ABSTRACT
The state of the art congestion control algorithms for wireless sensor networks respond to coarse-grained feedback regarding available capacity in the network with an additive increase multiplicative decrease mechanism to set source rates. Providing precise feedback is challenging in wireless networks because link capacities vary with traffic on interfering links. We address this challenge by applying a receiver capacity model that associates capacities with nodes instead of links, and use it to develop and implement the first explicit and precise distributed rate-based congestion control protocol for wireless sensor networks --- the wireless rate control protocol (WRCP). Apart from congestion control, WRCP has been designed to achieve lexicographic max-min fairness. Through extensive experimental evaluation on the USC Tutornet wireless sensor network testbed, we show that WRCP offers substantial improvements over the state of the art in flow completion times as well as in end-to-end packet delays.
- http://www.moteiv.com.Google Scholar
- http://www.tinyos.net/tinyos-2.x/doc/html/tep123.html.Google Scholar
- Bertsekas and Galagher. Data networks. Prentic Hall. Google ScholarDigital Library
- G. Bianchi. Performance Analysis of the IEEE 802.11 Distributed Coordination Function. IEEE JSAC, 18:535--547, March 2000. Google ScholarDigital Library
- F. Bonomi and KW Fendick. The Rate-based Flow Control Framework for the Available Bit Rate ATM service. Network, IEEE, 9(2):25--39, 1995. Google ScholarDigital Library
- M. Chiang. Balancing Transport and Physical Layers in Wireless Multihop Networks: Jointly optimal congestion control and power control. IEEE JSAC, 23(1):104--116, 2005. Google ScholarDigital Library
- C. Curescu and S. Nadjm-Tehrani. Price/utility-based Optimized Resource Allocation in Wireless Ad-hoc Networks. IEEE SECON 2005.Google ScholarCross Ref
- N. Dukkipati, M. Kobayashi, R. Zhang-Shen, and N. McKeown. Processor Sharing Flows in the Internet. IWQoS, 2005. Google ScholarDigital Library
- C. T. Ee and R. Bajcsy. Congestion Control and Fairness for Many-to-One Routing in Sensor Networks. ACM Sensys, 2004. Google ScholarDigital Library
- B. Hull, K. Jamieson, and H. Balakrishnan. Techniques for Mitigating Congestion in Sensor Networks. ACM Sensys, 2004. Google ScholarDigital Library
- S. Kalyanaraman, R. Jain, S. Fahmy, R. Goyal, and B. Vandalore. The ERICA switch algorithm for ABR traffic management in ATM networks. IEEE/ACM Transactions on Networking (TON), 8(1):87--98, 2000. Google ScholarDigital Library
- D. Katabi, M. Handley, and C. Rohrs. Congestion Control for High Bandwidth-Delay Product Networks. ACM SIGCOMM, 2002. Google ScholarDigital Library
- Embedded Networks Laboratory. http://testbed.usc.edu, 2007.Google Scholar
- H. Ohsaki, M. Murata, H. Suzuki, C. Ikeda, and H. Miyahara. Rate-based congestion control for ATM networks. ACM SIGCOMM Computer Communication Review, 25(2):60--72, 1995. Google ScholarDigital Library
- J. Paek and R. Govindan. RCRT: Rate-controlled Reliable Transport for Wireless Sensor Networks. ACM Sensys, 2007. Google ScholarDigital Library
- S. Rangwala, R. Gummadi, R. Govindan, and K. Psounis. Interference-Aware Fair Rate Control in Wireless Sensor Networks. ACM SIGCOMM, 2006. Google ScholarDigital Library
- S. Rangwala, A. Jindal, K. Y. Jang, K. Psounis, and R. Govindan. Understanding congestion control in multi-hop wireless mesh networks. In ACM MobiCom, pages 291--302, 2008. Google ScholarDigital Library
- Y. Sankarasubramaniam, O. B. Akan, and I. F. Akyildiz. ESRT: Event-to-Sink Reliable Transport in Wireless Sensor Networks. ACM Mobi-Hoc, 3:177--188, 2003. Google ScholarDigital Library
- D. Son, B. Krishnamachari, and J. Heidemann. Experimental Analysis of Concurrent Packet Transmissions in Low-Power Wireless Networks. ACM Sensys, 2006. Google ScholarDigital Library
- A. Sridharan and B. Krishnamachari. TDMA scheduling feasibility of the Receiver Capacity Model. WiOpt, 2009.Google Scholar
- C. Y. Wan, S. B. Eisenman, and A. T. Campbell. CODA: Congestion Detection and Avoidance in Sensor Networks. ACM Sensys, 2003. Google ScholarDigital Library
- X. Wang and K. Kar. Cross-layer Rate Control in Multi-hop Wireless Networks with Random Access. IEEE JSAC, February 2005.Google Scholar
- A. Woo and D. E. Culler. A Transmission Control scheme for Media Access in Sensor Networks. ACM MobiCom, pages 221--235, 2001. Google ScholarDigital Library
Index Terms
- Explicit and precise rate control for wireless sensor networks
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