Wireless Sensor Networks (CS 6115)

Lect #12
Spring 2023- 24

Prof. Suchismita Chinara

Department of Computer Science and Engineering
E-mail: suchismita@nitrkl.ac.in

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 ROUTING AND DATA DISSEMINATION
• Routing and data dissemination are an important
issue in WSN.
• The essential function of a WSN is to monitor a
phenomenon in a physical environment and report
sensed data to a central node called a sink , where
additional operations can be applied to the
gathered data.

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 Terminologies
• Sensing Range
• Transmission Range
• Neighbor Set
• Coverage
• Homogeneous versus Heterogeneous
Network
• Communication Graph
• Connectivity
• Voronoi Diagram

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 Sensing Range
The sensing range of a sensor ( si ) is a disk of
radius ( ri ), including its boundary, centered at
the location of si.

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 Transmission Range
The transmission range of a sensor si is a disk of
radius (Ri ), including its boundary, centered at
the location of si.

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 Neighbor Set
The neighbor set of a sensor ( si ) is given by N (
si ) = { sj : Dist ( si, sj) ≤ Ri }, where Ri is the radius
of the transmission range of si

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 Coverage
Let A be an area of the field. A point p ∈ A is said
to be covered (or sensed ) if and only if it
belongs to the sensing range of at least one
sensor. The area A is said to be covered if and
only if for every point p ∈ A is covered.

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 Homogeneous versus Heterogeneous Network

A WSN is said to be homogeneous if all its

sensors have the same storage, computation,
communication, sensing, and energy
capabilities. Otherwise, it is heterogeneous .

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 Communication Graph
A communication graph of a homogeneous (
heterogeneous ) WSN is an undirected ( directed )
graph, G = ( S , E ), where S is a set of sensors and
E is a set of ( directed ) edges between them such
that for all s i , s j ∈ S , ( s i , s j ) ∈ E if Dist(si, sj) ≤ R
i.

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 Connectivity
Let G = ( S , E ) be a communication graph
representing a network, where S is a set of
sensors and E is a set of communication links
between them such that for all si , sj ∈ S , ( si , s j
) ∈ E if Dist(si, sj)≤ Ri . The connectivity of G is
equal to K if and only if G can be disconnected
by the removal of at least K nodes.

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 Voronoi Diagram
Let S = { s0 , … , s m− 1 } be a finite set of m sites in
the plane. The Voronoi diagram of S , denoted by
Vor ( S ), is a subdivision of the plane containing S
into m Voronoi regions VR (si ), for 1 ≤ i ≤ m . Note
that VR (si) is possibly an unbounded open convex
polygonal region that consists of all points closer to
s i than any other site in S . The edges of this region
are called Voronoi edges . The Vor ( S ) is the union
of all Voronoi regions of sites si ∈ S . A WSN can be
modeled by a Voronoi diagram with sites
representing locations of sensors.

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 The Voronoi diagram of a WSN

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 Architecture of a wireless sensor
network

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 Architecture of a wireless sensor
network
• The architecture diagram shows a network composed of a
set of sensors randomly deployed in a square sensor field.
• The transmission range of a sensor is represented by a
circle.
• When a sensor needs to communicate with another
sensor that is inside its transmission range, the
communication can be single hop (or direct).
• Otherwise, it must be multihop (or indirect) via other
intermediate sensors that act as relays between the two
communicating sensors.
• While the sensor si can communicate directly with the sink
sm , the sensor sk can communicate with sm only through
other intermediate sensors, for example, sj .

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 ROUTING CHALLENGES AND DESIGN
ISSUES IN WSN
• Network Scale and Time-Varying
Characteristics
• Resource Constraints
• Sensor Applications Data Models
• Sensor Characteristics

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 Network Scale and Time-Varying
Characteristics
• Sensor nodes operate with limited
computing, storage, and communication
capabilities under severe energy constraints.
• The densities of the WSNs may vary widely,
ranging from very sparse to very dense
(scalable)
• In these networks, the behavior of sensor
nodes is dynamic and highly adaptive, as
they need to self-organize and conserve
energy.
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 Resource Constraints
• Sensor nodes are designed with minimal complexity
for large-scale deployment at a reduced cost.
• Energy is a key concern in WSNs, which must achieve
a long lifetime while operating on limited battery
reserves.
• Multihop packet transmission over wireless networks
is a major source of power consumption.
• Reducing energy consumption can be achieved by
dynamically controlling the duty cycle of the wireless
sensors.
• A question arises as to how to design scalable routing
algorithms that can operate efficiently for a wide
range of performance constraints and design
requirements.
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 Sensor Applications Data Models
• The data model describes the flow of information between the sensor
nodes and the data sink.
• These models are highly dependent on the nature of the application in
terms of how data are requested and used.
• A class of sensor applications requires data collection models that are
based on periodic sampling or are driven by the occurrence of specific
events.
• In other applications, data can be captured and stored, possibly
processed and aggregated by a sensor node, before they are forwarded
to the data sink.
• Yet a third class of sensor applications requires bidirectional data models
in which two-way interaction between sensors and data sinks is required.
• The need to support a variety of data models increases the complexity
of the routing design problem.
• Optimizing the routing protocol for an application’s specific data
requirements while supporting a variety of data models and delivering
the highest performance in scalability, reliability, responsiveness, and
power efficiency becomes a design and engineering problem of
enormous magnitude.

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 Sensor Characteristics
• energy is the most crucial resource because it determines
the lifetime of a sensor.
• Also, energy poses a big challenge for network designers
especially in hostile environments, for example, a
battlefield, where it is impossible to access the sensors
and recharge their batteries.
• Furthermore, when the energy of a sensor reaches a
certain threshold, the sensor will become faulty and will
not be able to function properly, which will have a major
impact on the network performance.
• Therefore, algorithms designed for sensors should be as
energy efficient as possible to extend their lifetime, and
hence prolong the network lifetime while guaranteeing
good performance overall.

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