Multi-Criteria Routing in Pervasive Environment with Sensors Santhanakrishnan, G., Li, Q., Beaver,...

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

Outline

Introduction Multi-Criteria Routing Protocol Performance Evaluation Conclusions and Future Work

Introduction

1. Wireless sensor networks (WSNs)

2. The major challenge in WSNs3. The contributions of this paper

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

Introduction- The major challenge in WSNs

Power conservation Communication costs Network processing

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

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

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

Multi-Criteria Routing Protocol

1. Credit-Based Dynamic Route Update2. Neighborhoods and Criteria Lists3. Updating Credits4. Proportional Credit Updates

Multi-Criteria Routing Protocol Tree structure

Traditionally, signal strength is the main factor

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.)

Multi-Criteria Routing Protocol

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)

Credit-Based Dynamic Route Update (cont.)

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

Neighborhoods and Criteria Lists

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

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

Performance Evaluation

1. Experimental Setup and Workload

2. Network Coverage3. Network Lifetime4. Survivability of Critical Nodes

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

Experimental Setup and Workload (cont.)

Some standard SQL aggregation functions were used for the experiments SUM AVERAGE MAX

Network Coverage

Network Coverage (cont.)

Network Lifetime

Survivability of Critical Nodes

Conclusions and Future Work

A multi-criteria routing scheme Minimal overhead

Considering varied query frequencies, and varied (e.g., non-uniform) distributions of nodes

Questions?

Thank you.Thank you.