Computing the State of Specknets: further analysis of an innate immune-inspired model.

Davoudani, Despina; Hart, Emma; Paechter, Ben

Abstract

Specknets consist of hundreds of miniature devices, which are each capable of processing data and communicating wirelessly across short distances. Such networks, with their great complexity, pose considerable challenges for engineers due to the unreliability and scarce resources of individual devices. Their limitations make it difficult to apply traditional engineering approaches. In this paper, we describe a model inspired by the dendritic cells of the innate immune system; often overlooked in artificial immune systems, dendritic cells possess a unique ability to scout the body environment and then present an integrated picture of the internal state of the body to the adaptive system. We adopt a model, inspired by this approach, to sense the state of a Specknet and provide experimental results to show that useful information can be gathered from the Specknet in order to determine local states. Experiments are conducted using realistic random topologies in a simulation environment, in a scenario which models sensing temperature changes.

Citation

Davoudani, D., Hart, E., & Paechter, B. (2008). Computing the State of Specknets: further analysis of an innate immune-inspired model. In P. Bentley, D. Lee, & S. Jung (Eds.), Artificial Immune Systems, 7th International Conference, ICARIS 2008, Phuket, Thailand, August 2008, Proceedings (95-106). https://doi.org/10.1007/978-3-540-85072-4

• Start Date: Aug 10, 2008
• End Date: Aug 13, 2008
• Publication Date: 2008
• Deposit Date: Jan 18, 2010
• Peer Reviewed: Peer Reviewed
• Volume: 5132
• Pages: 95-106
• Book Title: Artificial Immune Systems, 7th International Conference, ICARIS 2008, Phuket, Thailand, August 2008, Proceedings
• ISBN: 978-3-540-85071-7
• DOI: https://doi.org/10.1007/978-3-540-85072-4
• Keywords: Specknet; speckled computing; innate immune system; artificial immune systems; minute semi-conductor grains; wireless sensor networks
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/3496
• Publisher URL: http://www.springerlink.com/content/9175578u2l22478r/?p=0f3417182b6d42b7a77dd705530b7d52&pi=1