Performance Analysis of Routing Protocols of Wireless Sensor Networks

Performance Analysis of Routing Protocols of Wireless
Sensor Networks

BY
DARPAN DEKIVADIYA
09BCE008
VIVEK VADHARIA
09BCE090

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
AHMEDABAD-382481

November 2012

Performance Analysis of Routing Protocols of Wireless
Sensor Networks

Minor Project

Submitted in partial fulﬁllment of the requirements

For the degree of

Bachelor of Technology In Computer Engineering

By
DARPAN DEKIVADIYA
09BCE008
VIVEK VADHARIA
09BCE090

Guide
Prof. Vijay Ukani

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
AHMEDABAD-382481

November 2012

Certiﬁcate

This is to certify that the Minor Project entitled ”Performance Analysis of Routing
Protocols of Wireless Sensor Networks” submitted by DARPAN DEKIVADIYA(09BCE008),
towards the partial fulﬁllment of the requirements for the degree of Bachelor of Technology
in Computer Engineering of Nirma University of Science and Technology, Ahmedabad is the
record of work carried out by him under my supervision and guidance. In my opinion, the
submitted work has reached a level required for being accepted for examination. The results
embodied in this Seminar, to the best of my knowledge, haven’t been submitted to any other
university or institution for award of any degree or diploma.

Prof.Vijay Ukani Dr. Sanjay Garg
Guide and Assistant Professor, Professor and Head,
Dept. of Computer Science & Engg., Dept. of Computer Science & Engg.,
Institute of Technology, Institute of Technology,
Nirma University, Ahmedabad Nirma University, Ahmedabad

Dr. Sanjay Garg
Professor and Head,
Dept. of Computer Science & Engg.
Institute of Technology,
Nirma University, Ahmedabad

iii

Certiﬁcate

This is to certify that the Minor Project entitled ”Performance Analysis of Routing Pro-
tocols of Wireless Sensor Networks” submitted by VIVEK VADHARIA(09BCE090), towards
the partial fulﬁllment of the requirements for the degree of Bachelor of Technology in Com-
puter Engineering of Nirma University of Science and Technology, Ahmedabad is the record
of work carried out by him under my supervision and guidance. In my opinion, the sub-
mitted work has reached a level required for being accepted for examination. The results
embodied in this Seminar, to the best of my knowledge, haven’t been submitted to any other
university or institution for award of any degree or diploma.

Prof.Vijay Ukani Dr. Sanjay Garg
Guide and Assistant Professor, Professor and Head,
Dept. of Computer Science & Engg., Dept. of Computer Science & Engg.,
Institute of Technology, Institute of Technology,
Nirma University, Ahmedabad Nirma University, Ahmedabad

Prof. Tejal Upadhyay
Dept. of Computer Science & Engg.
Institute of Technology,
Nirma University, Ahmedabad

iv

Acknowledgements

I would like to express my heartfelt gratitude to Prof.Vijay Ukani,Professor in Department
of computer science and engineering for her valuable time and guidance that made the
seminar project work a success. Thanking all my friends and all those who had helped me
in carrying out this work. I am also indebted to the library resources centre and interest
services that enabled us to ponder over the vast subject of ”Performance Analysis of Routing
Protocols of Wireless Sensor Networks”.

- DARPAN DEKIVADIYA
09BCE008

- VIVEK VADHARIA
09BCE090

v

Abstract

This project involves implementation & Performance Analysis of Routing Protocols for
Wireless Sensor Network. A wireless sensor network (WSN) consists of spatially distributed
autonomous sensors to monitor physical or environmental conditions, such as temperature,
sound, vibration, pressure, humidity, motion or pollutants and to cooperatively pass their
data through the network to a main location. The sensor networks can be used in Disaster
Relief, Emergency Rescue operation, Military, Habitat Monitoring, Health Care, Environ-
mental monitoring, Home networks, detecting chemical, biological, radiological, nuclear,
and explosive material etc. Sensor network nodes are limited with respect to energy supply,
restricted computational capacity and communication bandwidth. Most of the attention,
however, has been given to the routing protocols since they might differ depending on the
application and network architecture. To prolong the lifetime of the sensor nodes, designing
efficient routing protocols is critical. Even though sensor networks are primarily designed
for monitoring and reporting events, since they are application dependent, a single routing
protocol cannot be efficient for sensor Networks across all applications. The Comparison of
Routing Protocols reveals the important features that need to be taken into consideration
While designing and evaluating new routing protocols for sensor networks.
Routing in WSNs is very challenging due to the inherent characteristics that distinguish
these networks from other wireless networks like mobile ad hoc networks or cellular networks.
First, due to the relatively large number of sensor nodes, it is not possible to build a global
addressing scheme for the deployment of a large number of sensor nodes as the overhead
of ID mainte-nance is high. Thus, traditional IP-based protocols may not be applied to
WSNs. In contrast to typical communication networks, almost all applications of sensor
networks require the flow of sensed data from multiple sources to a particular BS. This,
however, does not prevent the flow of data to be in other forms (e.g., multicast or peer
to peer). sensor nodes are tightly constrained in terms of energy, processing, and storage
capacities. Thus, they require careful resource management. sensor networks are application
specific, i.e., design requirements of a sensor network change with application. For example,
the challenging problem of low-latency precision tactical surveillance is different from that
required for a periodic weather-monitoring task.

vi

Contents

Acknowledgements v

Abstract vi

1 Introduction to WSN 1

2 Classification Of Routing Protocols 3
2.1 Based on Mode of Functioning and Type of Target Applications . . . . . . . 3
2.1.1 Proactive :- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.2 Reactive :- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.3 Hybrid :- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 According to the Participation style of the Nodes. . . . . . . . . . . . . . . . 4
2.2.1 Direct Communication :- . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2.2 Flat :- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2.3 Clustering Protocols :- . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Depending on the Network Structure . . . . . . . . . . . . . . . . . . . . . . 5
2.3.1 Data Centric :- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3.2 Hierarchical :- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3.3 Location Based :- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Routing Challenges and Design Issues in WSNs 6

4 Routing Protocols in WSNs 8
4.1 Direct Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.2 Sensor Protocol for Information via Negotiation . . . . . . . . . . . . . . . . 10
4.3 Low Energy Adaptive Clustering Hierarchy (LEACH). . . . . . . . . . . . . 12
4.4 CTP : Collection Tree Protocol . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.5 Greedy Perimeter Stateless Routing(GPSR) . . . . . . . . . . . . . . . . . . 17
4.5.1 States of Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.5.2 Beaconing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.5.3 Greedy Forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.5.4 Perimeter Forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.6 Power Efficient Gathering for Sensor Information Systems . . . . . . . . . . 18
4.7 Binary Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.8 Chain Based Three level Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 20

vii

5 Simulation And Analysis 21
5.1 Greedy Perimeter Stateless Routing(GPSR) . . . . . . . . . . . . . . . . . . 21
5.2 Collection Tree Protocol (CTP) . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.3 Low-Energy Adaptive Clustering Hierarchy (LEACH) . . . . . . . . . . . . . 26

6 References 30

viii

Chapter 1

Introduction to WSN

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors
to monitor physical or environmental conditions, such as temperature, sound, vibration,
pressure, motion or pollutants and to cooperatively pass their data through the network to
a main location[2].

The more modern networks are bi-directional, also enabling control of sensor activity.
The development of wireless sensor networks was motivated by military applications such
as battleﬁeld surveillance; today such networks are used in many industrial and consumer
applications, such as industrial process monitoring and control, machine health monitoring,
and so on.

Figure 1.1: Typical multi-hop wireless sensor network architecture

1

The WSN is built of ”nodes” from a few to several hundreds or even thousands, where
each node is connected to one (or sometimes several) sensors. Each such sensor network node
has typically several parts: a radio transceiver with an internal antenna or connection to an
external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and
an energy source, usually a battery or an embedded form of energy harvesting.

A sensor node might vary in size from that of a shoebox down to the size of a grain
of dust, although functioning ”motes” of genuine microscopic dimensions have yet to be
created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of
dollars, depending on the complexity of the individual sensor nodes. Size and cost constraints
on sensor nodes result in corresponding constraints on resources such as energy, memory,
computational speed and communications bandwidth.

The topology of the WSNs can vary from a simple star network to an advanced multi-hop
wireless mesh network. The propagation technique between the hops of the network can be
routing or ﬂooding.

2

Chapter 2

Classification Of Routing Protocols

Routing techniques are required for sending data between sensor nodes and the base
stations for communication. Different routing protocols are proposed for wireless sensor
network. These protocols can be classified according to different parameters.[1].

Figure 2.1: Routing protocols in WSNs

2.1 Based on Mode of Functioning and Type of Target
Applications

2.1.1 Proactive :-

In a Proactive Protocol the nodes switch on their sensors and transmitters, sense the
environment and transmit the data to a BS through the predefined route.
e.g. The Low Energy Adaptive Clustering hierarchy protocol (LEACH) utilizes this type
of protocol.

2.1.2 Reactive :-

3

if there are sudden changes in the sensed attribute beyond some pre-determined threshold
value, the nodes immediately react. This type of protocol is used in time critical applications.
e.g. The Threshold sensitive Energy Efficient sensor Network (TEEN) is an example of
a reactive protocol.

2.1.3 Hybrid :-

Hybrid protocols incorporate both proactive and reactive concepts. They first compute
all routes and then improve the routes at the time of routing. e.g. Adaptive Periodic TEEN
(APTEEN) is an example of a reactive protocol.

2.2 According to the Participation style of the Nodes.

2.2.1 Direct Communication :-

In this type of protocols, any node can send information to the Base Station(BS) directly.
When this is applied in a very large network, the energy of sensor nodes may be drained
quickly. Its scalability is very small.
e.g. SPIN is an example of this type of protocol.

2.2.2 Flat :-

In the case of flat protocols,if any node needs to transmit data, it first searches for a valid
route to the BS and then transmits the data. Nodes around the base station may drain their
energy quickly. Its scalability is average.
e.g. Rumor Routing is an example of this type of protocol.

2.2.3 Clustering Protocols :-

According to the clustering protocol,the total area is divided into numbers of clusters.
Each and every cluster has a cluster head (CH) and this cluster head directly communicates
with the BS. All nodes in a cluster send their data to their corresponding CH.
e.g. TEEN is an example of this type of protocol.

4

2.3 Depending on the Network Structure

2.3.1 Data Centric :-

Data centric protocols are query based and they depend on the naming of the desired
data, thus it eliminates much redundant transmissions. The BS sends queries to a certain
area for information and waits for reply from the nodes of that particular region. Since data
is requested through queries, attribute based naming is required to specify the properties of
the data. Depending on the query, sensors collect a particular data from the area of interest
and this particular information is only required to transmit to the BS and thus reducing the
number of transmissions.
e.g. SPIN was the first data centric protocol.

2.3.2 Hierarchical :-

Hierarchical routing is used to perform energy efficient routing, i.e., higher energy nodes
can be used to process and send the information; low energy nodes are used to perform the
sensing in the area of interest.
examples: LEACH, TEEN, APTEEN.

2.3.3 Location Based :-

Location based routing protocols need some location information of the sensor nodes.
Location information can be obtained from GPS (Global Positioning System) signals, re-
ceived radio signal strength, etc. Using location information, an optimal path can be formed
without using flooding techniques.
e.g. Geographic and Energy-Aware Routing(GEAR)

5

Chapter 3

Routing Challenges and Design Issues
in WSNs

Despite the innumerable applications of WSNs, these networks have several restrictions,
e.g., limited energy supply, limited computing power, and limited bandwidth of the wireless
links connecting sensor nodes. One of the main design goals of WSNs is to carry out data
communication while trying to prolong the lifetime of the network and prevent connectivity
degradation by employing aggressive energy management techniques. The design of routing
protocols in WSNs is inuenced by many challenging factors. These factors must be overcome
before ecient communication can be achieved in WSNs. In the following, we summarize some
of the routing challenges and design issues that affect routing process in WSNs[3].

ˆ Node deployment :- Node deployment in WSNs is application dependent and affects
the performance of the routing protocol. The deployment can be either deterministic or
randomized. In deterministic deployment, the sensors are manually placed and data
is routed through pre-determined paths. However, in random node deployment, the
sensor nodes are scattered randomly creating an infras- tructure in an ad hoc manner.
If the resultant distribution of nodes is not uniform, optimal clustering becomes nec-
essary to allow connectivity and enable energy ecient network operation. Inter-sensor
communication is normally within short transmission ranges due to energy and band-
width limita- tions. Therefore, it is most likely that a route will consist of multiple
wireless hops.

ˆ Energy Efficiency :- sensor nodes can use up their limited supply of energy perform-
ing computations and transmitting information in a wireless environment. As such,
energy- conserving forms of communication and computation are essential. Sensor
node lifetime shows a strong dependence on the battery lifetime[8]. In a multihop
WSN, each node plays a dual role as data sender and data router. The malfunctioning
of some sensor nodes due to power failure can cause signicant topological changes and
might require rerouting of packets and reorganization of the network.

ˆ Data Reporting Model:- Data sensing and reporting in WSNs is dependent on the
application and the time criticality of the data reporting. Data reporting can be catego-
rized as either time-driven (continuous), event-driven, query-driven, and hybrid[9].The
time-driven delivery model is suitable for applications that require periodic data moni-
toring. As such, sensor nodes will periodically switch on their sensors and transmitters,

6

sense the environment and transmit the data of interest at constant periodic time in-
tervals. In event-driven and query-driven models, sensor nodes react immediately to
sudden and drastic changes in the value of a sensed attribute due to the occurrence of
a certain event or a query is generated by the BS. As such, these are well suited for
time critical applications. A combination of the previous models is also possible. The
routing protocol is highly inuenced by the data reporting model with regard to energy
consumption and route stability.

ˆ Fault Tolerance:- Some sensor nodes may fail or be blocked due to lack of power,
physical damage, or environmental interference. The failure of sensor nodes should
not aect the overall task of the sensor network. If many nodes fail, MAC and routing
protocols must accommodate formation of new links and routes to the data collec-
tion base stations. This may require actively adjusting transmit powers and signaling
rates on the existing links to reduce energy consumption, or rerouting packets through
regions of the network where more energy is available. Therefore, multiple levels of
redundancy may be needed in a fault-tolerant sensor network.

ˆ Scalability:- The number of sensor nodes deployed in the sensing area may be in the
order of hundreds or thousands, or more. Any routing scheme must be able to work
with this huge number of sensor nodes. In addition, sensor network routing protocols
should be scalable enough to respond to events in the environment. Until an event
occurs, most of the sensors can remain in the sleep state, with data from the few
remaining sensors providing a coarse quality.

ˆ Quality of Service:- In some applications, data should be delivered within a cer-
tain period of time from the moment it is sensed, otherwise the data will be useless.
Therefore bounded latency for data delivery is another condition for time-constrained
applications. However, in many applications, conservation of energy, which is directly
related to network lifetime, is considered relatively more important than the quality
of data sent. As the energy gets depleted, the network may be required to reduce
the quality of the results in order to reduce the energy dissipation in the nodes and
hence lengthen the total network lifetime. Hence, energy-aware routing protocols are
required to capture this requirement.

7

Chapter 4

Routing Protocols in WSNs

Data dissemination is the process by which queries or data are routed in the sensor
network. The data collected by sensor nodes has to be communicated to the BS or to any
other node interested in the data. The node that generates data is called a source and the
information to be reported is called an event. Anode which is interested in an event and seeks
information about it is called a sink. Traffic Models have been developed for sensor networks
such as the data collection and data dissemination (diffusion) models. In the data collection
model, the source sends the data it collects to a collection entity such as the BS. This could
be periodic or on demand. The data is processed in the central collection entity[1]. Data
diffusion, on the other hand, consists of a two-step process of interest propagation and data
propagation. an interested is a descriptor for a particular, intrusion or presence of bio-agents.
For every event that a sink is interested in , it broadcasts its interest to its neighbors and
periodically refreshes its interest. The interest is propagated across the network and every
node maintains an interest cache of all events to be reported.

4.1 Direct Diffusion
Directed diffusion is a data-centric (DC) and application- aware paradigm in the sense that
all data generated by sensor nodes is named by attribute-value pairs. The main idea of
the DC paradigm is to combine the data coming from dierent sources enroute (in-network
aggregation) by eliminating redundancy, minimizing the number of transmissions; thus sav-
ing network energy and prolonging its lifetime. Unlike traditional end-to-end routing, DC
routing finds routes from multiple sources to a single destination that allows in-network con-
solidation of redundant data[3]. This potocol is useful in scenario where the sensor nodes
themselves generate requests/queries for data sensed by other nodes, instead of all queries
arising only from a BS. Hence the sink for the query could be a BS or a sensor node. The
direct diffusion routing protocol improves on data diffusion using interest gradients. Each
sensor node names its data with one or more attributes and other nodes express their interest
depending on these attributes. Attribute value pairs can be used to describe an interest in
intrusion data as follows.

The sink has to periodically refresh its interest if it still requires the data to be reported
it. Data is propagated along the reverse path of the interest propagation. Each path is
associated with a gradient that is formed at the time of interest propagation. Each path

8

is associated with the gradient that is formed at the time of interest propagation. While
the positive gradients encourage the data flow along the path, Negative gradients inhibit
the distribution of data along a perticular path. The strength of the interest is different
toward different neighbors, resulting into source to sink paths with different gradients. The
gradient coresponding to an interest is derived from the interval/data-rate field specified in
the interest.

In directed diffusion, sensors measure events and create gradients of information in their
respective neighborhoods. The base station requests data by broadcasting interests. Interest
describes a task required to be done by the network. Interest diuses through the network
hop-by-hop, and is broadcast by each node to its neighbors. As the interest is propagated
throughout the network, gradients are setup to draw data satisfying the query towards the
requesting node, i.e., a BS may query for data by disseminating interests and intermediate
nodes propagate these interests. Each sensor that receives the interest setup a gradient
toward the sensor nodes from which it receives the interest. This process continues until
gradients are setup from the sources back to the BS. More generally, a gradient specifies
an attribute value and a direction. The strength of the gradient may be different towards
different neighbors resulting in different amounts of information ow. At this stage, loops are
not checked, but are removed at a later stage.

Figure 4.1: An example of interest diffusion in sensor network

9

Figure 4.1 shows an example of the working of directed diffusion ((a) sending interests,
(b) building gradients, and (c) data dissemination). When interests fit gradients, paths of
information ow are formed from multiple paths and then the best paths are reinforced so as
to prevent further ooding according to a local rule. In order to reduce communication costs,
data is aggregated on the way. The goal is to nd a good aggregation tree which gets the
data from source nodes to the BS. The BS periodically refreshes and re-sends the interest
when it starts to receive data from the source(s). This is necessary because interests are not
reliably transmitted throughout the network.
All sensor nodes in a directed diusion-based network are application-aware, which enables
diffusion to achieve energy savings by selecting empirically good paths and by caching and
processing data in the network. Caching can increase the eciency, robustness and scalability
of coordination between sensor nodes which is the essence of the data diusion paradigm.
Other usage of directed diusion is to spontaneously propagate an important event to some
sections of the sensor network. Such type of information retrieval is well suited only for
persistent queries where requesting nodes are not expecting data that satisfy a query for
duration of time. This makes it unsuitable for one-time queries, as it is not worth setting
up gradients for queries, which use the path only once.
Directed diffusion differs from SPIN in two aspects. First, directed diffusion issues on
demand data queries as the BS send queries to the sensor nodes by flooding some tasks. In
SPIN, however, sensors advertise the availability of data allowing interested nodes to query
that data. Second, all communication in directed diffusion is neighbor-to-neighbor with each
node having the capability of performing data aggregation and caching. Unlike SPIN, there
is no need to maintain global network topology in directed diffusion. However, directed
diusion may not be applied to applications (e.g., environmental monitoring) that require
continuous data delivery to the BS. This is because the query- driven on demand data model
may not help in this regard. Moreover, matching data to queries might require some extra
overhead at the sensor nodes.

4.2 Sensor Protocol for Information via Negotiation

SPIN uses Negotiation and resources and adaption to address the defi-ciencies of flooding.
Negotiation Reduces overlap and implosion, and a threshold based resource-aware operation
is used to prolong network lifetime. Meta-data, or data describing data, is transmitted
instead of row data. This requires fewer bytes and can be in an application-specific format.
SPIN has three types of messages: ADV, REQ, and DATA. A sensor node broadcasts an
ADV containing meta-data describing actual data. If a neighbor is interested in the data, it
sends REQ for the data. Then the sensor node sends the actual DATA to the neighbor[3].

The neighbor again sends ADVs to its neighbors and this process con-tinues to dissemi-
nate the data throughout the network. The simple version is shown in figure.

The SPIN family is designed to address the deficiencies of classic flooding by negotiation
and resource adaptation. The SPIN family of protocols is designed based on two basic ideas:

10

Figure 4.2: Sensor Protocol for Information via Negotiation

ˆ Sensor nodes operate more efficiently and conserve energy by sending data that describe
the sensor data instead of sending all the data; for example, image and sensor nodes
must monitor the changes in their energy resources.

ˆ Conventional protocols like flooding or gossiping based routing protocols waste energy
and bandwidth when sending extra and un-necessary copies of data by sensors cover-
ing overlapping areas. The drawbacks of flooding include implosion, which is caused
by duplicate messages sent to the same node, overlap when two nodes sensing the
same region will send similar packets to the same neighbor and resource blindness by
consuming large amounts of energy without Consideration for the energy Constraints.
Gossiping avoids the problem of implosion by just selecting a random node to send the
packet to rather than broadcasting the packet blindly. However, this causes delays in
propagation of data through the nodes.

SPIN’s meta-data negotiation solves the classic problems of ooding, and thus achieving a
lot of energy eciency. SPIN is a 3-stage protocol as sensor nodes use three types of messages
ADV, REQ and DATA to communicate. ADV is used to advertise new data, REQ to request
data, and DATA is the actual message itself. The protocol starts when a SPIN node obtains
new data that it is willing to share. It does so by broadcasting an ADV message containing
meta-data. If a neighbor is interested in the data, it sends a REQ message for the DATA
and the DATA is sent to this neighbor node. The neighbor sensor node then repeats this
process with its neighbors. As a result, the entire sensor area will receive a copy of the data.

11

The SPIN family of protocols includes many protocols. The main two protocols are
called SPIN-1 and SPIN-2, which incorporate negotiation before transmitting data in order
to ensure that only useful information will be transferred. Also, each node has its own
resource manager which keeps track of resource consumption, and is polled by the nodes
before data transmission. The SPIN-1 protocol is a 3-stage protocol, as described above.
An extension to SPIN-1 is SPIN-2, which incorporates threshold-based resource awareness
mechanism in addition to negotiation. When energy in the nodes is abundant, SPIN-2
communicates using the 3-stage protocol of SPIN-1. However, when the energy in a node
starts approaching a low energy threshold, it reduces its participation in the protocol, i.e.,
it participates only when it believes that it can complete all the other stages of the protocol
without going below the low-energy threshold. In conclusion, SPIN-l and SPIN-2 are simple
protocols that eciently disseminate data, while maintaining no per-neighbor state. These
protocols are well-suited for an environment where the sensors are mobile because they base
their forwarding decisions on local neighborhood information.

ˆ SPIN-BC: This protocol is designed for broadcast channels.

ˆ SPIN-PP: This protocol is designed for a point to point communication, i.e., hop-by-
hop routing.

ˆ SPIN-EC: This protocol works similar to SPIN-PP, but with an energy heuristic added
to it.

ˆ SPIN-RL: When a channel is lossy, a protocol called SPIN-RL is used where adjust-
ments are added to the SPIN-PP protocol to account for the lossy channel.

The advantages of SPIN is that topological changes are localized since each node needs
to know only its single-hop neighbors. SPIN provides much energy savings than ﬂooding and
meta- data negotiation almost halves the redundant data. However, SPINs data advertise-
ment mechanism cannot guarantee the delivery of data. To see this, consider the application
of intrusion detection where data should be reliably reported over periodic intervals and
assume that nodes interested in the data are located far away from the source node and the
nodes between source and destination nodes are not interested in that data, such data will
not be delivered to the destination at all.

4.3 Low Energy Adaptive Clustering Hierarchy (LEACH).

LEACH is a cluster-based protocol, which includes distributed cluster formation. LEACH
randomly selects a few sensor nodes as clusterheads (CHs) and rotate this role to evenly
distribute the energy load among the sensors in the network. In LEACH, the clusterhead
(CH) nodes compress data arriving from nodes that belong to the respective cluster, and
send an aggregated packet to the base station in order to reduce the amount of information
that must be transmitted to the base station. LEACH uses a TDMA/CDMA MAC to
reduce inter-cluster and intra-cluster collisions. However, data collection is centralized and
is performed periodically. Therefore, this protocol is most appropriate when there is a need
for constant monitoring by the sensor network. A user may not need all the data immediately.

12

Hence, periodic data transmissions are unnecessary which may drain the limited energy of
the sensor nodes. After a given interval of time, a randomized rotation of the role of the
CH is conducted so that uniform energy dissipation in the sensor network is obtained. The
authors found, based on their simulation model, that only 5% of the nodes need to act as
cluster heads.
The operation of LEACH is separated into two phases, the setup phase and the steady
state phase. In the setup phase, the clusters are organized and CHs are selected. In the
steady state phase, the actual data transfer to the base station takes place. The duration of
the steady state phase is longer than the duration of the setup phase in order to minimize
overhead. During the setup phase, a predetermined fraction of nodes, p, elect themselves as
CHs as follows. A sensor node chooses a random number, r, between 0 and 1. If this random
number is less than a threshold value, T(n), the node becomes a cluster-head for the current
round. The threshold value is calculated based on an equation that incorporates the desired
percentage to become a cluster-head, the current round, and the set of nodes that have not
been selected as a cluster-head in the last (1/P) rounds, denoted by G. It is given by:
p
T (n) = if n G (4.1)
1 − p(rmod(1 = p))

where G is the set of nodes that are involved in the CH election[3]. Each elected CH
broadcast an advertisement message to the rest of the nodes in the network that they are
the new cluster-heads. All the non-cluster head nodes, after receiving this advertisement,
decide on the cluster to which they want to belong to. This decision is based on the signal
strength of the advertisement. The non cluster-head nodes inform the appropriate cluster-
heads that they will be a member of the cluster. After receiving all the messages from the
nodes that would like to be included in the cluster and based on the number of nodes in the
cluster, the cluster-head node creates a TDMA schedule and assigns each node a time slot
when it can transmit. This schedule is broadcast to all the nodes in the cluster. During the
steady state phase, the sensor nodes can begin sensing and transmitting data to the cluster-
heads. The cluster-head node, after receiving all the data, aggregates it before sending it
to the base-station. After a certain time, which is determined a priori, the network goes
back into the setup phase again and enters another round of selecting new CH. Each cluster
communicates using diﬀerent CDMA codes to reduce interference from nodes belonging to
other clusters. Although LEACH is able to increase the network lifetime, there are still a
number of issues about the assumptions used in this protocol. LEACH assumes that all
nodes can transmit with enough power to reach the BS if needed and that each node has
computational power to support diﬀerent MAC protocols. Therefore, it is not applicable to
networks deployed in large regions. It also assumes that nodes always have data to send, and
nodes located close to each other have correlated data. It is not obvious how the number
of the predetermined CHs (p) is going to be uniformly distributed through the network.
Therefore, there is the possibility that the elected CHs will be concentrated in one part of
the network. Hence, some nodes will not have any CHs in their vicinity. Furthermore, the
idea of dynamic clustering brings extra overhead, e.g. head changes, advertisements etc.,
which may diminish the gain in energy consumption. Finally, the protocol assumes that
all nodes begin with the same amount of energy capacity in each election round, assuming
that being a CH consumes approximately the same amount of energy for each node. The
protocol should be extended to account for non-uniform energy nodes, i.e., use energy-based
threshold. An extension to LEACH, LEACH with negotiation. The main theme of the

13

proposed extension is to precede data transfers with high-level negotiation using meta-data
descriptors as in the SPIN protocol discussed in the previous section. This ensures that
only data that provides new information is transmitted to the cluster-heads before being
transmitted to the base station.

Figure 4.3: Comparison between SPIN, LEACH and Directed Diffusion.

4.4 CTP : Collection Tree Protocol
Collecting information reliably and efficiently from the nodes in a sensor network is a chal-
lenging problem, particularly due to the wireless dynamics. Multihop routing in a dynamic
wireless environment requires that a protocol can adapt quickly to the changes in the network
(agility) while the energy-constrains of sensor networks dictate that such mechanisms not
require too much communication among the nodes (efficiency). CTP is a collection routing
protocol, that achieves both agility and efficiency, while offering highly reliable data delivery
in sensor networks.[5]

14

Figure 4.4: Multihop Wireless Routing Using CTP

CTP has been used in research, teaching, and in commercial products. Experiences with
CTP has also informed the design of the IPv6 Routing Protocol for Low power and Lossy
Networks (RPL). CTP is a distance vector routing protocol designed for sensor networks.
CTP computes the routes from each node in the network to the root (speciﬁed destinations)
in the network. CTP uses these three mechanisms to overcome the challenges faced by
distance vector routing protocol in a highly dynamic wireless network:

Agile and Accurate Link Quality Estimation: The links in a wireless network are
highly dynamic and exhibit bursty behavior over short time scales. This property of wireless
links suggests that a link quality must be agile for it to be accurate. The four-bit link
estimator used in CTP uses information from the physical, data link, and network layers to
provide accurate link quality estimates despite these challenges.
Datapath Validation: In a dynamic wireless environment, a routing path that is reli-
able at one point can become unreliable or even unavailable within a few seconds. Due to
changing link qualities, loops can form and cause network congestion and energy drain due
to looping packets. Thus, these problems in the routing path must be detected as quickly

15

as they occurr. CTP uses datapath validation to detect these problems at the timescale
of data packet transmission (a few tens of milliseconds). It does so by using data packets
transmissions and receptions as topology probes and quickly detecting the problem when
the packets do not make progress towards the destination in the routing metric space.

16

Adaptive Beaconing: Routing protocols typically broadcast control packets at a fixed
interval (e.g., every 30 seconds). This interval poses a basic tradeoff. A small interval, i.e.,
frequent beacons, makes the protocol more responsive to the changes in the network, but
uses more bandwidth and energy. A large interval uses less bandwidth and energy but can
let topological problems persist for a long time. CTP uses adaptive beaconing to break
this tradeoff. When the topology is inconsistent and has problems, it sends beacons faster.
Otherwise, it decreases the beaconing rate exponentially. Thus, CTP can quickly respond
to adverse wireless dynamics while incuring low control overhead in the long term.

4.5 Greedy Perimeter Stateless Routing(GPSR)

GPSR is a geographic routing for wireless sensor networks. Unlike traditional Internet
routing(DV, LS), each node keep states from immediate neighbors and uses only those states
for data forwarding. The state is geographic position that all sensor nodes can self-configure
through GPS devices or others. Source node propagates data with the position of destination
to wireless sensor network. In normal case, forwarding node runs greedy mode routing. The
greedy routing firstly selects a node whose distance to a destination is less than distance
from forwarding node to destination and shortest among all immediate neighbors. Then,
data are forwarded to it. If there is no neighbor whose distance to destination is greater
than distance from forwarding node to destination, forwarding node runs perimeter mode
routing. The perimeter routing is based on planarized graphs such as Relative Neighborhood
Graph(RNG) or Gabriel Graph(GG). When each node receives position information from
immediate nodes, it initially makes unit graph and then determine RNG or GG. If the node
receives data and it cant perform greedy routing, it selects a node among immediate nodes
by right-handed rule and sends data to the neighbor. However, the edge between sending
node and receiving neighbor should not cross the edge between origin node and destination.
During data forwarding in perimeter mode, if forwarding node run greedy routing, it returns
to perimeter mode into greedy mode[6].

4.5.1 States of Neighbors
ˆ Add and delete neighbor

ˆ Update and look-up the state of neighbor

ˆ Update RNG topology and GG topology

ˆ Find shortest-path for greedy forward or clockwise-path for perimeter forward

4.5.2 Beaconing
ˆ Receive beacon packet and beacon solicit packet

ˆ Periodically broadcast beacon packet to neighbor nodes

ˆ Periodically check connections to neighbor nodes.

17

4.5.3 Greedy Forwarding
ˆ Receive greedy-mode data packet

ˆ Send the pure application data except for GPSR protocol header to upper application
if the destination of packet is same as the position of local node.
ˆ Send the receiving whole packet to upper application if GPSR daemon runs on loosely-
coupled mod.
ˆ Forward the packet to shortest neighbor nodes if GPSR daemon runs on tightly-coupled
mod.

4.5.4 Perimeter Forwarding
ˆ Receive perimeter-mode data packet

ˆ Send the pure application data except for GPSR protocol header to upper application
if the destination of packet is same as the position of local node.
ˆ Send the receiving whole packet to upper application if GPSR daemon runs on loosely-
coupled mod.
ˆ Forward from on perimeter mode to on greedy mode if distance from local to destination
is shorter than one from previous node to destination.
ˆ Drop(send to application) the pure data except for protocol header if receiving packet
is the previous sent packet to neighbor.
ˆ Perform face change algorithm.

ˆ Forward the packet to counterclockwise neighbor nodes.

4.6 Power Eﬃcient Gathering for Sensor Information
Systems

Power Eﬃcient Gathering for Sensor Information Systems (PEGASIS) is a data-gathering
protocol based on the assumption that all sensor nodes know the location of every other node,
that is, the topology information is available to all nodes. Also,any node has the required
transmission range to reach the BS in one-hop, when it is select as a leader. The goals of
PEGASIS are as follows :

ˆ Minimize the distance over which each node transmits.

ˆ Minimize the broadcasting overhead.

ˆ Minimize the number of messages that need to be sent to the BS.

ˆ Distribute the energy consumption equally across all nodes.

18

Figure 4.5: Power Efficient Gathering for Sensor Information Systems

A greedy algorithm is used to construct a chain of sensor nodes, starting from the node
farthest from the BS. At each step, the nearest neighbor which has not been visited is added
to the chain. The chain is constructed a priory, before data transmission begins and is
reconstructed when nodes die out. At every node, data fussion is carried out. so, that only
one message is passed on from one node to next. A node which is designated as the leader
finally transmits one message to BS.

Leadership is transffered in sequential order and a token is passed. so that the nodes
know in which direction to pass messages in order to reach the leader. A possible chain
formation is illustrated in figure. The delay involved in message reaching the BS is O(N),
where N is the total number of nodes in the network[1].

4.7 Binary Scheme

This is also a chain-based scheme like PEGASIS, which classifies nodes into different
levels. All nodes which recieve messages at one level rises to the next level[1].

19

Step 1 :- S0 → S1 S2 → S3 S4 → S5 S6 → S7

Step 2 :- S1 → S3 S5 → S7

Step 3 :- S3 → S7

Step 4 :- S7 → BS

The number of nodes is halved from one level to the next. For instance,consider a network
with eight nodes labled from S0 to S7. In figure agreegated data reaches the BS in 4 steps
which is O(log2 N ).where N is the number of nodes in the network.

4.8 Chain Based Three level Scheme

For non CDMA sensor nodes, a binary scheme is not applicable. The chain based three
level scheme addresses this situation. In this scheme chain is constructed as in PEGASIS.
The chain is devided into into number of groups to space out simulteneous transmission in
order to minimize interference. Within a group , nodes transmit one at a time[1].

One node out of each group agreegates data from all group members and rises to the
next level. The index of this leader node is decided priori. In the second level all nodes are
devided into two groups and the third level consist of a message exchange between one node
from each group of second level. Finally the leader transmits a single message to BS.

Step 1 :- S0 → S1....S6 → S7 ← S8 ← S9 S10 → S11....S16 → S17 ← S18..........S97 ←
S98 ← S99

Step 2 :- S7 → S17 ← S27 ← S37 ← S47 S57 → S67 ← S77 ← S87 ← S97

Step 3 :- S17 ← S67

Step 4 :- S67 → BS

The working of this scheme is explain in above figure.Suppose Network has 100 nodes
and the group size is 10 for the first level and 5 for second level. Three levels have been
found to give the optimal energy X delay through simulation.

20

Chapter 5

Simulation And Analysis

we validate our analysis using simulations. We have also compared the performance of
different protocols under same settings of the network parameters.

5.1 Greedy Perimeter Stateless Routing(GPSR)

The source node and destination node are assumed. The source node transmits the
hello packet to all neighbourhood nodes through right hand flooding technique. The flooded
packets are tracked and a routing table is formed at the base station. Different path to deliver
thepackets is found, data reachability is ensured and a routing table is formed with all the
successful routes. Once the routing table is formed, the optimal route is selected based on
packet delivery delay, less energy consumption and number of hops. We simulated the GPSR
protocol using Castalia Simulator. No of Nodes in Simulation is 100. Node deployment is
Uniform . For the simulation experiment, following parameter was used:

21

Figure 5.1: Energy Consumed By Each Node

The Energy Consumption at every Node is shown in Figure:

The GPSR routing protocol strives to address the unique requirements for sensor network
applications. It provides a robust, energy-efficient routing protocol with the ability to route
messages from node to node and guarantees the delivery of packets under situations where
non-uniform transmission ranges exist. A geographical routing protocol was developed and
implemented for successful data delivery to any destination within the network or to the base
station. The results of proposed optimal routing indicate that the energy consumption can
be systematically decreased by optimizing the clusters and head set size which also increases
the efficiency of the network. The energy efficiency of the etwork is increased by ensuring
the data reachability using proper routing technique. If the path is being established from
source to destination, the particular path might get loaded due to the traffic conditions. If
traffic density is more, then an alternative path needs to be selected from the routing table
with optimal energy conservation and shortest path with less traffic. This selection of path
will further improve the routing technique by reducing the Packet Delivery Delay and energy
consumption.

5.2 Collection Tree Protocol (CTP)
We simulated the CTP cluster based routing protocol using Castalia Simulator. No of
Nodes in Simulation is 100. Node deployment is Uniform and area of ”250x250”. For the
simulation experiment, following parameter was used:
The Results of the simulation are shown in following figures.which shows the Application
Level Latency , Packets recieved per Node , Recieved packet breakdown, Transmitted Packets
and Energy Consumed By each Node.

22


23

Figure 5.3: Application Level Latency at Each Node

Figure 5.4: Packets recieved per Node

24

Figure 5.5: CTP data per Node

Figure 5.6: Packets Transmitted per Node

25

5.3 Low-Energy Adaptive Clustering Hierarchy (LEACH)
Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol for sensor networks is pro-
posed by W. R. Heinzelman et. which minimizes energy dissipation in sensor networks. It
is very famous hierarchical routing algorithms for sensor networks which make clusters of
the sensor nodes based on the received signal strength.The 5% of the total number of nodes
becomes the cluster head which act as router to the sink. Energy consumption is less as
transmission will only be done by cluster head.
We simulated the LEACH cluster based routing protocol using Castalia Simulator. No
of Nodes in Simulation is 100. Node deployment is Uniform and area of ”70x70”. For the
simulation experiment, following parameter was used:

The Results of the simulation are shown in following ﬁgures.which shows the Application
Level Latency , Packets recieved per Node , Recieved packet breakdown, Transmitted Packets
and Energy Consumed By each Node.

26


Figure 5.8: Application Level Latency at Each Node

27

Figure 5.9: Packets recieved per Node

Although LEACH is able to increase the network lifetime, there are still a number of issues
about the assumptions used in this protocol. LEACH assumes a homogeneous distribution
of sensor nodes in the given area. This scenario is not very realistic[7]. LEACH assumes that
all nodes can transmit with enough power to reach the BS if needed and that each node has
computational power to support different MAC protocols. Therefore, it is not applicable to
networks deployed in large regions. It also assumes that nodes always have data to send and
nodes located close to each other have correlated data. It is not obvious how the number of
predetermined Cluster Heads is going to be uniformly distributed throughout the network.
Therefore, there is a possibility that the elected CHs will be concentrated in one part of the
network.The Hierarchical routing protocol LEACH is energy efficient for the sensor network.
By varying the different no. of clusters heads/clusters in the network the performance of
network changed in terms of lifetime, throughput and average energy dissipation. From the
above results we concluded that if the clusters in network or cluster heads in network are
below or above 5 percentage of the total no. of nodes the performance of the network is
degraded in terms of energy, throughput and lifetime[7].

28

Figure 5.10: Comparison of Routing Protocols in WSN

29

Chapter 6

References

1. Ad Hoc Wireless Networks By,C.Shiva Ram Murthy and B.S.Manoj

2. www.wikipedia.org/wiki/Wireless sensor network Access on 20th June 2012

3. Routing Techniques in Wireless Sensor Networks: A Survey BY Jamal N. Al-Karaki
AND Ahmed E. Kamal

4. A Review on Enhanced GPSR protocol For Wireless Sensor Networks by P.Pranitha,G.Swamy,Aaku
Manjula.

5. http://sing.stanford.edu/gnawali/ctp Accessed on 28th October 2012

6. Greedy Perimeter Stateless Routing (GPSR) USER Speciﬁcation By Ramesh Govidan

7. Energy and Throughput Analysis of Hierarchical Routing Protocol (LEACH) for Wire-
less Sensor Network by prof.Vijay Ukani,Nirma University.

8. W. Heinzelman, A. Chandrakasan and H. Balakrishnan, ”Energy-Ecient Communica-
tion Protocol for Wireless Mi- crosensor Networks,” Proceedings of the 33rd Hawaii
International Conference on System Sciences (HICSS ’00), January 2000.

9. Y. Yao and J. Gehrke, The cougar approach to in-network query processing in sensor
networks”, in SIGMOD Record, September 2002.

10. Marc Greis tutorial For NS2

11. Castalia User Manual

30

Performance Analysis of Routing Protocols of Wireless Sensor Networks

More Related Content

What's hot

Viewers also liked

Similar to Performance Analysis of Routing Protocols of Wireless Sensor Networks

In this document

Performance Analysis of Routing Protocols of Wireless Sensor Networks