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concerned, routers 15, 19, 22 and 26 can relay the
data sent by two sensors, and the sensors are also
used with relays. This makes these sensors and
routers consume more power, and as a result, battery
runs out sooner.
This paper proposed a positioning algorithm for
router nodes in wireless network using immune
systems techniques. The algorithm creates redundant
paths to the data collected by the sensors to be sent
to the gateway by any two or more paths, meeting
the criteria of degree of failure, the number of
retransmission by routers and number of sensors to
neighbouring routers. The algorithm allows each
criterion is enabled at a time or that they be
combined with weights. Comparison between the
results obtained in this paper and those known from
the literature are not straightforward to produce
since their criteria were not the same as ours. Our
criteria considered as a top priority the fault tolerant
aspect guaranteeing, for instance, in the case studies
presented, at least two paths to the gateway for each
sensor node. The affinity function, which works as
an objective function, is multi-objective so several
other objectives could be jointly considered.
Another alternative could be to use some sort of
workbench problem and try to compare other
methods with ours, but the definition of a suitable
workbench problem has issues which are difficult to
deal with.
REFERENCES
Zheng, J., and Lee, M. J., 2006. A Comprehensive
Performance Study of IEEE 802.15.4. Sensor Network
Operations, IEEE Press, Wiley InterScience, Chapter
4, pp. 218-237.
Costa, M. S., Amaral, J. L.M., 2010. A tool for node
positioning analysis in wireless networks for industrial
automation. In XVIII Automation Brazilian Congress.
Bonito, pp. 1521-1527, in portuguese.
Moyne, J. R. and Tilbury, D. M., 2007. The Emergence of
Industrial Control, Diagnostics, and Safety Data.
Proceedings of the IEEE, 95(1), pp. 29-47.
Cannons, J., Milstein, L. B., Zeger, K., 2008. An
Algorithm for Wireless Relay Placement. IEEE
Transactions on Wireless Communications.USA.
Nov.2006. vol.8, n°11, pp. 5564-5574.
Coelho, P. H. G., Amaral, J. L. M., Amaral, J. F. M.,
Barreira, L.F.A. and, Barros, A. V., 2013. Deploying
Nodes for Industrial Wireless Networks by Artificial
Immune Systems Techniques. In 15
th
International
Conference on Enterprise Information Systems,
Angers, France.
Gersho, A., Gray, R. M., 1992. Vector Quantization and
Signal Compression. Norwell, MA. Kluwer Academic
Publishers.
Hoffert, J., Klues, K., and Orjih, O., 2007. Configuring the
IEEE 802.15.4 MAC Layer for Single-sink Wireless
Sensor Network Applications, Technical Report
http://www.dre.vanderbilt.edu/~jhoffert/802_15_4_Ev
al_Report.pdf.
Youssef, W., and Younis, M., 2007. Intelligent Gateways
Placement for Reduced Data Latency in Wireless
Sensor Networks. In ICC’07 International Conference
on Communications, Glasgow, pp.3805-3810.
Molina, G., Alba, E., and Talbi, E. G., 2008. Optimal
Sensor Network Layout Using MultiObjective
Metaheuristics. Journal of Universal Computer
Science, Vol.15, No. 15, pp.2549-2565.
Coelho, P. H. G., Amaral, J. L. M. and Amaral, J. F. M.,
2012. Node Positioning in Industrial Plants Wireless
Networks. In WES’12 International Conference on
Communications, Rio Grande, R.S., in portuguese.
Shi, Y. , Jia,F., Hai-Tao, Y., 2009. An Improved Router
Placement Algorithm Base on Energy Efficient
Strategy for Wireless Network. In ISECS International
Colloquium on Computing, Communication, Control
and Management (CCCM2009), pp. 421- 423.
Silva, L. N. C., 2001. Immune Engineering: Development
and Application of Computational Tools Inspired by
Artificial Immune Systems, Ph. D. Thesis, State
University of Campinas, Campinas, in portuguese.
Amaral, J. L. M., 2006. Artificial Immune Systems Applied
to Fault Detection, Ph. D. Thesis, Pontifical Catholic
University of Rio de Janeiro, Rio de Janeiro, in
portuguese.
Jerne, N. K., 1974. Towards a Network Theory of the
Immune System. Ann. Immunol. (Inst. Pasteur), 125C,
pp. 373-389.
Howard, A., Mataric, M. J., and Sukhatme, G. S., 2002.
Mobile Sensor Network Deployment using Potential
Fields: A Distributed, Scalable Solution to the Area
Coverage Problem heuristics. In DARS’02, 6th
International Symposium on Distributed Autonomous
Robotics Systems, Fukuoka, Japan.
Poduri, S., Pattem, S., Krishnamachari, B.; Sukhatme, G.,
2006. Controlled Deployments of Sensor Networks.
In Press.
Howard, A., Mataric, M. J., Sukhatme, G. S., 2002.
Mobile Sensor Network Deployment using Potential
Fields: A Distributed, Scalable Solution to the Area
Coverage Problem. Proceedings of the 6th
International Symposium on Distributed Autonomous
Robotics Systems (DARS02), Fukuoka, Japan, pp. 299-
308.
Timmis, J., and Neal, M. , 2001. A Resource Limited
Artificial Immune System for Data Analysis.
Knowledge Based Systems, Vol.3-4, No. 14, pp.121-
130.
Neal, M., 2002. An Artificial Immune System for
Continuous Analysis of Time-Varying Data. In 1
st
ICARIS.
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