Multi-Criteria Routing in Pervasive Environment with Sensors Santhanakrishnan, G., Li, Q., Beaver,...
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Multi-Criteria Routing in Pervasive Environment with Sensors
Santhanakrishnan, G., Li, Q., Beaver, J., Chrysanthis, P.K., Amer, A. and Labrinidis, A
Department of Computer ScienceUniversity of Pittsburgh
U.S.A.
International Conference on Pervasive Services, 2005. (ICPS '05)International Conference on Pervasive Services, 2005. (ICPS '05)
Chien-Ku Lai
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Outline
Introduction Multi-Criteria Routing Protocol Performance Evaluation Conclusions and Future Work
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Introduction
1. Wireless sensor networks (WSNs)
2. The major challenge in WSNs3. The contributions of this paper
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Introduction- Wireless sensor networks (WSNs)
Sensor networks will be an integral part of a pervasive computing environment Since they allow interaction with the
physical environment
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Introduction- The major challenge in WSNs
Power conservation Communication costs Network processing
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Introduction- The major challenge in WSNs (cont.)
In-network processing To perform computation in the
network itself Reducing the size of the data to be sent
higher up to other nodes Helps in reducing power consumption
Since computation is cheaper in terms of energy and power than communication
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Introduction- The major challenge in WSNs (cont.)
More and more approaches adopting in-network processing of data The creation of the routing tree
Base on the semantics of the query Energy remaining Power consumption model
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Introduction- The contributions of this paper
The introduction of a semantic and multi-criteria based routing protocol Self-optimizing
Performance improvements Network lifetime Network coverage Survivability of critical nodes
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Multi-Criteria Routing Protocol
1. Credit-Based Dynamic Route Update2. Neighborhoods and Criteria Lists3. Updating Credits4. Proportional Credit Updates
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Multi-Criteria Routing Protocol Tree structure
Traditionally, signal strength is the main factor
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Multi-Criteria Routing Protocol
Current System State(Overall)
Goal to be Satisfied by the System(eg. Network Coverage of 50%
Multi-Criteria
Algorithm(Per-node)
Multi-Criteria
Algorithm(Per-node)
Criteria Pool(Energy Remaining,
Power Consumption mode, etc.)
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Multi-Criteria Routing Protocol
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Credit-Based Dynamic Route Update The construction of the routing tree st
arts with a tree build request Initiated by the root node An identifier for the sender The query specification A value representing the current level in t
he tree level, L(sender)
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Credit-Based Dynamic Route Update (cont.)
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Credit-Based Dynamic Route Update (cont.)
For selecting a node’s parent Power consumption model per node
Watts Energy remaining at nodes
Joules The group membership information
For in-network aggregation Spatial locality Temporal locality
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Neighborhoods and Criteria Lists
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Updating Credits A set of goals are defined initially
Initially the credits are distributed uniformly
The base station updates credits among criteria Depending on the observed outcome
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Proportional Credit Updates The redistribution of credits is
done globally Checking periodically if the goal is
satisfied The credits are redistributed
proportionately The network is reconfigured
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Performance Evaluation
1. Experimental Setup and Workload
2. Network Coverage3. Network Lifetime4. Survivability of Critical Nodes
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Experimental Setup and Workload The simulator
was written using C++ and csim The credit points
were shaped from a pool of size 100 Various sensor network grid sizes
from 15 x 15 to 50 x 50
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Experimental Setup and Workload (cont.)
Some standard SQL aggregation functions were used for the experiments SUM AVERAGE MAX
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Network Coverage
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Network Coverage (cont.)
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Network Lifetime
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Survivability of Critical Nodes
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Conclusions and Future Work
A multi-criteria routing scheme Minimal overhead
Considering varied query frequencies, and varied (e.g., non-uniform) distributions of nodes
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Questions?
Thank you.Thank you.