formation in LEACH is randomly rotating and self-adaptive. This

is done through its CH selection algorithm and adaptive cluster fonnation scheme.

Both of them are explained in the next section in detail. In essence, LEACH is

designed to distribute energy dissipation evenly among all sensor nodes in the

network. As a result it increases the length of the period when all sensor nodes

remain operative ( alive).

In addition, LEACH is a single-hop protocol since the transmission of data

between the CH and the BS is one-hop. Data collected from sensor nodes within

the cluster are processed by the CH. This is defined as data aggregation. The most

common data aggregation technique is data suppression which eliminates

duplication. A single-hop protocol implies that all nodes within the WSN can reach

the BS. On the other hand, this also means that LEACH can only be applied to

WSNs with small network area.

1.4.5.2.2 LEACH protocol architecture

The operation of LEACH is divided into rounds. A round consists of two

phases. The first phase is the setup phase. In this phase, CHs are selected and the

adaptive clusters are formed. The second phase is called the steady-state phase. In

this phase, data is sent from cluster members to their corresponding CH according

to a TDMA schedule. In other words, when not transmitting, the sensor nodes

remain in sleep mode to conserve energy. At the end of the round, the CHs send

aggregated data back to the BS. The steady-state phase lasts for a certain amount of

time before the entire cluster configuration is destroyed. The process repeats until

all nodes run out of power. In Figure 5 [I], after the set-up is complete at the

19

beginning of the round, steady-state phase takes place. Each non-CH node

transmits data to its CH. They can only transmit during their time slot within a

frame. The duration of a frame is thus dependent on how many members that a CH

has. Several frames are transmitted before the clusters in the current round are

destroyed.

Figure 5: LEACH steady-state operations, diagram from [1]

1.4.5.2.3 Cluster head selection algorithm

The set-up phase of LEACH is consists of two parts, cluster head selection and

cluster formation. In this section, the cluster head selection algorithm of LEACH is

discussed. In LEACH CHs are self-eleeted with no central control. All the sensor

nodes take tum to become a CH to share the energy-intensive duty. At the

beginning of each round, each sensor node elects itself as a CH with a probability or

threshold value determined through a cluster head election algorithm. The threshold

value is a rational number between 0 and 1 and is subsequently compared with a

random number between 0 and 1. If the threshold value is greater than the random

number, the sensor node becomes a CH for the current round and vice versa. The

cluster head election threshold value is calculated according to the equation below,

which is equation 3 in [1].

Figure 6: Cluster selection algorithm for LEACH, equation 3 in [1]

In the above equation, Pi(t) stands for the threshold value, r represents the

20

current round, k is a pre-defined value for the optimal mnnber of clusters according

to the over all mnnber of nodes. This is a simulation result from [1]. N is the

overall number of sensor nodes within the network, NIk is the theoretical number of

rounds it takes for all nodes in the WSN to become a CH., and the set Ci(t) from

Figure 6 indicates the eligibility of a node i. When Ci(t) equals I, node i is eligl"le

to become a CH because it has not become a CH in the most recent (r mod (N1k»

rounds. On the contrary, after a node i has become a CH for the current round, it is

not eligible to become a CH again until after (N1k) - r rounds, and during this time

Ci(t) equals o. All the nodes are expected to become a CH after NIk rounds. In our

simulations, the number of rounds for all nodes to become a CH may not be the

theoretical NIk value every single time, but even if that is the case, this number

remains close to the theoretic value.

1.4.5.2.4 Cluster formadon algorithm

The second part of the set-up phase is cluster formation. After all the CHs are

selected for the current round, those CHs must inform all other nodes in the network

that they have been chosen. The CHs use non-persistent CSMA MAC protocol to

broadcast an ADV message. This is a very short message containing only the

node's ID and a header that labels the message as an announcement message. A

non-CH node can determine which cluster to join based on the received signal

strength of the ADV message.

Once a non-CH node decides which cluster to join, it will send back a Join-Req

message to the CH of that cluster. This message is also a short message, containing

non-CH and CH nodes' IDs. When this process is completed, all clusters are

21

formed for the current round. Next each CH sets up a TDMA transmission schedule

and sends this schedule to all other non-CHs within the cluster [4]. The TDMA

schedule assigns a time slot for each of the members within the cluster. By doing so,

non-CH nodes can conserve energy by going into sleep mode when it is not their

tum to transmit In addition, collisions of data messages are avoided. When the

TDMA schedule is received by all nodes within the cluster for all clusters, the setup

phase is complete. Intra-cluster and inter-cluster transmissions can begin. Unlike

some of the static cluster formation algorithms, clusters in LEACH are destroyed at

the end of the round, and the CH selection algorithm and cluster formation

algorithm will repeat again at the beginning of the next round.

1.4.5.2.5 Steady-state phase

LEACH's steady state operation is divided into frames. Owing each frame,

each node is allocated one transmission slot. The length of the frame in time

depends on the number of nodes within the cluster. For instance, if there are many

members for a particular cluster, then the time to send a single frame is longer. It is

also assumed that nodes always have data to send and they must be synchronized to

be able to follow the TDMA schedule sent by their CH.

After all the data is received by the CH, data aggregation is performed by the CH to

reduce the overall number of bits to transmit to the BS. In addition, common

(useful) signal is enhanced and uncorrelated noise of the signal is suppressed. To

reduce inter-cluster interference, direct-sequence spread spectrum (DSSS) is used

by each cluster in LEACH [1]. At the end of the frame, the combined data is sent to

the BS from the CH by using a fixed spreading code and CSMA to avoid

22

•

interference and collisions. In LEACH, each round 1asts about 20 seconds, so

multiple frames are sent to the CH during a single round. The time duration is an

arbitrary number. In general, by setting the round duration at 20 sec, nodes do not

drain their energy when they become the CH for the first time. The steady-state

phase completes at the end of the round Figure 7 shows the operation of the

steady-state phase in LEACH.

Figure 7: Steady-state operation in LEACH, diagram from [1]

1.5 Motivation

The motivation of this thesis is to design an energy efficient routing protocol for

large-area Wifeless Sensor Networks performing periodic data sensing applications.

Apparently LEACH is an energy efficient protocol and distributes energy

consumption among all the sensor nodes within the network. Yet it may not be

applicable for large-area WSN operations because of its one-hop transmission

nature. Our objectives are to retain the advantages of LEACH such as its CH

election algorithm and cluster formation scheme, and at the same time develop a

new routing protocol that can provide energy efficient transmissions of data for

sensor nodes very far from the base station through data forwarding.

1.6 Thesis contribution

In this thesis, we present ML-LEACH as a solution for large-area WSN routing.

The simulation results from MATLAB have shown that it outperformed LEACH

significantly in large-area WSN routing and variations of ML-LEACH have shown

23

even greater performance gain that are comparable to that of LEACH in a small­

area network. This thesis, we first analyzed 2-level LEACH's energy dissipation

model based on that of LEACH. By comparing the energy dissipation cost of

LEACH and 2-level LEACH, the extra energy cost to implement n-Ievel LEACH

protocol is derived. Then simulations of LEACH, two-level LEACH (2L-LEACH),

and 3L-LEACH routing protocols are performed in MATLAB. The simulations

estimated the number of rounds it takes for the first sensor node to run out of power

with each of the above protocols, energy dissipation per round, and data received by

the BS per I Joule of energy. In addition the simulation located the sensor nodes

that are more probable to run out of power with the given topologies, and based on

that, four strategies are studied carefully in hope to improve network lifetime and

energy dissipation efficiency of LEACH.

1.7 Thesis organization

The rest of the thesis is organized as follows. In section 2, ML-LEACH is

presented and its network architecture is discussed. In section 3, the energy

dissipation model of a 2-level LEACH is analyzed and is compared with that of

LEACH. Based on this result, the extra energy cost incurred from establishing an n­

level LEACH is derived and explained. In section 4, MATLAB simulation results

of ML-LEACH and LEACH are compared and demonstrated in detail. Several

ideas to improve network life time and energy dissipation efficiency are evaluated

by their simulation results. In addition, the interpretation of these results is studied

in full detail. Finally in section 5, future research directions are discussed in the

conclusion.

24

2. Multi-level LEACH architecture

2.1 Background information

ML-LEACH protocol is an extension based on LEACH. It is designed to be

implemented in a WSN tbat has large network diameter (currently between 30Om-

500rn), about three times as that of LEACH. As mentioned in the previous section,

LEACH is a single-hop protocol which limits the diameter of WSN. In con~

ML-LEACH is a multi-hop protocol. The sensor nodes in a WSN tbat implements

ML-LEACH are grouped into different levels based on their distances to the base

station, with the closest group of nodes from the BS being the highest level. In

addition, with ML-LEACH, sensor nodes form two different types of clusters. First

local clusters are formed at each level using LEACH's CH selection and cluster

formation algorithms. Secondly super-clusters (SC) are formed among CHs of

neighboring levels. As a result, the set-up phase of ML-LEACH is a little different

from that of LEACH.

2.2 ML-LEACH cluster head selection algorithm

The new ML-LEACH protocol inherits the self-configuring and randomized

cluster formation algorithms from LEACH despite an increase in network area. In

ML-LEACH, CHs at each level are elected simultaneously using LEACH's CH

election algorithm. This algorithm is shown in Figure 6 from the previous section.

One key difference is tbat in ML-LEACH an upper-level CH is eligible to become a

super-cluster-head (SCH) for the lower level CHs. In this case, the lower-level CHs

transmit aggregated data to an upper-level SCH instead to the BS. In other words,

data collected at lower levels are relayed back to the BS through one or more upper-

25

levelSCHs.

2.3 ML-LEACH cluster formation algorithm

First of all, clusters at local levels are formed exactly like how they are formed

in LEACH. Levels in ML-LEACH are determined by their distance to the BS.

Nodes closest to the BS are classified as Level 1. In ML-LEACH, nstead of

broadcasting the ADV message to all the nodes in the WSN, CHs in ML-LEACH

multicast the ADV message to sensor nodes within the same level and also those

from its neighboring lower-level only. For instance, a level-2 CH only sends the

ADV message to non-CH nodes in level 2 and to all the sensor nodes in level-I.

Thus higher level nodes do not receive the ADV messages from lower-level CHs.

CSMA MAC protocol is again used in LEACH to facilitate cluster formations in

ML-LEACH.

Figure 8 shows that after all the local clusters are formed, based on received

signal strength, super-clusters are formed between CHs of neighboring levels. A SC

is only formed between CHs from two neighboring levels, with an upper-level CH

serving as the SCH. The inter-level clusters are formed between the two lowest

levels initially, and eventually completing the process when super clusters are

formed between two highest-level CHs. It is assumed that every node in the lower

level can reach every node in a neighboring upper level.

26

Higher-level Super Cluster

1 .... 2do .. l(iJ , . Lev-2 SCH

Lower-level Super Cluster

Q ~

Iw-2 dolt • ...·1 dOlI ... · ldo ~ Lev-2 SCH

Higher-level Super Cluster Lower-level Super Cluster

Figure 8: Cluster fonnation diagram for ML-LEACH

2.4 Steady-state phase

The steady state operation in ML-LEACH is again divided into frames. In

LEACH, a TDMA schedule allows each non-CH member to transmit to its CH once

per frame, and at the end of the frame, aggregated data is sent from the CH to the

BS. The steady-state phase operation of ML-LEACH has a key difference from that

of LEACH. To send all the collected data back to the BS efficientl y, the steady-

state operation of ML-LEACH starts at the lowest level. At the beginning of the

frame, local data is collected simultaneous at all levels. Each sensor node takes tum

at transmitting data to their CH. At the end of a frame, the aggregated data is sent to

an upper-level SCH or the BS. The difference from the LEACH operation is that in

ML-LEACH, an upper level SCH does not begin transmitting to a higher level SCH

until it has received all the aggregated data coming from the lower level. After it

receives all the data coming from the lower level, it sends both the data from the

lower level and the same level to another upper-level SCH or to the BS, depending

27

on how many levels exist in the WSN. At the end of the round, data collected at

leach level is sent to the BS by level-I SCHs. Figure 9 shows that the steady-state

operation of ML-LEACH starts with the lowest-level transmission and ends with

the highest-level transmission. Only the highest level CHs transmit to the BS.

Local dUSIenI are fanned

Set-up phase

Lowest level -;:===~I~;.;~s:t~ea~d~y-state phase -I ~':: SC formed •••

mi n -Frame- Time

Figure 9: Setup and Steady-state operations of ML-LEACH

There are a couple of observations regarding the steady-state phase of ML-

LEACH. First, an upper-level SCH is most likely unable to perform data aggregation

on data received from the lower-level because this data is highly unlikely to be

correlated to the data collected at its same level since it is collected by nodes very far

away. As a result, whatever is received from the lower-level is accumulated into the

overall data to be sent. Secondly, as the levels increase in a WS , sensor nodes closer

to the BS are burdened with more bits to transmit. They are more susceptible to

running out of power. Furthermore, if the data collected faraway from the BS is to be

transmitted back to the BS, then it must be transmitted in a relay fashion . In ML-

LEACH, the CHs rotate among all nodes at each level , so the energy-intensive tasks

such as long range transmissions are distributed among all nodes. In section 4, the

simulation results from MATLAB will show that the network life time of a WSN

implementing ML-LEACH is also related to the number of sensor nodes at each level.

For example, if there are more number of nodes in a level, then it takes more rounds

for a node to become a CH, and consequently this extends lifetime of the networks.

28

3. Comparison of energy dissipation of 2-level LEACH and LEACH

Before we perform simulations of LEACH and ML-LEACH in MATLAB, we

need to study their energy dissipation model. By studying the energy dissipation

models of LEACH and ML-LEACH, we can estimate the energy dissipation

difference per round between them. This gives us an estimation of how much

additional energy is needed per round to establish a multi-level routing structure

based on LEACH. Furthermore by estimating the energy dissipation difference

between LEACH and ML-LEACH, we are able to verify the feasibility of

implementing ML-LEACH in a large-area network. In other words, based on our

estimation results, we determine that the energy cost to implement ML-LEACH in a

large-area network is acceptable and the lifetime of such a network can be fairly

long. With this in mind, the energy dissipation of 2-level LEACH is analyzed in

this section, and it is compared with the energy dissipation of LEACH.

To be consistent with LEACH and to be able to make comparisons, the 2-level

LEACH also follows the energy dissipation model of LEACH. Figure 10 shows the

radio energy dissipation model of LEACH [1]. ML-LEACH also follows LEACH's

energy dissipation model. In this model, the block on the left represents transmitter

circuit logic and the block on the right represents reception circuit logic. The

energy dissipation of a packet is the summation of transmitter electronic energy and

amplifier energy depending on the distance of transmission. For instance, Free

space (d2 power loss) channel model is used for short-distance intra-cluster

transmission and multi-path fading (d4 power loss) channel model is used for long

distance inter-cluster transmiS'lion.

29

•

TIt Amplifier

Figure 10: Energy dissipation model of LEACH and ML-LEACH, diagram from [1]

In both LEACH and ML-LEACH, to transmit an L-bit message a distance of

d, the radio expends:

Transmission: ETx(L,d) = ETx..,lec(L) + ETx-amp(L,d) (I) = L "'Eelec + L Em "'d2. for d < do = LOIIEelec + L"'E,.p*d4

, for d > do

The above equation is a translation of the energy dissipation model shown in

Figure 10. It is also used by LEACH as it is shown as equation 9 in [1]. The

equation states that to transmit L-bit of message, the overall energy dissipation is

the sum of transmitter electronic energy (Eelec x L) and amplifier energy (ETx-amp)

based on the distance of transmission. The parameters in (1) are defined as follows.

Bet.. stands for electronic energy. Em is the free-space loss coefficient used for

short-distance transmissions and E,.p is the multi-path fading loss used for long­

distance transmissions. In addition, do is a threshold distance that determines which

channel loss model is to be used. On the other hand, the reception electronic energy

of an L-bit message is equal to that of transmission electronic energy with no

amplifier energy needed. This is shown below.

Reception: Eax(L) = LOll Eelec (2) (equation 10 in [1])

To show the energy dissipation of a 2-level LEACH network, let

M2 = Area of the network

30

M2/ k = area of one cluster

N = # of nodes in the network

k = # of clusters in the network (both level 1 & 2)

NIk = # of nodes per cluster

NIk -1 = # of non-CH nodes in a cluster

L = # of bits in data transmission

do = threshold to determine which power loss model to be used

EOA = energy expended in data aggregation

For d < do, use Efs·d2; for d > do, use Emp·d4

Note: perfect data aggregation is assumed for intra-cluster data collection because

these data came from members from the same cluster and are correlated. In

addition, the network area of the WSN using 2-level LEACH is twice as large as

that of LEACH so that cluster area in 2-level LEACH is larger than that of LEACH

(2M2 instead ofM2).

The purpose of these calculations is to first find out how much energy is

expended in a 2-level LEACH network and then obtain the energy dissipation

difference between that and LEACH. Then from the above result, the energy

dissipation of n-Ievel LEACH can be derived. The energy dissipation of a WSN

implementing 2-level LEACH is divided two components, level-l and level-2

energy dissipation. The calculations of both of them are shown below.

3.1 Energy dlssipadon ofl-Ievel LEACH: Part I: 2nd level energy dlssipadon

Entire level 2: Elevel.2 = Elevel.2 eH + E1evel.21lO11-CH (3)

Single Non-CH node oflevel 2 in one frame:

31

•

Enon-CHLev2 = L *Eelec + L *E& *d2 loCH

= L*Eelec + L*Em * (M2/( pi*k» (4)

Single CH node oflevel 2 in one frame:

EcH-Lev2 = L*Eelec (N/k-l) + L*EDA*N/k + L*Eelec + L*Emp*d4tolevel_1 (S)

Equation (3) simply states that the energy dissipation of a level-2 cluster is the sum

of energy dissipation from the CH node and the non-CH nodes. Equation (4) shows

the energy dissipation of a level-2 non-CH node with M2 I( pi*k) ) term derived

from equations 13-1 S in [I]. In our case, the new in level-2 area is 2M2 instead of

M2 with the increase in overall network area. Equation 13-IS in [I] are shown

below with E[ d2 tocIJ equals to the expected squared distance. In addition (N/k) in

equation (4) is the average number of nodes in a cluster. Equation (S) shows the

energy dissipation of a level-2 CH node. The terms in (S) represent from left to

right, reception energy, data aggregation energy, transmitter electronic energy and

transmitter amplifier energy.

E[tPt.DCHl = J J (r + il)p(x,y)tb:du

'" J J ,.2p(r, fJ'prlTrfD.

Equation 13-IS in [I]

Energy dissipation of one cluster in level 2:

Elevel-2 cluster = Elevel-2 CH + (N/k - I) * Elevel-2 non-CH

= Elevel-2 CH + (N/k) * Elevel-21101l-CH (6)

32

The above equations are derived from equation 11-19 in [I].

The above equations calculated the expected square distance from the nodes to

the cluster head assuming the cluster area is a circle. But for 2-level LEACH, the

parameter k (number of clusters) is reduced to half because sensor nodes spread

over an area twice as large as before. The new E[d2tDCH] equals M2 / (pi*k), which

is substituted into equation (4).

Since we have obtained the energy dissipation estimation for a level-2 cluster, we

can calculate that of all clusters by multiplying the estimation with the number of

clusters at each level, kl2.

All clusters in level-2:

Eleve!-2 toIaI = (kI2) * Elevel-2 cluster

Eleve!-2 toIaI = (kI2)*(EcHLev2 + Emm-cHLev2)

Total energy dissipation oflevel 2 nodes in one frame:

~-Lev-2=

L*(Eelec*N) + (U2)*(N*EoA + k*E.,p*d41OIevel_1 + N*Et;,* (M2/( pi*k) )(7)

Since all the sensor nodes are divided evenly among all levels, half of the nodes

are allocated in level-2 and the other half is allocated in level-I for 2-level LEACH,

As such the overall number of clusters k is divided by 2 for level-2 energy

dissipation of2-level LEACH.

3.2 Energy dissipation of 2-level LEACH: Part II: t 8l level energy dissipation

Next calculation of energy expended by level-l sensor nodes is done. There are

a couple of observations to be noted. First, level-I nodes dissipated more energy

than level-2 nodes because they had to relay or forward level-2 data back to the BS.

33

The energy dissipation calculation of level-1 nodes in one frame is shown below.

Secondly, since the number of CHs in 1st and 2nd levels is approximately equal, on

average each CH receives data from one level-I Oower-level) cluster head.

1st level (highest) CH nodes in one frame:

EcH-Ievl = L *Eelec (N1k) + L *EDA *(NIk) + 2L *Eelec + 2L *Emp*d4 tnBS (8)

In the above equation, additional reception and data forwarding energy are added to

the highest-level CH nodes.

1st level non-CH nodes in one frame:

Enon-CHLevI = L *Eelec + L *Efs*d2 tnBS

= L *Eelec + L "'Efs*(M2/( pi*k) ) (9)

Again equation (9) is derived from equations 13-15 in [I].

One cluster in level 1:

Elevel-l cluster = Elevel-l eH + (N1k - 1) * Elevel-l non-CH

= Elevel-l eH + (N1k) * Elevel-l non-CH (10)

Equation (10) is an approximation oflevel-1 cluster energy. NIk is the number non­

CH members within a cluster. Notice that (N1k - 1) is replaced with NIk as an

approximation.

Total energy dissipation oflevel-1 nodes:

Etotat-Lev-l = kl2*(EcHLevI cluster+ Enon-CHLevI cluster)

Etotat-Lev-l =

L*(Eelec*N + k*Eelec + k*Emp*d4tnBs) + (U2)*(EDA*N + N*Ets* (M2/( pi*k) )

(11)

Assume Emp*d4tnBs = Emp*d4

tnlevel_t. sum of energy dissipated 2-level LEACH:

34

Ewtm 2-1evel LEACH = Ewtm-Lev-l + Ewtm-Lev-2

Ewtm 2-1evel LEACH = equation (7) + equation (11)

Ewtm 2-1evel LEACH = L "'(2EeIec"'N + N"'EoA + k"'Emp"'d4toBS + k"'Eelec +

N"'Efs'" (M2/( pi"'k) ) + (U2)"'(k"'Emp"'d4tolevel_l) (12)

Equation (12) gives the overall energy dissipation of2-level LEACH.

The difference In energy dissipation between 2-1evel LEACH and LEACH:

The total energy expended in one frame in LEACH is shown in equation 18 of [1]:

Total energy dissipation of LEACH: (equation 18 in [1])

Ewtm LEACH = L"'(2Eelec"'N + N*EoA + k"'Emp*d4toBs + N*Efs'" (1I2*pi)*(M2/ k»

(13)

The above equation is an estimation of the total energy dissipation of LEACH.

The terms in equation (13) stand for transmitter electronic energy, data aggregation

energy, long-distance transmission amplifier energy and intra-cluster energy from

left to right on the right side of the equation_ Next the energy dissipation difference

between the 2-level LEACH and LEACH is obtained as shown in equation (14)

below_ Equation (14) is the difference between Equation (12) and Equation (13)_

Energy dissipation difference between 2-level LEACH and LEACH:

Edill'erence 2-1evel LEACH and LEACH = Etotru 2-1evel LEACH - Etotal LEACH

= equation (12) - equation (13)

Edifference2-1evel LEACH and LEACH =

L"'(k*Eelec) + (U2)*(k*Emp"'d4toleve1_l) + U2(N*Efs"'(M2/( pi"'k) »

(14)

E.rur.....,., 3-1evel LEACH and LEACH =

35

2L *(k*Eelec) + (L)*(k*Emp*d4tolevel_l) + L(N"'Efs*(M2/( pi*k) ))

Additional energy required to build an n-level LEACH:

Erutrerence n-1evel LEACH and LEACH = (n-I)* Etimdam_l difference (IS)

The extra energy dissipated by 2-1evel LEACH comparing with LEACH is

shown in equation (14). The additional energy terms are the extra electronic energy

expended in reception of level-2 packets, the extra electrouic energy expended in

fOlWarding the level-2 packets, extra amplifier energy spent in fOlWarding the level-

2 packets to the BS, and finally the extra intra-cluster transmission energy caused by

the increase in cluster area. The first two extra energy dissipations are shown as the

first tenn in equation (14). The most energy-intensive term is the second term of

equation (14), which is the long-distance transmission amplifier energy. This is

because this term is a function of distance to the 4th power and it accounts for most

of the extra energy loss since its value is about 7 times as large as the first term in

equation (14) based on our simulation estimation. At last, an energy difference

equation is derived to calculate the extra energy required to build an n-level

LEACH. This is shown as equation (IS).

Proof by Induction:

Equation (15) can be proved by mathematical induction shown below. In our

proof we are going to show that the energy required to build an n-level LEACH

corresponds to our energy dissipation model. As a result its energy difference with

LEACH is the same as described in equation (15). Let Elong equals the long­

distance transmission energy. Let Emtra equals additional intra-area energy due to

36

area increase. Finally let Eelec equals additional transmission and reception

electronic energy. Notice that Eelec is the same for both transmission and reception.

Among these three energy factors, E10Dg and Eu.tm increase proportionally with the

number of levels. For instance, 3-level LEACH expends 3(Elong + Euwa) Joules of

energy. This is because 3 levels of data are accumulated to transmit to the BS and

the area of a cluster is increased by 3 times. Our proof focuses on the extra

transmission and reception electronic energy. For simplicity we refer it as

electronic energy or Eelec. We show that for n-Ievel LEACH, the electronic energy

equals (n-I )Eelec.

Proposition:

Show that the electronic energy to build an n-Ievel LEACH is:

[2[1 + 2 + ... + (n-I)] 1 n]Eelec = [2[(n-IXn)/2]/n]Eelec = (n-I)Eelec for alI n > I,

assuming that Eelec equals the electronic energy required for inter-level

transmission and reception.

Note: After the summation of all the extra Electronic Energy, the sum is divided by

n, because alI the sensor nodes are divided evenly into each level so that each level

has approximately same number of clusters.

Proof.

n = 1: Eelec = 0, True, no inter-level electronIc energy in single-level LEACH.

n = 2: [2(2-1 )(2)/2] / 2 = Eelec

Assume n = k holds: [2[ 1+2+ ... +(k-l )]Ik]Eelec = (k-l )Eelec (Induction Hypothesis)

Show n = k+1 holds: [2[1+2+ ... +(k-I)+k]lk+l]Eelec = kEelec

Let's first prove the inner bracket: 1+2+ ... +(k-I)+k = k(k+1)/2

37

1 +2+ ... +(k-I )+k = (I +2+ ... +(k- I )]+k = (k-I )k/2 + k, by Induction Hypothesis

(k-l )k/2 + k = «k-I )k + 2k) /2 by 2/2 = I and distribution of division over addition

«k-I)k + 2k) /2 = (k2 + k)/2 = k(k+I)/2 by distribution of multiplication

Now substitute k(k+I)/2 for n = k+1 into n = k+ 1 equation:

n = k+ I: [2[1+2+ . .. +(k-I )+k]lk+ I]Eelec = [2[(k)(k+ 1)/2]1k+ I]Eelec = kEelec

Thus Ediftcrenee n·level LEACH and LEACH = [n(Elong + Einlm) + (n-I )Eelec] - [(Elong + Eintm) 1

= (n-I )(Elol1g + Einlra + Eelec) (16)

The proof is complete. The above equation is the same as equation (IS)

The above proof can be better illustrated with the following diagram.

1 long trans r=:::-l <=~

1 trans

2~~:=;:~ AlienergYll112

~ 1 recep L:.:..J 2 trans 1 trans

3 long trans B <==B <==EJ <= level 1 level 2 level 3 All energy => ===::> It 113

2 recep 1 recep

n·1 trans n long Irans r:::;:l r.::;:l <=L:::J: L::J

n-1 recep

1 trans

: 8 1 recap

All energy J( 11n

In summary, this section shows that the additional energy required to

establishing a multi-level LEACH structure is within an acceptable range and such

an implementation is feasible . The energy difference estimations are proved by our

MATLAB simulation results shown in Section 4.

38

•

4. Simulation results analysis and discussion

4.1 Simulation description

In these MATLAB simulations, we simulate the energy dissipation of LEACH,

2-level LEACH, and 3-level LEACH protocols and compare their results in a

variety of ways. The purposes of this simulation are to verify the calculation results

from the previous section and to explore possible solutions to increase the network

lifetime of ML- LEACH in a large-area WSN. It is apparent that the transmission

energy cost is related to the network radius of the sensor network. But there are

also other factors involved which can be seen from the simulation results.

Because the focus of this thesis is on transmission energy cost which is the primary

energy dissipation source in WSN routing, the computing overhead is ignored in

this simulation. The following paragraph briefly descnbes about the coding method

used in this simulation.

First of all, we use a lOx 10 matrix to simulate 100 sensor nodes in a WSN

with area of 100m2• This makes the node distribution in the WSN uniform. Next,

for all Multi-Level LEACH protocols, the CH selection algorithm used is the same

as that of LEACH. The difference is that with a Multi-Level LEACH, the CH

selection algorithm is run in all M levels rather than in just one level. In LEACH

clusters are formed according to received signal strength of the ADV message by

the non-CH nodes. Stronger ADV message signal received by a non-CH node

means less energy required to transmit packets and less distance between the non­

CH node and the CH node. The non-CH nodes chose the closest CH node as their

CH. To simulate this, distance between two nodes is calculated as by subtractions

39

•

between their respective X and Y coordinates. For instance, the distance between

node (1,1) and node (2,2) is thus (2-1) + (2-1) = 2. The x and y coordinates of

these two nodes are subtracted separately to obtain a value and then their swn is

obtained. In the case where a tie occurs, a random selection is performed to break

the tie.

The topology diagrams used for LEACH, 2-Level LEACH, and 3-Level

LEACH are shown the Figure 11. In LEACH, 100 nodes are scattered within a

deployment area of 100m2• In 2-level LEACH, each level contains an equal amount

of nodes (50). In 3-level LEACH, 40 nodes are deployed for levell, and 30 nodes

are deployed for each of the remaining two levels. First Table 1 shows the

simulation parameters.

Bit rate 525bytesfs Nwnber of sensor nodes 100 Startingen~fureachnode 2J Electronic en~ cost (trans.& recept.) 50 nJlbit Data aggregation en~ cost 5 nJlbit Free space channel amplifier en~ cost 10 pJlbit Multipath-fading amplifier en~ cost 0.0013 pJlbit Network radius 175m, 275m, 375m Round operation 5 frames / round

Table 1: Simulation parameters

40

Base @ Station I

75m

I 00

100m ; 100 nod ••

0

10 X 10

LEACH

0

0

Base @ Station I

100m

100m

75m

I 00

, 50 nodes

0

50 nodes

10x5

2·level LEACH

Base @ Station I

75m

I 0

100m '0 nodes

0

100m 30 nodes

100m

3·level LEACH

Figure II : Simulation topology

4.2 Simulation proof to Section 3 and the validity of simulations

10x4

10x3

10 It 3

In section 3, we show that in order to implement an n-level LEACH with

network radius approximately n times of that of a WSN using LEACH, the extra

energy dissipation needed must be at least (n-I ) times of the energy difference

between LEACH and 2- level LEACH. To show that our simulations results are

indeed valid, the energy dissipation per round of ML-LEACH (2-level and 3-level)

is first obtained. Next we calculate the actual energy difference between ML-

LEACH and LEACH (both 2-level and 3-level \vith respect to LEACH). Table 5

below shows the simulation results.

41

Network Avg. Simulation. Theoretical Simulation radius energy per energydifI difference difference

(m) round (1) against factor factor LEACH(J)

LEACH 175 0.2737 -- -- --2-level 275 0.3468 0.0731 -- --LEACH 3-level 375 0.4351 0.1614 2 2.21 LEACH

Table 5: Energy dIfference between LEACH and 2-level LEACH

Table 5 shows that the energy dissipation difference between 3-1evel LEACH

and LEACH is 2.21 times of that between 2-level LEACH and LEACH. There is a

10.5% deviation from the theoretical difference of factor of 2 derived in Section 3.

However in the theoretical calculation, it is always assumed that equal nunlber of

sensor nodes are divided among each level, but in practice, we allocate 10 more

sensor nodes to level 1 of3-level LEACH. This along with the possibility that non-

optimal number of clusters is formed for ML-LEACH causes the energy dissipation

to increase for 3-level LEACH and thus deviates from the theoretical value.

4.3 Performance gain with ML-LEACB

The first set of simulation results shows the network lifetime of LEACH in

WSNs with network radius of 175m, 275m, and 375m respectively. This is to find

out LEACH's performance as network radius increases and 175m is used in the

testing for LEACH in [1]. We assunle that the sensor nodes can reach the BS

through long-distance transmissions. The network lifetime in the simulations is

defined as how many rounds the network lasted without a single sensor node

running out of power. Since each of the sensors started with 2J of energy, the total

network energy is 200J. The results are shown in Table 2. All the results shown in

this section are the averages of the results from 15 separate simulations.

42

• •

Protocol Radius #of Avg. Data/unit First Node (m) Rounds Round energy Out of

Energy (Bytes/I) Power (I) (row, col)

LEACH t75 552 0.2737 432974 (8,x) -OO,x)

LEACH 275 183 0.4852 245173 (l0,x) LEACH 375 63 l.0594 112436 (l0,x) ..

Table 2: LEACH snnulatton results WIth vanous network radll

The most important result from Table 2 is the 3rd column which indicates the

number of rounds the network last without a single node running out of power. For

WSNs that implement LEACH, as the network radius increases, the number of

rounds that the WSN has all of its sensors alive decreases. This is better illustrated

in Figure 12 below. The last column in Table 2 shows which set of nodes are more

likely to run out of energy first. In this case, (to,x) represents nodes from row to in

a matrix of 10 x 10. Row 10 is the last row in the matrix and nodes from this row

are farthest away from the BS. They are more likely to run out of power because

they expend more energy in long-distance transmission.

600 ,\2 _.----

\ \. _. ,

.........f

I--LEACHI

~ 500

'" g 400 .. ~ 300

~ 200 1 z 100

3 o

o 100 200 300 400 Network radius (m)

Figure 12: LEACH: Number of rounds before the first node runs out of energy at various network radii

43

M we increase the network radius, the average energy expended per round in

LEACH also increased. This means that energy dissipation has become

increasingly inefficient as the network radius increases. This is shown in Figure 13.

'0 § 1. 2 0 ... 1 ... 'CIl C. 0.8 >-~ 0.6 CIl

I--LEACHI c CIl 0.4 CIl bD ~ 0.2 --------i C!)

> 0 <: 0 100 200 300 400

Network radius em)

Figure 13: LEACH: Average energy dissipated per round at various network radii

Finally, energy dissipation efficiency of LEACH with different radius is

compared. The parameter used here is Data received by the BS per 1 Joule of

energy. The results for different radii are shown in Table 2 where there is

significant decrease in this performance measure as network radius increases. The

results are depicted in Figure 14.

44

,

160000 .,------------"1 - 60000 1--------.~~--_____1

r40000~-----~~,,----~ ~ 30000 +-----------'~_....;...___-t

100 400

11 20000 +--______ ~_'''''''-:::-ll1U___; ; 10000 ""-. 2441

l O~-~-~-~-~ ~ 0 200 300

Network radius 1m)

Figure 14: LEACH: Amount of data received by BS per 1 J of energy at various radii

ML-LEACH and LEACH are compared in perfonnance measures of network

lifetime, average energy dissipation per round, and data received per unit Joule of

energy. Table 3 and Table 4 compare the perfonnances of 2-level LEACH with

network radius of 275m and 3-level LEACH with network radius of 375m against

LEACH respectively.

Protocol Radius #of Avg. Data/unit First (m) Rounds Round energy Node Out

Energy (Bytes/J) of Power (J) (row, col)

LEACH 275 183 0.4852 27180 (l0,x) 2-level 275 248 0.3468 43388 (5,x) LEACH

Table 3: Companson of performances between LEACH and 2-level LEACH

Protocol Radius #of Avg. Data/unit First (m) Rounds Round energy Node Out

Energy (Bytes/J) of Power (J) (row, col)

LEACH 375 63 1.0594 12441 (1O,x) 3-level 375 213 0.4351 37674 (4,x) LEACH

Table 4: Companson of perfonnances between LEACH and 3-level LEACH

45

The above tables show the improvements in a number of areas when using

ML-LEACH in large-area network. First, there is a significant gain in network

lifetime. Secondly the average energy dissipated per round for Multi-level LEACH

decrease significantly as the network radius increases comparing with that of

LEACH. Finally more data is delivered to the BS with each Joule of energy in ML-

LEACH. Figure IS shows the improvement in network lifetime (number of rounds

before the first node demises). In Figure IS, at the network radius of 375m, the

number of rounds that the network has lasted before the first node runs out of

energy for ML-LEACH is 213 as is 63 for LEACH. The difference between the

protocols in this area widens as network radius increases. In the following graph,

LEACH is simulated for a network with network radius less than and equal to 175m,

2-level LEACH is simulated for a network with network radius less than and equal

to 275m, and 3-level LEACH is simulated for a network with network radius

greater than 275m.

600

500 . '" " " " 400 0 ...

'-< 300 0

... " 200 . .0 E

" z 100

0 0 100

248

183

200 300 Network radius (m)

-+- LEACH -+-ML-LEACH

13

400

Figure IS : Comparison between LEACH and ML-LEACH in # of rounds before the first node runs out of power

46

Figure 16 shows the improvement in energy dissipation per round by using

ML-LEACH. As shown, energy dissipated per round for ML-LEACH is much less

than that of LEACH at larger network radii (275m, 375m). The difference also

widens as network radius increases.

1. 2 ,------------,

'0

" ::l 8 0. 8 ·~-----------------r--_1

"" ~ O. 6 +-------->. .. ~ O. 4 +-------" w

0. 2

O+---r--r--.--~

o 100 200 300 400 Network radius (m)

-- LEACH

--- ML -LEACH

Figure 16: Comparison between LEACH and ML-LEACH in avg. energy dissipation per round

The next figure displays the improvement in data received per louie of energy

for ML-LEACH. The results are shown in Figure 17. ML-LEACH shows more

300% increase in data received per unit louie of energy over that of LEACH.

47

60000

'" !!' 50000 .. c .. - 40000 0

.!! " 0 30000 .., ~

20000 " ~ .. Co .. 10

10000 c

0

'" - ..... 674 "" -. +--------- ~180 1=~:QiI

r---------------~~~ ~ 1441

0 100 200 300 400

Network radius (m)

Figure 17: Comparison between LEACH and ML-LEACH in data received per I J of energy

From the above simulation results, it is apparent that ML-LEACH has shown

performance gain in three areas: number of rounds before the first sensor node runs

out of power, average energy dissipation per round, and data transmission efficiency.

The gains have become larger as the network radius increased. This indicates that

ML-LEACH performance better than LEACH for large-area WSN.

In addition, Table 3 and Table 4 also locate the nodes that are more probable to

drain its energy in LEACH, 2-level LEACH and 3-level LEACH. This information

helps us to identify the network "hot spots". If we can extend the life time of these

"bottleneck" sensor nodes, the network Ii fetime of the entire network shall increase.

We fo und out that sensor nodes that are farthest from the BS are most likely to run

out of power for LEACH due to heavy energy cost in long distance transmissions.

For 2-level LEACH, sensor nodes in row #5 or (5,x) are more likely to run out of

power due to the fact that they are closest to level-2 sensor nodes. If any of the

nodes in row 5 became a SCH, lower-level CHs would join them due to shorter

48

•

inter-cluster distances. In 3-level LEACH, sensor nodes in row #4 are more likely

to drain their power because of the same reason. Sensor nodes in row #4 are the last

row in level-} and therefore are closest to level-2 CHs. Note that level-} sensor

nodes dissipate the most energy because of data forwarding. We propose four

different strategies to improve network performances based on the above

information. Each them is explained in detail and their simulation results are also

analyzed.

4.4 Methods to improve network Ufetime and energy dIssipation efficiency

4.4.1 Method 1: Increase lnitial energy of level-l sensor nodes

Even though ML-LEACH has shown improvement in performance measures over

LEACH, there is a room for further gain. The following table demonstrates our

reason.

Total Network Radius(m) LEACH ML-LEACH Energy (1) Network Energy Network Energy

Dissipation (1) Dissipation (J)

200 }75 }51.2 --200 275 88.9 81.27 200 375 66.5 85.37 .

Table 6: Companson between LEACH and ML-LEACH: Total energy disSIpation when the first node runs out of power

The above table shows that when the first node in level } runs out of power for

ML-LEACH, the total energy dissipated by the network is not even 50% of the

starting network energy. This means that the entire network has more than half of

its total energy left and level-} sensor nodes are the bottleneck of the network. To

improve network lifetime and energy dissipation efficiency of ML-LEACH, the

focus should be on level-} sensor nodes; in other words, level-l sensor nodes have

to stay alive longer. Figure}8 displays the new node deployment for the first

49

strategy or Method I where the energy change is highlighted in red.

The first strategy is to increase the initial energy of the level-I nodes for 2-level

LEACH by 2 Joules and that for 3-level LEACH by 1.5 Joules. In our simulation,

if more Joules of energy are added to level- I sensor nodes for both of these

protocols, then the first node to run out of energy no longer exists in level I. Since

we only target level I in our strategy, we do not need to further increase their initial

energy. This is shown in Figure 18.

Base @ Station I

75m

t 00

100m 2Jlnod. 50 nodes

Level l

0

100m 50 nodes

l evel 2

100m

10 " 5

2·level LEACH

o

0

Base @ Station I

75m

t

100m U lnad. 40 nodes

l evel 1

100m 30 nodes

Level 2

100m 30 nodes

l evel :2

100m

3·level LEACH

10" 4

10" 3

10 " 3

Base @ Station I

75m

t 00

100m 4Jlnod. 50 nodes

l eva4 1

0

100m 50 nodes

level :2

100m

10" 5

o

0

2·level LEACH: Method 1

Base @ Station I

75m

I

100m l .,SJ/node 40 node ..

Level 1

100m 3D node ..

Level :z

100m 30 nodes

Level 2

100m

3·level LEACH. Method 1

Figure 18: Method I: Initial energy of level- I nodes is increased

Since level-I sensor nodes are most likely to run out of power, they are the

bottleneck of the entire WSN. By raising their initial energy, level- I sensor nodes

stay alive longer. However doing so, we need to take into consideration of

50

10 " 4

10" 3

10" 3

additional hardware cost. The simulation results from executing the first strategy

for 2-level LEACH and 3-level LEACH are shown in Table 7 and Table 8

respectively.

Protocol Radius #of Avg. Data/unit (m) Roun Round enet~)

ds Energy(J) LEACH 275 183 0.4852 27180 2-level LEACH 275 249 0.3266 46481

Method 1 : 2-level LEACH + 275 577 0.3336 38418 2J of energy for level-l nodes

Table 7: Method 1: 2-level LEACH with level-I sensor nodes' initial energy

increased by 2J

Protocol Radius #of Avg. Data/unit (m) Roun Round energy

ds Energy(J) (Bytes/I) LEACH 375 63 1.0594 12441 3-level LEACH 375 249 0.3913 41965

Method I: 3-level LEACH + 375 432 0.4098 30887 1.51 of energy for level-I nodes

Table 8 : Method I: 3-level LEACH with level-I sensor nodes' initial energy

increased by 1.51

By doubling the of initial energy for level-l sensor nodes, the network lifetime

demonstrated an additional 215% perfonnance gain in network lifetime over

LEACH at radius of 275m, and a 583% increase over LEACH at radius of 375m

(Column 3 of Table 7 and 8). However because of the extra energy introduced into

the network, the data per unit energy parameter for Method I decreases when

comparing with regular ML-LEACH (Column 5 of Table 7 and 8).

4.4.2 Method 2: Increase node density in level-l

51

The second strategy is to allocate more sensor nodes to level-I. The overall

number of sensor nodes is kept constant between all protocols, and to increase the

number of sensor nodes in level I, the number of sensor nodes in other levels must

be reduced. That meant some of the nodes from level 2 or level 3 went to level-I.

In the case of 2-level LEACH, level I has 80 nodes instead of 50 after re-

deployment and for 3-level LEACH, level I has 60 nodes instead of 40 after re-

deployment. Our simulation result in network lifetime shows the biggest

improvement when these two values are chosen. The node deployments before and

after are shown in the next figure. The differences are highlighted in red.

Base @ Station I

75m

I 00

100m 2J/node

50 nodes

l evel 1

0

100m

50 nodes

lev~ 2

100m

10 X 5

2-level LEACH

o

0

Base @ Station I

75m

I 100m 2JJnode

40 nodes

lev~ t

100m

30 nodes

Level 2:

100m 30 nodes

Level 2

100m

3·level LEACH

10 x4

10 X 3

10 X 3

Base @ Station I

75m

I 00

100m ZJlnode

80 nodes

Levelt

0

100m

20 nodes

Level 2

100m

10 X 5

o

0

2-level LEACH: Method 1

Base @ Station I

75m

I

100m 2J/node

60 nodes

Level 1

100m

20 nodes

level2

100m

20 nodes

Level 2

100m

3-level LEACH. Method 1

Figure 19: Method 2 node deployment, number of nodes increased in level I

10 X 4

10 X 3

10 X 3

Since the overall number of nodes in the most energy-intensive level (level-I )

increased, it is expected the network lifetime to increase as well. The next two

tables show the simulation results from implementing strategy 2 on ML-LEACH.

52

Protocol Radius #of Avg. Data/unit (m) Roun Round energy

ds Energy(J) (8yteS1J)

LEACH 275 183 0.4852 27180 2-level LEACH 275 249 0.3266 46481

Method I : 2-level LEACH + 275 577 0.3336 38418 2J of energy forlevel-l nodes

Method 2 : 2-level LEACH: 275 385 0.3219 43796 increase level I node density

Table 9: Method 2: 2-level ML-LEACH: increase level-l node density

Protocol Radius #of Avg. Data/unit (m) Roun Round energy

ds Energy(J) (Bytes/J) LEACH 375 63 1.0594 12441 3-level LEACH 375 218 0.3913 41965

Method I: 3-level LEACH + 375 432 0.4098 30887 1.5J of energy for level-l nodes

Method 2 : 2-level LEACH: 375 342 0.3925 38615 increase level I node density

Table 10: Method 2: 3-level ML-LEACH: increase level-l node density

Table 9 indicates that Method 2 strategy improves network lifetime of 2-level

LEACH by an additional 55% and that of 3-level LEACH by 57%. Even though

Method 1 demonstrates greater gain in network life time than Method 2 does, it is at

the cost of additional hardware. Method 2 is easier to implement and requires no

extra hardware cost. In addition, Method 2 performs better in data received per unit

energy.

4.4.3 Method 3: Decrease the Network radius of level!

53

Base on the above results, we modi fied Method 2 with the following change.

Since long-distance data forwarding costs the most energy, we choose to reduce

level-! radius (distance from farthest node in level 1 to BS). By experimenting

with our simulation code, it is found that the best performance is achieved when

level-! radius is reduced to 135m from the BS (40m is reduced from level-l radius).

This is true for both 2-level LEACH and 3-level LEACH. However by doing so,

the area of deployment for level 2 and level 3 are increased as a result. The

topology changes aare shown in the next figure. The differences are highlighted in

red.

Base @ SlatJon I

75m

I 00 0

100m 50 nodes

Level 1

0 0

100m 50 nodes

Levet 2

100m

10x 5

2·level LEACH

Base @ Slalion I

75m

I

00 m 'Dnodes

lltvet 1

00 m 30 nodes

Lavel 2

100 m 30nodn

Level 2:

100m

3·level LEACH

60m

1014

140m

Base @ Slation I

75m

I

50 nodes

level 1

50 nodes

1013 L .... 2

100m

2·level LEACH: Method 3

1013

Base @ Slation I

75m

I

60 m 40"000

Levet 1

120 m 30 nodes

levet 2

120 'm 30 nodes

LevelZ

100m Heve,

Method 3

Figure 20: Method 3: topology change in level-l network radius

There is an energy cost tradeoff between level-l transmission to BS and level-2

54

:

•

transmission to level-I Super Cluster Heads which can affect overall energy

dissipation. As we shrink the area oflevel I, level-l nodes are now closer to the BS.

This means that they expend less energy to forward data to the BS. On the other

hand, area oflevel 2 and level 3 are increased. As a result, nodes in these 2 levels

are more scattered and this causes the inter-level transmission energy to increase.

The overall network radius (275m for 2-level, 375m for 3-level) are kept the same

for this Method. Table II and Table 12 show the simulation results for Method 3.

Protocol Radius #of Avg. Data/unit (m) Roun Round energy

ds Energy(J) II II

LEACH 275 183 0.4852 27180 2-level LEACH 275 249 0.3266 46481

Method 1 : 2-level LEACH + 275 577 0.3336 38418 2J of energy for level-l nodes

Method 2 : 2-level LEACH: 275 385 0.3219 43796 increase level 1 node density

Method 3: 2-level LEACH: 275 481 0.2975 44976 reduce level-l radius

Table 11: Method 3: reduce level-I radius for 2-level LEACH

Protocol Radius #of Avg. Data/unit (m) Roun Round energy

ds Energy(J) r' :'T) LEACH 375 63 1.0594 12441 3-level LEACH 375 218 0.3913 41965

Method 1: 3-level LEACH + 375 432 0.4098 30887 1.5J of energy for level-l nodes

Method 2 : 2-level LEACH: 375 342 0.3925 38615 increase level 1 node density

55

•

Method 3: 3-level LEACH: 375 407 0.3677 34324 reduce level-I radius

Table 12: Method 3: Reduce level-I radius for 3-level LEACH

The comparison in performance between LEACH and Method 3 shows a 163%

gain in network lifetime at radius of 275m and a 546% gain at radius of 375m.

However Method 1 still performed best in this area. In terms of energy dissipation

per round, Method 3 did the best job among all protocols.

4.4.4 Method 4: Combine Method I, Method 3 together - increase initial energy of level-I sensor nodes and reduce network radius of level I.

Based on the results obtained from implementing the above strategies, we

combined Method I and Method 3. As a result, level-I nodes became closer to the

BS and their initial energy got increased. The changes in topology and energy level

are shown in Figure 21 where both the energy increase oflevel-l nodes and the

decrease in level-I area are highlighted in red. The results are shown in the Tables

13 and 14 below.

S6

Base ® Station I Base 0

Station ~I Base 0

Stallon ~I Base 0

Station ~I 75m

I 00 0

100m 2J/node 00 m 50 nodes

level 1

o o

75 m

I

2J/node 40 nodes

l Ox 4 Levell

80m

75m

I 4J/node 50 nodes

Lent 1 80m

120m

100m 00 m 140m 50 nodes 50 nodes 30 nodes

10 X 3 level 2

Levet 2 Level 2

100m 100m

lOx 5 2·level LEACH: Method 4

120m m 2.leYel LEACH 100 30 nodes

10 X 3 Levet 2

100m

3·leYel LEACH

Figure 21 : Method 4: Combine Method I and Method 3

Protocol Radius # of Avg. (m) Roun Round

ds Energy (J) LEACH 275 183 0.4852 2·level LEACH 275 249 0.3266

Method I : 2-level LEACH + 275 577 0.3336 2J of energy for level- I nodes

Method 2 : 2-level LEACH: 275 385 0.32 19 increase level I node density

Method 3: 2-level LEACH: 275 48 1 0.2975 reduce level- l radius

Method 4: 2-level LEACH: 275 514 0.2988 combined strategy

Table 13 : Method-3: 2-level LEACH perfo rmances

57

75m

I 3.5J/node

40 nodes

levet 1

30 nodes

level 2

30 nodes

Levei Z

100m

3·level : Method 4

Data/unit energy

(Byles/J) 27 180 46481

3841 8

43796

44976

43245

• •

Protocol Radius #of Avg. Data/unit (m) Roun Round

O::;J) ds Energy(J) LEACH 375 63 1.0594 12441 3-level LEACH 375 218 0.3913 41965

Method 1: 3-level LEACH + 375 432 0.4098 30887 1.5J of energy for level-l nodes

Method 2 : 2-level LEACH: 375 342 0.3925 38615 increase level 1 node density

Method 3: 3-level LEACH: 375 407 0.3677 34324 reduce level-l radius

Method 4: 3-level LEACH: 375 417 0.3680 34344 combined strategy

Table 14: Method-3: 3-level LEACH performances

Method 4 does not show significant improvement over Method 3. Comparing

with Method 3, the increase in initial energy level has not affected overall network

performance as much as Method 1 does. The reason for this is that although in

Method 3, the initial energy of level-l nodes get increased and their lifetime get

extended, but sensor nodes elsewhere became the "new bottleneck", and start to

demise before the level-l sensor nodes do. The following three figures compared

the overall simulation results of all the methods mentioned above. Figure 22

compares the six protocols in terms of network lifetime. Figure 23 compares them

in energy dissipation per round. Finally Figure 24 compares them in data receive

per unit Joule of energy.

S8

700

600

'" 500

" c

" 2 400 ..... 0 ...

300 -.. SJ E " z 200 -

100

0 0 100 200

Network radius (m)

300 400

-+-LEACH

--ML-LEACH

Method 1

--- Method 2

-><- Method 3

-lI!- Method 4

Figure 22: Comparison in of all protocols in number of rounds before the first node runs out of power (network lifetime)

1.2 ,--------------------,

~ 0.8 c o ~ 06 +---­.~

:;; 0.4 ,., '" ... .,

Lfi 0.2

o -·~------._------_.------_,------~

o 100 200

Network radius (m)

300 400

-+-LEACH

--- ML-LEACH Method 1

--- Method 2 ___ Method 3

--- Method 4

Figure 23: Comparison of all protocols in energy dissipation per round

59

•

,., '" ~ ., c ., -'" " ~ 8. ~ .. c

60000

50000

40000

30000

20000

10000

0 0 100 200 300

Network radius (m)

400

-+- LEACH

--M..-LEACH

Method 1

--Method 2

--><- Method 3

--IIf- Method 4

Figure 24: Comparison of all protocols in data received per unit energy

Based on Figure 22, Method I shows greatest gain in network lifetime, but it

requires an upgrade in hardware. The extra bardware complexity and additional

hardware cost must be considered. In addition, with some of the nodes in the

network baving more energy than otbers, the network is no longer homogeneous.

This further increases the complexity of the network. The peak shown in Figure 22

by Method I is because of the additional energy added to all level-l sensor nodes.

When the network radius is relatively short, the increase in initial energy of level-l

sensor nodes gives ML-LEACH an advantage over LEACH. [n Figure 23 , Method

4 shows the lowest energy dissipation per round. Finally in Figure 24, Method 4

shows the best perfonnance again with a slight edge over regular ML-LEACH. [n

contrast witb Method I, Method 2 and Method 3 don't require additional hardware

modification, but they required change in node deployment and network topology.

In some situations where terrains are tough, it may be difficult to implement these

60

• •

changes. Method 4 combines Method 1 and Method 3 together and provides the

overall best performance but it is significantly more complex than the other three

methods. Network complexity has to be considered when implementing Method 4

since these sensor nodes are low in computation power so implementing a complex

protocol can be energy wasting. In summary, ML-LEACH and its variations are

simulated in the WSN with sensor node deployment area enlarged by as much as

300% (300m2 for ML-LEACH comparing with 100m2 in LEACH). The simulation

results show significant performance gain in network lifetime, average energy

dissipation per round, and data received per unit energy. Based on the simulation

results, ML-LEACH is a suitable routing protocol for large-area WSN. However

there are some new problems caused by the performance improvement strategies

proposed in this thesis and they are discussed in the next section.

61

•

5. Discussion and Conclusion

Our goal is to design an energy efficient routing protocol for large-area Wireless

Sensor Networks performing periodic data sensing applications. LEACH is chosen

because of its random rotation of cluster heads, energy efficient data aggregation,

robustness, long network lifetime, uniform energy dissipation, and self-configuring

nature. LEACH is studied extensively. The conclusion is that it isn't an energy

efficient routing protocol for large-area WSN. At the same time we hope to retain

many of its characteristics. To accomplish this goal, we have designed a new

routing protocol called Multi-level LEACH. Unlike LEACH, Multi-level LEACH

is a multi-hop, multi-level routing protocol that performs data forwarding through

cluster heads at different levels.

The simulation results from Section 4 have shown that as network radius

increased, ML-LEACH shows better overall performance over LEACH in network

lifetime, energy dissipation efficiency, and data received by BS per unit Joule of

energy. To make ML-LEACH more energy efficient, four variations of ML­

LEACH are proposed and they all demonstrate performance gain in the above three

performance measures. Among them, Method 4 shows the best overall performance.

However these variations of ML-LEACH also raised new questions as well as

pointing out our future research directions. The first question has to do with

Method 1. Recall that with Method I, initial energy of level-l nodes is increased.

As a result, level-l sensor nodes are eliminated as the bottleneck of the WSN. On

the other hand this means there are two different types of hardware existing at the

same time in the network, which makes the WSN heterogeneous. Unlike

62

• •

homogeneous networks, heterogeneous networks usually contain devices with

different starting energy, and are different in sensing capability. To fully extend the

network lifetime and make ML-LEACH energy efficient for heterogeneous

networksO, its cluster-head selection algorithm has to be revised to incorporate a

remaining energy function. Furthermore it is also feasible to establish a threshold

value regarding every sensor node's remaining energy so that some sensor nodes

can not be eligible to become a CH if their energy levels fall below a certain

threshold.

The second question is raised by Method 3 where the network radius of level I

is reduced so that all the sensor nodes in level I are brought closer to the BS. As the

network radius of level 1 deployment is reduced, that of level 2 and level 3 also

increased. This causes the cluster-area of level 2 and level 3 to increase. In other

words, sensor nodes in level 2 and level 3 now expend more energy for intra-cluster

transmissions. Thus there is a need to study multi-hop intra-cluster transmission. In

fact, there is a research paper published in 2004 on k-hop intra-cluster transmission

for LEACH [21. The reason behind it is that in some rough terrains, implementing

single-hop intra-cluster transmissions might not be energy efficient. Likewise in the

simulations we go through, single-hop intra-cluster transmissions may not be energy

efficient and multi-hop intra-cluster transmissions may extend the network lifetime

even longer. However the downside of this is the computational and network

complexity. Because we must now consider a multi-level and multi-hop (intra

cluster) routing scheme which prolongs the set-up phase. As a result more energy is

expended. As such a detailed study in the energy tradeoff of transmission cost and

63

•

computational cost has to be done in order to gain full understanding in this matter.

Finally this thesis searches for an energy efficient routing solution for large-area

WSNs in the Hierarchical routing approach. Our simulation results in this thesis

have shown that ML-LEACH is an energy efficient approach and deserves a lot of

research attention.

64

.. •

Glossary

BS - Base Station, terminal that collects data from sensor network

CH - Cluster Head, performs data aggregation for sensor nodes within the cluster

GEAR - Geographic and Energy Aware Routing

LEACH - Low Energy Adaptive Clustering Hierarchy

ML-LEACD - Multi-level LEACH, proposed in this thesis as a solution for large­

area Wireless Sensor Network

SC - Super Cluster, inter-level clusters formed by cluster heads from neighboring

levels

SCD - Super Cluster Head, upper-level cluster head that forwards data from lower­

level cluster heads.

SPIN - Sensor Protocols for Information via Negotiation, a protocol that includes

meta-data negotiation scheme

TEEN - Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network

WSN - Wireless Sensor Network

6S

·' •

Reference

[I] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Application-specific protocol architecture for wireless microsensor networks", IEEE Transactions on Wireless Communications, 1(4):660-670, October 2002.

[2) Vivek Mhatre, Catherine Rosenberg, "Homogeneous vs. Heterogeneous Clustered Sesnsor Networks: A Comparative Study", in the proc of International Conference on Communications (ICC 2004), Paris, France, June 2004.

[3] A. Manjeshwar and D. P. Agarwal, "TEEN: a routing protocol for enhanced efficiency in wireless sensor networks," In 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, April 2001 .

)4) W. Heinzelman, A. Chandrakasan and H. Balakrishnan, "Energy-Efficient Communication Protocol for Wireless Mi-crosensor Networks", Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00), January 2000.

[S] Holger Karl , Andreas Willig, "A short survey of Wireless Sensor Networks", 2003. TKN Technical Report TKN-03-01S, Technical University Berlin, October 2003.http://citeseer.ist.psu.edulkarl03.html

(6) F.L. Lewis, "Wireless Sensor Networks", 2004. http://arri.uta.eduiacs/networks/wirelessSensorNetChap04.pdf

)7) K. Akkaya, M. Younis, "A survey on routing protocols for wireless sensor networks". http://www.cs.umbc.edul- kemai llmypapers/Akkaya Younis JoAdHocRevised.pdf

)8] W. Heinzelman, J. Kulik, and H. Balakrishnan, "Adaptive protocols for information dissemination in wireless sensor networks," in the Proceedings of the 5th Annual ACMlrEEE international Conference on Mobile Computing and Networking (MobiCom '99), Seattle, W A, August 1999.

)9) Jamal N. Al-Karaki, Ahmed E. Kamal, "Routing Techniques in Wireless Sensor Networks: A Survey", Wireless Communications, rEEE, Vol. 11 , Issue 6, pp 6-28, Dec, 2004.

[10] C. Intanagonwiwat, R. Govindan, and D. Estrin, "Directed diffusion: a scalable and robust communication paradigm for sensor networks," Proceedings of ACM MobiCom '00, Boston, MA, 2000, pp. 56-67.

66

[11] Y. Yu, D. Estrin, and R. Govindan, "Geographical and Energy-Aware Routing: A Recursive Data Dissemination Protocol for Wireless Sensor Networks," UCLA Computer Science Department Technical Report, UCLA-CSD TR-OI-0023, May 2001.

[12] K. Sohrabi, 1. Pottie, "Protocols for self-organization of a wireless sensor network", IEEE Personal Communications, Volume 7, Issue 5, pp. 16-27,2000.

[13] A. Manjeshwar and D. P. Agrawal, "TEEN: A Protocol for Enhanced Efficiency in Wireless Sensor Networks," in the Proceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, CA, April 2001.

[14] A. Manjeshwar and D. P. Agarwal, "APTEEN: A hybrid protocol for effiecient routing and comprehensive information retrieval in wireless sensor networks," Parallel and Distributed Processing Symposium., Proceedings International, lPDPS 2002, pp. 195-202.

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