Route Aware Predictive Congestion Control Protocol for Wireless Sensor Networks

Congestion in wireless sensor networks (WSN) may lead to packet losses or delayed delivery of important information rendering the WSN-based monitoring or control system useless. In this paper a routing-aware predictive congestion control (RPCC) yet decentralized scheme for WSN is presented that uses a combination of a hop by hop congestion control mechanism to maintain desired level of buffer occupancy, and a dynamic routing scheme that works in concert with the congestion control mechanism to forward the packets through less congested nodes. The proposed adaptive approach restricts the incoming traffic thus preventing buffer overflow while maintaining the rate through an adaptive back-off interval selection scheme. In addition, the optimal routing scheme diverts traffic from congested nodes through alternative paths in order to balance the load in the network, alleviating congestion. This load balancing of the routes will even out the congestion level throughout the network thus increasing throughput and reducing end to end delay. Closed-loop stability of the proposed hop-by-hop congestion control is demonstrated by using the Lyapunov-based approach. Simulation results show that the proposed scheme results in reduced end-to-end delays.

[1]  Sarangapani Jagannathan End to end congestion control in high-speed networks , 2002, 27th Annual IEEE Conference on Local Computer Networks, 2002. Proceedings. LCN 2002..

[2]  Sanjay Shakkottai,et al.  Hop-by-Hop Congestion Control Over a Wireless Multi-Hop Network , 2004, IEEE/ACM Transactions on Networking.

[3]  H. Balakrishnan,et al.  Mitigating congestion in wireless sensor networks , 2004, SenSys '04.

[4]  Jagannathan Sarangapani,et al.  Neural Network Control of Nonlinear Discrete-Time Systems , 2018 .

[5]  Wen-Kuang Kuo,et al.  Enhanced backoff scheme in CSMA/CA for IEEE 802.11 , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[6]  Paramvir Bahl,et al.  Distributed fair scheduling in a wireless LAN , 2000, IEEE Transactions on Mobile Computing.

[7]  B. Pasik-Duncan,et al.  Adaptive Control , 1996, IEEE Control Systems.

[8]  Chieh-Yih Wan,et al.  CODA: congestion detection and avoidance in sensor networks , 2003, SenSys '03.

[9]  Frank L. Lewis,et al.  Robust implicit self tuning regulator/MRAC convergence and stability , 1995, Proceedings of Tenth International Symposium on Intelligent Control.

[10]  Frank L. Lewis,et al.  Robust implicit self-tuning regulator: Convergence and stability , 1996, Autom..

[11]  Tsern-Huei Lee,et al.  Performance evaluations for hybrid IEEE 802.11b and 802.11g wireless networks , 2005, PCCC 2005. 24th IEEE International Performance, Computing, and Communications Conference, 2005..

[12]  QueueingJon,et al.  WF 2 Q : Worst-case Fair Weighted Fair , 1996 .

[13]  Hui Zhang,et al.  WF/sup 2/Q: worst-case fair weighted fair queueing , 1996, Proceedings of IEEE INFOCOM '96. Conference on Computer Communications.