Fuzzy logic rate adjustment controls using a circuit breaker for persistent congestion in wireless sensor networks

Wireless Networks - Tập 26 - Trang 3603-3627 - 2020
Phet Aimtongkham1, Sovannarith Heng1,2, Paramate Horkaew3, Tri Gia Nguyen1, Chakchai So-In1
1Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand
2Department of Computer Science, Faculty of Science, Royal University of Phnom Penh, Phnom Penh, Cambodia
3School of Computer Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand

Tóm tắt

Congestion control is necessary for enhancing the quality of service in wireless sensor networks (WSNs). With advances in sensing technology, a substantial amount of data traversing a WSN can easily cause congestion, especially given limited resources. As a consequence, network throughput decreases due to significant packet loss and increased delays. Moreover, congestion not only adversely affects the data traffic and transmission success rate but also excessively dissipates energy, which in turn reduces the sensor node and, hence, network lifespans. A typical congestion control strategy was designed to address congestion due to transient events. However, on many occasions, congestion was caused by repeated anomalies and, as a consequence, persisted for an extended period. This paper thus proposes a congestion control strategy that can eliminate both types of congestion. The study adopted a fuzzy logic algorithm for resolving congestion in three key areas: optimal path selection, traffic rate adjustment that incorporates a momentum indicator, and an optimal timeout setting for a circuit breaker to limit persistent congestion. With fuzzy logic, decisions can be made efficiently based on probabilistic weights derived from fitness functions of congestion-relevant parameters. The simulation and experimental results reported herein demonstrate that the proposed strategy outperforms state-of-the-art strategies in terms of the traffic rate, transmission delay, queue utilization, and energy efficiency.

Tài liệu tham khảo

Intel. A Guide to the Internet of Things Infographic. https://www.intel.com/content/www/us/en/internet-of-things/infographics/guide-to-iot.html. Accessed 5 Jan 2019. 2017 Roundup of Internet of Things Forecasts. https://www.gartner.com/newsroom/id/3598917. Accessed 11 Jan 2019. Ovsthus, K., & Kristensen, L. M. (2014). An industrial perspective on wireless sensor networks—A survey of requirements, protocols, and challenges. IEEE Communications Surveys & Tutorials,16(3), 1391–1412. Mahmood, M. A., Seah, W. K., & Welch, I. (2015). Reliability in wireless sensor networks: A survey and challenges ahead. Computer Networks,79, 166–187. Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks,67, 104–122. Khan, J. A., Qureshi, H. K., & Iqbal, A. (2015). Energy management in wireless sensor networks: A survey. Computers & Electrical Engineering,41, 159–176. Erdelj, M., Król, M., & Natalizio, E. (2017). Wireless sensor networks and multi-UAV systems for natural disaster management. Computer Networks,124, 72–86. Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications,60, 192–219. Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys & Tutorials,19(2), 828–854. Ghaffari, A. (2015). Congestion control mechanisms in wireless sensor networks: A survey. Journal of Network and Computer Applications,52, 101–115. Hasan, M. Z., Al-Rizzo, H., & Al-Turjman, F. (2017). A survey on multipath routing protocols for QoS assurances in real-time wireless multimedia sensor networks. IEEE Communications Surveys & Tutorials,19(3), 1424–1456. Shah, S. A., Nazir, B., & Khan, I. A. (2017). Congestion control algorithms in wireless sensor networks: Trends and opportunities. Journal of King Saud University-Computer and Information Sciences,29(3), 236–245. Khan, I., Belqasmi, F., Glitho, R., Crespi, N., Morrow, M., & Polakos, P. (2016). Wireless sensor network virtualization: A survey. IEEE Communications Surveys & Tutorials,18(1), 553–576. Lara, R., Benítez, D., Caamaño, A., Zennaro, M., & Rojo-Álvarez, J. L. (2015). On real-time performance evaluation of volcano-monitoring systems with wireless sensor networks. IEEE Sensors Journal,15(6), 3514–3523. Harrison, D. C., Seah, W. K., & Rayudu, R. (2016). Rare event detection and propagation in wireless sensor networks. ACM Computing Surveys (CSUR),48(4), 58. Xu, C., Zhao, J., & Muntean, G. M. (2016). Congestion control design for multipath transport protocols: A survey. IEEE Communications Surveys & Tutorials,18(4), 2948–2969. Pham, Q. V., & Hwang, W. J. (2017). Network utility maximization-based congestion control over wireless networks: A survey and potential directives. IEEE Communications Surveys & Tutorials,19(2), 1173–1200. Zhou, D., Song, W., & Cheng, Y. (2013). A study of fair bandwidth sharing with AIMD-based multipath congestion control. IEEE Wireless Communications Letters,2(3), 299–302. Ding, W., Tang, L., & Ji, S. (2016). Optimizing routing based on congestion control for wireless sensor networks. Wireless Networks,22(3), 915–925. Wan, C. Y., Eisenman, S. B., & Campbell, A. T. (2003). CODA: congestion detection and avoidance in sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems (pp. 266–279). ACM. Bentaleb, A., Taani, B., Begen, A. C., Timmerer, C., & Zimmermann, R. (2018). A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE Communications Surveys & Tutorials. Herrero, R. (2017). Integrating HEC with circuit breakers and multipath RTP to improve RTC media quality. Telecommunication Systems,64(1), 211–221. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal,16(1), 137–144. Tao, L. Q., & Yu, F. Q. (2010). ECODA: enhanced congestion detection and avoidance for multiple class of traffic in sensor networks. IEEE Transactions on Consumer Electronics,56(3), 1387–1394. Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., & Silva, F. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking (ToN),11(1), 2–16. Gholipour, M., Haghighat, A. T., & Meybodi, M. R. (2017). Hop-by-Hop Congestion Avoidance in wireless sensor networks based on genetic support vector machine. Neurocomputing,223, 63–76. Narawade, V., & Kolekar, U. D. (2018). ACSRO: Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks. Alexandria Engineering Journal,57(1), 131–145. Singh, K., Singh, K., & Aziz, A. (2018). Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Computer Networks,138, 90–107. Jaiswal, S., & Yadav, A. (2013). Fuzzy based adaptive congestion control in wireless sensor networks. In Contemporary computing (IC3), 2013 sixth international conference on (pp. 433–438). IEEE. Sonmez, C., Incel, O. D., Isik, S., Donmez, M. Y., & Ersoy, C. (2014). Fuzzy-based congestion control for wireless multimedia sensor networks. EURASIP Journal on Wireless Communications and Networking,2014(1), 63. Hatamian, M., Bardmily, M. A., Asadboland, M., Hatamian, M., & Barati, H. (2016). Congestion-aware routing and fuzzy-based rate controller for wireless sensor networks. Radioengineering,25(1), 114–123. Callaway, E., Gorday, P., Hester, L., Gutierrez, J. A., Naeve, M., Heile, B., et al. (2002). Home networking with IEEE 802.15. 4: A developing standard for low-rate wireless personal area networks. IEEE Communications Magazine,40(8), 70–77. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications,1(4), 660–670. Logambigai, R., & Kannan, A. (2016). Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks,22(3), 945–957. Crossbow Technology Inc. (2006) MiCaZ Datasheet, Document Part No. 6020-0060-04. Low, R. K. Y., & Tan, E. (2016). The role of analyst forecasts in the momentum effect. International Review of Financial Analysis,48, 67–84. Gao, L., Han, Y., Li, S. Z., & Zhou, G. (2018). Market intraday momentum. Journal of Financial Economics,129, 394–414. Marshall, B. R., Nguyen, N. H., & Visaltanachoti, N. (2017). Time series momentum and moving average trading rules. Quantitative Finance,17(3), 405–421. Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing,36, 534–551. Vu, V. H., Mashal, I., & Chung, T. Y. (2017). A novel bandwidth estimation method based on MACD for DASH. KSII Transactions on Internet & Information Systems, 11(3). Kua, J., Armitage, G., & Branch, P. (2017). A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Communications Surveys & Tutorials,19(3), 1842–1866. Fall, K., & Varadhan, K. (2007) The network simulator NS-2. https://www.isi.edu/nsnam/ns. Accessed 15 Jan 2019. Manna Research Group, Mannasim framework. (2010). https://www.mannasim.dcc.ufmg.br/index.htm. Accessed 15 Jan 2019. Eaton, J. W., Bateman, D., & Hauberg, S. (2013). Gnu octave. GNU Octave.