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An Adaptive Congestion Alleviating Protocol for Healthcare Applications in Wireless Body Sensor Networks: Learning Automata Approach | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 3، دوره 44، شماره 1، تیر 2012، صفحه 31-41 اصل مقاله (687.31 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2012.38 | ||
| نویسندگان | ||
| Nazbanoo Farzaneh Bahalgardi1؛ Mohammad Hossein Yaghmaee؛ Donald Adjeroh2 | ||
| 1Nazbanoo Farzaneh. Computer Engineering Department, Ferdowsi University of Mashhad, Azadi Square, Mashhad, IRAN. (email: Farzaneh@stu-mail.um.ac.ir) | ||
| 2Donald Adjeroh, Full Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506 (email: don@csee.wvu.edu) | ||
| چکیده | ||
| Wireless Body Sensor Networks (WBSNs) involve a convergence of biosensors, wireless communication and networks technologies. WBSN enables real-time healthcare services to users. Wireless sensors can be used to monitor patients’ physical conditions and transfer real time vital signs to the emergency center or individual doctors. Wireless networks are subject to more packet loss and congestion. To alleviate congestion, the source transmission rate and node arrival rate should be controlled. In this paper, we propose Learning based Congestion Control Protocol (LCCP) for wireless body sensor networks. LCCP joins active queue management and rate adjustment mechanism to alleviate congestion. The proposed system is able to discriminate different physiological signals and assign them different priorities. Thus, it would be possible to provide better quality of service for transmitting highly important vital signs. The simulation results confirm that the proposed protocol improves system throughput and reduces delay and packet dropping. We also evaluate the performance of the AQM mechanism with no rate adjustment mechanism to show the advantage of using both AQM and rate adjustment mechanism together. | ||
| کلیدواژهها | ||
| Active Queue Management؛ Congestion Control؛ Learning Automata؛ Transport protocol؛ Wireless Body Sensor Network | ||
| عنوان مقاله [English] | ||
| An Adaptive Congestion Alleviating Protocol for Healthcare Applications in Wireless Body Sensor Networks: Learning Automata Approach | ||
| چکیده [English] | ||
| Wireless Body Sensor Networks (WBSNs) involve a convergence of biosensors, wireless communication and networks technologies. WBSN enables real-time healthcare services to users. Wireless sensors can be used to monitor patients’ physical conditions and transfer real time vital signs to the emergency center or individual doctors. Wireless networks are subject to more packet loss and congestion. To alleviate congestion, the source transmission rate and node arrival rate should be controlled. In this paper, we propose Learning based Congestion Control Protocol (LCCP) for wireless body sensor networks. LCCP joins active queue management and rate adjustment mechanism to alleviate congestion. The proposed system is able to discriminate different physiological signals and assign them different priorities. Thus, it would be possible to provide better quality of service for transmitting highly important vital signs. The simulation results confirm that the proposed protocol improves system throughput and reduces delay and packet dropping. We also evaluate the performance of the AQM mechanism with no rate adjustment mechanism to show the advantage of using both AQM and rate adjustment mechanism together. | ||
| کلیدواژهها [English] | ||
| Active Queue Management, Congestion Control, Learning Automata, Transport protocol, Wireless Body Sensor Network | ||
| مراجع | ||
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[1] H. L. Ren, M. Q. H. Meng, and X. J. Chen, “Physiological Information acquisition through wireless biomedical sensor networks”, in Proc. 2005 IEEE International Conference on Information Acquisition. [2] G.Zhou, J. Liu, C. Wan, M. Yarvis, and J. Stankovic, “BodyQoS: Adaptive and Radio-Agnostic QoS for Body Sensor Networks”, in Proc. 2008 Infocom. [3] M.H. Yaghmaee, and D. Adjeroh, “Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks”. Computer network Journal, pp. 1798-1811,2009. [4] J. Paek, and R. Govindan, ”RCRT: Rate-Controlled Reliable Transport for Wireless Sensor Networks”, in Proc. 2007 ACM Conference on Embedded Networked Sensor Systems (Sensys). [5] M.H. Yaghmaee, and D. Adjeroh, “A Novel Congestion Control Protocol for Vital Signs Monitoring in Wireless Biomedical Sensor Networks,” in Proc. 2010 WCNC, pp. 1-6. [6] S. Misra, V. Tiwari, and M.S. Obaidat, “LACAS: Learning Automata-Based Congestion Avoidance Scheme for Healthcare Wireless Sensor Networks,” IEEE Journal on Selected Areas in Communications, vol. 27, no. 4, pp. 466-479, 2009. [7] R. Gunasundari, R. Arthi, and S. Priya, “An Efficient Congestion Avoidance Scheme for Mobile Healthcare Wireless Sensor Networks,” Int. J. Advanced Networking and Applications, vol. 2, no,3, pp. 693-698, 2010.
[8] S. Kim, “Study on the Dynamic QoS Provisioning Scheme for U-Healthcare over WMSAN,” Journal of Convergence Information Technology, vol. 6, no. 2, pp. 1-8, 2011. [9] F. Hu, Y. Xiao, and Q. Hao, “Congestion-aware, loss-resilient biomonitoring sensor networking for mobile health applications.” IEEE J. Selected. Areas Communication, vol. 27, no. 4, pp. 450–465, 2009. [10] N. Farzaneh, M.H. Yaghmaee, “Joint Active Queue Management and Congestion Control Protocol for Healthcare Applications in Wireless Body Sensor Networks”, In Procc. 9th International Conference on Smart Homes and Health Telematics(ICOST), doi: 10.1007/978-3-642-21535-3_12, 2011. [11] K.S. Narendra, and M.A. Thathachar, “Learning Automaton: An Introduction,” Prentice Hall, 1989. [12] http://www.opnet.com/ [13] CC2430 Preliminary Data Sheet (rev. 2.1) SWRS036F, Jun. 2007, Chipcon Products from Texas Instruments [14] H. Sabbineni, K. Chakrabarty, ”Location-aided floooding: An energy efffcient data dissemination protocol for wireless sensor networks,” IEEE Trans. on Computers, vol. 51, no. 1, pp. 36-46, 2005. | ||
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