Robust Deployment of Wireless Sensor Networks Using Gene Regulatory Networks

Sensor nodes in a Wireless Sensor Network (WSN) are responsible for sensing the environment and propagating the collected data in the network. The communication between sensor nodes may fail due to different factors, such as hardware failures, energy depletion, temporal variations of the wireless channel and interference. To maximize efficiency, the sensor network deployment must be robust and resilient to such failures. One effective solution to this problem has been inspired by Gene Regulatory Networks (GRNs). Owing to millions of years of evolution, GRNs display intrinsic properties of adaptation and robustness, thus making them suitable for dynamic network environments. In this paper, we exploit real biological gene structures to deploy wireless sensor networks, called bio-inspired WSNs. Exhaustive structural analysis of the network and experimental results demonstrate that the topology of bio-inspired WSNs is robust, energy-efficient, and resilient to node and link failures.

[1]  Denis C. Daly,et al.  Energy efficiency of the IEEE 802.15.4 standard in dense wireless microsensor networks: modeling and improvement perspectives , 2005, Design, Automation and Test in Europe.

[2]  Nicole Immorlica,et al.  Power optimization in fault-tolerant topology control algorithms for wireless multi-hop networks , 2007, TNET.

[3]  Xiangyu Yu,et al.  A Novel Virtual Force Approach for Node Deployment in Wireless Sensor Network , 2012, 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems.

[4]  Levente Buttyán,et al.  Designing robust network topologies for wireless sensor networks in adversarial environments , 2013, Pervasive Mob. Comput..

[5]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[6]  Agathoniki Trigoni,et al.  Discrete Gene Regulatory Networks (dGRNs): A Novel Approach to Configuring Sensor Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[7]  Miguel A. Labrador,et al.  A3: A Topology Construction Algorithm for Wireless Sensor Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[8]  Özgür B. Akan,et al.  Bio-inspired networking: from theory to practice , 2010, IEEE Communications Magazine.

[9]  Sajal K. Das,et al.  Performance of wireless sensor topologies inspired by E. coli genetic networks , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications Workshops.

[10]  Alvis Brazma,et al.  Current approaches to gene regulatory network modelling , 2007, BMC Bioinformatics.

[11]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[12]  Hongliang Ren,et al.  Biologically Inspired Approaches for Wireless Sensor Networks , 2006, 2006 International Conference on Mechatronics and Automation.

[13]  P. Bourgine,et al.  Topological and causal structure of the yeast transcriptional regulatory network , 2002, Nature Genetics.

[14]  Dario Floreano,et al.  GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods , 2011, Bioinform..

[15]  Daniel Marbach,et al.  Evolutionary reverse engineering of gene networks , 2009 .

[16]  Ivan Stojmenovic,et al.  Topology Construction and Maintenance in Wireless Sensor Networks , 2005, Handbook of Sensor Networks.

[17]  Sajal K. Das,et al.  Principles of genomic robustness inspire fault-tolerant WSN topologies: A network science based case study , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[18]  Xinye Cai,et al.  The gene regulatory network: an application to optimal coverage in sensor networks , 2008, GECCO '08.

[19]  Andre Levchenko,et al.  Dynamic Properties of Network Motifs Contribute to Biological Network Organization , 2005, PLoS biology.

[20]  L. MacNeil,et al.  Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. , 2011, Genome research.