Biology-Inspired Approach for Communal Behavior in Massively Deployed Sensor Networks

<p>Research in wireless sensor networks has accelerated rapidly in recent years. The promise of ubiquitous control of the physical environment opens the way for new applications that will redefine the way we live and work. Due to the small size and low cost of sensor devices, visionaries promise smart systems enabled by deployment of massive numbers of sensors working in concert. To date, most of the research effort has concentrated on forming ad hoc networks under centralized control, which is not scalable to massive deployments. This thesis proposes an alternative approach based on models inspired by biological systems and reports significant results based on this new approach. This perspective views sensor devices as autonomous <em>organisms</em> in a <em>community</em> interacting as part of an <em>ecosystem</em> rather than as nodes in a computing network. The networks that result from this design make local decisions based on local information in order for the network to achieve global goals, thus we must engineer for emergent behavior in wireless sensor networks. First we implemented a simulator based on cellular automata to be used in algorithm development and assessment. Then we developed efficient algorithms to exploit emergent behavior for finding the average of distributed values, synchronizing distributed clocks, and conducting distributed binary voting. These algorithms are shown to be convergent and efficient by analysis and simulation. Finally, an extension of this perspective is used and demonstrated to provide significant progress on the noise abatement problem for jet aircraft. Using local information and actions, optimal impedance values for an acoustic liner are determined <em>in situ</em> providing the basis for an adaptive noise abatement system that provides superior noise reduction compared with current technology and previous research efforts.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2008

Author dc:creator

• Jones, Kennie H.

Contributors dc:contributor

• Stephan Olariu
• Larry Wilson
• Irwin Levinstein
• Chunsheng Xin
• Douglas Nark

• Biologically-inspired networks
• Biomimetics
• Communal behavior
• Massive deployments
• Sensor networks
• Computer Sciences
• Electrical and Computer Engineering
• Systems Engineering

Statement dc:rights

• <p>In Copyright. URI: <a href="http://rightsstatements.org/vocab/InC/1.0/">http://rightsstatements.org/vocab/InC/1.0/</a> This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).</p>

Identifier

9781109033892

OAI identifier oai:identifier

oai:digitalcommons.odu.edu:computerscience_etds-1058