It is short document of WSN

1. Routing in a Wireless Sensor Network
A project report submitted to
The Department of Computer Science and Engineering, Indian School Mines
Dhanbad
In partial fulfillment of the requirement for the award of degree of
Bachelor of Technology in Computer Science and Engineering
By
Mohammad Kafee Uddin (2009JE0651)
Aloukik Mishra(2009JE0640)
Under the guidance of
Prof P. K. Jana
Dept. of Computer Science and Engg
ISM, Dhanbad

2. Department of Computer Science and Engineering
Indian School of Mines, Dhanbad-826004
Department of Computer Science and Engineering
Indian School of Mines, Dhanbad
Dated: 3-12-2012
CERTIFICATE
This is to certify that the dissertation entitled “ Routing in a Wireless Sensor
Network” is being submitted to Indian School of Mines, Dhanbad by Mohammad
Kafee Uddin(2009Je0651) and Aloukik Mishra(2009JE0640) and in partial
fulfillment of their Bachelor of Technology degree in Computer Science &
Engineering of the same institution incorporates the result of his own work,
carried out under my supervision and guidance. This dissertation has not been
submitted for any other degree, elsewhere to the best of my knowledge.
Prof P. K. Jana
HOD, CSE ISM Dhanbad

3. Abstract
Recent advances in wireless sensor networks have led to many new
protocols specifically designed for sensor networks where energy
awareness is an essential consideration. Most of the attention, however,
has been given to the routing protocols since they might differ depending
on the application and network architecture. This paper surveys recent
routing protocols for sensor networks and presents a classification for the
various approaches pursued. We have classified routing protocols
according to three different parameters, namely Mode of Functioning and
Type of Target Applications, Participation style of the Nodes, and the
Network Structure. Each routing protocol is described and discussed under
the appropriate category.

4. Acknowledgement
First and foremost, we would like to express our sincere gratitude
to our Project guide, Prof P. K. Jana, Head of the Department of
Computer Science and Engineering for providing us with such an
opportunity and permitting this project in the department.
We were privileged to experience a sustained and involved
interest on the part of our mentors. This encouraged us to boldly step
into what was an unexplored expanse before us. Exploring new realms
in the field of Computer Science and Engineering helped unveil new
insights into the field expanding our knowledge of the same.
We would also like to thank our friends who were ready with a
positive comment all the time, whether it was an off-hand comment to
encourage us or a constructive piece of criticism.
Last but not least, we would like to thank the faculty members
and the Department, in general, for extending a helping hand at every
juncture of need.
Mohammad Kafee Uddin Aloukik Mishra
Location: Location:
Date: Date:

5. Contents
Certificate
Abstract
Acknowledgements
1 Introduction to WSN
2 Classification Of Routing Protocols
2.1 Based on Mode of Functioning and Type of Target Applications
2.1.1 Proactive :-
2.1.2 Reactive :-
2.1.3 Hybrid :-
2.2 According to the Participation style of the Nodes
2.2.1 Direct Communication :-
2.2.2 Flat :-
2.2.3 Clustering Protocols :-
2.3 Depending on the Network Structure
2.3.1 Data Centric :-
2.3.2 Hierarchical :-
2.3.3 Location Based :-
3 Data Dissemination Protocols
3.1 Flooding
3.2 Gossiping
3.3 Rumor Routing
3.4 Sequential Assignment Routing
3.5 Direct Diffusion
3.6 Sensor Protocol for Information via Negotiation
3.7 Geographic Hash Table
4 Data Gathering Protocols
4.1 Direct Transmission
4.2 Power Efficient Gathering for Sensor Information Systems
4.3 LEACH

6. 1. Introduction to WSN
Recent advances in micro-electro-mechanical systems and low power and
highly integrated digital electronics have led to the development of micro-
sensors. Such sensors are generally equipped with data processing and
communication capabilities. The sensing circuitry measures the ambient
conditions related to the environment surrounding the sensor and
transforms them into an electric signal. Processing such a signal reveals
some properties about objects located and/or events happening in the
vicinity of the sensor. The sensor sends such collected data, usually via
radio transmitter, to a command center (sink) either directly or through a
data concentration center (a gateway). The decrease in the size and cost of
sensors, resulting from such technological advances, has fueled interest in
the possible use of large set of disposable unattended sensors. Such
interest has motivated intensive research in the past few years addressing
the potential of collaboration among sensors in data gathering and
processing and the coordination and management of the sensing activity
and data flow to the sink. A natural architecture for such collaborative
distributed sensors is a network with wireless links that can be formed
among the sensors in an ad hoc manner. A general sensor node is made
up of four basic components as shown in Fig. 1: a sensing unit, a
processing unit, a transceiver unit and a power unit. They may also have
application dependent additional components such as a location finding
system, a power generator and a mobilizer. Sensing units are usually
composed of two subunits: sensors and analog to digital converters
(ADCs). The analog signals produced by the sensors based on the
observed phenomenon are converted to digital signals by the ADC, and
then fed into the processing unit. The processing unit, which is generally
associated with a small storage unit, manages the procedures that make
the sensor node collaborate with the other nodes to carry out the assigned
sensing tasks. A transceiver unit connects the node to the network. One of
the most important components of a sensor node is the power unit. Power
units may be supported by a power scavenging unit such as solar cells.
There are also other subunits, which are application dependent. Most of the
sensor network routing techniques and sensing tasks require the
knowledge of location with high accuracy. Thus, it is common that a sensor
node has a location finding system. A mobilizer may sometimes be needed
to move sensor nodes when it is required to carry out the assigned tasks.

7. Networking unattended sensor nodes are expected to have significant
impact on the efficiency of many military and civil applications such as
combat field surveillance, security and disaster management. These
systems process data gathered from multiple sensors to monitor events in
an area of interest. For example, in a disaster management setup, a large
number of sensors can be dropped by a helicopter. Networking these
sensors can assist rescue operations by locating survivors, identifying risky
areas and making the rescue crew more aware of the overall situation.
Such application of sensor networks not only can increase the efficiency of
rescue operations but also ensure the safety of the rescue crew. On the
military side, applications of sensor networks are numerous. For example,
the use of networked set of sensors can limit the need for personnel
involvement in the usually dangerous reconnaissance missions. In addition,
sensor networks can enable a more civic use of landmines by making them
remotely controllable and target specific in order to prevent harming
civilians and animals. Security applications of sensor networks include
intrusion detection and criminal hunting. However, sensor nodes are
constrained in energy supply and bandwidth. Such constraints combined
with a typical deployment of large number of sensor nodes have posed
many challenges to the design and management of sensor networks.
These challenges necessitate energy awareness at all layers of networking
protocol stack. The issues related to physical and link layers are generally
common for all kind of sensor applications, therefore the research on these
areas has been focused on system-level power awareness such as
dynamic voltage scaling, radio communication hardware, low duty cycle
issues, system partitioning, energy-aware MAC protocols. At the network

8. layer, the main aim is to find ways for energy-efficient route setup and
reliable relaying of data from the sensor nodes to the sink so that the
lifetime of the network is maximized. Routing in sensor networks is very
challenging due to several characteristics that distinguish them from
contemporary communication and wireless ad hoc networks. First of all, it
is not possible to build a global addressing scheme for the deployment of
sheer number of sensor nodes. Therefore, classical IP-based protocols
cannot be applied to sensor networks. Second, in contrary to typical
communication networks almost all applications of sensor networks require
the flow of sensed data from multiple regions (sources) to a particular sink.
Third, generated data traffic has significant redundancy in it since multiple
sensors may generate same data within the vicinity of a phenomenon.
Such redundancy needs to be exploited by the routing protocols to improve
energy and bandwidth utilization. Fourth, sensor nodes are tightly
constrained in terms of transmission power, on-board energy, processing
capacity and storage and thus require careful resource management. Due
to such differences, many new algorithms have been proposed for the
problem of routing data in sensor networks. These routing mechanisms
have considered the characteristics of sensor nodes along with the
application and architecture requirements. Data-centric protocols are
query-based and depend on the naming of desired data, which helps in
eliminating many redundant transmissions. Hierarchical protocols aim at
clustering the nodes so that cluster heads can do some aggregation and
reduction of data in order to save energy. Location based protocols utilize
the position information to relay the data to the desired regions rather than
the whole network. We will explore the routing mechanisms for sensor
networks developed in recent years. Each routing protocol is discussed
under the proper category. Our aim is to help better understanding of the
current routing protocols for wireless sensor networks.

9. 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.
(a)Routing Protocols can be classified as Proactive, Reactive and Hybrid,
based on their Mode of Functioning and Type of Target
Applications.
(b)Routing protocols can be classified as Direct Communication, Flat and
Clustering Protocols, according to the Participation style of the Nodes.
(c)Routing Protocols can be classified as Hierarchical, Data Centric and
location based, depending on the Network Structure.
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. Examples: The Low Energy Adaptive Clustering hierarchy
protocol (LEACH) utilizes this type of protocol.
2.1.2 Reactive:-
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.
Examples: 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.
Examples: 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:-

10. 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.
Examples: SPIN is an example of this type of protocol.
2.2.2 Flat:-
In this protocol, 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.
Examples: 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.
Examples: TEEN is an example of this type of protocol.
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.
Examples: 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:-

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

12. 3.Data Dissemination Protocols
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. A node 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. 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.
3.1 Flooding
In Flooding, Each node which receives a packet broadcasts it, if the
maximum hop count of the packet is not reached and node itself is not the
destination of the packet. This technique does not require complex
topology maintenance or route discovery algorithms.
Flooding has following disadvantages:
Implosion: This is situation when duplicate messages are sent to the same
node. This occurs when a node receives copies of the same message from
many of its neighbours.
Overlap: The same event may be sensed by more than one node due to
overlapping of regions of coverage. This results in their neighbors receiving
duplicate reports of the same event.
Resource Blindness: The flooding protocol does not consider the
available energy at the nodes and results in many redundant
transmissions. So, it reduces the network lifetime.

13. 3.2 Gossiping
Gossiping is modified version of flooding, where the nodes do not
broadcast a packet, but send packets to a randomly selected neighbor.
This avoids the problem of Implosion. It takes a long time for a message to
propagate throughout the network. Though gossiping has considerably
lower overhead than flooding, it does not guarantee that all nodes of the
network will receive the message. It relies on the random neighbor
selection to eventually propagate the message throughout the network.
3.3 Rumor Routing
Rumor Routing is an agent based path creation algorithm. Agents are long-
lived entities created at random by nodes. These are basically packets
which are circulated in the network to establish shortest path to events that
they encounter. They can also perform path optimizations at nodes they
visit. When agent finds a node whose path to an event is longer than its
own, it updates the nodes routing table.
Figure 3.1 illustrates the working of Rumor Routing algorithm. In figure
3.1(a), the agent has initially recorded a path distance 2 to event E1. Node
A's table shows that it is at a distance 3 from event E1 and distance 2 from
E2. When the agent visits node A, i+t updates its own path state
information to include the path to event E2. The updating is with one hop
greater distance than what it found in A, to account for the hop between

14. any neighbor of A that the agent will visit next, and A. It also optimizes the
path to e1 recorded at node A to the shorter path through node B. The
updated status of the agent and node table is shown in figure 3.1(b).
When a query is generated at a sink, it is sent on a random walk with
the hope that it will find a path leading to the required event. This is based
on high probability of two straight lines intersecting on a planar graph,
assuming the network topology is like a planar graph, and the paths
established can be approximated by straight lines owing to high density of
the nodes. If a query does not find an event path, the sink times out and
uses flooding as last resort to propagate the query.
For instance, as in figure 3.1(c), suppose a query for event E1 is
generated by node P. Through a random walk, it reaches A, where it finds
the previously established path to E1. Hence, the query is directed to E1
through node B, as indicated by A's table.
3.4 Sequential Assignment Routing :-
The Sequential Assignment Routing(SAR) creates multiple trees ,where the
root of each tree is a one hop neighbor of sink. Each tree grows outward
from the sink and avoids nodes with low throughput or high delay. At the
end of the procedure, most nodes belong to multiple trees. An instance of
tree formation is illustrated in figure.

15. The tree rooted at A and B. Two of the one hop neighbors of the sink are
shown. Node C belongs to both trees and has path length of 3 and 5
respectively to the sink, using the two trees. Each sensor node records two
parameters about each path through it:
1. The available energy resources on the path.
2. An additive quality of service(QoS) metric such as delay.
This allows a node to choose one path from among many to relay its
message to the sink. The SAR chooses a path with the high estimated
energy resources and provisions can be made to accommodate packets of
different properties. A weighted QoS metric is used to handle prioritized
packets which computed as a product of priority level and delay. The
routing ensures that the same weighted QoS metric is maintained. Thus,
higher priority packets take lower delay paths and lower priority packets
have to use the paths of greater delay. e.g. If node C generates a packet of
priority 3, it follows the longer path along tree B, and a packet of priority 5

16. will follow the shorter path along tree A. So that the priority X delay QoS
metric is maintained. SAR minimizes the average weighted QoS metric
over the lifetime of the network. The sink periodically triggers a metric
update to reflect the changes in available energy resources after some
transmissions.
3.5 Direct Diffusion
This protocol 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 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 particular
path. The strength of the interest is different toward different neighbors,
resulting into source to sink paths with different gradients. The gradient
corresponding to an interest is derived from the interval/data-rate field
specified in the interest.
This model uses data naming by attributes and local data transformation to
reflect the data centric nature of sensor network operations. The local
operations of Data aggregation are application-specific gradient model. The
network wide results of local interaction by regulating the flow of data along
different paths depending on the expressed interest.
3.6 Sensor Protocol for Information via Negotiation(SPIN)
SPIN uses negotiation and resources and adaption to address the
deficiencies 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.

17. 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. The neighbor again
sends ADVs to its neighbors and this process continues to disseminate the
data throughout the network. the simple version is shown in figure.
SPIN is based on data-centric routing, where the nodes advertise the
available data through an ADV and wait for requests from interested nodes.
SPIN-2 expands on SPIN, using an energy or resource threshold to reduce
participation. A node may participate in the ADV-REQ-DATA handshake
only if it has sufficient resources above a threshold.
3.7 Geographic Hash Table
Geographic Hash Table is a system based on data centric storage inspired
by internet scale distributed hash table systems such as chard and
Tapestry, GHT hashes keys into geographic co-ordinates and stores a pair
at the sensor node nearest to the hash value. The calculated hash value is
mapped onto a unique node consistently, so that queries for the data can
be routed to the correct node. Stored data is replicated to ensure
redundancy in case of node failures and consistently protocol is used to
maintain the replicated data. The data is distributed among nodes such that
it is scalable and the storage load is balanced. GHT is more effective in

18. large network where a large number of events are detected but not all are
queried. In this case data observed is stored in a distributed manner across
all nodes, instead of being routed to central external storage. Queries are
routed to the nearest node which contains a copy of the relevant data. This
makes the storage and traffic distribution uniform.

19. 4.Data Gathering Protocols
The objective of the data-gathering problem is to transmit the sensed data
from each sensor node to a BS. One round is defined as the BS collecting
data from all the sensor
nodes once. The goal of algorithms which implement data gathering is to
maximize the number of rounds of communication before the nodes die
and the network becomes inoperable. This means minimum energy should
be consumed and the transmission should occur with minimum delays,
which are conflicting requirements. Hence, the energy X delay metric is
used to compare algorithms, since this metric measures speedy and
energy efficient data gathering. A few algorithms that implement data
gathering are discussed below.
4.1 Direct Transmission
All sensor nodes transmit their data directly to BS. This is extremely
expensive in terms of energy consumed, since the BS may be very far
away from some nodes. Also, nodes must take turns while transmitting to
the BS to avoid collision , so the media access delay is also large. Hence,
this scheme performs poorly with respect to the energy X delay matrix.
4.2 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:
Minimize the distance over which each node transmits.
Minimize the broadcasting overhead.
Minimize the number of messages that need to be sent to the BS.
Distribute the energy consumption equally across all nodes.

20. 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 diffusion 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 transferred 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.
4.3. LEACH
Low-energy adaptive clustering hierarchy is one of the most popular
hierarchical routing algorithms for sensor networks. The idea is to form
clusters of the sensor nodes based on the received signal strength and use
local cluster heads as routers to the sink. This will save energy since the
transmissions will only be done by such cluster heads rather than all sensor
nodes. Optimal number of cluster heads is estimated to be 5% of the total
number of nodes.
All the data processing such as data fusion and aggregation are local to the
cluster. Cluster heads change randomly over time in order to balance the

21. energy dissipation of nodes. This decision is made by the node choosing a
random number between 0 and 1. The node becomes a cluster head for
the current round if the number is less than the following threshold:
where p is the desired percentage of cluster heads (e.g. 0.05), r is the
current round, and G is the set of nodes that have not been cluster heads
in the last 1=p rounds. LEACH achieves over a factor of 7 reduction in
energy dissipation compared to direct communication and a factor of 4–8
compared to the minimum transmission energy routing protocol. The nodes
die randomly and dynamic clustering increases lifetime of the system.
LEACH is completely distributed and requires no global knowledge of
network. However, LEACH uses single-hop routing where each node can
transmit directly to the cluster-head and the sink. Therefore, it is not
applicable to networks deployed in large regions. Furthermore, the idea of
dynamic clustering brings extra overhead, e.g. head changes,
advertisements etc., which may diminish the gain in energy consumption.

22. 5.References :-
1. Wireless sensor networks: a survey
I.F. Akyildiz, W. Su*, Y. Sankarasubramaniam, E. Cayirci
2. Ad Hoc Wireless Networks By,C.Shiva Ram Murthy and B.S.Manoj
3. A survey on routing protocols for wireless sensor networks
Kemal Akkaya *, Mohamed Younis
4.www.wikipedia.org/wiki/Wireless sensor network