A Bio-Inspired Approach to Design Robust and Energy-Efficient Communication Network Topologies
With the advent of Internet of Things (IoT), sensor networks are being utilized widely for data collection and dissemination. In order to ensure seamless communication, it is imperative to design topologically robust and energy-efficient networks. In this work, we introduce bio-inspired approaches for network topology construction based on the innate graph robustness of a biological network called the Gene Regulatory Network (GRN). We briefly discuss some of the topological properties of GRN, followed by its application in the design of wireless sensor network, disaster response network as well as a distributed event sensing and data collection framework for IoT and smart city applications. Finally, we demonstrate some graph experimental results that suggest that our proposed bio-inspired solutions exhibit greater robustness compared to existing graph topologies.
S. Roy and S. K. Das, "A Bio-Inspired Approach to Design Robust and Energy-Efficient Communication Network Topologies," Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications Workshops ( 2019, Kyoto, Japan), pp. 449 - 450, Institute of Electrical and Electronics Engineers (IEEE), Mar 2019.
The definitive version is available at https://doi.org/10.1109/PERCOMW.2019.8730691
2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019 (2019: Mar. 11-15, Kyoto, Japan)
Center for High Performance Computing Research
Keywords and Phrases
Data acquisition; Energy efficiency; Internet of things; Ubiquitous computing; Wireless sensor networks, Bio-inspired approach; Biological networks; Energy efficient communications; Energy efficient networks; Gene regulatory networks; Internet of Things (IOT); Seamless communication; Topological properties, Topology
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Mar 2019