Node Localization in Sensor Networks
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Transcript Node Localization in Sensor Networks
Routing Considerations for Sensor
Networks
Lecture 12
October 12, 2004
EENG 460a / CPSC 436 / ENAS 960
Networked Embedded Systems &
Sensor Networks
Andreas Savvides
[email protected]
Office: AKW 212
Tel 432-1275
Course Website
http://www.eng.yale.edu/enalab/courses/eeng460a
Announcements
Feng Zhao’s talk tomorrow 4:00pm @ AKW 500
Student session 3:20 – 4:00pm AKW 500
Reading for this lecture
• Zhao & Guibas Section 3.3 through 3.6
Reading for next lecture
• Directed Diffusion – paper posted on the class website
Today’s presentation IDSQ
Routing Considerations in Sensor Networks
Traditional TCP/IP routing not attractive for
sensor networks
• Too much overhead and large routing tables
Sensor networks are more ad-hoc
• Each node acts as a router
• Still different than ad-hoc networks
o Proactive routing is too expensive
o Some possibility for reactive routing such as
– Fish-eye routing, AODV, DSR
Routing Goal
Focus on localized state-less routing
• Consider only local neighborhood
Classical separation of address and content does not hold
• Care about reaching the nodes rather than a particular address – what can
be sensed by a node can most probably be sensed by neighboring nodes
• Interested in routing by attributes – data centric
o Node’s location
o Node’s type of sensors
o Range of values in the sensed data
Notion of optimality can vary
• QoS routing – latency is important => shortest path
• Energy aware routing – longer paths are ok => avoid nodes with less
energy
Geographic Routing
Aims to route based on very limited state
information
Geographic routing protocols assume
•
•
•
•
All nodes know their geographic location
Each node knows its 1-hop neighbors
Destination is a node with a given location
Each packet can hold a limited amount of information
as to where it has been in the network
Any issues with this?
• Needs to maintain information between node IDs and
node location (referred to as location service)
Geographic Forwarding Approaches
Greedy distance routing: select the neighbor
geographically closest to the destination and
forward the data to that neighbor
Compass routing: pick the next node as the one
that minimizes the angle to destination
What are the problems with the basic approaches
• Greedy distance routing – may get stuck in local
minima
• Compass routing – may go in loops
Planarization of Routing Graph
To get protocols that guarantee data delivery,
make graph planar
Remove some edges from your network graph G
• Aim: Keep the same connectivity but make the graph
planar
o no two edges in G should intersect each other
• In the planar subdivision of G each node is assumed to
know the circular order of its neighbors
• Convex perimeter routing and other face routing
protocols use this property
Common Planarization Methods
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
x
Grabriel Graph
•
The edge xy is introduced if the diameter xy is free of other nodes
x
y
y
Both graphs RNG and Gabriel graphs can be found with distributed construction
Greedy Perimeter Stateless Routing(GPSR)
Geographic protocol based on the offline
construction of planar graphs
• RDG, Gabriel, later on RDG suggested
Has 2 main phases forwarding and recovery
Forwarding is greedy
Recovery – uses a right-hand rule to recover
from holes. It stops as soon as a node closer
to the destination is found
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.
Attribute Based Routing & Directed Diffusion
Nodes desire certain information and other
nodes have some information. How do they
find each other?
Use attribute value pairs to describe the data
Attribute value record
type = animal
instance = horse
location = [89, 154]
time = 2:45:23
Information request record
type = animal
instance = horse
rect = [0,200,0,200]
Directed Diffusion
Each node names data with one or more attributes
Other nodes express interests based on these attributes
Network nodes propagate the interests and results back to
the sink
Negative gradients inhibit the propagation of information
& positive gradients encourage information propagation
Assumption: the sink will be interested in repeated
measurements from a source for a period of time
Paa-Kwesi will give a detailed presentation of directed
diffusion next time
Next Lecture
Geographic Hash Tables – Andreas
Directed Diffusion – Paa Kwesi