Abstract
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.
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References
Castalia, a simulator for wsns, http://castalia.npc.nicta.com.au/index.php
Cc2420 - single-chip 2.4 ghz ieee 802.15.4 compliant and zigbee ready rf transceiver, http://www.ti.com/product/cc2420
Ieee standard for local and metropolitan area networks–part 15.4: Low-rate wireless personal area networks (lr-wpans), http://standards.ieee.org/about/get/802/802.15.html
Bonabeau, A.B.E.: Scale free networks. Scientific American, 60–69 (2003)
Bougard, B., Catthoor, F., Daly, D.C., Chandrakasan, A., Dehaene, W.: Energy efficiency of the ieee 802.15.4 standard in dense wireless microsensor networks: Modeling and improvement perspectives. In: Conf. on Design, Automation and Test in Europe, pp. 196–201 (2005)
Das, S., Koduru, P., Cai, X., Welch, S., Sarangan, V.: The gene regulatory network: an application to optimal coverage in sensor networks. In: 10th Annual Conf. on Genetic and Evolutionary Computation, pp. 1461–1468 (2008)
Dressler, F., Akan, O.: Bio-inspired networking: from theory to practice. IEEE Communications Magazine 48(11), 176–183 (2010)
Ghosh, P., Mayo, M., Chaitankar, V., Habib, T., Perkins, E., Das, S.: Principles of genomic robustness inspire fault-tolerant wsn topologies: A network science based case study. In: PERCOM 2011 Workshops, pp. 160–165 (2011)
Guelzim, N., Bottani, S., Bourgine, P., Kepes, F.: Topological and causal structure of the yeast transcriptional regulatory network. Nature Genetics 31(1), 60–63 (2002)
Hajiaghayi, M., Immorlica, N., Mirrokni, V.: Power optimization in fault-tolerant topology control algorithms for wireless multi-hop networks. IEEE/ACM Transactions on Networking 15(6), 1345–1358 (2007)
Hou, J.C., Li, N., Stojmenovi, I.: Topology Construction and Maintenance in Wireless Sensor Networks, pp. 311–341. John Wiley & Sons, Inc. (2005)
de Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 9(1), 67–103 (2002)
Kamapantula, B.K., Abdelzaher, A., Ghosh, P., Mayo, M., Perkins, E., Das, S.K.: Performance of wireless sensor topologies inspired by e. coli genetic networks. In: IEEE Int’l Workshop on PerSeNS, pp. 308–313 (2012)
Laszka, A., Buttyn, L., Szeszlr, D.: Designing robust network topologies for wireless sensor networks in adversarial environments. Pervasive and Mobile Computing (2012)
Macneil, L.T., Walhout, A.J.: Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Research (March 2011)
Marbach, D.: Evolutionary reverse engineering of gene networks. Ph.D. thesis, Lausanne (2009), http://library.epfl.ch/theses/?nr=4503
Markham, A., Trigoni, N.: Discrete gene regulatory networks (dgrns): A novel approach to configuring sensor networks. In: INFOCOM, pp. 1–9 (2010)
Prill, R.J., Iglesias, P.A., Levchenko, A.: Dynamic properties of network motifs contribute to biological network organization. Public Library of Science (2005)
Ren, H., Meng, M.H.: Biologically inspired approaches for wireless sensor networks. In: Int’l Conf. on Mechatronics and Automation, pp. 762–768 (June 2006)
Schaffter, T., Marbach, D., Floreano, D.: Genenetweaver: In silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27, 2263–2270 (2011)
Thomas, S., Alvis, B.: Current approaches to gene regulatory network modelling. BioMed Central (2007)
Wightman, P., Labrador, M.: A3: A topology construction algorithm for wireless sensor networks. In: IEEE GLOBECOM, pp. 1–6 (December 2008)
Yu, X., Huang, W., Lan, J., Qian, X.: A novel virtual force approach for node deployment in wireless sensor network, pp. 359–363 (2012)
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Nazi, A., Raj, M., Di Francesco, M., Ghosh, P., Das, S.K. (2013). Robust Deployment of Wireless Sensor Networks Using Gene Regulatory Networks. In: Frey, D., Raynal, M., Sarkar, S., Shyamasundar, R.K., Sinha, P. (eds) Distributed Computing and Networking. ICDCN 2013. Lecture Notes in Computer Science, vol 7730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35668-1_14
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DOI: https://doi.org/10.1007/978-3-642-35668-1_14
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