Qiang Ma
Zhichao Cao
Skip Abstract Section
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%.
- 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).Google Scholar
- 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).Google ScholarDigital Library
- 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).Google Scholar
- 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).Google Scholar
- M. Li and Y. Liu. 2009. Underground coal mine monitoring with wireless sensor networks. ACM Trans. Sens. Netw. 5, 2 (2009), 10.Google ScholarDigital Library
- 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.Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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.Google ScholarDigital Library
- L. R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 2 (1989), 257–286.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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).Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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).Google Scholar
- 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.Google ScholarDigital Library
- 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).Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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).Google ScholarDigital Library
- 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. DOI:http://dx.doi.org/10.1145/2809695.2809721Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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.Google ScholarDigital Library
- 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).Google Scholar
- 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).Google ScholarCross Ref
- 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).Google ScholarDigital Library
- 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).Google ScholarDigital Library
Index Terms
- BOND: Exploring Hidden Bottleneck Nodes in Large-scale Wireless Sensor Networks
Recommendations
Distributed Bottleneck Node Detection in Wireless Sensor Network
CIT '10: Proceedings of the 2010 10th IEEE International Conference on Computer and Information TechnologyWireless 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 AccessA 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 systemsSensor 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