Sensors for Everyday Life pp 159-183

Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 22)

Smart Textiles for Smart Home Control and Enriching Future Wireless Sensor Network Data

  • Olivia Ojuroye
  • Russel Torah
  • Steve Beeby
  • Adriana Wilde
Chapter

Abstract

The increasing number of objects within homes connected to the Cloud is not going to recede. Our growing acceptance of automated appliances and items connected in wireless sensor networks (WSN) is gradually making our homes smart. This occurrence is a reflection of the technological advancement of societies around the world. We predict that the future applications of WSN will incorporate smart textiles. These will appear in smart homes, as well as in commercial spaces, in automobile vehicles, in personal or business-owned clothing, and even toys. As the electronics become available to industry, smart textiles could be embedded with electronics capable of receiving and transmitting data packets. The implications are that soft furnishings or any surfaces with a textile have the potential capability of connecting to the Cloud. Considering future applications of smart textiles, whether for personal or commercial usage, we can predict data contents that would be stored in a WSN and discuss how to ensure safety and network stability.

References

  1. 1.
    I. Thoma, L. Fedon, A. Jara, Y. Bocchi, Towards a human centric intelligent society: using cloud and the web of everything to facilitate new social infrastructures, in 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, (2015)Google Scholar
  2. 2.
    D. Ilišević, Mtel, B&H, B. Luka, Bosnia-Herzegovina, N. Banović-Ćurguz, Evolution toward digital society in B&H, in 2015 23rd Telecommunications Forum Telfor (TELFOR). IEEE, 2015Google Scholar
  3. 3.
    D. Bremner, The IoT Tree of Life (2015)Google Scholar
  4. 4.
    O. Ojuroye, A. Wilde, Prototyping a Voice-Controlled Smart Home Hub, Wirelessly Integrated with a Wearable Device, in International Conference on Sensing Technology (ICST), 2015Google Scholar
  5. 5.
    D. Foti, J.S. Koketsu, Activities of daily living, in Pedretti’s Occupational Therapy: Practice Skills for Physical Dysfunction (2008), pp. 157–232Google Scholar
  6. 6.
    S. Katz, Assessing self‐maintenance: activities of daily living, mobility, and instrumental activities of daily living. J. Am. Geriatr. Soc. 31(12), 721–727 (1983)Google Scholar
  7. 7.
    K. Avgerinakis, A. Briassouli, I. Kompatsiaris, Recognition of activities of daily living for smart home environments, in 2013 9th International Conference on Intelligent Environments (IE). IEEE, (2013)Google Scholar
  8. 8.
    P. Rashidi, D.J. Cook, Mining and monitoring patterns of daily routines for assisted living in real world settings, in Proceedings of the 1st ACM International Health Informatics Symposium. ACM, 2010Google Scholar
  9. 9.
    SMART CITIES, Trace analysis and mining for smart cities: issues, methods, and applications. IEEE Commun. Mag. 121 (2013)Google Scholar
  10. 10.
    L. Wang, T. Gu, X. Tao, H. Chen, J. Lu, Recognizing multi-user activities using wearable sensors in a smart home. Pervasive Mob. Comput. 7(3), 287–298 (2011)Google Scholar
  11. 11.
    A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic, ALARM-NET: wireless sensor networks for assisted-living and residential monitoring, in University of Virginia Computer Science Department Technical Report, vol. 2 (2006)Google Scholar
  12. 12.
    A. Pathak, Y.C. Hu; M. Zhang, Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof, in Proceedings of the 7th ACM european conference on Computer Systems, pp. 29–42. ACM (2012)Google Scholar
  13. 13.
    K.K. Peetoom, M.A. Lexis, M. Joore, C.D. Dirksen, L.P. De Witte, Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil. Rehabil. Assistive Technol. 10(4), 271–294 (2015)CrossRefGoogle Scholar
  14. 14.
    T. Nef, P. Urwyler, M. Büchler, I. Tarnanas, R. Stucki, D. Cazzoli, R. Müri, U. Mosimann, Evaluation of three state-of-the-art classifiers for recognition of activities of daily living from smart home ambient data. Sensors 15(5), 11725–11740 (2015)Google Scholar
  15. 15.
    P. Augustyniak, E. Kantoch, Turning domestic appliances into a sensor network for monitoring of activities of daily living. J. Med. Imaging Health Inform. 5(8), 1662–1667 (2015)Google Scholar
  16. 16.
    G. Sen Gupta, S.C. Mukhopadhyay, M. Sutherland, S. Demidenko, Wireless sensor network for selective activity monitoring in a home for the elderly, in Instrumentation and Measurement Technology Conference Proceedings. IMTC, IEEE (2007)Google Scholar
  17. 17.
    A. Dittmar, F. Axisa, G. Delhomme, C. Gehin, New concepts and technologies in home care and ambulatory monitoring. Stud. Health Technol. Inform. 9–35 (2004)Google Scholar
  18. 18.
    C-H. Lu, Y-C. Ho, L-C. Fu, Creating robust activity maps using wireless sensor network in a smart home, in IEEE International Conference on Automation Science and Engineering, 2007. CASE 2007. IEEE, 2007Google Scholar
  19. 19.
    J.A. Stankovic, A.D. Wood, T. He, Realistic applications for wireless sensor networks, in Theoretical Aspects of Distributed Computing in Sensor Networks (Springer Heidelberg, 2011), pp. 835–863Google Scholar
  20. 20.
    M. Meeker, Internet trends 2014-code conference. Retrieved 28 May 2014Google Scholar
  21. 21.
    A. Pathak, Y.C. Hu, M. Zhang, Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof, in Proceedings of the 7th ACM European Conference on Computer Systems, pp. 29–42. ACM (2012, April)Google Scholar
  22. 22.
    H. Cheng, Z. Liu, L. Yang, X. Chen, Sparse representation and learning in visual recognition: theory and applications. Sig. Process. 93(6), 1408–1425 (2013)CrossRefGoogle Scholar
  23. 23.
    V.S. Gunge, P.S. Yalagi, Smart Home Automation: A Literature Review (2016), http://research.ijcaonline.org/rtdm2016/number1/rtdm2568.pdf. Accessed 23 Sep 2016
  24. 24.
    A.P.O.Y. Wrist, Introducing The Samsung Galaxy Smartwatch, by Joseph Milord, posted Sep. 4, 2013, elitedaily.com. 10, 1Google Scholar
  25. 25.
    D.J. Skiba, The connected age and wearable technology. Nurs. Educ. Perspect. 35(5), 346–347 (2014)CrossRefGoogle Scholar
  26. 26.
    L. Yang, S. Hanneke, Activized learning with uniform classification noise, in Proceedings of The 30th International Conference on Machine Learning, pp. 370–378 (2013)Google Scholar
  27. 27.
    S. Leppënen, M. Jokinen, Daily routines and means of communication in a smart home, in Inside the Smart Home (Springer, London, 2003), pp. 207–225Google Scholar
  28. 28.
    A.B. Lynggaard, M.G. Petersen, S. Hepworth, I had a dream and I built it: power and self-staging in ubiquitous high-end homes, in CHI’12 Extended Abstracts on Human Factors in Computing Systems, pp. 201–210. ACM (2012, May)Google Scholar
  29. 29.
    L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  30. 30.
    R. Vipperla, M. Wolters, K. Georgila, S. Renals, Speech input from older users in smart environments: challenges and perspectives, in Universal Access in Human-Computer Interaction. Intelligent and Ubiquitous Interaction Environments (Springer, Berlin, 2009), pp. 117–126Google Scholar
  31. 31.
    C. Bell, Tiny talking modules: an introduction to XBee wireless modules, in Beginning Sensor Networks with Arduino and Raspberry Pi (Apress, 2013), pp. 19–50Google Scholar
  32. 32.
    XBee/Xbee Pro OEM RF Modules: Product Manual v1.xEx802.15.4 Protocol, Digi International, Inc., (2009, Sept)Google Scholar
  33. 33.
    L. Francioso et al., Flexible thermoelectric generator for ambient assisted living wearable biometric sensors. J. Power Sources 196(6), 3239–3243 (2011)CrossRefGoogle Scholar
  34. 34.
    H.R. Khaleel, Design and Fabrication of Compact Inkjet Printed Antennas for Integration Within Flexible and Wearable Electronics. Compon. Packag. Manuf. Technol. IEEE Trans. 4(10), 1722–1728 (2014)CrossRefGoogle Scholar
  35. 35.
    Y. Li, et al., An all-inkjet printed flexible capacitor for wearable applications, in 2012 Symposium on Design, Test, Integration and Packaging of MEMS/MOEMS (DTIP). IEEE, 2012Google Scholar
  36. 36.
    L. Francioso, et al., PDMS/Kapton interface plasma treatment effects on the polymeric package for a wearable thermoelectric generator. ACS Appl. Mater. Interfaces 5(14), 6586–6590 (2013)Google Scholar
  37. 37.
    L. Francioso, et al., Structural reliability and thermal insulation performance of flexible thermoelectric generator for wearable sensors, in 2013 IEEE SENSORS. IEEE, 2013Google Scholar
  38. 38.
    J-H. Moon, et al., Wearable polyimide–PDMS electrodes for intrabody communication. J. Micromech. Microeng. 20(2), 025032 (2010)Google Scholar
  39. 39.
    A. Arevalo, et al., A versatile multi-user polyimide surface micromachining process for MEMS applications, in 2015 IEEE 10th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS). IEEE, 2015Google Scholar
  40. 40.
    S. Shibata, Y. Niimi, M. Shikida, Flexible thermal MEMS flow sensor based on Cu on polyimide substrate, in 2014 IEEE SENSORS. IEEE, 2014Google Scholar
  41. 41.
    B. Plovie, et al., 2.5 D smart objects using thermoplastic stretchable interconnects, in International Symposium on Microelectronics. Vol. 2015. No. 1. International Microelectronics Assembly and Packaging Society, 2015Google Scholar
  42. 42.
    J. van den Brand, et al., Flexible and stretchable electronics for wearable healthcare, in 2014 44th European Solid State Device Research Conference (ESSDERC). IEEE, 2014Google Scholar
  43. 43.
    Y. Ahn, S. Song, K.-S. Yun, Woven flexible textile structure for wearable power-generating tactile sensor array. Smart Mater. Struct. 24(7), 075002 (2015)CrossRefGoogle Scholar
  44. 44.
    H. Rogier, Textile antenna systems: design, fabrication, and characterization, in Handbook of Smart Textiles (2015), pp. 433–458Google Scholar
  45. 45.
    A.R. Maria, P. Radu, Smart sensor grid networks for analyze the energy efficiency of the enveloped building by textile composites. Rom. Rev. Precis. Mech. Opt. Mechatron. 48, 12 (2015)Google Scholar
  46. 46.
    K. Koski, et al., Electro-textiles—The enabling technology for wearable antennas in wireless body-centric systems, in 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. IEEE, 2015Google Scholar
  47. 47.
    J. Cheng, et al., Smart-surface: large scale textile pressure sensors arrays for activity recognition. Pervasive Mob. Comput. (2016)Google Scholar
  48. 48.
    N. Labonnote, K. Høyland, Smart home technologies that support independent living: challenges and opportunities for the building industry–a systematic mapping study, in Intelligent Buildings International (2015), pp. 1–26Google Scholar
  49. 49.
    S.C. Mukhopadhyay, Wearable sensors for human activity monitoring: a review. Sens. J. IEEE 15(3), 1321–1330 (2015)Google Scholar
  50. 50.
    K. Cherenack, L. van Pieterson, Smart textiles: challenges and opportunities. J. Appl. Phys. 112(9), 091301 (2012)CrossRefGoogle Scholar
  51. 51.
    A. Mazzoldi et al., Smart textiles for wearable motion capture systems. AUTEX Res. J. 2(4), 199–203 (2002)Google Scholar
  52. 52.
    S. Park, S. Jayaraman, Smart textiles: wearable electronic systems. MRS Bull. 28(08), 585–591 (2003)CrossRefGoogle Scholar
  53. 53.
    L. Van Langenhove, C. Hertleer, Smart clothing: a new life. Int. J. Clothing Sci. Technol. 16, 63–72 (2004)CrossRefGoogle Scholar
  54. 54.
    Project Jacquard, Google, https://www.google.com/atap/project-jacquard/. Last accessed 9 Feb 2016
  55. 55.
    D. Marculescu, et al., Electronic textiles: a platform for pervasive computing. Proc. IEEE 91(12), 1995–2018 (2003)Google Scholar
  56. 56.
    M. Chan, et al., Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56(3), 137–156 (2012)Google Scholar
  57. 57.
    T. Dou, L. Yi, The development and trend of wireless body area networks in the smart clothing field (2015)Google Scholar
  58. 58.
    WHO, World report on disability, 2011Google Scholar
  59. 59.
    A. Nadeem, et al., Application specific study, analysis and classification of body area wireless sensor network applications. Comput. Netw. 83, 363–380 (2015)Google Scholar
  60. 60.
    R. Pailes-Friedman, Smart Textiles for Designers: Inventing the Future of Fashion (2016)Google Scholar
  61. 61.
    V. Kan, et al., Social textiles: social affordances and icebreaking interactions through wearable social messaging, in Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction. ACM, 2015Google Scholar
  62. 62.
    C. Zysset, et al., Textile integrated sensors and actuators for near-infrared spectroscopy. Opt. Expr. 21(3), 3213–3224 (2013)Google Scholar
  63. 63.
    K. Liang, Transistor Circuits For A MEMS Based Transceiver (2015)Google Scholar
  64. 64.
    S. Nihtianov, A. Luque (eds.), Smart Sensors and MEMS: Intelligent Devices and Microsystems for Industrial Applications (Woodhead Publishing, 2014)Google Scholar
  65. 65.
    N. Labonnote, K. Høyland, Smart home technologies that support independent living: challenges and opportunities for the building industry–a systematic mapping study. Intell. Buildings Int. 1–26 (2015)Google Scholar
  66. 66.
    L. Russell, R. Goubran, F. Kwamena, Personalization using sensors for preliminary human detection in an IoT environment, in 2015 International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2015Google Scholar
  67. 67.
    J. Cheng, et al., Designing sensitive wearable capacitive sensors for activity recognition. Sens. J. IEEE 13(10), 3935–3947 (2013)Google Scholar
  68. 68.
    H. Xia, T. Grossman, G. Fitzmaurice, NanoStylus: enhancing input on ultra-small displays with a finger-mounted stylus, in Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology. ACM, 2015Google Scholar
  69. 69.
    H. Kim, I. Kim, J. Kim, Designing the Smart Foot Mat and Its Applications: as a User Identification Sensor for Smart Home Scenarios (2015)Google Scholar
  70. 70.
    S. Parmar, T. Malik, Application of Textiles in Automobiles, www.fibre2fashion.com/industry-article/1807/application-of-textiles-in-automobile. Last accessed 23 Sep 2016
  71. 71.
    L. Jul, Adding values, upholstery concepts for automotives using smart textiles (2007)Google Scholar
  72. 72.
    A. Braun, et al. CapSeat-capacitive proximity sensing for automotive activities recognition, in Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 2015Google Scholar
  73. 73.
    J. Krajewski, et al., Steering wheel behavior based estimation of fatigue, in Proceedings of the Fifth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 2009Google Scholar
  74. 74.
    A. Sahayadhas, K. Sundaraj, M. Murugappan, Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953 (2012)CrossRefGoogle Scholar
  75. 75.
    O. Postolache, et al., Toward developing a smart wheelchair for user physiological stress and physical activity monitoring, in 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2014Google Scholar
  76. 76.
    L. Lagenhove, Advances in Smart Medical Textiles: Treatments and Health Monitoring (Woodhead Publishing, 2015)Google Scholar
  77. 77.
    A. Fleury, M. Sugar, T. Chau, E-textiles in Clinical Rehabilitation: A Scoping Review. Electronics 4(1), 173–203 (2015)CrossRefGoogle Scholar
  78. 78.
    P. Vanhems, et al., Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS One 8(9), e73970 (2013)Google Scholar
  79. 79.
    V. Ferraro, Smart textiles and wearable technologies for sportswear: a design approach, in 2nd International Electronic Conference on Sensors and Applications. Multidisciplinary Digital Publishing Institute, 2015Google Scholar
  80. 80.
    J. McLoughlin, et al., A Smart Textile for Measuring the Impact of Energy used in Competitive Contact Sports (2013)Google Scholar
  81. 81.
    R. Shishoo, Textiles for Sport (Woodhead Publishing, 2015), ISBN: 978-1-78242-229-7Google Scholar
  82. 82.
    K. Förster, M. Bächlin, G. Tröster, Non-interrupting user interfaces for electronic body-worn swim devices, in Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments. ACM, 2009Google Scholar
  83. 83.
    L. Berglin, Spookies: combining smart materials and information technology in an interactive toy, in Proceedings of the 2005 conference on Interaction design and children. ACM, 2005Google Scholar
  84. 84.
    E. Rossi, G. Crampton-Smith, P. Tabor, I Mirabilia: taking care of the emotional life of hospitalized children. Stud. Mater. Thinking 7 (2012)Google Scholar
  85. 85.
    S. Johansson, Sniff, 2009. Available at www.nearfield.org/sniff
  86. 86.
    S. Park, S. Jayaraman, Smart textile-based wearable biomedical systems: a transition plan for research to reality. Inf. Technol. Biomed. IEEE Trans. 14(1), 86–92 (2010)CrossRefGoogle Scholar
  87. 87.
    C. Linti, H. Horter, P. Österreicher, H. Planck, Sensory baby vest for the monitoring of infants, in Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks (BSN’06), Cambridge, MA, USA, 3–5 April 2006, pp. 3–137Google Scholar
  88. 88.
    Y. Rimet, et al., Surveillance of infants at risk of apparent life threatening events (ALTE) with the BBA bootee: a wearable multiparameter monitor, in Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE. IEEE, 2007Google Scholar
  89. 89.
    Z. Zhu, et al. Wearable sensor systems for infants. Sensors 15(2), 3721–3749 (2015)Google Scholar
  90. 90.
    A. Pantelopoulos, N.G. Bourbakis, A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. 40, 1–12 (2010)Google Scholar
  91. 91.
    A. Mainwaring, et al., Wireless sensor networks for habitat monitoring, in Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. ACM, 2002Google Scholar
  92. 92.
    I. Kivelä, I. Hakala, Area-based environmental noise measurements with a wireless sensor network (2015)Google Scholar
  93. 93.
    T.D.P. Mendes, et al., Smart home communication technologies and applications: Wireless protocol assessment for home area network resources. Energies 8(7), 7279–7311 (2015)Google Scholar
  94. 94.
    B. Płaczek, M. Bernaś, Uncertainty-based information extraction in wireless sensor networks for control applications. Ad Hoc Netw. 14, 106–117 (2014)CrossRefGoogle Scholar
  95. 95.
    D. Macedonio, M. Merro, A semantic analysis of key management protocols for wireless sensor networks. Sci. Comput. Progr. 81, 53–78 (2014)CrossRefGoogle Scholar
  96. 96.
    X. Pu, et al., A self‐charging power unit by integration of a textile triboelectric nanogenerator and a flexible lithium‐ion battery for wearable electronics. Adv. Mater. 27(15), 2472–2478 (2015)Google Scholar
  97. 97.
    X. Pu, et al., Wearable self‐charging power textile based on flexible yarn supercapacitors and fabric nanogenerators. Adv. Mater. 28(1), 98–105 (2016)Google Scholar
  98. 98.
    T.D.P. Mendes, et al., Smart and energy-efficient home implementation: Wireless communication technologies role, in 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). IEEE, 2015Google Scholar
  99. 99.
    H. Ye, et al., Research on Location Privacy Protection Methods of Wireless Sensor Network in Smart Grid (2015)Google Scholar
  100. 100.
    G.M. Paul, et al., A smart textile based facial EMG and EOG computer interface. Sens. J. IEEE 14(2), 393–400 (2014)Google Scholar
  101. 101.
    M. Rezvani, et al., Secure data aggregation technique for wireless sensor networks in the presence of collusion attacks. Dependable Secure Comput. IEEE Trans. 12(1), 98–110 (2015)Google Scholar
  102. 102.
    R. Roman, C. Fernandez-Gago, J. Lopez, H.H. Chen, Trust and reputation systems for wireless sensor networks, in Security and Privacy in Mobile and Wireless Networking, ed. by S. Gritzalis, T. Karygiannis, C. Skianis (Troubador Publishing Ltd, Leicester, 2009), pp. 105–128Google Scholar
  103. 103.
    M. Conti, Secure Wireless Sensor Networks (Springer, 2015)Google Scholar
  104. 104.
    S. Gonzalez, Improving Time Synchronization Protocols in Wireless Sensor Networks (Diss. Colorado School of Mines, 2016)Google Scholar
  105. 105.
    Y. Kadowaki, H. Ishii, Event-based distributed clock synchronization for wireless sensor networks. Autom. Control IEEE Trans. 60(8), 2266–2271 (2015)MathSciNetCrossRefGoogle Scholar
  106. 106.
    T.O. Ayodele, Introduction to machine learning, in New Advances in Machine Learning (InTech, Rijeka, Croatia, 2010)Google Scholar
  107. 107.
    M. Abu Alsheikh et al., Machine learning in wireless sensor networks: algorithms, strategies, and applications. Commun. Surv. Tutorials IEEE 16(4), 1996–2018 (2014)CrossRefGoogle Scholar
  108. 108.
    A.H. Duffy, The ‘what’ and ‘how’ of learning in design. IEEE Expert 12(3), 71–76 (1997)Google Scholar
  109. 109.
    P. Langley, H.A. Simon, Applications of machine learning and rule induction. Commun. ACM 38(11), 54–64 (1995)Google Scholar
  110. 110.
    Y.S. Abu-Mostafa, M. Magdon-Ismail, H.-T. Lin, Learning From Data (2012)Google Scholar
  111. 111.
    S Suthaharan, et al., Labelled data collection for anomaly detection in wireless sensor networks, in 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2010Google Scholar
  112. 112.
    S. Rajasegarar, C. Leckie, M. Palaniswami, R. Beyah, J. McNair, C. Corbett, Security in Ad-hoc and Sensor Networks (World Scientific Publishing, Inc, 2009), pp. 231–260Google Scholar
  113. 113.
    G Mao, (ed.), Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and/Surveillance Techniques for Target Tracking: Monitoring and Surveillance Techniques for Target Tracking (IGI Global, 2009)Google Scholar
  114. 114.
    G. Han et al., Localization algorithms of wireless sensor networks: a survey. Telecommun. Syst. 52(4), 2419–2436 (2013)CrossRefGoogle Scholar
  115. 115.
    H.M. Ammari, The art of wireless sensor networks, in Fundamentals, vol. 1 (Springer, 2013)Google Scholar
  116. 116.
    X. Yan, et al., An improved multihop-based localization algorithm for wireless sensor network using learning approach. Comput. Electr. Eng. 48, 247–257 (2015)Google Scholar
  117. 117.
    D.A. Tran, T. Nguyen, Localization in wireless sensor networks based on support vector machines. Parallel Distrib. Syst. IEEE Trans. 19(7), 981–994 (2008)CrossRefGoogle Scholar
  118. 118.
    H. Martins, et al., A support vector machine based technique for online detection of outliers in transient time series, in 2015 10th Asian Control Conference (ASCC). IEEE, 2015Google Scholar
  119. 119.
    D. Wang, Y. Zhou, Distributed support vector machines: An overview, in 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, (2012)Google Scholar
  120. 120.
    J.B. Predd, S.R. Kulkarni, H.V. Poor, Distributed learning in wireless sensor networks. IEEE Signal Process. Mag. 23(4), 56–69 (2006)CrossRefGoogle Scholar
  121. 121.
    K. Fluri, B. Beferull-Lozano, P. Tsakalides, Optimal gossip algorithm for distributed consensus SVM training in wireless sensor networks, in Proceedings of 16th international Conference on Digital Signal Processing, Santorini-Hellas (2009)Google Scholar
  122. 122.
    K. Wu, Y. Gao, F. Li, Y. Xiao, Lightweight deployment-aware scheduling for wireless sensor networks. Mob. Netw. Appl. 10(6), 837–852 (2005)CrossRefGoogle Scholar
  123. 123.
    M. Abu Alsheikh et al., Markov decision processes with applications in wireless sensor networks: A survey. Commun. Surv. Tutorials IEEE 17(3), 1239–1267 (2015)CrossRefGoogle Scholar
  124. 124.
    A. Kobbane, M. Koulali, H. Tembine, M. Koutbi, J. Ben-Othman, Dynamic power control with energy constraint for multimedia wire-less sensor networks, in Proceedings of IEEE International Conference on Communications, pp. 518–522 (2012)Google Scholar
  125. 125.
    K. Etessami, M. Kwiatkowska, M. Y. Vardi, M. Yannakakis, Multi-objective model checking of Markov decision processes, in Tools and Algorithms for the Construction and Analysis of Systems (Springer, Berlin, 2007), pp. 50–65Google Scholar
  126. 126.
    M. Di Francesco, S.K. Das, G. Anastasi, Data collection in wireless sensor networks with mobile elements: a survey. ACM Trans. Sens. Netw. 8(1), 7 (2011)Google Scholar
  127. 127.
    S. Rashid, U. Akram, S.A. Khan, WML: wireless sensor network based machine learning for leakage detection and size estimation. Procedia Comput. Sci. 63, 171–176 (2015)CrossRefGoogle Scholar
  128. 128.
    R. Daniel, K. Nageswara Rao, An optimal power conservation cluster based routing algorithm using Fuzzy Verdict Mechanism for Wireless Sensor Networks, in 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Olivia Ojuroye
    • 1
  • Russel Torah
    • 1
  • Steve Beeby
    • 1
  • Adriana Wilde
    • 1
  1. 1.University of SouthamptonSouthamptonUK

Personalised recommendations