Annals of Telecommunications

, Volume 71, Issue 9–10, pp 489–502 | Cite as

A network-assisted flow mobility architecture for optimized mobile medical multimedia transmission

Article

Abstract

A large part of mobile Health (mHealth) use-cases such as remote patient monitoring/diagnosis, teleconsultation, and guided surgical intervention requires advanced and reliable mobile communication solutions to provide efficient multimedia transmission with strict medical level Quality of Service (QoS) and Quality of Experience (QoE) provision. The increasing deployment of overlapping wireless access networks enables the possibility to offer the required network resources for ubiquitous and pervasive mHealth services. To address the challenges and support the above use-cases in today’s heterogeneous network (HetNet) environments, we propose a network-assisted flow-based mobility management architecture for optimized real-time mobile medical multimedia communication. The proposed system is empirically evaluated in a Pan-European HetNet testbed with multi-access Android-based mobile devices. We observed that the proposed scheme significantly improves the objective QoE of simultaneous real-time high-resolution electrocardiography and high-definition ultrasound transmissions while also enhances traffic load balancing capabilities of wireless architectures.

Keywords

mHealth Mobile medical multimedia Medical video quality assessment Flow mobility Heterogeneous networks Dynamic network discovery and selection Cross-layer optimization 

References

  1. 1.
    mHealth (2011) New horizons for health through mobile technologies. In: World Health Organization Global Observatory for eHealth series, vol 3Google Scholar
  2. 2.
    3GPP TS 24.312. Access Network Discovery and Selection Function (ANDSF) Management Object (MO), Rel. 11. 2013Google Scholar
  3. 3.
    Aura T Cryptographically Generated Addresses (CGA). Number 3972 in Request for Comments. IETF. Published: RFC 3972 (Proposed Standard) Updated by RFCs 4581, 4982Google Scholar
  4. 4.
    Bennis M, Simsek M, Czylwik A, Saad W, Valentin S, Debbah M (2013) When cellular meets wifi in wireless small cell networks. In: Communications magazine, vol 51. IEEE, pp 44–50Google Scholar
  5. 5.
    Bokor L, Jeney G, Kovács J (2014) A study on the performance of an advanced framework for prediction-based NEMO handovers in multihomed scenarios. In: Infocommunications journal VI, pp 16–27Google Scholar
  6. 6.
    Busra US, Rahman MZ Mobile phone based telemedicine service for rural bangladesh: ECG. In: 2013 16th International Conference on Computer and Information Technology (ICCIT), pp 203–208Google Scholar
  7. 7.
    Chen J-C, Chen T-C, Zhang T, van den Berg E (2006) WLC19-4: effective AP selection and load balancing in IEEE 802.11 wireless LANs. In: GLOBECOM ’06. IEEE, pp 1–6Google Scholar
  8. 8.
    Cisco Cisco visual networking index: global mobile data traffic forecast update 2015 – 2020Google Scholar
  9. 9.
    LifeSync Corp Improve the quality of patient care in your facilityGoogle Scholar
  10. 10.
    Damnjanovic A, Montojo J, Wei Y, Ji T, Luo T, Vajapeyam M, Yoo T, Song O, Malladi D (2011) A survey on 3gpp heterogeneous networks. In: Wireless communications, vol 18. IEEE, pp 10–21Google Scholar
  11. 11.
    Dugan J, Elliott S, Mah BA, Poskanzer J, Prabhu K iPerf - the network bandwidth measurement tool: active measurements in TCP, UDP and SCTP. https://iperf.fr/
  12. 12.
    Dutta A, Chakravarty S, Taniuchi K, Fajardo V, Ohba Y, Famolari D, Schulzrinne H (2007) An experimental study of location assisted proactive handover. In: GLOBECOM ’07. IEEE, pp 2037–2042Google Scholar
  13. 13.
    Eysenbach G (2001) What is e-health? J Med Internet ResGoogle Scholar
  14. 14.
    Linux Foundation Linux traffic control with network emulation. http://www.linuxfoundation.org/collaborate/workgroups/networking/netem/
  15. 15.
    Ghini V, Ferretti S, Panzieri F (2012) M-hippocrates: enabling reliable and interactive mobile health services. In: IT Professional, vol 14, pp 29–35Google Scholar
  16. 16.
    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank: physiotoolkit, and PhysioNet: components of a new research resource for complex physiologic signals. In: Circulation, vol 101, pp 215–220Google Scholar
  17. 17.
    IEEE (2009) IEEE Standard for Local and metropolitan area networks- Part 21: Media independent handover. IEEEGoogle Scholar
  18. 18.
    Jirka K, Berthold R, Adam W (2003) Evalvid—a framework for video transmission and quality evaluation. vol 2794 of LNICST, pp 255–272Google Scholar
  19. 19.
    Kyriacou E, Pattichis MS, Pattichis CS, Panayides A, Pitsillides A (2007) M-health e-emergency systems: current status and future directions [wireless corner]. In: Antennas and propagation magazine, vol 49. IEEE, pp 216–231Google Scholar
  20. 20.
    Makela J, Luoto M, Sutinen T, Pentikousis K (2011) Distributed information service architecture for overlapping multiaccess networks. In: Multimedia tools appl., vol 55, pp 289–306Google Scholar
  21. 21.
    Makela J, Pentikousis K (2007) Trigger management mechanisms. In: ISWPC ’07, pp 378–383Google Scholar
  22. 22.
    MobiSante. Smartphone ultrasound: the MobiUS SP1 systemGoogle Scholar
  23. 23.
    Narten T, Draves R Privacy Extensions for Stateless Address Autoconfiguration in IPv6. Number 3041 in Request for Comments. IETF. Published: RFC 3041 (Proposed Standard) Obsoleted by RFC 4941Google Scholar
  24. 24.
    PRZOOM Press ∖& Newswire. HealthFrontier announces launch of microtel ecgAnywhere with their remote monitoring system. http://www.przoom.com/news/70459/print_preview/
  25. 25.
    Ojanper T, Luoto M, Majanen M, Mannersalo P, Savolainen PT Cognitive network management framework and approach for video streaming optimization in heterogeneous networks. In: Wireless Personal Communications, vol 84, pp 1739–1769Google Scholar
  26. 26.
    Olla P, Shimskey C mHealth taxonomy: a literature survey of mobile health applications. In: Health and Technology, vol 4, pp 299–308Google Scholar
  27. 27.
    Panayides A, Eleftheriou I, Pantziaris M (2013) Open-source telemedicine platform for wireless medical video communication. In: International journal of telemedicine and applications, vol 2013, pp 1–12Google Scholar
  28. 28.
    Panayides A, Pattichis MS, Pattichis CS (2008) Wireless medical ultrasound video transmission through noisy channels. In: EMBS 2008, pp 5326–5329Google Scholar
  29. 29.
    Pedersen PC, Dickson BW, Chakareski J Telemedicine applications of mobile ultrasound. In: IEEE International Workshop on Multimedia Signal Processing, 2009. MMSP ’09, pp 1–6Google Scholar
  30. 30.
    Pedersen PC, Dickson BW, Chakareski J (2009) Telemedicine applications of mobile ultrasound. In: MMSP ’09. IEEE, pp 1–6Google Scholar
  31. 31.
    Perkins C, Johnson D, Arkko J (2011) Mobility Support in IPv6 Number 6275 in Request for Comments IETFGoogle Scholar
  32. 32.
    Philips Philips patient monitoring: monitored, but mobile. http://www.usa.philips.com/healthcare/solutions/patient-monitoring/wireless-monitoring/
  33. 33.
    Piri E (2014) Cell coverage area information service to improve cell se-lection in HetNets. In: 11Th CCNC 2014. IEEE, pp 47–52Google Scholar
  34. 34.
    Piri E, Schulzrinne H (2014) Scaling network information services to support HetNets and dynamic spectrum access. In: Journal of Communications and Networks, vol 16, pp 202– 208Google Scholar
  35. 35.
    Piri E, Varela M, Prokkola J (2015) A network information service for quality-driven mobility. In: 11Th CCNC 2014, IEEEGoogle Scholar
  36. 36.
    Bousseljot R, Kreiseler D, Schnabel A (1995) Nutzung der EKG-signaldatenbank CARDIODAT der PTB ber das internet. In: Biomedizinische technik, vol 40, pp 317– 318Google Scholar
  37. 37.
    Razaak M, Martini MG (2014) Rate-distortion and rate-quality performance analysis of hevc compression of medical ultrasound videos. In: (MoWNet2014), volume 40 of procedia computer science, pp 230–236Google Scholar
  38. 38.
    Saaty TL, Ozdemir MS (2003) Why the magic number seven plus or minus two. In: Mathematical and computer modelling, vol 38, pp 233–244Google Scholar
  39. 39.
    Takács A, Bokor L (2013) A distributed dynamic mobility architecture with integral cross-layered and context-aware interface for reliable provision of high bitrate mHealth services. In: Wireless mobile communication and healthcare, volume 61 of LNICST, pp 369–379Google Scholar
  40. 40.
    Tobias RJ (2015) Wireless communication of real-time ultrasound data and control. In: Proceedings of SPIE, vol 9419Google Scholar
  41. 41.
    Varga N, Bokor L, Bouroz S, Lecroart B, Takács A (2014) Client-based and cross-layer optimized flow mobility for android devices in heterogeneous femtocell/wi-fi networks. volume 40 of Proc. Computer Science, pp 26–36Google Scholar
  42. 42.
    Varga N, Bokor L, Takács A (2014) Context-aware IPv6 flow mobility for multi-sensor based mobile patient monitoring and tele-consultation. volume 40 of Proc. Computer Science, pp 222–229Google Scholar
  43. 43.
    Wang Y (2006) Survey of objective video quality measurements. Worcester Polytechnic Institute, USAGoogle Scholar
  44. 44.
    Xiao Y, Chen H (2008) Mobile Telemedicine: A Computing and Networking Perspective, 1st edn. Auerbach PublicationsGoogle Scholar
  45. 45.
    Xu F, Zhu X, Tan CC, Li Q, Yan G, Wu J (2013) SmartAssoc: decentralized access point selection algorithm to improve throughput. In: IEEE transactions on Parallel and distributed systems, vol 24, pp 2482–2491Google Scholar

Copyright information

© Institut Mines-Télécom and Springer-Verlag France 2016

Authors and Affiliations

  1. 1.Department of Networked Systems and ServicesBudapest University of Technology and EconomicsBudapestHungary
  2. 2.VTT Technical Research Centre of FinlandOuluFinland

Personalised recommendations