Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1109/SECON52354.2021.9491582guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
research-article

COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle Networks

Published: 06 July 2021 Publication History

Abstract

For energy constrained wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding is a promising approach to improve the performance of network flooding. In concurrent flooding, a key challenge is how to select a set of concurrent senders to improve both flooding speed and energy efficiency. We observe that selecting neither large nor small number of concurrent senders can achieve the optimal performance in different deployments. In this paper, we propose COFlood (Concurrent Opportunistic Flooding), a practical and effective concurrent flooding protocol in asynchronous duty cycle networks. The basic idea is based on an energy-efficient flooding tree, COFlood opportunistically selects extra concurrent senders that can speed up network flooding. First, COFlood constructs an energy-efficient flooding tree in distributed manner. The non-leaf nodes are selected as senders and they can cover the entire network with low energy consumption. Moreover, we find that exploiting both early wakeup nodes and long lossy links can speed up the flooding tree based network flooding. Then, COFlood develops a light-weight method to select the nodes that meet the conditions of these two opportunities as opportunistic senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.

References

[1]
X. Mao, X. Miao, Y. He, X.-Y. Li, and Y. Liu, “Citysee: Urban co 2 monitoring with sensors,” in Proceedings of INFOCOM, 2012.
[2]
L. Mo, Y. He, Y. Liu, J. Zhao, S.-J. Tang, X.-Y. Li, and G. Dai, “Canopy closure estimates with greenorbs: sustainable sensing in the forest,” in Proceedings of Sensys, 2009.
[3]
X. Zheng, Z. Cao, J. Wang, Y. He, and Y. Liu, “Zisense: towards interference resilient duty cycling in wireless sensor networks,” in Proceedings of Sensys, 2014.
[4]
F. Ferrari, M. Zimmerling, L. Thiele, and O. Saukh, “Efficient network flooding and time synchronization with glossy,” in Proceedings of IPSN, 2011.
[5]
J. Wang, Z. Cao, X. Mao, and Y. Liu, “Sleep in the dins: Insomnia therapy for duty-cycled sensor networks,” in Proceedings of INFOCOM, 2014.
[6]
X. Zheng, J. Wang, W. Dong, Y. He, and Y. Liu, “Bulk data dissemination in wireless sensor networks: analysis, implications and improvement,” IEEE Transactions on Computers, vol. 65, no. 5, pp. 1428–1439, 2016.
[7]
Z. Cao, D. Liu, J. Wang, and X. Zheng, “Chase: Taming concurrent broadcast for flooding in asynchronous duty cycle networks,” IEEE/ACM Transactions on Networking, 2017.
[8]
M. Doddavenkatappa, M. C. Chan, and A. L. Ananda, “Indriya: A lowcost, 3d wireless sensor network testbed,” in Proceedings of TRIDENTCOM, 2011.
[9]
O. Landsiedel, E. Ghadimi, S. Duquennoy, and M. Johansson, “Low power, low delay: opportunistic routing meets duty cycling,” in Proceedings of IPSN, 2012.
[10]
W. Dong, Y. Liu, C. Wang, X. Liu, C. Chen, and J. Bu, “Link quality aware code dissemination in wireless sensor networks,” in Proceedings of ICNP, 2011.
[11]
L. Huang and S. Setia, “Cord: Energy-efficient reliable bulk data dissemination in sensor networks,” in Proceedings of INFOCOM, 2008.
[12]
J. W. Hui and D. Culler, “The dynamic behavior of a data dissemination protocol for network programming at scale,” in Proceedings of Sensys, 2004.
[13]
V. Naik, A. Arora, P. Sinha, and H. Zhang, “Sprinkler: A reliable and energy efficient data dissemination service for extreme scale wireless networks of embedded devices,” IEEE Transactions on Mobile Computing, vol. 6, no. 7, pp. 777–789, 2007.
[14]
G. Tolle and D. E. Culler, “Design of an application-cooperative management system for wireless sensor networks.” in Proceedings of EWSN, 2005.
[15]
T. Zhu, Z. Zhong, T. He, and Z.-L. Zhang, “Exploring link correlation for efficient flooding in wireless sensor networks.” in Proceedings of NSDI, 2010.
[16]
S. Guo, L. He, Y. Gu, B. Jiang, and T. He, “Opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links,” IEEE Transactions on Computers, vol. 63, no. 11, pp. 2787–2802, 2014.
[17]
Y. Sun, O. Gurewitz, S. Du, L. Tang, and D. B. Johnson, “Adb: an efficient multihop broadcast protocol based on asynchronous duty-cycling in wireless sensor networks,” in Proceedings of Sensys, 2009.
[18]
F. Wang and J. Liu, “On reliable broadcast in low duty-cycle wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 11, no. 5, pp. 767–779, 2012.
[19]
M. Doddavenkatappa, M. C. Chan, and B. Leong, “Splash: fast data dissemination with constructive interference in wireless sensor networks,” in Proceedings of NSDI, 2013.
[20]
Y. Wang, Y. He, X. Mao, Y. Liu, and X.-Y. Li, “Exploiting constructive interference for scalable flooding in wireless networks,” IEEE/ACM Transactions on Networking, vol. 21, no. 6, pp. 1880–1889, 2013.
[21]
F. Ferrari, M. Zimmerling, L. Mottola, and L. Thiele, “Low-power wireless bus,” in Proceedings of Sensys, 2012.
[22]
C. Sarkar, R. V. Prasad, R. T. Rajan, and K. Langendoen, “Sleeping beauty: Efficient communication for node scheduling,” in Proceedings of MASS, 2016.
[23]
O. Landsiedel, F. Ferrari, and M. Zimmerling, “Chaos: Versatile and efficient all-to-all data sharing and in-network processing at scale,” in Proceedings of Sensys, 2013.
[24]
Z. Cao, J. Wang, D. Liu, X. Miao, Q. Ma, and X. Mao, “Chase++: Fountain-enabled fast flooding in asynchronous duty cycle networks,” in Proceedings of INFOCOM, 2018.

Cited By

View all
  • (2024)A Liquidity Analysis System for Large-scale Video Streams in the OilfieldACM Transactions on Sensor Networks10.1145/364922220:3(1-22)Online publication date: 13-Apr-2024
  • (2023)Understanding Concurrent Transmissions: The Impact of Carrier Frequency Offset and RF Interference on Physical Layer PerformanceACM Transactions on Sensor Networks10.1145/360443020:1(1-39)Online publication date: 10-Jun-2023

Index Terms

  1. COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle Networks
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image Guide Proceedings
        2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
        Jul 2021
        347 pages

        Publisher

        IEEE Press

        Publication History

        Published: 06 July 2021

        Qualifiers

        • Research-article

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)0
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 22 Dec 2024

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)A Liquidity Analysis System for Large-scale Video Streams in the OilfieldACM Transactions on Sensor Networks10.1145/364922220:3(1-22)Online publication date: 13-Apr-2024
        • (2023)Understanding Concurrent Transmissions: The Impact of Carrier Frequency Offset and RF Interference on Physical Layer PerformanceACM Transactions on Sensor Networks10.1145/360443020:1(1-39)Online publication date: 10-Jun-2023

        View Options

        View options

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media