Introduction to Wireless Sensor Networks

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A. Kruger

Introduction to Wireless Sensor Networks

Routing in WSNs

28 February 2005

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Organizational

Monday  4:30-5:20 Room 4511 SC

Thursday 12:30-1:20 Room 3220 SC

Please note that the room numbers are different for Mondays and Thursdays.

Class Website www.engineering.uiowa.edu/~ece195/2005/

Class Time

Midterm Exam Time: March 10, 2005

Monday  5:20-620 Room 1126 SC

Thursday 1:30-2:30 Room 1126 SC

Office Hours

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Routing• What is meant by “routing”?

• Internet (TCP/IP)– Routing tables often large– Can be updated frequently

• WSN– Frequent topology changes– Modest local storage– Expensive to update frequently– => Need local, stateless algorithms where

nodes know only immediate neighbors

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Routing• Consider the following

– The fundamental difference between classical routing and routing for sensor networks is that the separation between address and content of packet no longer viable

• What does it mean?– Network is a system, individual nodes come and go,

information sensed by one node can be sensed by another close by

• Data-centric view– Routing decision as based not on destination address, but

rather on destination attributes and relation to attribute of packet content

– Information providers and information seekers must be matched using data attributes and not (hard) network address

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Examples of Attributes• Node location

– But is this not just its address?– Get the rain data from the nodes at the Iowa City airport

• Types of sensor connected to a node– Send a control packet to all nodes that have a light sensor

connected to it

• Certain range of values in certain type of sensed data– Get max, min temperature values in from the sensor

network

• Pull model– Network is queried similar to a database

• Push model– Network can initiate flow of information based on events

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WSN Routing• Geographic routing (more traditional view)

– Greedy distance– Compass– Convex perimeter routing– Routing on a curve– Energy-minimizing broadcast

• Attribute-based routing (data-centric view)– Directed diffusion– Rumor routing– Geographic hash tables

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Graphs

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Greedy Distance and Compass Routing

• Greedy distance –pick the locally optimum (distance) neighbor

• Compass routing – pick the locally optimum (angle) neighbor

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Problem With Greedy Distance• Here both x’s neighbors are further

than destination

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Side-Bar Maze Solver

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Planar Graphs

not planar planar

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PlanerizationBasic idea – keep connectivity between nodes

Convex Polygon

Concave Polygon

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Planarization Requirements for WSN

• WSNs: local planarization algorithms, where edge xy is introduced if a geometric region (witness region) around xy is free of other nodes.

• Require accurate information about location of nodes

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Planerization• Basic idea – keep connectivity between nodes• Relative Neighborhood Graph (RNG)

– The edge xy is introduced if the intersection of circles centered at x and y with radius the distance d(x,y) is free of other nodes

• Grabriel Graph– The edge xy is introduced if the diameter xy is free of other

nodes

• Key for WSN: RNG and Gabriel graphs can be found with distributed construction

x y

x y

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Examples

RNG Gabriel

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Convex Perimeter Routing

• Objective: route from s to d (assume planar graph)• Start in the face just beyond s along sd and walk

around that face. Stop if d is reached. If the segment sd is about to be crossed, cross over to the next face along sd, and repeat

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Variations• Non-convex routing adaptation

• OFR – Other face routing

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Side-Bar Parametric Equations• Circle

– Non parametric: x2 + y2 = a2 – Parametric: x = a cos(t), y = a sin(t), t the

parameter

• Straight Line– Non parametric: y = mx+c– Parametric: line through point (a, b)

parallel to vector (u, v) is given by

(x, y) = (a, b) + t·(u, v), t the parameter

• Given t one can compute x and y

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Routing on A Curve• Specify a curve a packet should follow• Analytical description of a curve carried by the

packet• Curves may correspond to natural features of the

environment where the network is deployed• Can be implemented in a local greedy fashion that

requires no global knowledge• Curve specified in parametric form C(t)=(x(t),y(t))

– t – time parameter – could be just relative time

• Each node makes use of nodes trajectory information and neighbor positions to decide the next hop for the packet

• Also called trajectory-based routing

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Optimal Path• What do we mean by “optimal”

– Minimum delay => fewest hops– Minimum Energy => frequent hops (why)

• Formally, cost of a path

– Where l(e) is the length of the edge in the graph– k is in range 1…5 – k = 0 => Hop length, measure delay– k = 1 => Euclidian path length– k > 1 => Capture energy of path, depending on

attenuation model

e

k elc )()(

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Review Questions• Write a short (5 sentence) paragraph contrasting the

needs and resources available in WSN as opposed to, say, the Internet.

• Explain the statement “When routing a packet in a WSN, more hops increase delay, but the advantage is that it increases energy efficiency for the WSN as a whole”

• Write a 6-7 sentence paragraph explaining the term “routing on a curve”

• Write a paragraph explaining the term “convex perimeter routing”

• True of False – a major disadvantage of perimeter routing in WSN is that path construction require knowledge of the global topology

• With the aid of a figure, explain how a greedy forwarding strategy can result in a packet being stuck at a node in a WSN

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Review Questions• Below is a connectivity graph for a WSN. (a)

Planerize it using the RNG method. Planerize it using the Grabriel method.

(figure goes here)

• True or False – a problem with “Routing on a Curve”

is that each nodes must know the location of all nodes along the routing path.

• Write a short (5 sentence) paragraph explaining what Trajectory-Based Routing is.