research-article

BOND: Exploring Hidden Bottleneck Nodes in Large-scale Wireless Sensor Networks

Authors:

Xiaolong ZhengAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 17, Issue 2

Article No.: 13, Pages 1 - 21

https://doi.org/10.1145/3439956

Published: 12 March 2021 Publication History

Abstract

In a large-scale wireless sensor network, hundreds and thousands of sensors sample and forward data back to the sink periodically. In two real outdoor deployments GreenOrbs and CitySee, we observe that some bottleneck nodes strongly impact other nodes’ data collection and thus degrade the whole network performance. To figure out the importance of a node in the process of data collection, system manager is required to understand interactive behaviors among the parent and child nodes. So we present a management tool BOND (BOttleneck Node Detector), which explains the concept of Node Dependence to characterize how much a node relies on each of its parent nodes, and also models the routing process as a Hidden Markov Model and then uses a machine learning approach to learn the state transition probabilities in this model. Moreover, BOND can predict the network dataflow if some nodes are added or removed to avoid data loss and flow congestion in network redeployment. We implement BOND on real hardware and deploy it in an outdoor network system. The extensive experiments show that Node Dependence indeed help to explore the hidden bottleneck nodes in the network, and BOND infers the Node Dependence with an average accuracy of more than 85%.

References

[1]

Y. Liu, Y. He, M. Li, J. Wang, K. Liu, L. Mo, W. Dong, Z. Yang, M. Xi, J. Zhao, et al. 2011. Does wireless sensor network scale? A measurement study on GreenOrbs. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’11).

[2]

G. Tolle, J. Polastre, R. Szewczyk, D. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, et al. 2005. A macroscope in the redwoods. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’05).

Digital Library

[3]

G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh. 2006. Fidelity and yield in a volcano monitoring sensor network. In Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI’06).

[4]

L. Mo, Y. He, Y. Liu, J. Zhao, S. J. Tang, X. Y. Li, and G. Dai. 2009. Canopy closure estimates with greenorbs: Sustainable sensing in the forest. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’09).

[5]

M. Li and Y. Liu. 2009. Underground coal mine monitoring with wireless sensor networks. ACM Trans. Sens. Netw. 5, 2 (2009), 10.

Digital Library

[6]

P. Vicaire, T. He, Q. Cao, T. Yan, G. Zhou, L. Gu, L. Luo, R. Stoleru, J. A. Stankovic, and T. F. Abdelzaher. 2009. Achieving long-term surveillance in vigilnet. ACM Trans. Sens. Networks. 5, 1 (2009), 9.

[7]

N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. 2004. A wireless sensor network for structural monitoring. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’04).

[8]

O. Gnawali, R. Fonseca, K. Jamieson, D. Moss, and P. Levis. 2009. Collection tree protocol. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’09).

[9]

C. Schurgers and M. B. Srivastava. 2001. Energy efficient routing in wireless sensor networks. In Proceedings of the IEEE IEEE/AFCEA Military Communications Conference (MILCOM’01).

[10]

N. Ramanathan, K. Chang, R. Kapur, L. Girod, E. Kohler, and D. Estrin. 2005. Sympathy for the sensor network debugger. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’05).

[11]

K. Liu, Q. Ma, X. Zhao, and Y. Liu. 2011. Self-diagnosis for large scale wireless sensor networks. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’11).

[12]

E. Magistretti, O. Gurewitz, and E. Knightly. 2010. Inferring and mitigating a link’s hindering transmissions in managed 802.11 wireless networks. In Proceedings of the ACM Annual International Conference on Mobile Computing and Networking (MobiCom’10).

[13]

Mohammad Abdur Razzaque, Chris J. Bleakley, and Simon Dobson. 2013. Compression in wireless sensor networks: A survey and comparative evaluation. ACM Trans. Sens. Netw. 10, 1 (2013), 5:1–5:44.

Digital Library

[14]

L. R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 2 (1989), 257–286.

[15]

L. E. Baum and J. A. Eagon. 1967. An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bull. Am. Math. Soc 73, 3 (1967), 360–363.

[16]

X. Mao, X. Miao, Y. He, T. Zhu, J. Wang, W. Dong, X. LI, and Y. Liu. 2012. Citysee: Urban CO2 monitoring with sensors. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’12).

[17]

Beshr Al Nahas and Olaf Landsiedel. 2017. Competition: Towards low-power wireless networking that survives interference with minimal latency. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN’17). 268–269.

[18]

Peilin Zhang, Olaf Landsiedel, and Oliver E. Theel. 2017. MOR: Multichannel opportunistic routing for wireless sensor networks. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN’17). 36–47.

[19]

J. Zhao and R. Govindan. 2003. Understanding packet delivery performance in dense wireless sensor networks. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’03).

[20]

Wei Dong, Yunhao Liu, Yuan He, Tong Zhu, and Chun Chen. 2014. Measurement and analysis on the packet delivery performance in a large-scale sensor network. IEEE/ACM Trans. Netw. 22, 6 (2014), 1952–1963.

Digital Library

[21]

K. Srinivasan, M. Jain, J. I. Choi, T. Azim, E. S. Kim, P. Levis, and B. Krishnamachari. 2010. The κ factor: Inferring protocol performance using inter-link reception correlation. In Proceedings of the ACM Annual International Conference on Mobile Computing and Networking (MobiCom’10).

[22]

K. Srinivasan, M. A. Kazandjieva, S. Agarwal, and P. Levis. 2008. The β-factor: Measuring wireless link burstiness. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’08).

[23]

Roman Lim, Reto Da Forno, Felix Sutton, and Lothar Thiele. Competition: Robust flooding using back-to-back synchronous transmissions with channel-hopping. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN’17). 270–271.

[24]

Zhichao Cao, Daibo Liu, Jiliang Wang, and Xiaolong Zheng. 2017. Chase: Taming concurrent broadcast for flooding in asynchronous duty cycle networks. IEEE/ACM Trans. Netw. 25, 5 (2017), 2872–2885.

Digital Library

[25]

Zhichao Cao, Jiliang Wang, Daibo Liu, Xin Miao, Qiang Ma, and Xufei Mao. 2018. Chase++: Fountain-enabled fast flooding in asynchronous duty cycle networks. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’18).

Digital Library

[26]

Wan Du, Jansen Christian Liando, Huanle Zhang, and Mo Li. 2015. When pipelines meet fountain: Fast data dissemination in wireless sensor networks. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys’15). 365–378.

Digital Library

[27]

Manjunath Doddavenkatappa, Mun Choon Chan, and Ben Leong. 2013. Splash: Fast data dissemination with constructive interference in wireless sensor networks. In Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI’13). 269–282.

Digital Library

[28]

J. Yang, M.L. Soffa, L. Selavo, and K. Whitehouse. 2007. Clairvoyant: A comprehensive source-level debugger for wireless sensor networks. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’07).

[29]

Q. Cao, T. Abdelzaher, J. Stankovic, K. Whitehouse, and L. Luo. 2008. Declarative tracepoints: A programmable and application independent debugging system for wireless sensor networks. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’08).

[30]

M. M. H. Khan, H. Ahmadi, G. Dogan, K. Govindan, R. K. Ganti, T. Brown, J. Han, Mohapatra P., and Abdelzaher T. F.2011. DustDoctor: A self-healing sensor data collection system. In Proceedings of the ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN’11).

[31]

B. Chen, G. Peterson, G. Mainland, and M. Welsh. 2008. Livenet: Using passive monitoring to reconstruct sensor network dynamics. In Proceedings of the IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS’08).

[32]

Q. Ma, K. Liu, X. Miao, and Y. Liu. 2012. Sherlock is around: Detecting network failures with local evidence fusion. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’12).

[33]

L. Fu, P. Cheng, Y. Gu, J. Chen, and T. He. 2013. Minimizing charging delay in wireless rechargeable sensor networks. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’13).

[34]

Q. Ma, K. Liu, X. Xiao, Z Cao, and Y. Liu. 2013. Link scanner: Faulty link detection for wireless sensor networks. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’13).

[35]

Nguyen Quoc Viet Hung, Hoyoung Jeung, and Karl Aberer. 2013. An evaluation of model-based approaches to sensor data compression. IEEE Trans. Knowl. Data Eng. 25, 11 (2013), 2434–2447.

Digital Library

[36]

Razvan Musaloiu-Elefteri, Chieh-Jan Mike Liang, and Andreas Terzis. 2008. Koala: Ultra-low power data retrieval in wireless sensor networks. In Proceedings of the ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN’08).

[37]

Nicolas Burri, Pascal von Rickenbach, and Roger Wattenhofer. 2007. Dozer: Ultra-low power data gathering in sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN’07).

[38]

Timofei Istomin, Amy L. Murphy, Gian Pietro Picco, and Usman Raza. 2016. Data Prediction + Synchronous Transmissions = Ultra-low Power Wireless Sensor Networks. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’16).

Digital Library

[39]

Timofei Istomin, Matteo Trobinger, Amy L. Murphy, and Gian Pietro Picco. 2018. Interference-resilient ultra-low power aperiodic data collection. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN’18).

Digital Library

Cited By

Xu YDang FLiu KZhu ZChen XWang XMiao XZhao H(2024)BEANet: An Energy-efficient BLE Solution for High-capacity Equipment Area NetworkACM Transactions on Sensor Networks10.1145/364128020:3(1-23)Online publication date: 17-Jan-2024
https://dl.acm.org/doi/10.1145/3641280
Alizadeh RSavaria YNerguizian C(2024)Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation SystemIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33367955(55-69)Online publication date: 2024
https://doi.org/10.1109/OJITS.2023.3336795
Said O(2023)A bandwidth control scheme for reducing the negative impact of bottlenecks in IoT environments: Simulation and performance evaluationInternet of Things10.1016/j.iot.2023.10068221(100682)Online publication date: Apr-2023
https://doi.org/10.1016/j.iot.2023.100682
Show More Cited By

Index Terms

BOND: Exploring Hidden Bottleneck Nodes in Large-scale Wireless Sensor Networks
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
2. Networks
  1. Network properties
    1. Network reliability

Recommendations

Distributed Bottleneck Node Detection in Wireless Sensor Network
CIT '10: Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology

Wireless sensor networks (WSNs) have been considered as a promising method for reliably monitoring both civil and military environments under hazardous or dangerous conditions. Due to the special property and difference from the traditional wireless ...
Integrated Connectivity and Coverage Techniques for Wireless Sensor Networks
MobiWac '16: Proceedings of the 14th ACM International Symposium on Mobility Management and Wireless Access

A wireless sensor network (WSN) consists of a group of energy-constrained sensor nodes with the ability of both sensing and communication, which can be deployed in a field of interesting (FoI) for detecting or monitoring some special events and then ...
Movement-assisted sensor redeployment scheme for network lifetime increase
MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems

Sensor deployment in mobile sensor networks has received significant attention in recent years. Goals during sensor deployment include improving coverage, achieving load balance, and prolonging the network lifetime. To improve the initial deployment, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 17, Issue 2

May 2021

296 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3447946

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 12 March 2021

Accepted: 01 November 2020

Revised: 01 November 2020

Received: 01 September 2019

Published in TOSN Volume 17, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Key R&D Program of China
NSFC

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
148
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)1

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xu YDang FLiu KZhu ZChen XWang XMiao XZhao H(2024)BEANet: An Energy-efficient BLE Solution for High-capacity Equipment Area NetworkACM Transactions on Sensor Networks10.1145/364128020:3(1-23)Online publication date: 17-Jan-2024
https://dl.acm.org/doi/10.1145/3641280
Alizadeh RSavaria YNerguizian C(2024)Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation SystemIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33367955(55-69)Online publication date: 2024
https://doi.org/10.1109/OJITS.2023.3336795
Said O(2023)A bandwidth control scheme for reducing the negative impact of bottlenecks in IoT environments: Simulation and performance evaluationInternet of Things10.1016/j.iot.2023.10068221(100682)Online publication date: Apr-2023
https://doi.org/10.1016/j.iot.2023.100682
Cao ZZheng XMa QMiao X(2022)COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle NetworksACM Transactions on Sensor Networks10.1145/357016319:3(1-21)Online publication date: 3-Nov-2022
https://dl.acm.org/doi/10.1145/3570163
Elhoseny MLakhan ARashid AMohammed MAbdulkareem K(2022)Underwater Sensor Multi-Parameter Scheduling for Heterogenous Computing NodesACM Transactions on Sensor Networks10.1145/347651318:3(1-23)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3476513
Li CGuo HTong SZeng XCao ZZhang MYan QXiao LWang JLiu Y(2021)NELoRaProceedings of the 19th ACM Conference on Embedded Networked Sensor Systems10.1145/3485730.3485928(56-68)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3485730.3485928

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents