Nanonetwork Minimum Energy coding

Affiliation auteurs!!!! Error affiliation !!!!
TitreNanonetwork Minimum Energy coding
Type de publicationConference Paper
Year of Publication2014
AuteursZainuddin MAgus, Dedu E, Bourgeois J
EditorApduhan BO, Zheng Y, Nakamoto Y, Thulasiraman P, Ning H, Sun Y
Conference Name2014 IEEE 11TH INTL CONF ON UBIQUITOUS INTELLIGENCE AND COMPUTING AND 2014 IEEE 11TH INTL CONF ON AUTONOMIC AND TRUSTED COMPUTING AND 2014 IEEE 14TH INTL CONF ON SCALABLE COMPUTING AND COMMUNICATIONS AND ITS ASSOCIATED WORKSHOPS
PublisherInst Elect & Elect Engineers; IEEE Comp Soc; IEEE Tech Comm Scalable Comp; Northwestern Polytechn Univ; STIKOM Bali; StFX Univ; Kyushu Sangyo Univ
Conference Location345 E 47TH ST, NEW YORK, NY 10017 USA
ISBN Number978-1-4799-7646-1
Mots-clésEnergy Consumption, Huffman coding, Nanonetwork
Résumé

Nanotechnology is generally considered a technology of the future. It promises to have many implications in various fields, and create revolutionary methods in some circumstances. Due to their size, nanodevices have limited capacities in terms of energy, computation and transmission among others. Networking them allows to increase their effectiveness, and also their communication range. However, data transmission consumes power, which is very precious in such devices. As such, communication between nanodevices in the Terahertz band have been investigated using low-power Time Spread-On Off Keying (TS-OOK) modulation. A characteristic of this modulation is that energy is required only for transmitting bit 1, since bit 0 is ``transmitted'' as silence (no energy). We exploit this property in the Nanonetwork Minimum Energy coding we propose in this paper. This coding reduces the number of 1s in data transmitted by source by encoding more often used symbols with fewer 1s. As such, it yields energy efficiency, but also reduces absorption noise and interference between devices, and increases information capacity. Results of this algorithm with various types of real files show notable improvements. It is able to reduce the energy up to 100%, depending on probabilities of 0 and 1 in input data.

DOI10.1109/UIC-ATC-ScalCom.2014.117