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A passive means based privacy protection method for the perceptual layer of IoTs

Published: 28 November 2016 Publication History

Abstract

Privacy protection in Internet of Things (IoTs) has long been the topic of extensive research in the last decade. The perceptual layer of IoTs suffers the most significant privacy disclosing because of the limitation of hardware resources. Data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. Therefore, in this paper we derive an innovative and passive method called Horizontal Hierarchy Slicing (HHS) method to detect the existence of unknown wireless devices which could result negative means to the privacy. PAM algorithm is used to cluster the HHS curves and analyze whether unknown wireless devices exist in the communicating environment. Link Quality Indicator data are utilized as the network parameters in this paper. The simulation results show their effectiveness in privacy protection.

References

[1]
Atzori L, Iera A, Morabito G. The Internet of Things: a survey. Computer Networks, 54(15): 2787--2805, 2010.
[2]
Adrian Perrig, Robert Szewczyk, J. D. Tygar, Victor Wen, and David E. Culler: "SPINS: security protocols for sensor networks", ACM Trans. Wirel. Netw. 8, 5 pp. 521--534, 2002.
[3]
AlMheiri S.M., and AlQamzi H.S: "Data link layer security protocols in Wireless Sensor Networks: a survey " Networking, Sensing and Control (ICNSC), 10th IEEE International Conference on, pp.312--317, 2013.
[4]
Taejoon Park, and Kang G. Shin: "LiSP: A Lightweight Security Protocol for Wireless Sensor Networks" ACM Transactions on Embedded Computing Systems, Vol.3(3), pp. 634--660, 2004.
[5]
Z. Zhang, Z. Lu, V. Saakian, X. Qin, Q. Chen, and L.-R. Zheng : "Item-level indoor localization with passive UHF RFID based on tag interaction analysis" IEEE Transactions on Industrial Electronics, vol. 61, No.4, pp. 2122--2135, 2014.
[6]
Idris M. Atakli, Hongbing Hu, Yu Chen, Wei Shinn Ku, and Zhou Su, "Malicious node detection in wireless sensor networks using weighted trust evaluation," In Proceedings of the 2008 Spring Simulation Multiconference (SpringSim'08), Society for Computer Simulation International, San Diego, CA, USA, pp.836--843, 2008.
[7]
Nouha Baccour, Anis Koubaa, Luca Mottola, Marco Antonio Zuniga, Habib Youssef, Carlo Alberto Boano, and Mario Alves., "Radio link quality estimation in wireless sensor networks: A survey," ACM Trans. Sen. Netw., 8, 4, Article 34, 33, 2012.
[8]
Tao Liu and Alberto E. Cerpa., "Data-driven link quality prediction using link features," ACM Trans. Sen. Netw., 10, 2, Article 37, 35 pages, January 2014.
[9]
Guoan Hu, "A Link Quality Evaluation Model Based on the Three-dimensional Space in Wireless Sensor Network," Information Technology Journal, 13: 720--724.
[10]
J. Serra, Image Analysis and Mathematical Morphology. London: Academic Press, 1982.
[11]
P. Soille, Morphological Image Analysis. Principles and Applications, 2nd. Berlin, Germany: Springer-Verlag, 2003.
[12]
G. Ayala, M. Gaston, T. Leon, and F. Mallor, "Measuring dissimilarity between curves by means of their granulometric size distributions," in Functional and Operatorial Statistics, Contributions to Statistics. S. Dabo-Niang and F. Ferraty, Eds. Heidelberg: Physica-Verlag/Springer, pp. 35--41, 2008.
[13]
T. Leon, G. Ayala, M. Gaston, and F. Mallor, "Using mathematical morphology for unsupervised classification of functional data," J. Statist. Comput. Simul., vol. 81, no. 8, pp. 1001--1016, 2011.
[14]
M. Gaston,T. Leon, F. Mallor, and L. Ramirez, "A morphological clustering method for daily solar radiation curves," J. Solar Energy, vol. 85, pp. 1824--1836, 2011.
[15]
L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344, John Wiley & Sons, New York, NY, USA, 2009.
[16]
www.xbow.com.
[17]
http://webs.cs.berkeley.edu.

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iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services
November 2016
528 pages
ISBN:9781450348072
DOI:10.1145/3011141
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

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Publication History

Published: 28 November 2016

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Author Tags

  1. horizontal hierarchy slicing
  2. internet of things
  3. link quality indicator
  4. privacy protection

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