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2015 | 5 | 1273--1278
Tytuł artykułu

Influence of on-Device Measurement Analysis on Energy Efficiency in Machine-to-Machine Systems

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Machine-to-Machine Communication (M2M) en- ables communication between heterogeneous devices without hu- man intervention. It is considered to be a key enabler technology for the concept of Internet of Things (IoT) and Cyber Physical Systems (CPS). With M2M's integration with Wireless Sensor Networks (WSN), information from different kinds sensors can be obtained. In order to discover useful knowledge from sensor data, various data mining techniques need to be applied. Due to the development of microprocessors on end devices in M2M system which collect data from sensors, data processing can also be executed on those devices. However, since end devices are often battery powered, energy consumption when running those algorithms needs to be taken into account. In this paper we implement an algorithm in M2M system, on Libelium Waspmote devices, which detects temperature plummeting in an indoor space. Afterwards, energy consumption of Waspmote devices is analyzed for two cases: when algorithm is executed on-device and when algorithm is executed on gateway or on back-end system.(original abstract)
Słowa kluczowe
Rocznik
Tom
5
Strony
1273--1278
Opis fizyczny
Twórcy
autor
  • University of Zagreb
autor
  • University of Zagreb
autor
  • University of Zagreb
Bibliografia
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  • J. Zhang, L. Shan, H. Hu, and Y. Yang, "Mobile cellular networks and wireless sensor networks: toward convergence," Communications Magazine, IEEE, vol. 50, no. 3, pp. 164-169, 2012. doi: http://dx.doi.org/10.1109/MCOM.2012.6163597
  • J. Wan, H. Yan, Q. Liu, K. Zhou, R. Lu, and D. Li, "Enabling cyberphysical systems with machine-to-machine technologies," Int. J. Ad Hoc Ubiquitous Comput., vol. 13, no. 3/4, pp. 187-196, 2013. doi: http://dx.doi.org/10.1504/IJAHUC.2013.055454
  • J. Hller, V. Tsiatsis, C. Mulligan, S. Karnouskos, S. Avesand, and D. Boyle, From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence. Academic Press, 2014. ISBN 978-0-12-407684-6
  • F. Chen, P. Deng, J. Wan, D. Zhang, A. V. Vasilakos, and X. Rong, "Data mining for the internet of things: Literature review and challenges," International Journal of Distributed Sensor Networks, in press.
  • N. K. Suryadevara and S. C. Mukhopadhyay, Smart Homes: Design, Implementation and Issues. Springer, 2015. ISBN 978-3-319-13556-4
  • I. Stojmenovic, "Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems," Internet of Things Journal, IEEE, vol. 1, no. 2, pp. 122-128, 2014. doi: http://dx.doi.org/10.1109/JIOT.2014.2311693
  • I. Stojmenovic and S. Wen, "The fog computing paradigm: Scenarios and security issues," in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, vol. 2. IEEE, 2014, pp. 1-8. doi: http://dx.doi.org/10.15439/2014F503
  • R. Bruns, J. Dunkel, H. Masbruch, and S. Stipkovic, "Intelligent M2M: Complex event processing for machine-to-machine communication," Expert Systems with Applications, vol. 42, no. 3, pp. 1235 - 1246, 2015. doi: http://dx.doi.org/10.1016/j.eswa.2014.09.005
  • A. Mahmood, K. Shi, S. Khatoon, and M. Xiao, "Data mining techniques for wireless sensor networks: A survey," International Journal of Distributed Sensor Networks, vol. 2013, pp. 1-24, 2013. doi: http://dx.doi.org/10.1155/2013/406316
  • M. Abu Alsheikh, S. Lin, D. Niyato, and H.-P. Tan, "Machine learning in wireless sensor networks: Algorithms, strategies, and applications," Communications Surveys Tutorials, IEEE, vol. 16, no. 4, pp. 1996-2018, 2014. doi: http://dx.doi.org/10.1109/COMST.2014.2320099
  • oneM2M, "M2M Functional Architecture," Technical Specification, draft, 2015. [Online]. Available: http://www.onem2m.org/images/files/deliverables/TS-0001- Functional Architecture-V1 6 1.pdf
  • ETSI, "Machine-to-Machine communications (M2M); Functional architecture," Technical Specification ETSI TS 102690 V2.1.1, 2013. [Online]. Available: http://www.etsi.org/deliver/etsi ts/102600 102699/102690/02.01.01 60/ ts 102690v020101p.pdf
  • S. Kitagami, M. Yamamoto, H. Koizumi, and T. Suganuma, "An M2M Data Analysis Service System Based on Open Source Software Environments," in Advanced Information Networking and Applications Workshops (WAINA), 2013 27th International Conference on, 2013, pp. 953-958. doi: http://dx.doi.org/10.1109/WAINA.2013.124
  • Cisco Systems, "Proposed Computation and Analytics Use Case," Input contribution, 2013. [Online]. Available: ftp.onem2m.org/Meetings/REQ/2013%20meetings/20130225 REQARC19 REQ20 San%20Francisco/oneM2M-REQ-2012-0102R01- Analytics for oneM2M.DOC
  • B. Hamrick, "Discrete calculus." [Online]. Available: http://homepages.math.uic.edu/ kauffman/DCalc.pdf
  • N. C. Krishnan and D. J. Cook, "Activity recognition on streaming sensor data," Pervasive Mob. Comput., vol. 10, pp. 138-154, 2014. doi: http://dx.doi.org/10.1016/j.pmcj.2012.07.003
  • Libelium Comunicaciones Distribuidas S.L., "Waspmote," Technical Guide, 2013. [Online]. Available: http://www.libelium.com/uploads/2013/02/waspmotetechnical guide eng.pdf
  • M. Kusek, I. Lovrek, and H. Maracic, "Rich presence information in agent based machine-to-machine communication," Procedia Computer Science, vol. 22, no. 0, pp. 321 - 329, 2013. doi: http://dx.doi.org/10.1016/j.procs.2013.09.109
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171423126

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