Distributed genetic evolution in WSN

Valencia, Philip, Lindsay, Peter and Jurdak, Raja (2010). Distributed genetic evolution in WSN. In: Proceedings 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010. Proc 9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Stockholm, Sweden, (13-23). 12-16 April 2010. doi:10.1145/1791212.1791215

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Author Valencia, Philip
Lindsay, Peter
Jurdak, Raja
Title of paper Distributed genetic evolution in WSN
Conference name Proc 9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
Conference location Stockholm, Sweden
Conference dates 12-16 April 2010
Proceedings title Proceedings 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010
Journal name Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10
Place of Publication Piscataway, NJ, United States
Publisher IEEE - Computer Society
Publication Year 2010
Sub-type Fully published paper
DOI 10.1145/1791212.1791215
ISBN 9781605589886
Start page 13
End page 23
Total pages 11
Collection year 2011
Language eng
Abstract/Summary Wireless Sensor Actuator Networks (WSANs) extend wireless sensor networks through actuation capability. Designing robust logic for WSANs however is challenging since nodes can affect their environment which is already inherently complex and dynamic. Fixed (offline) logic does not have the ability to adapt to significant environmental changes and can fail under changed conditions. To address this challenge, we present In situ Distributed Genetic Programming (IDGP) as a framework for evolving logic post-deployment (online) and implement this framework on a physically deployed WSAN. To demonstrate the features of the framework including individual, cooperative and heterogeneous evolution, we apply it to two simple optimisation problems requiring sensing, communications and actuation. The experiments confirm that IDGP can evolve code to achieve a system wide objective function and is resilient to unexpected environmental changes. © 2010 ACM.
Keyword Distributed
Genetic program
Learning
Online
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Session --- Programming

 
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Created: Tue, 08 Mar 2011, 10:10:57 EST by Professor Peter Lindsay on behalf of School of Information Technol and Elec Engineering