Toward Uniform Smart Healthcare Ecosystems: A Survey on Prospects, Security, and Privacy Considerations



A plethora of interwoven social enablers and technical advancements have elevated smart healthcare from once a supplemental feature to now an indispensable necessity crucial to addressing intractable problems our modern cities face, which range from gradual population aging to ever surging healthcare expenses. State-of-the-art smart healthcare implementations now span a wide array of smart city applications including smart homes, smart environments, and smart transportation to take full advantage of the existing synergies among these services. This engagement of exogenous sources in smart healthcare systems introduces a variety of challenges; chief among them, it expands and complicates the attack surface, hence raising security and privacy concerns. In this chapter, we study the emerging trends in smart healthcare applications as well as the key technological developments that give rise to these transitions. Particularly, we emphasize threats, vulnerabilities, and consequences of cyberattacks in modern smart healthcare systems and investigate their corresponding proposed countermeasures.


Privacy Security Wearable sensors Access control Authentication 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    C.J. Truffer, S. Keehan, S. Smith, J. Cylus, A. Sisko, J.A. Poisal, J. Lizonitz, M.K. Clemens, Health spending projections through 2019: the recession’s impact continues. Health Aff. 29(3), 522–529 (2010)CrossRefGoogle Scholar
  2. 2.
    D. Stuckler, S. Basu, M. Suhrcke, A. Coutts, M. McKee, The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis. Lancet 374(9686), 315–323 (2009)CrossRefGoogle Scholar
  3. 3.
    J. Andreu-Perez, D.R. Leff, H.M.D. Ip, G.Z. Yang, From wearable sensors to smart implants-toward pervasive and personalized healthcare. IEEE Trans. Biomed. Eng. 62(12), 2750–2762 (2015)CrossRefGoogle Scholar
  4. 4.
    L.E. Hebert, P.A. Scherr, J.L. Bienias, D.A. Bennett, D.A. Evans, Alzheimer disease in the us population: prevalence estimates using the 2000 census. Arch. Neurol. 60(8), 1119–1122 (2003)CrossRefGoogle Scholar
  5. 5.
    M. Estai, Y. Kanagasingam, M. Tennant, S. Bunt, A systematic review of the research evidence for the benefits of teledentistry. J. Telemed. Telecare 24(3), 147–156 (2017). 1357633X16689433CrossRefGoogle Scholar
  6. 6.
    K.A. Al Mamun, M. Alhussein, K. Sailunaz, M.S. Islam, Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications. Futur. Gener. Comput. Syst. 66, 36–47 (2017)CrossRefGoogle Scholar
  7. 7.
    A. Page, M. Hassanalieragh, T. Soyata, M.K. Aktas, B. Kantarci, S. Andreescu, Conceptualizing a real-time remote cardiac health monitoring system, in Enabling Real-Time Mobile Cloud Computing Through Emerging Technologies, ed. by T. Soyata (IGI Global, Hershey, 2015), pp. 1–34Google Scholar
  8. 8.
    F. Casino, C. Patsakis, E. Batista, F. Borràs, A. Martínez-Ballesté, Healthy routes in the smart city: a context-aware mobile recommender. IEEE Softw. 34(6), 42–47 (2017)CrossRefGoogle Scholar
  9. 9.
    B. Reeder, A. David, Health at hand: a systematic review of smart watch uses for health and wellness. J. Biomed. Inform. 63, 269–276 (2016)CrossRefGoogle Scholar
  10. 10.
    J. Tavares, T. Oliveira, Electronic health record patient portal adoption by health care consumers: an acceptance model and survey. J. Med. Internet Res. 18(3), e49 (2016)CrossRefGoogle Scholar
  11. 11.
    G. Manogaran, R. Varatharajan, D. Lopez, P.M. Kumar, R. Sundarasekar, C. Thota, A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Futur. Gener. Comput. Syst. 82, 375–387 (2018)CrossRefGoogle Scholar
  12. 12.
    G. Muhammad, M. Alsulaiman, S.U. Amin, A. Ghoneim, M.F. Alhamid, A facial-expression monitoring system for improved healthcare in smart cities. IEEE Access 5, 10871–10881 (2017)CrossRefGoogle Scholar
  13. 13.
    E. Spanò, S.D. Pascoli, G. Iannaccone, Low-power wearable ECG monitoring system for multiple-patient remote monitoring. IEEE Sens. J. 16(13), 5452–5462 (2016)CrossRefGoogle Scholar
  14. 14.
    H. Samani, R. Zhu, Robotic automated external defibrillator ambulance for emergency medical service in smart cities. IEEE Access 4, 268–283 (2016)CrossRefGoogle Scholar
  15. 15.
    R. Sundar, S. Hebbar, V. Golla, Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sens. J. 15(2), 1109–1113 (2015)CrossRefGoogle Scholar
  16. 16.
    F. Mwasilu, J.J. Justo, E.K. Kim, T.D. Do, J.W. Jung, Electric vehicles and smart grid interaction: a review on vehicle to grid and renewable energy sources integration. Renew. Sustain. Energy Rev. 34, 501–516 (2014)CrossRefGoogle Scholar
  17. 17.
    A. Alaiad, L. Zhou, Patients’ Adoption of WSN-Based Smart Home Healthcare Systems: An Integrated Model of Facilitators and Barriers. IEEE Transactions on Professional Communication 60(1), 4–23 (2017)CrossRefGoogle Scholar
  18. 18.
    A.L. Young, M. Yung, Cryptovirology: the birth, neglect, and explosion of ransomware. Commun. ACM 60(7), 24–26 (2017)CrossRefGoogle Scholar
  19. 19.
    A. Page, S. Hijazi, D. Askan, B. Kantarci, T. Soyata, Research directions in cloud-based decision support systems for health monitoring using Internet-of-Things driven data acquisition. Int. J. Serv. Comput. 4(4), 18–34 (2016)Google Scholar
  20. 20.
    American Diabetes Association, About Us: American Diabetes Association. Accessed 02 August 2018
  21. 21.
    P. Kakria, N.K. Tripathi, P. Kitipawang, A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. Int. J. Telemed. Appl. 2015, 8:8–8:8 (2015)Google Scholar
  22. 22.
    American Heart Association, Building healthier lives free of cardiovascular diseases and strokes. Accessed 02 August 2018
  23. 23.
    R. Pandey, N.C. Dingari, N. Spegazzini, R.R. Dasari, G.L. Horowitz, I. Barman, Emerging trends in optical sensing of glycemic markers for diabetes monitoring. Trends Anal. Chem. 64, 100–108 (2015)CrossRefGoogle Scholar
  24. 24.
    O. Arias, K. Ly, Y. Jin, Security and Privacy in IoT Era (Springer, Cham, 2018), pp. 351–378Google Scholar
  25. 25.
    U.E. Bauer, P.A. Briss, R.A. Goodman, B.A. Bowman, Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet 384(9937), 45–52 (2014)CrossRefGoogle Scholar
  26. 26.
    B. Veeravalli, C.J. Deepu, D. Ngo, Real-Time, Personalized Anomaly Detection in Streaming Data for Wearable Healthcare Devices (Springer, Cham, 2017), pp. 403–426Google Scholar
  27. 27.
    X. Wang, Q. Gui, B. Liu, Z. Jin, Y. Chen, Enabling smart personalized healthcare: a hybrid mobile-cloud approach for ECG telemonitoring. IEEE J. Biomed. Health Inform. 18(3), 739–745 (2014)CrossRefGoogle Scholar
  28. 28.
    M. Chen, Y. Ma, J. Song, C.F. Lai, B. Hu, Smart clothing: connecting human with clouds and big data for sustainable health monitoring. Mobile Netw. Appl. 21(5), 825–845 (2016)CrossRefGoogle Scholar
  29. 29.
    V.L. West, D. Borland, W.E. Hammond, Innovative information visualization of electronic health record data: a systematic review. J. Am. Med. Inform. Assoc. 22(2), 330–339 (2014)Google Scholar
  30. 30.
    A. Page, T. Soyata, J. Couderc, M. Aktas, B. Kantarci, S. Andreescu, Visualization of health monitoring data acquired from distributed sensors for multiple patients, in IEEE Global Telecommunications Conference, San Diego (2015), pp. 1–7Google Scholar
  31. 31.
    A. Page, M.K. Aktas, T. Soyata, W. Zareba, J. Couderc, QT clock to improve detection of QT prolongation in long QT syndrome patients. Heart Rhythm 13(1), 190–198 (2016)CrossRefGoogle Scholar
  32. 32.
    G. Fico, A. Fioravanti, M.T. Arredondo, J. Gorman, C. Diazzi, G. Arcuri, C. Conti, G. Pirini, Integration of personalized healthcare pathways in an ICT platform for diabetes managements: a small-scale exploratory study. IEEE J. Biomed. Health Inform. 20(1) (2016), pp. 29–38CrossRefGoogle Scholar
  33. 33.
    J.Y. Lucisano, T.L. Routh, J.T. Lin, D.A. Gough, Glucose monitoring in individuals with diabetes using a long-term implanted sensor/telemetry system and model. IEEE Trans. Biomed. Eng. 64(9), 1982–1993 (2017)CrossRefGoogle Scholar
  34. 34.
    J.D. Stewart, Foot drop: where, why and what to do? Pract. Neurol. 8(3), 158–169 (2008)CrossRefGoogle Scholar
  35. 35.
    M. Abtahi, S. Barlow, M. Constant, N. Gomes, O. Tully, S. D’Andrea, K. Mankodiya, MagicSox: an E-textile IoT system to quantify gait abnormalities. Smart Health 5–6, 4–14 (2017)Google Scholar
  36. 36.
    A.C.B. Garcia, A.S. Vivacqua, N. Sánchez-Pi, L. Martí, J.M. Molina, Crowd-based ambient assisted living to monitor the elderly’s health outdoors. IEEE Softw. 34(6), 53–57 (2017)CrossRefGoogle Scholar
  37. 37.
    M. da Silva Cameirão, S. Bermúdez i Badia, E. Duarte, P.F. Verschure, Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: a randomized controlled pilot study in the acute phase of stroke using the rehabilitation gaming system. Restor. Neurol. Neurosci. 29(5), 287–298 (2011)Google Scholar
  38. 38.
    P. Standen, K. Threapleton, A. Richardson, L. Connell, D. Brown, S. Battersby, F. Platts, A. Burton, A low cost virtual reality system for home based rehabilitation of the arm following stroke: a randomised controlled feasibility trial. Clin. Rehabil. 31(3), 340–350 (2017). PMID: 27029939CrossRefGoogle Scholar
  39. 39.
    N.H. Alkahtani, S. Almohsen, N.M. Alkahtani, G. Abdullah Almalki, S.S.Meshref, H. Kurdi, A semantic multi-agent system to exchange information between hospitals. Procedia Comput. Sci. 109, 704–709 (2017). 8th International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, 16–19 May 2017, Madeira, PortugalCrossRefGoogle Scholar
  40. 40.
    M.S. Hossain, G. Muhammad, Cloud-assisted industrial Internet of Things (IIoT) – enabled framework for health monitoring. Comput. Netw. 101, 192–202 (2016). Industrial Technologies and Applications for the Internet of ThingsGoogle Scholar
  41. 41.
    X. Chen, L. Wang, J. Ding, N. Thomas, Patient flow scheduling and capacity planning in a smart hospital environment. IEEE Access 4, 135–148 (2016)CrossRefGoogle Scholar
  42. 42.
    A. Alessa, M. Faezipour, A review of influenza detection and prediction through social networking sites. Theor. Biol. Med. Model. 15(1), 2 (2018)Google Scholar
  43. 43.
    L. Fernandez-Luque, M. Imran, Humanitarian health computing using artificial intelligence and social media: a narrative literature review. Int. J. Med. Inform. 114, 136–142 (2018)CrossRefGoogle Scholar
  44. 44.
    M.A. Al-Taee, W. Al-Nuaimy, Z.J. Muhsin, A. Al-Ataby, Robot assistant in management of diabetes in children based on the internet of things. IEEE Internet Things J. 4(2), 437–445 (2017)CrossRefGoogle Scholar
  45. 45.
    A.G. Ferreira, D. Fernandes, S. Branco, J.L. Monteiro, J. Cabral, A.P. Catarino, A.M. Rocha, A smart wearable system for sudden infant death syndrome monitoring, in 2016 IEEE International Conference on Industrial Technology (ICIT) (2016), pp. 1920–1925Google Scholar
  46. 46.
    G. Janjua, D. Guldenring, D. Finlay, J. McLaughlin, Wireless chest wearable vital sign monitoring platform for hypertension, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2017), pp. 821–824Google Scholar
  47. 47.
    K. Kaiya, A. Koyama, Design and implementation of meal information collection system using IoT wireless tags, in 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS) (2016), pp. 503–508Google Scholar
  48. 48.
    S. Clarke, L.G. Jaimes, M.A. Labrador, mStress: a mobile recommender system for just-in-time interventions for stress, in 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC) (2017), pp. 1–5Google Scholar
  49. 49.
    A. Gomez-Sacristan, M.A. Rodriguez-Hernandez, V. Sempere, Evaluation of quality of service in smart-hospital communications. J. Med. Imaging Health Inform. 5(8), 1864–1869 (2015)CrossRefGoogle Scholar
  50. 50.
    B. Fabian, T. Ermakova, P. Junghanns, Collaborative and secure sharing of healthcare data in multi-clouds. Inf. Syst. 48, 132–150 (2015)CrossRefGoogle Scholar
  51. 51.
    P. Dayal, N.M. Hojman, J.L. Kissee, J. Evans, J.E. Natale, Y. Huang et al., Impact of telemedicine on severity of illness and outcomes among children transferred from referring emergency departments to a children’s hospital PICU. Pediatr. Crit. Care Med. 17(6), 516–521 (2016). CrossRefGoogle Scholar
  52. 52.
    M. Habibzadeh, Z. Qin, T. Soyata, B. Kantarci, Large scale distributed dedicated- and non-dedicated smart city sensing systems. IEEE Sens. J. 17(23), 7649–7658 (2017)CrossRefGoogle Scholar
  53. 53.
    M. Habibzadeh, T. Soyata, B. Kantarci, A. Boukerche, C. Kaptan, Sensing, communication and security planes: a new challenge for a smart city system design. Comput. Netw. 144, 163–200 (2018)CrossRefGoogle Scholar
  54. 54.
    M. Liggins II, D. Hall, J. Llinas, Handbook of Multisensor Data Fusion: Theory and Practice (CRC Press, Boca Raton, 2017)CrossRefGoogle Scholar
  55. 55.
    G. Fortino, S. Galzarano, R. Gravina, W. Li, A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Inf. Fusion 22, 50–70 (2015)CrossRefGoogle Scholar
  56. 56.
    Y. Zhang, M. Qiu, C.W. Tsai, M.M. Hassan, A. Alamri, Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88–95 (2017)CrossRefGoogle Scholar
  57. 57.
    H.L. Peng, J.Q. Liu, H.C. Tian, B. Xu, Y.Z. Dong, B. Yang, X. Chen, C.S. Yang, Flexible dry electrode based on carbon nanotube/polymer hybrid micropillars for biopotential recording. Sens. Actuators A Phys. 235, 48–56 (2015)CrossRefGoogle Scholar
  58. 58.
    M. Habibzadeh, M. Hassanalieragh, A. Ishikawa, T. Soyata, G. Sharma, Hybrid solar-wind energy harvesting for embedded applications: supercapacitor-based system architectures and design tradeoffs. IEEE Circuits Syst. Mag. 17(4), 29–63 (2017)CrossRefGoogle Scholar
  59. 59.
    M. Habibzadeh, M. Hassanalieragh, T. Soyata, G. Sharma, Solar/wind hybrid energy harvesting for supercapacitor-based embedded systems, in IEEE Midwest Symposium on Circuits and Systems, Boston (2017), pp. 329–332Google Scholar
  60. 60.
    M. Habibzadeh, M. Hassanalieragh, T. Soyata, G. Sharma, Supercapacitor-based embedded hybrid solar/wind harvesting system architectures, in Proceedings of the 30th IEEE International System-on-Chip Conference, Munich (2017)Google Scholar
  61. 61.
    B.A. Reyes, N. Reljin, Y. Kong, Y. Nam, K.H. Chon, Tidal volume and instantaneous respiration rate estimation using a volumetric surrogate signal acquired via a smartphone camera. IEEE J. Biomed. Health Inf. 21(3), 764–777 (2017)CrossRefGoogle Scholar
  62. 62.
    K. Arning, M. Ziefle, “get that camera out of my house!” conjoint measurement of preferences for video-based healthcare monitoring systems in private and public places, in Inclusive Smart Cities and e-Health, ed. by A. Geissbühler, J. Demongeot, M. Mokhtari, B. Abdulrazak, H. Aloulou (Springer, Cham, 2015), pp. 152–164Google Scholar
  63. 63.
    S. Kianoush, S. Savazzi, F. Vicentini, V. Rampa, M. Giussani, Device-free RF human body fall detection and localization in industrial workplaces. IEEE Internet Things J. 4(2), 351–362 (2017)CrossRefGoogle Scholar
  64. 64.
    X. Liu, J. Cao, S. Tang, J. Wen, P. Guo, Contactless respiration monitoring via off-the-shelf WiFi devices. IEEE Trans. Mob. Comput. 15(10), 2466–2479 (2016)CrossRefGoogle Scholar
  65. 65.
    F. Adib, H. Mao, Z. Kabelac, D. Katabi, R.C. Miller, Smart homes that monitor breathing and heart rate, in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. CHI ’15 (ACM, New York, 2015), pp. 837–846Google Scholar
  66. 66.
    M. Kachuee, M.M. Kiani, H. Mohammadzade, M. Shabany, Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Trans. Biomed. Eng. 64(4), 859–869 (2017)CrossRefGoogle Scholar
  67. 67.
    D.L. Carnì, D. Grimaldi, A. Nastro, V. Spagnuolo, F. Lamonaca, Blood oxygenation measurement by smartphone. IEEE Instrum. Meas. Mag. 20(3), 43–49 (2017)CrossRefGoogle Scholar
  68. 68.
    C.Y. Huang, M.C. Chan, C.Y. Chen, B.S. Lin, Novel wearable and wireless ring-type pulse oximeter with multi-detectors. Sensors 14(9), 17586–17599 (2014)CrossRefGoogle Scholar
  69. 69.
    S. Acharya, A. Rajasekar, B.S. Shender, L. Hrebien, M. Kam, Real-time hypoxia prediction using decision fusion. IEEE J. Biomed. Health Inform. 21(3), 696–707 (2017)CrossRefGoogle Scholar
  70. 70.
    V.P. Rachim, W.Y. Chung, Wearable noncontact armband for mobile ECG monitoring system. IEEE Trans. Biomed. Circuits Syst. 10(6), 1112–1118 (2016)CrossRefGoogle Scholar
  71. 71.
    P. Müller, M.A. Bégin, T. Schauer, T. Seel, Alignment-free, self-calibrating elbow angles measurement using inertial sensors. IEEE J. Biomed. Health Inform. 21(2), 312–319 (2017)CrossRefGoogle Scholar
  72. 72.
    N.J. Cleven, J.A. Müntjes, H. Fassbender, U. Urban, M. Görtz, H. Vogt, M. Gräfe, T. Göttsche, T. Penzkofer, T. Schmitz-Rode, W. Mokwa, A novel fully implantable wireless sensor system for monitoring hypertension patients. IEEE Trans. Biomed. Eng. 59(11), 3124–3130 (2012)CrossRefGoogle Scholar
  73. 73.
    D. Shao, C. Liu, F. Tsow, Y. Yang, Z. Du, R. Iriya, H. Yu, N. Tao, Noncontact monitoring of blood oxygen saturation using camera and dual-wavelength imaging system. IEEE Trans. Biomed. Eng. 63(6), 1091–1098 (2016)CrossRefGoogle Scholar
  74. 74.
    T.M. Seeberg, J.G. Orr, H. Opsahl, H.O. Austad, M.H. Røed, S.H. Dalgard, D. Houghton, D.E.J. Jones, F. Strisland, A novel method for continuous, noninvasive, cuff-less measurement of blood pressure: evaluation in patients with nonalcoholic fatty liver disease. IEEE Trans. Biomed. Eng. 64(7), 1469–1478 (2017)CrossRefGoogle Scholar
  75. 75.
    B. Zhou, M. Sundholm, J. Cheng, H. Cruz, P. Lukowicz, Measuring muscle activities during gym exercises with textile pressure mapping sensors. Pervasive Mob. Comput. 38, 331–345 (2017). Special Issue IEEE International Conference on Pervasive Computing and Communications (PerCom) 2016CrossRefGoogle Scholar
  76. 76.
    A.Q. Javaid, H. Ashouri, A. Dorier, M. Etemadi, J.A. Heller, S. Roy, O.T. Inan, Quantifying and reducing motion artifacts in wearable seismocardiogram measurements during walking to assess left ventricular health. IEEE Trans. Biomed. Eng. 64(6), 1277–1286 (2017)CrossRefGoogle Scholar
  77. 77.
    A. Page, O. Kocabas, T. Soyata, M.K. Aktas, J. Couderc, Cloud-based privacy-preserving remote ECG monitoring and surveillance. Ann. Noninvasive Electrocardiol. 20(4), 328–337 (2014)CrossRefGoogle Scholar
  78. 78.
    M. Habibzadeh, A. Boggio-Dandry, Z. Qin, T. Soyata, B. Kantarci, H. Mouftah, Soft sensing in smart cities: handling 3Vs using recommender systems, machine intelligence, and data analytics. IEEE Commun. Mag. 56(2), 78–86 (2018)CrossRefGoogle Scholar
  79. 79.
    ZigBee Alliance, ZigBee Alliance Web page (2017). Accessed 10 November 2017
  80. 80.
    T. de Almeida Oliveira, E.P. Godoy, Zigbee wireless dynamic sensor networks: feasibility analysis and implementation guide. IEEE Sens. J. 16(11), 4614–4621 (2016)CrossRefGoogle Scholar
  81. 81.
    Y. Kim, S. Lee, S. Lee, Coexistence of ZigBee-based WBAN and WiFi for health telemonitoring systems. IEEE J. Biomed. Health Inform. 20(1), 222–230 (2016)CrossRefGoogle Scholar
  82. 82.
    Bluetooth Special Interest Group (SIG), Core Specifications - Bluetooth Technology Website (2017). Accessed 17 October 2017
  83. 83.
    M. Collotta, G. Pau, A novel energy management approach for smart homes using bluetooth low energy. IEEE J. Sel. Areas Commun. 33(12), 2988–2996 (2015)CrossRefGoogle Scholar
  84. 84.
    A. Basalamah, Sensing the crowds using bluetooth low energy tags. IEEE Access 4, 4225–4233 (2016)CrossRefGoogle Scholar
  85. 85.
    O. Bello, S. Zeadally, M. Badra, Network layer inter-operation of device-to-device communication technologies in Internet of Things (IoT). Ad Hoc Netw. 57(C), 52–62 (2017)CrossRefGoogle Scholar
  86. 86.
    M. Agiwal, A. Roy, N. Saxena, Next generation 5G wireless networks: a comprehensive survey. IEEE Commun. Surv. Tutorials 18(3), 1617–1655 (2016)CrossRefGoogle Scholar
  87. 87.
    N.A. Johansson, Y.P.E. Wang, E. Eriksson, M. Hessler, Radio access for ultra-reliable and low-latency 5G communications, in 2015 IEEE International Conference on Communication Workshop (ICCW) (June 2015), pp. 1184–1189Google Scholar
  88. 88.
    O. Galinina, S. Andreev, M. Komarov, S. Maltseva, Leveraging heterogeneous device connectivity in a converged 5G-IoT ecosystem. Comput. Netw. 128(Supplement C), 123–132 (2017). Survivability Strategies for Emerging Wireless NetworksCrossRefGoogle Scholar
  89. 89.
    M.N. Tehrani, M. Uysal, H. Yanikomeroglu, Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions. IEEE Commun. Mag. 52(5), 86–92 (2014)CrossRefGoogle Scholar
  90. 90.
    J. Qiao, X.S. Shen, J.W. Mark, Q. Shen, Y. He, L. Lei, Enabling device-to-device communications in millimeter-wave 5G cellular networks. IEEE Commun. Mag. 53(1), 209–215 (2015)CrossRefGoogle Scholar
  91. 91.
    A. Mukherjee, J.F. Cheng, S. Falahati, H. Koorapaty, D.H. Kang, R. Karaki, L. Falconetti, D. Larsson, Licensed-assisted access LTE: coexistence with IEEE 802.11 and the evolution toward 5G. IEEE Commun. Mag. 54(6), 50–57 (2016)CrossRefGoogle Scholar
  92. 92.
    M. Habibzadeh, W. Xiong, M. Zheleva, E.K. Stern, B.H. Nussbaum, T. Soyata, Smart city sensing and communication sub-infrastructure, in IEEE Midwest Symposium on Circuits and Systems, Boston (Aug 2017), pp. 1159–1162Google Scholar
  93. 93.
    Y. Lu, P. Kuonen, B. Hirsbrunner, M. Lin, Benefits of data aggregation on energy consumption in wireless sensor networks. IET Commun. 11(8), 1216–1223 (2017)CrossRefGoogle Scholar
  94. 94.
    P. Sridhar, A.M. Madni, M. Jamshidi, Hierarchical aggregation and intelligent monitoring and control in fault-tolerant wireless sensor networks. IEEE Syst. J. 1(1), 38–54 (2007)CrossRefGoogle Scholar
  95. 95.
    U. Shaukat, E. Ahmed, Z. Anwar, F. Xia, Cloudlet deployment in local wireless networks: motivation, architectures, applications, and open challenges. J. Netw. Comput. Appl. 62(Supplement C), 18–40 (2016)CrossRefGoogle Scholar
  96. 96.
    Y. Chen, Y. Chen, Q. Cao, X. Yang, Packetcloud: a cloudlet-based open platform for in-network services. IEEE Trans. Parallel Distrib. Syst. 27(4), 1146–1159 (2016)CrossRefGoogle Scholar
  97. 97.
    T. Soyata, H. Ba, W. Heinzelman, M. Kwon, J. Shi, Accelerating mobile cloud computing: a survey, in Communication Infrastructures for Cloud Computing, ed. by H.T. Mouftah, B. Kantarci (IGI Global, Hershey, 2013), pp. 175–197Google Scholar
  98. 98.
    M. Almorsy, J. Grundy, I. Müller, An analysis of the cloud computing security problem (2016). Preprint arXiv:1609.01107Google Scholar
  99. 99.
    Google LLC, Cloud IoT Core, Google Cloud Platform.
  100. 100.
    Microsoft Corp., Microsoft Azure Cloud Computing Platform and Services.
  101. 101.
    Amazon Inc., Amazon Web Services (AES) - Cloud Computing Services.
  102. 102.
    IBM Corp., IBM Watson Internet of Things (IoT).
  103. 103.
    L. Hu, M. Qiu, J. Song, M.S. Hossain, A. Ghoneim, Software defined healthcare networks. IEEE Wirel. Commun. 22(6), 67–75 (2015)CrossRefGoogle Scholar
  104. 104.
    J. Li, Y.K. Li, X. Chen, P.P. Lee, W. Lou, A hybrid cloud approach for secure authorized deduplication. IEEE Trans. Parallel Distrib. Syst. 26(5), 1206–1216 (2015)CrossRefGoogle Scholar
  105. 105.
    P.T. Endo, A.V. de Almeida Palhares, N.N. Pereira, G.E. Goncalves, D. Sadok, J. Kelner, B. Melander, J.E. Mangs, Resource allocation for distributed cloud: concepts and research challenges. IEEE Netw. 25(4), 42–46 (2011)CrossRefGoogle Scholar
  106. 106.
    S. Hijazi, A. Page, B. Kantarci, T. Soyata, Machine learning in cardiac health monitoring and decision support. IEEE Comput. Mag. 49(11), 38–48 (2016)CrossRefGoogle Scholar
  107. 107.
    S. Li, L. Da Xu, X. Wang, Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Trans. Ind. Inform. 9(4), 2177–2186 (2013)CrossRefGoogle Scholar
  108. 108.
    M. Alhussein, Monitoring Parkinson’s disease in smart cities. IEEE Access 5, 19835–19841 (2017)CrossRefGoogle Scholar
  109. 109.
    D. Zhou, J. Luo, V.M. Silenzio, Y. Zhou, J. Hu, G. Currier, H.A. Kautz, Tackling mental health by integrating unobtrusive multimodal sensing, in AAAI (2015), pp. 1401–1409Google Scholar
  110. 110.
    L. Calderoni, M. Ferrara, A. Franco, D. Maio, Indoor localization in a hospital environment using random forest classifiers. Expert Syst. Appl. 42(1), 125–134 (2015)CrossRefGoogle Scholar
  111. 111.
    J. Qin, W. Fu, H. Gao, W.X. Zheng, Distributed k-means algorithm and fuzzy c-means algorithm for sensor networks based on multiagent consensus theory. IEEE Trans. Cybern. 47(3), 772–783 (2017)CrossRefGoogle Scholar
  112. 112.
    W. Kim, M.S. Stanković, K.H. Johansson, H.J. Kim, A distributed support vector machine learning over wireless sensor networks. IEEE Trans. Cybern. 45(11), 2599–2611 (2015)CrossRefGoogle Scholar
  113. 113.
    M.M.A. Patwary, D. Palsetia, A. Agrawal, W.k. Liao, F. Manne, A. Choudhary, A new scalable parallel DBSCAN algorithm using the disjoint-set data structure, in 2012 International Conference for High Performance Computing, Networking, Storage and Analysis (SC) (Nov 2012), pp. 1–11Google Scholar
  114. 114.
    FDA Safety Communication, Cybersecurity Vulnerabilities Identified in St. Jude Medical’s Implantable Cardiac Devices and Merlin@home Transmitter: FDA Safety Communication. Accessed 03 December 2018
  115. 115.
    B. Barrett, Hack Brief: Hackers are Holding an LA Hospital’s Computers Hostage. Accessed 12 March 2018
  116. 116.
    S. Balasubramanlan, The Global Cyberattack and the Need to Revisit Health Care Cybersecurity. Accessed 12 March 2018
  117. 117.
    S. Larson, Why Hospitals are so Vulnerable to Ransomware Attacks. Accessed 19 March 2018
  118. 118.
    J. Rogers, Fitness Tracking Data on Strava App Reveal US Military Bases Details, Sparking Security Concerns. Accessed 19 March 2018
  119. 119.
    J.L. Fernández-Alemán, I.C. Señor, P. Ángel Oliver Lozoya, A. Toval, Security and privacy in electronic health records: a systematic literature review. J. Biomed. Inform. 46(3), 541–562 (2013)CrossRefGoogle Scholar
  120. 120.
    C. Cerrudo, An emerging us (and world) threat: cities wide open to cyber attacks. Securing Smart Cities (2015)Google Scholar
  121. 121.
    O. Arias, J. Wurm, K. Hoang, Y. Jin, Privacy and security in internet of things and wearable devices. IEEE Trans. Multi-Scale Comput. Syst. 1(2), 99–109 (2015)CrossRefGoogle Scholar
  122. 122.
    H. Takabi, J.B.D. Joshi, G.J. Ahn, Security and privacy challenges in cloud computing environments. IEEE Secur. Priv. 8(6), 24–31 (2010)CrossRefGoogle Scholar
  123. 123.
    A.B. Budurusubmi, S.S. Yau, An effective approach to continuous user authentication for touch screen smart devices, in IEEE International Conference on Software Quality, Reliability and Security (QRS) (Aug 2015), pp. 219–226Google Scholar
  124. 124.
    K. Zhang, K. Yang, X. Liang, Z. Su, X. Shen, H.H. Luo, Security and privacy for mobile healthcare networks: from a quality of protection perspective. IEEE Wirel. Commun. 22(4), 104–112 (2015)CrossRefGoogle Scholar
  125. 125.
    I. Hwang, Y. Kim, Analysis of security standardization for the internet of things, in 2017 International Conference on Platform Technology and Service (PlatCon) (Feb 2017), pp. 1–6Google Scholar
  126. 126.
    P. Kumari, M. López-Benítez, G.M. Lee, T. Kim, A.S. Minhas, Wearable internet of things - from human activity tracking to clinical integration, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (July 2017), pp. 2361–2364Google Scholar
  127. 127.
    J. Rajamäki, R. Pirinen, Towards the cyber security paradigm of ehealth: resilience and design aspects. AIP Conf. Proc. 1836(1), 020029 (2017)Google Scholar
  128. 128.
    J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, W. Zhao, A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)CrossRefGoogle Scholar
  129. 129.
    B. Ondiege, M. Clarke, G. Mapp, Exploring a new security framework for remote patient monitoring devices. Computers 6(1), 11 (2017)CrossRefGoogle Scholar
  130. 130.
    M.A. Ferrag, L. Maglaras, A. Derhab, A.V. Vasilakos, S. Rallis, H. Janicke, Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues (2018). Preprint arXiv:1803.10281Google Scholar
  131. 131.
    O. Kocabas, T. Soyata, M.K. Aktas, Emerging security mechanisms for medical cyber physical systems. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(3), 401–416 (2016)CrossRefGoogle Scholar
  132. 132.
    Z. Liu, J. Großschädl, Z. Hu, K. Järvinen, H. Wang, I. Verbauwhede, Elliptic curve cryptography with efficiently computable endomorphisms and its hardware implementations for the Internet of Things. IEEE Trans. Comput. 66(5), 773–785 (2017)MathSciNetzbMATHCrossRefGoogle Scholar
  133. 133.
    K. Zhang, J. Ni, K. Yang, X. Liang, J. Ren, X.S. Shen, Security and privacy in smart city applications: challenges and solutions. IEEE Commun. Mag. 55(1), 122–129 (2017)CrossRefGoogle Scholar
  134. 134.
    X. Wang, C. Konstantinou, M. Maniatakos, R. Karri, Confirm: detecting firmware modifications in embedded systems using hardware performance counters, in Proceedings of the IEEE/ACM International Conference on Computer-Aided Design. ICCAD ’15 (IEEE, Piscataway, 2015), pp. 544–551Google Scholar
  135. 135.
    S. Agrawal, M.L. Das, A. Mathuria, S. Srivastava, Program integrity verification for detecting node capture attack in wireless sensor network, in Information Systems Security, ed. by S. Jajoda, C. Mazumdar (Springer, Cham, 2015), pp. 419–440CrossRefGoogle Scholar
  136. 136.
    L.S. Sindhuja, G. Padmavathi, Replica node detection using enhanced single hop detection with clonal selection algorithm in mobile wireless sensor networks. J. Comput. Netw. Commun. 2016, 1:1–1:1 (2016)CrossRefGoogle Scholar
  137. 137.
    L. Hu, Z. Wang, Q.L. Han, X. Liu, State estimation under false data injection attacks: security analysis and system protection. Automatica 87, 176–183 (2018)MathSciNetzbMATHCrossRefGoogle Scholar
  138. 138.
    A. Abbaspour, K.K. Yen, S. Noei, A. Sargolzaei, Detection of fault data injection attack on UAV using adaptive neural network. Procedia Comput. Sci. 95, 193–200 (2016)CrossRefGoogle Scholar
  139. 139.
    M. Ryan, et al., Bluetooth: with low energy comes low security. WOOT 13, 4–4 (2013)Google Scholar
  140. 140.
    C. Mclvor, M. McLoone, J.V. McCanny, Fast Montgomery modular multiplication and RSA cryptographic processor architectures, in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2004, vol. 1 (IEEE, Piscataway, 2003), pp. 379–384Google Scholar
  141. 141.
    A. Boscher, E.V. Trichina, H. Handschuh, Randomized RSA-based cryptographic exponentiation resistant to side channel and fault attacks (20 March 2012) US Patent 8139763Google Scholar
  142. 142.
    Y. Li, L. Shi, P. Cheng, J. Chen, D.E. Quevedo, Jamming attacks on remote state estimation in cyber-physical systems: a game-theoretic approach. IEEE Trans. Autom. Control 60(10), 2831–2836 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  143. 143.
    M. Brownfield, Y. Gupta, N. Davis, Wireless sensor network denial of sleep attack, in Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop (June 2005), pp. 356–364Google Scholar
  144. 144.
    D.R. Raymond, R.C. Marchany, S.F. Midkiff, Scalable, cluster-based anti-replay protection for wireless sensor networks, in Information Assurance and Security Workshop, 2007. IAW’07. IEEE SMC (IEEE, Piscataway, 2007), pp. 127–134Google Scholar
  145. 145.
    E.Y. Vasserman, N. Hopper, Vampire attacks: draining life from wireless ad hoc sensor networks. IEEE Trans. Mob. Comput. 12(2), 318–332 (2013)CrossRefGoogle Scholar
  146. 146.
    Y. Liu, M. Dong, K. Ota, A. Liu, Activetrust: secure and trustable routing in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 11(9), 2013–2027 (2016)CrossRefGoogle Scholar
  147. 147.
    H. Suo, J. Wan, C. Zou, J. Liu, Security in the Internet of Things: a review, in 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), vol. 3 (IEEE, Piscataway, 2012), pp. 648–651Google Scholar
  148. 148.
    M.J. Covington, R. Carskadden, Threat implications of the Internet of Things, in 2013 5th International Conference on Cyber Conflict (CyCon) (IEEE, Piscataway, 2013), pp. 1–12Google Scholar
  149. 149.
    D. Puthal, S. Nepal, R. Ranjan, J. Chen, Threats to networking cloud and edge datacenters in the Internet of Things. IEEE Cloud Comput. 3(3), 64–71 (2016)CrossRefGoogle Scholar
  150. 150.
    S.M. Muzammal, M.A. Shah, H.A. Khattak, S. Jabbar, G. Ahmed, S. Khalid, S. Hussain, K. Han, Counter measuring conceivable security threats on smart healthcare devices. IEEE Access 6, 20722–20733 (2018)CrossRefGoogle Scholar
  151. 151.
    C. Kolias, A. Stavrou, J. Voas, I. Bojanova, R. Kuhn, Learning Internet-of-Things Security “Hands-On”. IEEE Secur. Priv. 14(1), 37–46 (2016)CrossRefGoogle Scholar
  152. 152.
    C. Bormann, Z. Shelby, K. Hartke, Constrained application protocol (coap), draft-ietf-core-coap-18 (2013)Google Scholar
  153. 153.
    E. Rescorla, N. Modadugu, Datagram transport layer security version 1.2. Technical report (2012)Google Scholar
  154. 154.
    S.R. Moosavi, T.N. Gia, E. Nigussie, A.M. Rahmani, S. Virtanen, H. Tenhunen, J. Isoaho, End-to-end security scheme for mobility enabled healthcare internet of things. Futur. Gener. Comput. Syst. 64, 108–124 (2016)CrossRefGoogle Scholar
  155. 155.
    A. Zhang, L. Wang, X. Ye, X. Lin, Light-weight and robust security-aware D2D-assist data transmission protocol for mobile-health systems. IEEE Trans. Inf. Forensics Secur. 12(3), 662–675 (2017)CrossRefGoogle Scholar
  156. 156.
    J. Shen, D. Liu, J. Shen, Q. Liu, X. Sun, A secure cloud-assisted urban data sharing framework for ubiquitous-cities. Pervasive Mob. Comput. 41, 219–230 (2017)CrossRefGoogle Scholar
  157. 157.
    S. Tonyali, K, A., N. Saputro, A.S. Uluagac, M. Nojoumian, Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled smart metering systems. Futur. Gener. Comput. Syst. 78(Part 2), 547–557 (2018)CrossRefGoogle Scholar
  158. 158.
    O. Kocabas, T. Soyata, Towards privacy-preserving medical cloud computing using homomorphic encryption, in Enabling Real-Time Mobile Cloud Computing through Emerging Technologies, ed. by T. Soyata (IGI Global, Hershey, 2015), pp. 213–246CrossRefGoogle Scholar
  159. 159.
    K. Yang, Z. Liu, X. Jia, X.S. Shen, Time-domain attribute-based access control for cloud-based video content sharing: a cryptographic approach. IEEE Trans. Multimedia 18(5), 940–950 (2016)CrossRefGoogle Scholar
  160. 160.
    T. Jung, X.Y. Li, Z. Wan, M. Wan, Control cloud data access privilege and anonymity with fully anonymous attribute-based encryption. IEEE Trans. Inform. Forensics Secur. 10(1), 190–199 (2015)CrossRefGoogle Scholar
  161. 161.
    M. Li, S. Yu, Y. Zheng, K. Ren, W. Lou, Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE Trans. Parallel Distrib. Syst. 24(1), 131–143 (2013)CrossRefGoogle Scholar
  162. 162.
    S.R. Moosavi, T.N. Gia, A.M. Rahmani, E. Nigussie, S. Virtanen, J. Isoaho, H. Tenhunen, SEA: a secure and efficient authentication and authorization architecture for IoT-based healthcare using smart gateways. Procedia Comput. Sci. 52, 452–459 (2015). The 6th International Conference on Ambient Systems, Networks and Technologies (ANT-2015), the 5th International Conference on Sustainable Energy Information Technology (SEIT-2015)Google Scholar
  163. 163.
    A. Sahi, D. Lai, Y. Li, Security and privacy preserving approaches in the ehealth clouds with disaster recovery plan. Comput. Biol. Med. 78, 1–8 (2016)CrossRefGoogle Scholar
  164. 164.
    D. Mishra, A. Chaturvedi, S. Mukhopadhyay, Design of a lightweight two-factor authentication scheme with smart card revocation. J. Inform. Secur. Appl. 23, 44–53 (2015)Google Scholar
  165. 165.
    L.E. Holmquist, F. Mattern, B. Schiele, P. Alahuhta, M. Beigl, H.W. Gellersen, Smart-its friends: a technique for users to easily establish connections between smart artefacts, in International Conference on Ubiquitous Computing (Springer, Berlin, 2001), pp. 116–122zbMATHGoogle Scholar
  166. 166.
    L. Ding, P. Shi, B. Liu, The clustering of internet, internet of things and social network, in 2010 3rd International Symposium on Knowledge Acquisition and Modeling (KAM) (IEEE, Piscataway, 2010), pp. 417–420Google Scholar
  167. 167.
    M.L. Gavrilova, F. Ahmed, S. Azam, P.P. Paul, W. Rahman, M. Sultana, F.T. Zohra, Emerging Trends in Security System Design Using the Concept of Social Behavioural Biometrics (Springer, Cham, 2017), pp. 229–251Google Scholar
  168. 168.
    J. Tian, Y. Cao, W. Xu, S. Wang, Challenge-response authentication using in-air handwriting style verification. IEEE Trans. Dependable Secure Comput. PP(99), 1–1 (2018)Google Scholar
  169. 169.
    M. Sultana, P.P. Paul, M. Gavrilova, A concept of social behavioral biometrics: motivation, current developments, and future trends, in International Conference on Cyberworlds (IEEE, Piscataway, 2014), pp. 271–278Google Scholar
  170. 170.
    L. Zhang, S. Zhu, S. Tang, Privacy protection for telecare medicine information systems using a chaotic map-based three-factor authenticated key agreement scheme. IEEE J. Biomed. Health Inform. 21(2), 465–475 (2017)CrossRefGoogle Scholar
  171. 171.
    T. Kumar, A. Braeken, M. Liyanage, M. Ylianttila, Identity privacy preserving biometric based authentication scheme for naked healthcare environment, in 2017 IEEE International Conference on Communications (ICC) (May 2017), pp. 1–7Google Scholar
  172. 172.
    C. Prandi, S. Ferretti, S. Mirri, P. Salomoni, Trustworthiness in crowd-sensed and sourced georeferenced data, in 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) (IEEE, Piscataway, 2015), pp. 402–407CrossRefGoogle Scholar
  173. 173.
    B. Kantarci, K.G. Carr, C.D. Pearsall, SONATA: social network assisted trustworthiness assurance in smart city crowdsensing. Int. J. Distrib. Syst. Technol. 7(1), 59–78 (2016)CrossRefGoogle Scholar
  174. 174.
    M. Pouryazdan, B. Kantarci, T. Soyata, H. Song, Anchor-assisted and vote-based trustworthiness assurance in smart city crowdsensing. IEEE Access 4, 529–541 (2016)CrossRefGoogle Scholar
  175. 175.
    T.M. Fernández-Caramés, P. Fraga-Lamas, A review on the use of blockchain for the internet of things. IEEE Access 6, 32979–33001 (2018)CrossRefGoogle Scholar
  176. 176.
    Y. Huo, X. Dong, W. Xu, M. Yuen, Cellular and WiFi co-design for 5G user equipment (2018). Preprint arXiv:1803.06943Google Scholar
  177. 177.
    M.N. Kamel Boulos, J.T. Wilson, K.A. Clauson, Geospatial blockchain: promises, challenges, and scenarios in health and healthcare. Int. J. Health Geogr. 17(1), 25 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University at AlbanySUNYAlbanyUSA

Personalised recommendations