Position-Relative Identities in the Internet of Things: An Evolutionary GHT Approach

The Internet of Things (IoT) will result in the deployment of many billions of wireless embedded systems creating interactive pervasive environments. It is envisaged that devices will cooperate to provide greater system knowledge than the sum of its parts. In an emergency situation, the flow of data...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 1; no. 5; pp. 497 - 507
Main Authors: Attwood, Andrew, Lamb, David J., Abuelmaatti, Omar
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Embedded systems
Genetic algorithms
Global Positioning System
Internet of Things
Peer-to-peer computing
Routing protocols
Wireless communication
Wireless sensor networks
evolutionary computing and genetic algorithms
load balancing
wireless sensor networks
Data-centric storage
information dispersal
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:The Internet of Things (IoT) will result in the deployment of many billions of wireless embedded systems creating interactive pervasive environments. It is envisaged that devices will cooperate to provide greater system knowledge than the sum of its parts. In an emergency situation, the flow of data across the IoT may be disrupted, giving rise to a requirement for machine-to-machine interaction within the remaining ubiquitous environment. Geographic hash tables (GHTs) provide an efficient mechanism to support fault-tolerant rendezvous communication between devices. However, current approaches either rely on devices being equipped with a GPS or being manually assigned an identity. This is unrealistic when the majority of these systems will be located inside buildings and will be too numerous to expect manual configuration. Additionally, when using GHT as a distributed data store, imbalance in the topology can lead to storage and routing overhead. This causes unfair work load, exhausting limited power supplies as well as causing poor data redundancy. To deal with these issues, we propose an approach that balances graph-based layout identity assignment, through the application of multifitness genetic algorithms. Our experiments show through simulation that our multifitness evolution technique improves on the initial graph-based layout, providing devices with improved balance and reachability metrics.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2014.2353194