Commenced in January 2007
Paper Count: 31100
Multipath Routing Sensor Network for Finding Crack in Metallic Structure Using Fuzzy Logic
Abstract:For collecting data from all sensor nodes, some changes in Dynamic Source Routing (DSR) protocol is proposed. At each hop level, route-ranking technique is used for distributing packets to different selected routes dynamically. For calculating rank of a route, different parameters like: delay, residual energy and probability of packet loss are used. A hybrid topology of DMPR(Disjoint Multi Path Routing) and MMPR(Meshed Multi Path Routing) is formed, where braided topology is used in different faulty zones of network. For reducing energy consumption, variant transmission ranges is used instead of fixed transmission range. For reducing number of packet drop, a fuzzy logic inference scheme is used to insert different types of delays dynamically. A rule based system infers membership function strength which is used to calculate the final delay amount to be inserted into each of the node at different clusters. In braided path, a proposed 'Dual Line ACK Link'scheme is proposed for sending ACK signal from a damaged node or link to a parent node to ensure that any error in link or any node-failure message may not be lost anyway. This paper tries to design the theoretical aspects of a model which may be applied for collecting data from any large hanging iron structure with the help of wireless sensor network. But analyzing these data is the subject of material science and civil structural construction technology, that part is out of scope of this paper.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079110Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1269
 C. Huang, M. Chatterjee, W. Cui and R. Guha, "Multipath source routing in sensor networks based on route ranking", IWDC, 2005.
 S. De, C. Qiao and H. Wu. ,"Meshed multipath routing with selective forwarding:.." WCNC, 2003.
 A. Woo, and D. Culler, "A Transmission control Scheme for Media Access in Sensor Networks" Proc. ACM Mobile com -01, Rome, Italy, pp 221-35, July 2001.
 D. Ganesan, R. Govindan, S. Shenker, D. Estrin, "Highly-Resilient, Energy-Efficient Multipath Routing in Wireless Sensor Networks", Mobile Computing and Communications Review, volume 1, Number 2.
 D. B Johnson, D.A. Maltz , "Dynamic Source Routing in Ad Hoc Networks", book "Mobile computing" by Kluwer Academic Publishers, 1996.
 A. L. Toledo and X. Wang, "Efficient multipath in sensor networks usingdiffusion and network coding", in 40th Annual Conference on Information Sciences and Systems, Princeton University, NJ, USA, March 22-24, 2006.
 H K Dass , " Engineering Mathematics", S. Chand & Co Ltd. ,2001 edition, pgs 780-90.
 K L Chung, " Elementary probability theory", Narosa Publishing house, New Delhi,1995 edition, pgs. 192-200.
 Qilian Liang and Qingchun Ren, "Energy and Mobility Aware Geographical Multipath Routing for Wireless Sensor Networks", IEEE Communications Society / WCNC 2005.
 J W Wilson, G Y Tian and S Barrans, "Residual Magnetic Field Sensing For Stress Measurement", University of Huddersfield, UK., ECNDT, 2006.
 V Singh, M L Wang and G M Lloyd, "Measuring and modeling of corrosion in structural steels using magnetoelastic sensors", University of Illinois, Chicago, USA, 2005.
 D.Acharjee, N.Sharma, "Slope based shortest path routing for wireless sensor network", ADCOM-2007, IIT- Guwahati, India.
 www.virtualacquisitionshowcase.com/docs /2008/JENTEK3-Brief.pdf :access on 24/10/08
 Saka M, Yang Ju, Daying Luo, Abc H, "Infrared and Millimeter waves,2000" conference digest, 25th International conference on vol, Issues, pgs 423-424.
 Feng Xia, W. Zhao, et. el., "Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Network", Sensors 2007, 7, pgs. 3179-3191.
 X. Cut, T. Hardian and et. el., "A Swarm-based fuzzy logic control mobile sensor network for hazardous contaminants localization", IEEE 2004.
 J.-S.R. Jang, C.-T.Sun and E.Mizutani, "Neuro-Fuzzy and Soft Computing" by PHI, Eastern economy edition, pgs. 74-79.
 Vojislav Kecman, "Learning and Soft Computing", by Pearson Education, 1st Indian reprint, 2004, pgs. 391-394.