UNIVERSITY OF
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RUGGeD: RoUting on finGerprint
GraDients in Sensor Networks
Jabed Faruque, Ahmed Helmy
Wireless Networking Laboratory
Department of Electrical Engineering
University of Southern California
faruque@usc.edu, helmy@usc.edu
URL: http://nile.usc.edu, http://ceng.usc.edu/~helmy
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Introduction
Sensor networks consist of sensor nodes with
-Limited Energy source
-Sensor devices
-Short range radio
-On-board processing capability
Mica2 mote and sensor board
Use of Sensor networks is tightly coupled with physical phenomena
-May be most widely used for habitat and environment monitoring
(e.g. temperature, humidity)
-For unattended and fine grained monitoring of natural phenomena
-Self configuration capability
-Also others e.g., for defense purpose …
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Motivation
Every physical event produces a fingerprint in the environment,
e.g.,
-Fire event increases temperature
-Nuclear leakage causes radiation
Many physical phenomena follow diffusion law
f(d)  1/d, where
d = distance from the source,
 = diffusion parameter, depends on the type of effect
(e.g. for temperature  ~ 1, light  ~ 2)
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Example (of diffusion):
Isoseismal (intensity) maps
(North Palm Springs earthquake of July 8, 1986 )
Ref.: Southern California Earthquake Center. (http://www.scec.org)
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Why Using Natural Information Gradient
is Important?
• This natural information gradient is FREE
• Routing protocols can use it to forward query packet (greedily)
- Locate event(s); e.g., fire, nuclear leakage.
• Can be extended for other notions of gradients
- Example: Time gradients can be used for mobile target tracking
• Existing approaches – flooding, expanding ring search,
random-walk, etc. do not utilize this information gradient
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Challenges
-In real life, sensors are unable to detect or measure the event’s
effect below certain threshold. So, diffusion curve has finite tail
- Lack of sensitivity of sensor device(s)
-Erroneous reading of malfunctioning sensors
- Due to calibration errors or obstacle
- Cause local maxima or minima
-Environmental noise
100
magnitude of effect
80
60
40
20
0
0
50
100
150
200
distance
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Objective
Design an efficient algorithm to locate source(s) in
sensor networks, exploiting natural information gradients
i.e., the diffusion pattern of the event’s effect
- Gradient based
- Fully distributed
- Robust to node or sensor failure or malfunction
- Capable of finding multiple sources
Environment Model
• Event’s effect follows the diffusion law
• Discontinuity exists in the diffusion curve with finite tail
• Environmental noise
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Related Work
[1,2,3]
• Traditional routing protocols for sensor networks are based
on Flooding (directed-diffusion) or Random-walk (Rumorrouting, ACQUIRE, etc.)
- Flooding based methods cause huge energy overhead
- Random-walk increases latency and failure probability
- Do not utilizes the natural information gradient
• Existing Information driven protocols [4,5] use single path
approaches with/without look-ahead parameter
- Use a proactive phase to prepare information repository
 Cause significant overhead at low query rate
- Unable to handle local maxima or minima
- Unable to find multiple sources
- Robustness depends on the proactive phase and the lookahead parameter
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Protocol

A node can exist in one of two modes/states
- flat-region mode
- gradient-region mode
A
node forwards the query to neighbors with its information level
 To forward the query, each node uses following algorithm:
1. Information gradient region follows greedy approach
- Forwards the query to the neighbors if the information level about the
event improves
2. Unsmooth gradient region use probabilistic forward based
on Simulated Annealing
- Probabilistic function is fp(x) = 1/xa, where x = hop count in the information
gradient region and ‘a’ depends on the diffusion parameter ( )
3. Use flooding for the flat (i.e., zero) information region
- Decrease latency to reach gradient information region
- Handles query in the absence of events
 Query
ID prevents looping
 Once query is resolved, a node uses the reverse path to reply
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E
E
Q’ Q’
Q’
np
Q’ Q’
Q’
np
Q’ Q’
Q
Q’
np nngp ng ng
Mx ngnp Mn ng
np np nngp ng ng
Q
• All neighbors (np) of Mx have less information, so they forward the
query to their neighbors probabilistically
• All neighbors (ng) of Mn have more information, so they forward the
query to their neighbors
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Simulation Model
• Two different sensor network layouts
1. 100 X 100 regular grid of 10000 nodes. Event located at (74,49)
2. 15 X 6 grid of 90 nodes in 225 x 375 m2 sensor field with 50m
communication radius. Grid points are perturbed by Gaussian noise (0,25)
• Diffusion parameter  set to 0.8
• Two regions exist in each layout
- Flat or zero information region
- Gradient information region
• Malfunctioning nodes are uniformly
distributed in both region
• Environmental noise is present in the
gradient information region
• Malfunctioning nodes have arbitrary readings
- For global maxima search, protocol uses a filter to prohibit replies
from nodes having arbitrary high value
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Query Types
• Single-value query
- Search for a specific value and have a single response
• Global Maxima search (only sensor layout 1 is used)
- Search for the maximum value of information in the system
- Intermediate nodes suppress non-promising replies
• Multiple Events detection (only sensor layout 1 is used)
- Search for multiple events of the same type
Performance Metrics
• Reachability i.e., success probability
- Probability that the query will reach the source
• Overhead in terms of average energy dissipation
- Number of transmissions required to forward the query and to get the reply
from the source
• For multiple events detection, ratio of sources found to actual
number of sources
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Single-value query-
effect of flat information region nodes
(3% environmental noise and 15% malfunctioning nodes)
- With increase of flat region
- Flooding overhead becomes dominant increasing energy consumption
- Malfunctioning nodes cause query to switch to gradient mode erroneously
- Decrease in ‘a’ creates more paths, increasing reachability and energy consumption
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Single-value query-
effect of the malfunctioning nodes
(3% environmental noise and 36% flat information region nodes)
- With increase of malfunctioning nodes the protocol switches from the flat region
mode to the gradient region mode rapidly
- Reduces flooding overhead
- Increases failure rate
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Single-value query-
route a query around the sensors hole
(3% environmental noise and 20% malfunctioning nodes)
- For smaller value of ‘a’ (e.g., a ~0.65), reachability is above 98% even at the presence
of 55% flat information region
• For the probabilistic function fp(x) = 1/xa, a <  is recommended, but
close to  gives optimal trade-off between reachability and overhead
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Global Maxima Search-effect of flat information region nodes
(3% environmental noise and 15% malfunctioning nodes)
(without Filter)
(with Filter)
- Average energy dissipation reduces significantly due to use of the simple filter
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Multiple Events Detection-effect of flat information region nodes
(3% environmental noise and 15% malfunctioning nodes)
- With the increase of number of sources, some plateaux regions are created in the
resultant gradient information region that require more probabilistic forwarding
- for five or more sources, a ~ 0.35 is a good setting in the simulated scenario
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Conclusion
• Developed a multiple-path exploration protocol to
discover events in sensor networks efficiently
• The protocol is fully reactive, effectively exploits the
natural information gradients and controls the
instantiation of multiple paths probabilistically
• The performance of the probabilistic function is closely
tied to the diffusion parameter
• Three different problems were studied
• Single-value, Global maximum, Multiple events
• Obtained high success rate to route around the
sensors hole, with proper setting of the probability
function parameters
• More efficient than existing approaches
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On-going and Future work
• Establish analytical relationship between
diffusion pattern and the probabilistic
forwarding function
• Develop protocol for target tracking and target
counting using the multiple path exploration
mechanisms
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Backup Slides
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Environment Model
• f(di) = f*(di) ± fEN(f*(di)),
• fEN(f(*di))  fmax - f*(di)
–
–
–
–
–
–
where,
di = distance of the location from peak information point (i.e., the event)
f(di) = gradient information of the location with environmental noise,
fmax = peak information,
f*(di) = gradient information without environmental noise.
The proportional constant is considered 0.03 to model the environmental for
our protocol, i.e., 3% environmental noise is considered
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Filtering of Malfunctioning Nodes
• Let distance of sensors S1 and S2 from the event’s
location are d and d+1 hops with readings R1 and R2
In our simulations  = 0.8
We use the filter
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Reply Suppression Mechanism
Intermediate nodes suppress the non-promising replies
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References
[1] C. Intanagonwiwat, R. Govindan and D. Estrin, ``Directed Diffusion:
A Scalable and Robust Communication Paradigm for Sensor
Networks,” MobiCom 2000.
[2] D. Braginsky and D. Estrin, ``Rumor Routing Algorithm for Sensor
Networks", WSNA 2002.
[3] N. Sadagopan, B. Krishnamachari, and A. Helmy, ``Active Query
Forwarding in Sensor Networks (ACQUIRE)", SNPA 2003.
[4] M. Chu, H. Haussecker, and F. Zhao, ``Scalable Information-Driven
Sensor Querying and Routing for ad hoc Heterogeneous Sensor
Networks", Int'l J. High Performance Computing Applications,
16(3):90-110, Fall 2002.
[5] J. Liu, F. Zhao, and D. Petrovic, ``Information-Directed Routing in
Ad Hoc Sensor Networks", WSNA 2003.
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