research-article

Leveraging Fog Analytics for Context-Aware Sensing in Cooperative Wireless Sensor Networks

Authors:

Kriti Bhargava,

Diarmuid McSweeney,

William DonnellyAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 15, Issue 2

Article No.: 23, Pages 1 - 35

https://doi.org/10.1145/3306147

Published: 25 March 2019 Publication History

Abstract

In this article, we present a fog computing technique for real-time activity recognition and localization on-board wearable Internet of Things(IoT) devices. Our technique makes joint use of two light-weight analytic methods—Iterative Edge Mining(IEM) and Cooperative Activity Sequence-based Map Matching(CASMM). IEM is a decision-tree classifier that uses acceleration data to estimate the activity state. The sequence of activities generated by IEM is analyzed by the CASMM method for identifying the location. The CASMM method uses cooperation between devices to improve accuracy of classification and then performs map matching to identify the location. We evaluate the performance of our approach for activity recognition and localization of animals. The evaluation is performed using real-world acceleration data of cows collected during a pilot study at a Dairygold-sponsored farm in Kilworth, Ireland. The analysis shows that our approach can achieve a localization accuracy of up to 99%. In addition, we exploit the location-awareness of devices and present an event-driven communication approach to transmit data from the IoT devices to the cloud. The delay-tolerant communication facilitates context-aware sensing and significantly improves energy profile of the devices. Furthermore, an array-based implementation of IEM is discussed, and resource assessment is performed to verify its suitability for device-based implementation.

References

[1]

T. Wang, Z. Peng, J. Liang, S. Wen, Md Z. A. Bhuiyan, Y. Cai, and J. Cao. 2016. Following targets for mobile tracking in wireless sensor networks. ACM Trans. Sen. Netw. 12, 4 (2016).

Digital Library

[2]

CERES TAG. 2018. What is ceres tag. Retrieved from cerestag.com.

[3]

T. Wark, D. Swain, C. Crossman, P. Valencia, G. Bishop-Hurley, and R. Handcock. 2009. Sensor and actuator networks: Protecting environmentally sensitive areas. IEEE Perv. Comput. 8, 1 (2009), 30--36.

Digital Library

[4]

A. Maddumabandara, H. Leung, and M. Liu. 2015. Experimental evaluation of indoor localization using wireless sensor networks. IEEE Sens. J. 15, 9 (2015), 5228--5237.

[5]

T. Stoyanova, F. Kerasiotis, C. Antonopoulos, and G. Papadopoulos. 2014. RSS-based localization for wireless sensor networks in practice. In Proceedings of the 9th International Symposium on Communication Systems, Networks Digital Sign (CSNDSP’14). 134--139.

[6]

SmartBow. 2014. User manual: E093 SmartBow eartag. Retrieved from smartbow.com. (accessed Oct. 2017).

[7]

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. 2012. Fog computing and its role in the internet of things. In Proceedings of the 1st Edition of the MCC Workshop on Mobile Cloud Computing. 13--16.

Digital Library

[8]

B. Zhou, Q. Li, Q. Mao, W. Tu, and X. Zhang. 2015. Activity sequence-based indoor pedestrian localization using smartphones. IEEE Trans. Hum.-Mach. Syst. 45, 5 (2015), 562--574.

[9]

DAIRYMASTER. 2013. What is moomonitor+. Retrieved from moomonitor.dairymaster.com.

[10]

HerdInsights. 2015. How HerdInsights works. Retrieved from herdinsights.com.

[11]

E. I. Gaura, J. Brusey, M. Allen, R. Wilkins, D. Goldsmith, and R. Rednic. 2013. Edge mining the internet of things. IEEE Sens. J. 13, 10 (2013), 3816--3825.

[12]

K. Bhargava, S. Ivanov, C. Kulatunga, and W. Donnelly. 2017. Fog-enabled WSN system for animal behavior analysis in precision dairy. In Proceedings of the International Conference on Computing, Networking and Communications (ICNC’17). 504--510.

[13]

L. Mainetti, L. Patrono, A. Secco, and I. Sergi. 2016. An IoT-aware AAL system for elderly people. In Proceedings of the International Multidisciplinary Conference on Computer and Energy Science (SpliTech’16). 1--6.

[14]

X. Huang, Y. Li, Y. Wang, X. Chen, Y. Xiao, and L. Zhang. 2018. CTS: A cellular-based trajectory tracking system with GPS-level accuracy. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4, Article 140 (2018), 140:1--140:29 pages.

Digital Library

[15]

R. Mohamed, H. Aly, and M. Youssef. 2017. Accurate real-time map matching for challenging environments. IEEE Trans. Intell. Transport. Syst. 18, 4 (2017), 847--857.

Digital Library

[16]

X. Li, D. Zhang, Q. Lv, J. Xiong, S. Li, Y. Zhang, and H. Mei. 2017. IndoTrack: Device-free indoor human tracking with commodity Wi-Fi. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 72 (2017), 72:1--72:22 pages.

Digital Library

[17]

B. Xie, K. Chen, G. Tan, M. Lu, Y. Liu, J. Wu, and T. He. 2016. LIPS: A light intensity--based positioning system for indoor environments. ACM Trans. Sen. Netw. 12, 4 (2016).

Digital Library

[18]

Q. Yuan and I. M. Chen. 2013. 3-D localization of human based on an inertial capture system. IEEE Trans. Robot. 29, 3 (2013), 806--812.

Digital Library

[19]

D. Ayllón, H. A. Sánchez-Hevia, R. Gil-Pita, M. U. Manso, and M. R. Zurera. 2016. Indoor blind localization of smartphones by means of sensor data fusion. IEEE Trans. Instr. Meas. 65, 4 (2016), 783--794.

[20]

Z. Chen, Q. Zhu, and Y. C. Soh. 2016. Smartphone inertial sensor-based indoor localization and tracking with iBeacon corrections. IEEE Trans. Industr. Inform. 12, 4 (2016), 1540--1549.

[21]

OpenFog Consortium Architecture Working Group. 2017. OpenFog reference architecture for fog computing. Retrieved from openfogconsortium.org.

[22]

S. Sarkar, S. Chatterjee, and S. Misra. 2015. Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans. Cloud Comput. PP, 99 (2015).

[23]

R. Tan, G. Xing, B. Liu, J. Wang, and X. Jia. 2012. Exploiting data fusion to improve the coverage of wireless sensor networks. IEEE/ACM Trans. Netw. 20, 2 (2012), 450--462.

Digital Library

[24]

S. Gomes Soares, A. F. da Rocha, T. M. G. de A. Barbosa, and R. A. de Matos Araújo. 2010. Embedding a neural network into WSN furniture. In Proceedings of the 10th International Conference on Hybrid Intelligent Systems. 219--222.

[25]

D. Goldsmith and J. Brusey. 2010. The Spanish Inquisition protocol: Model-based transmission reduction for wireless sensor networks. In IEEE Sensors. 2043--2048.

[26]

E. I. Gaura, J. Brusey, and R. Wilkins. 2011. Bare necessities—Knowledge-driven WSN design. In Proceedings of the IEEE Concerence on Sensors 66--70.

[27]

R. Rednic, E. Gaura, and J. Brusey. 2009. ClassAct: Accelerometer-based real-time activity classifier. In Proceedings of the Sensors 8 Instrumentation KTN: Wireless Sensing Demonstrator Showcase (WiSIG’09). 1--6.

[28]

M. Abo-Zahhad, O. Amin, M. Farrag, and A. Ali. 2014. Survey on energy consumption models in wireless sensor networks. Open Trans. Wireless Comm. 1, 1 (2014), 63--79.

[29]

C. J. Rutten, A. G. Velthuis, W. Steeneveld, and H. Hogeveen. 2013. Invited review: Sensors to support health management on dairy farms. J. Dairy Sci. 96, 4 (2013), 1928--1952.

[30]

M. Alsaaod, C. Römer, J. Kleinmanns, K. Hendriksen, S. Rose-Meierhöfer, L. Plümer, and W. Büscher. 2012. Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior. Appl. Animal Behav. Sci. 142, 3 (2012), 134--141.

[31]

M. Trotter et al. 2012. Monitoring and managing landscape variability in grazing systems. In Proceedings of the 15th Precision Agriculture Symposium. 42--48.

[32]

J. Werner et al. 2017. Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows. J. Neurosci. Meth., Elsevier (2017).

[33]

Advanticsys. 2017. MTM-CM5000-MSP. Retrieved from advanticsys.com.

[34]

Invensense. 2014. MPU-9255 product specification (rev. 1.0). Retrieved from stanford.edu.

[35]

Wiki. 2015. TinyOS documentation wiki. Retrieved from tinyos.stanford.edu/tinyos-wiki.

[36]

Raspberry Pi Foundation. 2016. Raspberry Pi 2 model B. Retrieved from raspberrypi.org.

[37]

Md. B. Uddin and C. Castelluccia. 2010. Toward clock skew--based wireless sensor node services. In Proceedings of the 5th ICST Wireless Internet Conference (WICON’10). 1--9.

[38]

K. Shinghal, A. Noor, N. Srivastava, and R. Singh. 2011. Power measurements of wireless sensor network node. Int. J. Comput. Eng. Sci. 1, 1 (2011), 8--13.

[39]

Antônio Dâmaso, Davi Freitas, Nelson Rosa, Bruno Silva, and Paulo Maciel. 2013. Evaluating the power consumption of wireless sensor network applications using models. Sensors 13, 3 (2013), 3473--3500.

[40]

David Gay, Philip Levis, Robert von Behren, Matt Welsh, Eric Brewer, and David Culler. 2003. The nesC language: A holistic approach to networked embedded systems. SIGPLAN Not. 38, 5 (May 2003), 1--11.

Digital Library

[41]

Antônio Dâmaso, Davi Freitas, and Nelson Rosa. 2013. nesc2cpn. Retrieved from github.com/sensor2model-group/nesc2cpn.

Cited By

Besler BMojabi PLasemiimeni ZMurphy JWang ZBaker RPearson JFear E(2024)Scoping review of precision technologies for cattle monitoringSmart Agricultural Technology10.1016/j.atech.2024.1005969(100596)Online publication date: Dec-2024
https://doi.org/10.1016/j.atech.2024.100596
Aral ADe Maio VBrandic I(2022)ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor NetworksIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.30498507:4(761-773)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2021.3049850
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Index Terms

Leveraging Fog Analytics for Context-Aware Sensing in Cooperative Wireless Sensor Networks

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 15, Issue 2

May 2019

339 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3311822

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2019 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]

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 25 March 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 July 2018

Published in TOSN Volume 15, Issue 2

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Cited By

Besler BMojabi PLasemiimeni ZMurphy JWang ZBaker RPearson JFear E(2024)Scoping review of precision technologies for cattle monitoringSmart Agricultural Technology10.1016/j.atech.2024.1005969(100596)Online publication date: Dec-2024
https://doi.org/10.1016/j.atech.2024.100596
Aral ADe Maio VBrandic I(2022)ARES: Reliable and Sustainable Edge Provisioning for Wireless Sensor NetworksIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.30498507:4(761-773)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TSUSC.2021.3049850
Peng YLiu CLiu SLiu YWu Y(2022)SmartTRO: Optimizing topology robustness for Internet of Things via deep reinforcement learning with graph convolutional networksComputer Networks10.1016/j.comnet.2022.109385218(109385)Online publication date: Dec-2022
https://doi.org/10.1016/j.comnet.2022.109385
Chen NQiu TDaneshmand MWu D(2021)Robust Networking: Dynamic Topology Evolution Learning for Internet of ThingsACM Transactions on Sensor Networks10.1145/344693717:3(1-23)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3446937
Divan MSanchez-Reynoso M(2020)Optimizing Data Transmission from IoT devices through Weighted Online Data Changing DetectorsAdvances in Data Science and Adaptive Analysis10.1142/S2424922X20410016Online publication date: 13-Aug-2020
https://doi.org/10.1142/S2424922X20410016
O'Grady MLangton DO'Hare G(2019)Edge computing: A tractable model for smart agriculture?Artificial Intelligence in Agriculture10.1016/j.aiia.2019.12.001Online publication date: Dec-2019
https://doi.org/10.1016/j.aiia.2019.12.001

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